reach thesis award

REACH THESIS AWARD 2023

Kontakt i contact.

• [email protected]
• [email protected]

The REACH THESIS AWARD 2023 is an initiative of the REACH – EUREGIO Start-up Center to reward highly innovative bachelor, master and doctoral theses. The focus is on applied science and research that can be used as a basis for product development and innovation. In each of the three categories two graduates will be selected and awarded with prize money of 300 € (Bachelor), 500 € (Master) and 700 € (PhD). Applications are accepted from WWU and FH Münster University of Applied Sciences. It is possible to submit in english or german language. Der REACH THESIS AWARD 2023 ist eine Initiative des REACH – EUREGIO Start-up Centers zur Auszeichnung hoch innovativer Bachelor-, Master- und Doktorarbeiten. Der Fokus liegt auf angewandter Wissenschaft und Forschung, die als Grundlage für Produktentwicklung und Innovation genutzt werden können. In jeder der drei Kategorien werden zwei Absolvent*innen ausgewählt und mit Preisgeldern von 300 € (Bachelor), 500 € (Master) und 700 € (PhD) ausgezeichnet. Bewerbungen werden von der WWU und der FH Münster entgegengenommen. Die Einreichung ist auf Englisch oder Deutsch möglich. Who can apply? Wer kann sich bewerben? Authorized for application are official supervisors of bachelor, master, and doctoral theses that have been submitted between 30.06.2022 and 30.06.2023. Antragsberechtigt sind Betreuer*innen von Bachelor-, Master- und Promotionsarbeiten, die zwischen dem 30.06.2022 und dem 30.06.2023 eingereicht wurden.  

What to submit? Was wird eingereicht? One-pager describing the innovative potential and applicability of the selected theses. A visualization of the core results is desired. One-Pager, der das innovative Potenzial und die Anwendbarkeit der ausgewählten Thesen beschreibt. Eine Visualisierung der Kernergebnisse ist erwünscht.  

• Datenschutzerklärung

+49 2505 939 20 70

Internet:  evis-solutions.de Umsatzsteuer-ID-Nummer: DE 315171187 Amtsgericht Steinfurt: HRB 11776 Die Event Information Systems (EVIS) GmbH entwickelt, pflegt und be- und vertreibt Informationssysteme. Redaktionell verantwortlich gemäß §5 TMG und vertretungsberechtigt: Geschäftsführer: Dr. Friedrich Chasin (Anschrift wie oben)   

Das Internetangebot dient der Kontaktaufnahme und der Informationsverbreitung. Ihr Inhalt wurde mit größtmöglicher Sorgfalt zusammengestellt. Dennoch kann keinerlei Gewähr für Aktualität, Richtigkeit, Vollständigkeit und Qualität der bereitgestellten Daten übernommen werden. Die Event Information Systems (EVIS) GmbH übernimmt keine Garantie, weder ausdrücklich noch implizit, für die Art oder Richtigkeit des dargebotenen Materials und übernimmt keine Haftung (einschließlich der Haftung für indirekten Verlust oder Gewinn- oder Umsatzverluste) bezüglich des Materials bzw. der Nutzung dieses Materials. Sollten Inhalte von Web-Seiten der Event Information Systems (EVIS) GmbH gegen geltende Rechtsvorschriften verstoßen, dann bitten wir um umgehende Benachrichtigung. Wir werden die Seite oder den betreffenden Inhalt dann schnellstmöglich entfernen.

Verweise auf externe Web-Seiten:

Für alle auf den Internetseiten der Event Information Systems (EVIS) GmbH befindlichen Hyperlinks gilt:  Die Event Information Systems (EVIS) GmbH bemüht sich um Sorgfalt bei der Auswahl dieser Seiten und deren Inhalte, hat aber keinerlei Einfluss auf die Inhalte oder Gestaltung der verlinkten Seiten. Der Inhalt dieser Websites liegt vollständig außerhalb des Verantwortungsbereiches der Event Information Systems (EVIS) GmbH. Die Websites waren jedoch zur Zeit der Verlinkung frei von illegalen Inhalten. Auf die Gestaltung der gelinkten Websites kann nicht Einfluss genommen werden. Für Schäden, die aus fehlerhaften oder unvollständigen Inhalten auf den mittels Link verwiesenen Websites resultieren, haften die Event Information Systems (EVIS) GmbH und die Autoren bzw. Verantwortlichen dieser Website nicht. Eine ständige Kontrolle dieser externen Links ist für die Event Information Systems (EVIS) GmbH ohne konkrete Hinweise auf Rechtsverstöße nicht zumutbar. Bei Kenntnis von Rechtsverstößen werden jedoch derartige externe Links unverzüglich gelöscht.

I.         Name und Anschrift des Verantwortlichen

Der Verantwortliche im Sinne der Datenschutz-Grundverordnung und anderer nationaler Datenschutzgesetze der Mitgliedsstaaten sowie sonstiger datenschutzrechtlicher Bestimmungen ist die:

Event Information Systems (EVIS) GmbH

vertreten durch Geschäftsführer Dr. Friedrich Chasin Lütke Berg 6 48341 Altenberge Deutschland Tel.: +49 2505 939 20 70 E-Mail: [email protected] Website: evis-solutions.de

II.     Allgemeines zur Datenverarbeitung

1.        umfang der verarbeitung personenbezogener daten.

Wir verarbeiten personenbezogene Daten unserer Nutzer grundsätzlich nur, soweit dies zur Bereitstellung einer funktionsfähigen Website sowie unserer Inhalte und Leistungen erforderlich ist. Die Verarbeitung personenbezogener Daten unserer Nutzer erfolgt regelmäßig nur nach Einwilligung des Nutzers. Eine Ausnahme gilt in solchen Fällen, in denen eine vorherige Einholung einer Einwilligung aus tatsächlichen Gründen nicht möglich ist und die Verarbeitung der Daten durch gesetzliche Vorschriften gestattet ist.

2.        Rechtsgrundlage für die Verarbeitung personenbezogener Daten

Soweit wir für Verarbeitungsvorgänge personenbezogener Daten eine Einwilligung der betroffenen Person einholen, dient Art. 6 Abs. 1 lit. a EU-Datenschutzgrundverordnung (DSGVO) als Rechtsgrundlage.

Bei der Verarbeitung von personenbezogenen Daten, die zur Erfüllung eines Vertrages, dessen Vertragspartei die betroffene Person ist, erforderlich ist, dient Art. 6 Abs. 1 lit. b DSGVO als Rechtsgrundlage. Dies gilt auch für Verarbeitungsvorgänge, die zur Durchführung vorvertraglicher Maßnahmen erforderlich sind.

Soweit eine Verarbeitung personenbezogener Daten zur Erfüllung einer rechtlichen Verpflichtung erforderlich ist, der unser Unternehmen unterliegt, dient Art. 6 Abs. 1 lit. c DSGVO als Rechtsgrundlage.

Für den Fall, dass lebenswichtige Interessen der betroffenen Person oder einer anderen natürlichen Person eine Verarbeitung personenbezogener Daten erforderlich machen, dient Art. 6 Abs. 1 lit. d DSGVO als Rechtsgrundlage.

Ist die Verarbeitung zur Wahrung eines berechtigten Interesses unseres Unternehmens oder eines Dritten erforderlich und überwiegen die Interessen, Grundrechte und Grundfreiheiten des Betroffenen das erstgenannte Interesse nicht, so dient Art. 6 Abs. 1 lit. f DSGVO als Rechtsgrundlage für die Verarbeitung.

3.        Datenlöschung und Speicherdauer

Die personenbezogenen Daten der betroffenen Person werden gelöscht oder gesperrt, sobald der Zweck der Speicherung entfällt. Eine Speicherung kann darüber hinaus erfolgen, wenn dies durch den europäischen oder nationalen Gesetzgeber in unionsrechtlichen Verordnungen, Gesetzen oder sonstigen Vorschriften, denen der Verantwortliche unterliegt, vorgesehen wurde. Eine Sperrung oder Löschung der Daten erfolgt auch dann, wenn eine durch die genannten Normen vorgeschriebene Speicherfrist abläuft, es sei denn, dass eine Erforderlichkeit zur weiteren Speicherung der Daten für einen Vertragsabschluss oder eine Vertragserfüllung besteht.

III.     Bereitstellung der Website und Erstellung von Logfiles

1.        beschreibung und umfang der datenverarbeitung.

Bei jedem Aufruf unserer Internetseite erfasst unser System automatisiert Daten und Informationen vom Computersystem des aufrufenden Rechners.

Folgende Daten werden hierbei erhoben:

• Informationen über den Browsertyp und die verwendete Version
• Das Betriebssystem des Nutzers
• Den Internet-Service-Provider des Nutzers
• Die IP-Adresse des Nutzers
• Datum und Uhrzeit des Zugriffs
• Websites, von denen das System des Nutzers auf unsere Internetseite gelangt
• Websites, die vom System des Nutzers über unsere Website aufgerufen werden

Die Daten werden ebenfalls in den Logfiles unseres Systems gespeichert. Eine Speicherung dieser Daten zusammen mit anderen personenbezogenen Daten des Nutzers findet nicht statt.

2.        Rechtsgrundlage für die Datenverarbeitung

Rechtsgrundlage für die vorübergehende Speicherung der Daten und der Logfiles ist Art. 6 Abs. 1 lit. f DSGVO.

3.        Zweck der Datenverarbeitung

Die vorübergehende Speicherung der IP-Adresse durch das System ist notwendig, um eine Auslieferung der Website an den Rechner des Nutzers zu ermöglichen. Hierfür muss die IP-Adresse des Nutzers für die Dauer der Sitzung gespeichert bleiben.

Die Speicherung in Logfiles erfolgt, um die Funktionsfähigkeit der Website sicherzustellen. Zudem dienen uns die Daten zur Optimierung der Website und zur Sicherstellung der Sicherheit unserer informationstechnischen Systeme. Eine Auswertung der Daten zu Marketingzwecken findet in diesem Zusammenhang nicht statt.

In diesen Zwecken liegt auch unser berechtigtes Interesse an der Datenverarbeitung nach Art. 6 Abs. 1 lit. f DSGVO.

4.        Dauer der Speicherung

Die Daten werden gelöscht, sobald sie für die Erreichung des Zweckes ihrer Erhebung nicht mehr erforderlich sind. Im Falle der Erfassung der Daten zur Bereitstellung der Website ist dies der Fall, wenn die jeweilige Sitzung beendet ist.

Im Falle der Speicherung der Daten in Logfiles ist dies nach spätestens sieben Tagen der Fall. Eine darüberhinausgehende Speicherung ist möglich. In diesem Fall werden die IP-Adressen der Nutzer gelöscht oder verfremdet, sodass eine Zuordnung des aufrufenden Clients nicht mehr möglich ist.

5.        Widerspruchs- und Beseitigungsmöglichkeit

Die Erfassung der Daten zur Bereitstellung der Website und die Speicherung der Daten in Logfiles ist für den Betrieb der Internetseite zwingend erforderlich. Es besteht folglich seitens des Nutzers keine Widerspruchsmöglichkeit.

IV.       Newsletter

Mit den nachfolgenden Hinweisen informieren wir Sie über die Inhalte unseres Newsletters sowie das Anmelde-, Versand- und das statistische Auswertungsverfahren sowie Ihre Widerspruchsrechte auf. Indem Sie unseren Newsletter abonnieren, erklären Sie sich mit dem Empfang und den beschriebenen Verfahren einverstanden.

Inhalt des Newsletters: Wir versenden Newsletter, E-Mails und weitere elektronische Benachrichtigungen mit werblichen Informationen (nachfolgend „Newsletter“) nur mit der Einwilligung der Empfänger oder einer gesetzlichen Erlaubnis. Sofern im Rahmen einer Anmeldung zum Newsletter dessen Inhalte konkret umschrieben werden, sind sie für die Einwilligung der Nutzer maßgeblich. Im Übrigen enthalten unsere Newsletter Informationen zu unseren Leistungen und uns.

Double-Opt-In und Protokollierung: Die Anmeldung zu unserem Newsletter erfolgt in einem sog. Double-Opt-In-Verfahren. D.h. Sie erhalten nach der Anmeldung eine E-Mail, in der Sie um die Bestätigung Ihrer Anmeldung gebeten werden. Diese Bestätigung ist notwendig, damit sich niemand mit fremden E-Mailadressen anmelden kann. Die Anmeldungen zum Newsletter werden protokolliert, um den Anmeldeprozess entsprechend den rechtlichen Anforderungen nachweisen zu können. Hierzu gehört die Speicherung des Anmelde- und des Bestätigungszeitpunkts, als auch der IP-Adresse. Ebenso werden die Änderungen Ihrer bei dem Versanddienstleister gespeicherten Daten protokolliert.

Anmeldedaten: Um sich für den Newsletter anzumelden, reicht es aus, Ihre E-Mailadresse, Anrede und Ihren Namen, zwecks persönlicher Ansprache im Newsletters anzugeben.

Der Versand des Newsletters und die mit ihm verbundene Erfolgsmessung erfolgt auf Grundlage einer Einwilligung der Empfänger gem. Art. 6 Abs. 1 lit. a, Art. 7 DSGVO i.V.m § 7 Abs. 2 Nr. 3 UWG bzw. auf Grundlage der gesetzlichen Erlaubnis gem. § 7 Abs. 3 UWG.

Die Protokollierung des Anmeldeverfahrens erfolgt auf Grundlage unserer berechtigten Interessen gem. Art. 6 Abs. 1 lit. f DSGVO. Unser Interesse richtet sich auf den Einsatz eines nutzerfreundlichen sowie sicheren Newslettersystems, das sowohl unseren geschäftlichen Interessen dient, als auch den Erwartungen der Nutzer entspricht und uns ferner den Nachweis von Einwilligungen erlaubt.

Rechtsgrundlage für die Verarbeitung der Daten nach Anmeldung zum Newsletters durch den Nutzer ist bei Vorliegen einer Einwilligung des Nutzers Art. 6 Abs. 1 lit. a DSGVO.

Die Erhebung der E-Mail-Adresse des Nutzers dient dazu, den Newsletter zuzustellen.

Die Erhebung sonstiger personenbezogener Daten im Rahmen des Anmeldevorgangs dient dazu, einen Missbrauch der Dienste oder der verwendeten E-Mail-Adresse zu verhindern.

Die Daten werden gelöscht, sobald sie für die Erreichung des Zweckes ihrer Erhebung nicht mehr erforderlich sind. Die E-Mail-Adresse des Nutzers wird demnach solange gespeichert, wie das Abonnement des Newsletters aktiv ist.

Die sonstigen im Rahmen des Anmeldevorgangs erhobenen personenbezogenen Daten werden in der Regel nach einer Frist von sieben Tagen gelöscht.

Das Abonnement des Newsletters kann durch den betroffenen Nutzer jederzeit gekündigt werden. Zu diesem Zweck findet sich in jedem Newsletter ein entsprechender Link.

Hierdurch wird ebenfalls ein Widerruf der Einwilligung der Speicherung der während des Anmeldevorgangs erhobenen personenbezogenen Daten ermöglicht.

V.         Kontaktformular und E-Mail-Kontakt

1.            beschreibung und umfang der datenverarbeitung.

Auf unserer Internetseite ist ein Kontaktformular vorhanden, welches für die elektronische Kontaktaufnahme genutzt werden kann. Nimmt ein Nutzer diese Möglichkeit wahr, so werden die in der Eingabemaske eingegeben Daten an uns übermittelt und gespeichert. Diese Daten sind mindestens:

• Der Name des Nutzers
• Die E-Mailadresse des Nutzers

Im Zeitpunkt der Absendung der Nachricht werden zudem folgende Daten gespeichert:

• Datum und Uhrzeit der Registrierung

Für die Verarbeitung der Daten wird im Rahmen des Absendevorgangs Ihre Einwilligung eingeholt und auf diese Datenschutzerklärung verwiesen.

Alternativ ist eine Kontaktaufnahme über die bereitgestellte E-Mail-Adresse möglich. In diesem Fall werden die mit der E-Mail übermittelten personenbezogenen Daten des Nutzers gespeichert.

Es erfolgt in diesem Zusammenhang keine Weitergabe der Daten an Dritte. Die Daten werden ausschließlich für die Verarbeitung der Konversation verwendet.

Rechtsgrundlage für die Verarbeitung der Daten ist bei Vorliegen einer Einwilligung des Nutzers Art. 6 Abs. 1 lit. a DSGVO.

Rechtsgrundlage für die Verarbeitung der Daten, die im Zuge einer Übersendung einer E-Mail übermittelt werden, ist Art. 6 Abs. 1 lit. f DSGVO. Zielt der E-Mail-Kontakt auf den Abschluss eines Vertrages ab, so ist zusätzliche Rechtsgrundlage für die Verarbeitung Art. 6 Abs. 1 lit. b DSGVO.

Die Verarbeitung der personenbezogenen Daten aus der Eingabemaske dient uns allein zur Bearbeitung der Kontaktaufnahme. Im Falle einer Kontaktaufnahme per E-Mail liegt hieran auch das erforderliche berechtigte Interesse an der Verarbeitung der Daten.

Die sonstigen während des Absendevorgangs verarbeiteten personenbezogenen Daten dienen dazu, einen Missbrauch des Kontaktformulars zu verhindern und die Sicherheit unserer informationstechnischen Systeme sicherzustellen.

Die Daten werden gelöscht, sobald sie für die Erreichung des Zweckes ihrer Erhebung nicht mehr erforderlich sind. Für die personenbezogenen Daten aus der Eingabemaske des Kontaktformulars und diejenigen, die per E-Mail übersandt wurden, ist dies dann der Fall, wenn die jeweilige Konversation mit dem Nutzer beendet ist. Beendet ist die Konversation dann, wenn sich aus den Umständen entnehmen lässt, dass der betroffene Sachverhalt abschließend geklärt ist.

Die während des Absendevorgangs zusätzlich erhobenen personenbezogenen Daten werden spätestens nach einer Frist von sieben Tagen gelöscht.

Der Nutzer hat jederzeit die Möglichkeit, seine Einwilligung zur Verarbeitung der personenbezogenen Daten zu widerrufen. Nimmt der Nutzer per E-Mail Kontakt mit uns auf, so kann er der Speicherung seiner personenbezogenen Daten jederzeit widersprechen. In einem solchen Fall kann die Konversation nicht fortgeführt werden.

Zum Widerruf der Einwilligung in die Verarbeitung seiner personenbezogenen Daten setzt sich der Nutzer mit der Event Information Systems (EVIS) GmbH in Kontakt (siehe oben) und erklärt dieser seinen Widerruf auf sämtlichen oben angegebenen Wegen.

Alle personenbezogenen Daten, die im Zuge der Kontaktaufnahme gespeichert wurden, werden in diesem Fall gelöscht.

VI.    Onlinepräsenzen in sozialen Medien

Wir unterhalten Onlinepräsenzen innerhalb sozialer Netzwerke und Plattformen, um mit den dort aktiven Kunden, Interessenten und Nutzern kommunizieren und sie dort über unsere Leistungen informieren zu können. Beim Aufruf der jeweiligen Netzwerke und Plattformen gelten die Geschäftsbedingungen und die Datenverarbeitungsrichtlinien deren jeweiligen Betreiber.

Soweit nicht anders im Rahmen unserer Datenschutzerklärung angegeben, verarbeiten wir die Daten der Nutzer sofern diese mit uns innerhalb der sozialen Netzwerke und Plattformen kommunizieren, z.B. Beiträge auf unseren Onlinepräsenzen verfassen oder uns Nachrichten zusenden.

VII.         Rechte der betroffenen Person

Werden personenbezogene Daten von Ihnen verarbeitet, sind Sie Betroffener i.S.d. DSGVO und es stehen Ihnen folgende Rechte gegenüber dem Verantwortlichen zu:

1.        Auskunftsrecht

Sie können von dem Verantwortlichen eine Bestätigung darüber verlangen, ob personenbezogene Daten, die Sie betreffen, von uns verarbeitet werden.

Liegt eine solche Verarbeitung vor, können Sie von dem Verantwortlichen über folgende Informationen Auskunft verlangen:

(1)       die Zwecke, zu denen die personenbezogenen Daten verarbeitet werden;

(2)       die Kategorien von personenbezogenen Daten, welche verarbeitet werden;

(3)       die Empfänger bzw. die Kategorien von Empfängern, gegenüber denen die Sie betreffenden personenbezogenen Daten offengelegt wurden oder noch offengelegt werden;

(4)       die geplante Dauer der Speicherung der Sie betreffenden personenbezogenen Daten oder, falls konkrete Angaben hierzu nicht möglich sind, Kriterien für die Festlegung der Speicherdauer;

(5)       das Bestehen eines Rechts auf Berichtigung oder Löschung der Sie betreffenden personenbezogenen Daten, eines Rechts auf Einschränkung der Verarbeitung durch den Verantwortlichen oder eines Widerspruchsrechts gegen diese Verarbeitung;

(6)       das Bestehen eines Beschwerderechts bei einer Aufsichtsbehörde;

(7)       alle verfügbaren Informationen über die Herkunft der Daten, wenn die personenbezogenen Daten nicht bei der betroffenen Person erhoben werden;

(8)       das Bestehen einer automatisierten Entscheidungsfindung einschließlich Profiling gemäß Art. 22 Abs. 1 und 4 DSGVO und – zumindest in diesen Fällen – aussagekräftige Informationen über die involvierte Logik sowie die Tragweite und die angestrebten Auswirkungen einer derartigen Verarbeitung für die betroffene Person.

Ihnen steht das Recht zu, Auskunft darüber zu verlangen, ob die Sie betreffenden personenbezogenen Daten in ein Drittland oder an eine internationale Organisation übermittelt werden. In diesem Zusammenhang können Sie verlangen, über die geeigneten Garantien gem. Art. 46 DSGVO im Zusammenhang mit der Übermittlung unterrichtet zu werden.

2.        Recht auf Berichtigung

Sie haben ein Recht auf Berichtigung und/oder Vervollständigung gegenüber dem Verantwortlichen, sofern die verarbeiteten personenbezogenen Daten, die Sie betreffen, unrichtig oder unvollständig sind. Der Verantwortliche hat die Berichtigung unverzüglich vorzunehmen.

3.        Recht auf Einschränkung der Verarbeitung

Unter den folgenden Voraussetzungen können Sie die Einschränkung der Verarbeitung der Sie betreffenden personenbezogenen Daten verlangen:

(1)       wenn Sie die Richtigkeit der Sie betreffenden personenbezogenen für eine Dauer bestreiten, die es dem Verantwortlichen ermöglicht, die Richtigkeit der personenbezogenen Daten zu überprüfen;

(2)       die Verarbeitung unrechtmäßig ist und Sie die Löschung der personenbezogenen Daten ablehnen und stattdessen die Einschränkung der Nutzung der personenbezogenen Daten verlangen;

(3)       der Verantwortliche die personenbezogenen Daten für die Zwecke der Verarbeitung nicht länger benötigt, Sie diese jedoch zur Geltendmachung, Ausübung oder Verteidigung von Rechtsansprüchen benötigen, oder

(4)       wenn Sie Widerspruch gegen die Verarbeitung gemäß Art. 21 Abs. 1 DSGVO eingelegt haben und noch nicht feststeht, ob die berechtigten Gründe des Verantwortlichen gegenüber Ihren Gründen überwiegen.

Wurde die Verarbeitung der Sie betreffenden personenbezogenen Daten eingeschränkt, dürfen diese Daten – von ihrer Speicherung abgesehen – nur mit Ihrer Einwilligung oder zur Geltendmachung, Ausübung oder Verteidigung von Rechtsansprüchen oder zum Schutz der Rechte einer anderen natürlichen oder juristischen Person oder aus Gründen eines wichtigen öffentlichen Interesses der Union oder eines Mitgliedstaats verarbeitet werden.

Wurde die Einschränkung der Verarbeitung nach den o.g. Voraussetzungen eingeschränkt, werden Sie von dem Verantwortlichen unterrichtet bevor die Einschränkung aufgehoben wird.

4.        Recht auf Löschung

A)        löschungspflicht.

Sie können von dem Verantwortlichen verlangen, dass die Sie betreffenden personenbezogenen Daten unverzüglich gelöscht werden, und der Verantwortliche ist verpflichtet, diese Daten unverzüglich zu löschen, sofern einer der folgenden Gründe zutrifft:

(1)       Die Sie betreffenden personenbezogenen Daten sind für die Zwecke, für die sie erhoben oder auf sonstige Weise verarbeitet wurden, nicht mehr notwendig.

(2)       Sie widerrufen Ihre Einwilligung, auf die sich die Verarbeitung gem. Art. 6 Abs. 1 lit. a oder Art. 9 Abs. 2 lit. a DSGVO stützte, und es fehlt an einer anderweitigen Rechtsgrundlage für die Verarbeitung.

(3)       Sie legen gem. Art. 21 Abs. 1 DSGVO Widerspruch gegen die Verarbeitung ein und es liegen keine vorrangigen berechtigten Gründe für die Verarbeitung vor, oder Sie legen gem. Art. 21 Abs. 2 DSGVO Widerspruch gegen die Verarbeitung ein.

(4)       Die Sie betreffenden personenbezogenen Daten wurden unrechtmäßig verarbeitet.

(5)       Die Löschung der Sie betreffenden personenbezogenen Daten ist zur Erfüllung einer rechtlichen Verpflichtung nach dem Unionsrecht oder dem Recht der Mitgliedstaaten erforderlich, dem der Verantwortliche unterliegt.

(6)       Die Sie betreffenden personenbezogenen Daten wurden in Bezug auf angebotene Dienste der Informationsgesellschaft gemäß Art. 8 Abs. 1 DSGVO erhoben.

b)        Information an Dritte

Hat der Verantwortliche die Sie betreffenden personenbezogenen Daten öffentlich gemacht und ist er gem. Art. 17 Abs. 1 DSGVO zu deren Löschung verpflichtet, so trifft er unter Berücksichtigung der verfügbaren Technologie und der Implementierungskosten angemessene Maßnahmen, auch technischer Art, um für die Datenverarbeitung Verantwortliche, die die personenbezogenen Daten verarbeiten, darüber zu informieren, dass Sie als betroffene Person von ihnen die Löschung aller Links zu diesen personenbezogenen Daten oder von Kopien oder Replikationen dieser personenbezogenen Daten verlangt haben.

c)        Ausnahmen

Das Recht auf Löschung besteht nicht, soweit die Verarbeitung erforderlich ist

(1)       zur Ausübung des Rechts auf freie Meinungsäußerung und Information;

(2)       zur Erfüllung einer rechtlichen Verpflichtung, die die Verarbeitung nach dem Recht der Union oder der Mitgliedstaaten, dem der Verantwortliche unterliegt, erfordert, oder zur Wahrnehmung einer Aufgabe, die im öffentlichen Interesse liegt oder in Ausübung öffentlicher Gewalt erfolgt, die dem Verantwortlichen übertragen wurde;

(3)       aus Gründen des öffentlichen Interesses im Bereich der öffentlichen Gesundheit gemäß Art. 9 Abs. 2 lit. h und i sowie Art. 9 Abs. 3 DSGVO;

(4)       für im öffentlichen Interesse liegende Archivzwecke, wissenschaftliche oder historische Forschungszwecke oder für statistische Zwecke gem. Art. 89 Abs. 1 DSGVO, soweit das unter Abschnitt a) genannte Recht voraussichtlich die Verwirklichung der Ziele dieser Verarbeitung unmöglich macht oder ernsthaft beeinträchtigt, oder

(5)       zur Geltendmachung, Ausübung oder Verteidigung von Rechtsansprüchen.

5.        Recht auf Unterrichtung

Haben Sie das Recht auf Berichtigung, Löschung oder Einschränkung der Verarbeitung gegenüber dem Verantwortlichen geltend gemacht, ist dieser verpflichtet, allen Empfängern, denen die Sie betreffenden personenbezogenen Daten offengelegt wurden, diese Berichtigung oder Löschung der Daten oder Einschränkung der Verarbeitung mitzuteilen, es sei denn, dies erweist sich als unmöglich oder ist mit einem unverhältnismäßigen Aufwand verbunden.

Ihnen steht gegenüber dem Verantwortlichen das Recht zu, über diese Empfänger unterrichtet zu werden.

6.        Recht auf Datenübertragbarkeit

Sie haben das Recht, die Sie betreffenden personenbezogenen Daten, die Sie dem Verantwortlichen bereitgestellt haben, in einem strukturierten, gängigen und maschinenlesbaren Format zu erhalten. Außerdem haben Sie das Recht diese Daten einem anderen Verantwortlichen ohne Behinderung durch den Verantwortlichen, dem die personenbezogenen Daten bereitgestellt wurden, zu übermitteln, sofern

(1)       die Verarbeitung auf einer Einwilligung gem. Art. 6 Abs. 1 lit. a DSGVO oder Art. 9 Abs. 2 lit. a DSGVO oder auf einem Vertrag gem. Art. 6 Abs. 1 lit. b DSGVO beruht und

(2)       die Verarbeitung mithilfe automatisierter Verfahren erfolgt.

In Ausübung dieses Rechts haben Sie ferner das Recht, zu erwirken, dass die Sie betreffenden personenbezogenen Daten direkt von einem Verantwortlichen einem anderen Verantwortlichen übermittelt werden, soweit dies technisch machbar ist. Freiheiten und Rechte anderer Personen dürfen hierdurch nicht beeinträchtigt werden.

Das Recht auf Datenübertragbarkeit gilt nicht für eine Verarbeitung personenbezogener Daten, die für die Wahrnehmung einer Aufgabe erforderlich ist, die im öffentlichen Interesse liegt oder in Ausübung öffentlicher Gewalt erfolgt, die dem Verantwortlichen übertragen wurde.

7.        Widerspruchsrecht

Sie haben das Recht, aus Gründen, die sich aus ihrer besonderen Situation ergeben, jederzeit gegen die Verarbeitung der Sie betreffenden personenbezogenen Daten, die aufgrund von Art. 6 Abs. 1 lit. e oder f DSGVO erfolgt, Widerspruch einzulegen; dies gilt auch für ein auf diese Bestimmungen gestütztes Profiling.

Der Verantwortliche verarbeitet die Sie betreffenden personenbezogenen Daten nicht mehr, es sei denn, er kann zwingende schutzwürdige Gründe für die Verarbeitung nachweisen, die Ihre Interessen, Rechte und Freiheiten überwiegen, oder die Verarbeitung dient der Geltendmachung, Ausübung oder Verteidigung von Rechtsansprüchen.

Werden die Sie betreffenden personenbezogenen Daten verarbeitet, um Direktwerbung zu betreiben, haben Sie das Recht, jederzeit Widerspruch gegen die Verarbeitung der Sie betreffenden personenbezogenen Daten zum Zwecke derartiger Werbung einzulegen; dies gilt auch für das Profiling, soweit es mit solcher Direktwerbung in Verbindung steht.

Widersprechen Sie der Verarbeitung für Zwecke der Direktwerbung, so werden die Sie betreffenden personenbezogenen Daten nicht mehr für diese Zwecke verarbeitet.

Sie haben die Möglichkeit, im Zusammenhang mit der Nutzung von Diensten der Informationsgesellschaft – ungeachtet der Richtlinie 2002/58/EG – Ihr Widerspruchsrecht mittels automatisierter Verfahren auszuüben, bei denen technische Spezifikationen verwendet werden.

8.        Recht auf Widerruf der datenschutzrechtlichen Einwilligungserklärung

Sie haben das Recht, Ihre datenschutzrechtliche Einwilligungserklärung jederzeit zu widerrufen. Durch den Widerruf der Einwilligung wird die Rechtmäßigkeit der aufgrund der Einwilligung bis zum Widerruf erfolgten Verarbeitung nicht berührt.

9.        Automatisierte Entscheidung im Einzelfall einschließlich Profiling

Sie haben das Recht, nicht einer ausschließlich auf einer automatisierten Verarbeitung – einschließlich Profiling – beruhenden Entscheidung unterworfen zu werden, die Ihnen gegenüber rechtliche Wirkung entfaltet oder Sie in ähnlicher Weise erheblich beeinträchtigt. Dies gilt nicht, wenn die Entscheidung

(1)       für den Abschluss oder die Erfüllung eines Vertrags zwischen Ihnen und dem Verantwortlichen erforderlich ist,

(2)       aufgrund von Rechtsvorschriften der Union oder der Mitgliedstaaten, denen der Verantwortliche unterliegt, zulässig ist und diese Rechtsvorschriften angemessene Maßnahmen zur Wahrung Ihrer Rechte und Freiheiten sowie Ihrer berechtigten Interessen enthalten oder

(3)       mit Ihrer ausdrücklichen Einwilligung erfolgt.

Allerdings dürfen diese Entscheidungen nicht auf besonderen Kategorien personenbezogener Daten nach Art. 9 Abs. 1 DSGVO beruhen, sofern nicht Art. 9 Abs. 2 lit. a oder g DSGVO gilt und angemessene Maßnahmen zum Schutz der Rechte und Freiheiten sowie Ihrer berechtigten Interessen getroffen wurden.

Hinsichtlich der in (1) und (3) genannten Fälle trifft der Verantwortliche angemessene Maßnahmen, um die Rechte und Freiheiten sowie Ihre berechtigten Interessen zu wahren, wozu mindestens das Recht auf Erwirkung des Eingreifens einer Person seitens des Verantwortlichen, auf Darlegung des eigenen Standpunkts und auf Anfechtung der Entscheidung gehört.

10.     Recht auf Beschwerde bei einer Aufsichtsbehörde

Unbeschadet eines anderweitigen verwaltungsrechtlichen oder gerichtlichen Rechtsbehelfs steht Ihnen das Recht auf Beschwerde bei einer Aufsichtsbehörde, insbesondere in dem Mitgliedstaat ihres Aufenthaltsorts, ihres Arbeitsplatzes oder des Orts des mutmaßlichen Verstoßes, zu, wenn Sie der Ansicht sind, dass die Verarbeitung der Sie betreffenden personenbezogenen Daten gegen die DSGVO verstößt.

Die Aufsichtsbehörde, bei der die Beschwerde eingereicht wurde, unterrichtet den Beschwerdeführer über den Stand und die Ergebnisse der Beschwerde einschließlich der Möglichkeit eines gerichtlichen Rechtsbehelfs nach Art. 78 DSGVO.

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Andreas Dombret doctoral thesis award

The doctoral thesis award of the School of Business and Economics is awarded once per semester for the best dissertation.

Beta Gamma Sigma

The international business honor society of AACSB-accredited schools includes the top graduates of the SBE.

Young Economy Award

The graduate award in the "young economy" theme area is awarded by the SBE alumni association AlumniUM e.V.

REACH Thesis Award

The graduate award for innovative theses is awarded by the REACH EUREGIO Start-up Center.

WWU dissertation awards

Subject related dissertation awards are handed by the Rectorate of the University of Münster.

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Dr. Naivy Nava was awarded the first REACH Thesis Award in the Category PhD thesis

Today, we were informed that based on Prof. Bruno Moerschbacher’s application, our recent doctoral graduate Dr. Naivy Nava was selected for the REACH Thesis Award for her doctoral thesis entitled „Biotechnological approaches of chitosan polymer and oligosaccharides to control the plant parasitic nematode, Meloidogyne incognita, and their mode of action in plant defense“. The REACH thesis awards were given for the first time this year, by the REACH EUREGIO Start-up Center of the University of Münster, for the most innovative and, at the same time, most practically relevant theses. In her doctoral project, Naivy had found that chitosan can protect crop plants from nematode attack to their roots, provided the right chitosan is used, and that it is applied at the right time in the right way. Nematode infestation of the roots leads to reduced root performance and, as a consequence, reduced plant growth and, in the case of crop plants, reduced harvest yields. However, as conventional nematicides are toxic not only to the nematodes but also to other animals including humans, their use is severely restricted in Europe, leading to substantial losses particularly in many legumes. Chitosans, in contrast, act mostly via inducing the plants’ own defense mechanisms, i.e. like a vaccine stimulating our immune system, and – as Naivy is currently demonstrating – by positively influencing the microbiome associated with the roots, the so called rhizobiome, i.e. like a prebiotic positively influencing our gut microbiota. Small wonder the REACH jury felt that this work is not only innovative, but also highly relevant, and worth one of the first REACH thesis awards. Congratulations Naivy!

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CS Faculty Earned Outstanding Dissertation Award

Assistant Professor Yu Meng , who joined the UVA Department of Computer Science in January of 2024, received the ACM SIGKDD 2024 Dissertation Award for his Ph.D. thesis, "Efficient and Effective Learning of Text Representations". 

This award recognizes outstanding research by doctoral candidates in data science, data mining, and knowledge discovery and is considered the highest honor in the data mining field for Ph.D. research. 

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REACH | Office of the Vice Chancellor for Research | SIU

Office of the vice chancellor for research.

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Office of the Vice Chancellor for Research 900 S. Normal Avenue, MC 4344 Woody Hall 350 Carbondale, IL 62901

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The REACH ( Research-Enriched Academic Challenge ) award offers the opportunity for SIU Carbondale undergraduate students to work with a faculty member on independent creative activities or research.  

 Who is eligible to apply for REACH?

• Degree-seeking undergraduates in all SIU Carbondale majors are encouraged to apply.
• Applicants who are currently enrolled as a full-time Southern Illinois University Carbondale student with a GPA of 2.8 or higher.
• Applicants who are enrolled as a full-time SIU Carbondale undergraduate student during at least the fall and spring semester of the 2024-2025 academic year.
• Applicants who will work closely with a faculty mentor in their area of research/creative interest to plan the project, prepare the application, and carry out the project.
• Only one application may be submitted per student.
• Past recipients of this award are not eligible to reapply.
• 2019-2020 Award Winners

Congratulations to the  2019-2020 REACH students.       

The following students were awarded the REACH award at the Student Creative Activities and Research Forum on Monday, April 8, 2019.  

• Angelina Arcuri
• Kevin Marmo
• Sarah Booth
• Tristin Miller
• Kailey Brown
• Garrett Murry
• Cierra Crowell
• Caroline Page
• Tra'Deidra Davis
• Bennett Phan
• Connor Gartner
• Logan Phillips
• Matt Herman 
• Benjamin Polo
• Madelyn Heinecke
• Carly Kasicki
• Elizabeth Saery
• Karalyn Rich
• Christian Rose
• Omar Khader
• Terry Ann Sneed
• Amanda Leppert-Gomes
• Emma Vogelsberg
• Miranda Limbach
• Lincoln Weber
• Cassidy Lounsbury
• Megan Welty
• Reise Malone
• Michaela Hoots
• 2020-2021 Award Winners

CONGRATULATIONS TO THE  2020-2021 REACH STUDENTS.         

The following students were awarded the REACH award at the Student Creative Activities and Research Forum held virtually on Tuesday, April 21, 2020. The winners were announced in the Awards Ceremony.

Emily Duran

Claire Talbert

Samantha Dennis

Lily Becker

Elyshia Lewis

Anuj Sharad Pawar

Kaitlyn Dodson

Valerie Bates

Tiana C Daniels

Bethany Egge

Elle Lanier

Daisy Kaplan

Grace Lehnhoff

Hannah Gahagan

Nicholas Reames

Nicholas Bartelsmeyer

Tamara Keene

Clark Lindsay

Julia Cicero

Emily O'Brien

David Hernanadez

Arianna Goss

Chole Grover

Darwin Koch

Secilia G Ho

Zachary Noehl

Nelson Fernanadez

Lauren Troutt

Maurnice Scott

Reece Davis

• 2022-2023 Award Winners
• Ayers, Claire
• Bartsch, Caitlyn
• Burkett, Gabriella
• Carson, Azareah
• Coogan, Vanessa
• Glowinski, Lucas
• Hoffman, Caleb
• Iriarte, Aleida
• Karl, Katie
• Ligon, Ryaan
• Morrical, Claire
• Olajuwon, Sally
• Overton, Isaiah
• Parks, Benjamin
• Price, Trinity
• Sanchez, Amber
• Sedlacek, Erin
• Skinner, Savannah
• Sparks, Abigail
• Wallace, Mae
• 2023-2024 Award Winners

Aaflaq, Sophia

Abell, Aren

Atchison, Benjamin

Barnes, Andrew

Barnes, Tony

Cantrell, Denae

Dillenburg, Georgia

Echols, Lisa

Francis, Isaiah

George, Lauren

Grey, Kayla

Hamon, Alexis

Heffren, Anna

Hillyer, Jared

Karl, Hannah

Lukavsky, Sarah

McKenna, Taylor

Qasem, Elana

Schwartzberg, Cameron

Stuart, Jayden

Zhan, Alyssa

• 2024-2025 Award Winners

REACH Awards competition applications opens in D2L on Decemeber 4 th  

Full-time SIU Carbondale undergraduate students can begin applying for the 2024-25 Research-Enriched Academic Challenge (REACH) award competition on Dec. 4th. Students with a GPA of 2.8 or higher are eligible. The application deadline is Jan. 29 th , 2024.

 Full-time undergraduate students working with a faculty member can qualify for REACH awards of up to $2,000 to support expenses associated with original creative activity or research projects.

Faculty need to submit recommendation forms and letters of support to REACH 2024 D2L class by 11:59 p.m. Feb. 5 th , 2024.

 Students will find application instructions, as well as eligibility and additional information, in the REACH 2025 class in D2L Use the Discover feature and type in the class name if you do not see it upon log in.

 Team and individual projects from all disciplines are encouraged. Successful team applications can receive $500 per additional team member in addition to the up to $2,000 REACH award.

Eligible awardees can be paid 10 hours per week for work on their projects during the FALL 2024 and SPRING 2025 semesters.

 Award recipients will be required to present a poster detailing their research or creative activity projects, and faculty mentors will be required to serve as judges at the undergraduate research showcase forum or SCARF 2025 on April 11 th , 2025, in the Student Center Art Gallery. A final report of their work will be due May 10, 2024, in the D2L class REACH 2024

More information is available at the SIU REACH Award website, at 618-453-5289 or by email at [email protected].

Congratulations 2024-2025 REACH Students                                                                              

Alabdullah, Raghad

Bergschneider, Bethany

Biggs, Mollie

Buchannan, Liliana

TEAM Christopher, Thierra & Samuel Wang

Eves, Natalie

Fieber, Max

Garcia, Eli

Ghosh, Ashlishya

TEAM Gusewelle, Brenna & Yin Sun

Hoeft, Abigail

TEAM Howton, Eli & James Martin

Johnson, Zachary

Kleiss, Caitlyn

TEAM Patel, Meet & Morgan Meinicke

Phillips, Hannah

Vogel, Josh

White, Kaleigh

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Student Contributions ACM Doctoral Dissertation Award

Superior research and writing by doctoral candidates in computer science and engineering

• ACM Doctoral Dissertation Award

About ACM Doctoral Dissertation Award

Presented annually to the author(s) of the best doctoral dissertation(s) in computer science and engineering.  The Doctoral Dissertation Award is accompanied by a prize of $20,000, and the Honorable Mention Award is accompanied by a prize totaling $10,000. Winning dissertations will be published in the ACM Digital Library as part of the ACM Books Series.

Recent Doctoral Dissertation Award News

2023 acm doctoral dissertation award.

Nivedita Arora of Northwestern University is the recipient of the ACM Doctoral Dissertation Award for her dissertation “ Sustainable Interactive Wireless Stickers: From Materials to Devices to Applications ,” which demonstrated wireless and batteryless sensor nodes using novel materials and radio backscatter.

Arora’s research envisions creating sustainable computational materials that operate by harvesting energy from the environment and, at the end of their life cycle, can be responsibly composted or recycled. Her research process involves working at the intersection of materials, methods of fabrication, low-power systems, and HCI . She actively looks to apply her work to application domains such as smart homes, health, climate change, and wildlife monitoring.

Arora’s dissertation makes truly groundbreaking contributions to the fields of Ubiquitous Computing and Human-Computer Interaction. Today’s Internet of Things (IoT) devices are bulky, require battery maintenance, and involve costly installation. In contrast, Arora shows how the computational capabilities of sensing, communication, and display can be diffused into materials and everyday objects. She builds interactive stickers that are inexpensive, and easy to deploy and sustainably operate by harvesting energy from body heat or indoor light. She demonstrates this idea over a series of projects. Her first effort,  SATURN , is a thin, flexible multi-layer material that is a self-sustaining audio sensor. Specifically, it uses the vibration itself to power the ability to capture and encode the vibration sensor. SATURN was extended to ZEUSSS  to use passive RF backscatter for wireless transmission on the vibration signal. She followed this up with the MARS platform that produces an extremely low-power (less than a microwatt) resonance circuit that varies its frequency based on user interaction with interfaces that create inductive or capacitive loads on the circuit. Coupling this circuit with FM passive backscatter and ambient power harvesting allows user interfaces such as touch-sensitive buttons, sliders, and vibration sensors to communicate at a distance. The result of these three projects is a flat user interface in a post-it note form factor that can be deployed in the environment simply by sticking it to a flat surface. The flat user interface and mobile design allows for applications such as light switches or audio volume sliders that can simply be pasted where they are needed without worrying about wiring the infrastructure or maintaining batteries.

The final project, VENUS , adds output in the form of low-power display technologies to provide immediate feedback on the surface of the computational material, opening a wide variety of user-facing interaction scenarios. Her work also showed that it is possible to power these circuits through the transfer of body heat when a user touches the button, which can also be used to protect privacy.

Arora is an Assistant Professor in the Electrical and Computer Engineering and (by courtesy) Computer Science Department, as well as the Allen K. and Johnnie Cordell Breed Jr. Professor of Design at Northwestern University. Her research involves rethinking the computing stack from a sustainability-first approach for its entire life-cycle: manufacturing, operation, and disposal. Arora received a PhD in Computer Science and an MS In Human-Computer Interaction from the Georgia Institute of Technology.

Honorable Mentions

Honorable Mentions for the ACM Doctoral Dissertation Award go to Gabriele Farina of the Massachusetts Institute of Technology, and William Kuszmaul   of Harvard University.

Farina’s   dissertation, “ Game-Theoretic Decision Making in Imperfect-Information Games ” was recognized for laying modern learning foundations for decision-making in imperfect-information sequential games, resolving long-standing questions, and demonstrating state-of-the-art theoretical and practical performance.

Farina is an Assistant Professor in the Electrical Engineering and Computer Science Department (EECS) at the Massachusetts Institute of Technology. His research interests include artificial intelligence, machine learning, optimization, and game theory. He received a PhD in Computer Science from Carnegie Mellon University.

Kuszmaul’s dissertation, “ Randomized Data Structures: New Perspectives and Hidden Surprises ,” is recognized for contributions to the field of randomized data structures that overturn conventional wisdom and widely believed conjecture.

Kuszmaul’s research focuses on algorithms, data structures, and probability. He received a PhD in Computer Science from the Massachusetts Institute of Technology and is presently doing Post Doctoral work at Harvard University. In August, he will be starting as an assistant professor in the Computer Science Department at Carnegie Mellon University.

Nivedita Arora of Northwestern University is the recipient of the ACM Doctoral Dissertation Award for her dissertation “ Sustainable Interactive Wireless Stickers: From Materials to Devices to Applications . Honorable Mentions for the ACM Doctoral Dissertation Award go to Gabriele Farina of the Massachusetts Institute of Technology, and William Kuszmaul   of Harvard University.

2023 ACM Doctoral Dissertation Award Honorable Mention

2022 acm doctoral dissertation award.

Aayush Jain is the recipient of the 2022 ACM Doctoral Dissertation Award  for his dissertation “ Indistinguishability Obfuscation From Well-Studied Assumptions ,” which established the feasibility of mathematically rigorous software obfuscation from well-studied hardness conjectures.

The central goal of software obfuscation is to transform source code to make it unintelligible without altering what it computes. Additional conditions may be added, such as requiring the transformed code to perform similarly, or even indistinguishably, from the original. As a software security mechanism, it is essential that software obfuscation have a firm mathematical foundation.

The mathematical object that Jain’s thesis constructs, indistinguishability obfuscation, is considered a theoretical “master tool” in the context of cryptography—not only in helping achieve long-desired cryptographic goals such as functional encryption, but also in expanding the scope of the field of cryptography itself. For example, indistinguishability obfuscation aids in goals related to software security that were previously entirely in the domain of software engineering.

Jain’s dissertation was awarded the Best Paper Award at the ACM Symposium on Theory of Computing (STOC 2021) and was the subject of an article in Quanta Magazine titled “Scientists Achieve Crown Jewel of Cryptography.”

Jain is an Assistant Professor at Carnegie Mellon University. He is interested in theoretical and applied cryptography and its connections with related areas of theoretical computer science. Jain received a BTech in Electrical Engineering, and an MTech in Information and Communication Technology from the Indian Institute of Technology, Delhi. He received a PhD in Computer Science from the University of California, Los Angeles.

Honorable Mentions for the 2022 ACM Doctoral Dissertation Award go to Alane Suhr  whose PhD was earned at Cornell University, and Conrad Watt ,  who earned his PhD at the University of Cambridge.

Suhr’s   dissertation, “ Reasoning and Learning in Interactive Natural Language Systems ,” was recognized for formulating and designing algorithms for continual language learning in collaborative interactions, and designing methods to reason about context-dependent language meaning. Suhr’s dissertation made transformative contributions in several areas of Natural Language Processing (NLP).

Suhr is an Assistant Professor at the University of California, Berkeley. Suhr’s research is focused on natural language processing, machine learning, and computer vision. Suhr received a BS in Computer Science and Engineering from Ohio State University, as well as a PhD in Computer Science from Cornell University.

Watt’s dissertation, “ Mechanising and Evolving the Formal Semantics of WebAssembly: the Web’s New Low-Level Language ,” establishes a mechanized semantics for WebAssembly and defines its concurrency model. The model will underpin current and future web engineering. His dissertation is considered a stand-out example of developing and using fully rigorous mechanized semantics to directly affect and improve the designs of major pieces of our industrial computational infrastructure.

Watt is a Research Fellow (postdoctoral) at the University of Cambridge, where he focuses on mechanized formal verification, concurrency, and the WebAssembly language. He received a MEng in Computer Science from Imperial College London and a PhD in Computer Science from the University of Cambridge.

Aayush Jain is the recipient of the 2022 ACM Doctoral Dissertation Award for his dissertation “ Indistinguishability Obfuscation From Well-Studied Assumptions .” Honorable Mentions for the 2022 ACM Doctoral Dissertation Award go to Alane Suhr whose PhD was earned at Cornell University, and Conrad Watt , who earned his PhD at the University of Cambridge.

Jain's dissertation established the feasibility of mathematically rigorous software obfuscation from well-studied hardness conjectures.The central goal of software obfuscation is to transform source code to make it unintelligible without altering what it computes. Additional conditions may be added, such as requiring the transformed code to perform similarly, or even indistinguishably, from the original. As a software security mechanism, it is essential that software obfuscation have a firm mathematical foundation.

2022 ACM Doctoral Dissertation Award Honorable Mention

Suhr’s dissertation, “ Reasoning and Learning in Interactive Natural Language Systems ,” was recognized for formulating and designing algorithms for continual language learning in collaborative interactions, and designing methods to reason about context-dependent language meaning. Suhr’s dissertation made transformative contributions in several areas of Natural Language Processing (NLP).

Watt’s dissertation, “ Mechanising and Evolving the Formal Semantics of WebAssembly: The Web’s New Low-Level Language ,” establishes a mechanized semantics for WebAssembly and defines its concurrency model. The model will underpin current and future web engineering. His dissertation is considered a stand-out example of developing and using fully rigorous mechanized semantics to directly affect and improve the designs of major pieces of our industrial computational infrastructure.

2021 ACM Doctoral Dissertation Award

Manish Raghavan is the recipient of the 2021 ACM Doctoral Dissertation Award for his dissertation " The Societal Impacts of Algorithmic Decision-Making ." Raghavan’s dissertation makes significant contributions to the understanding of algorithmic decision making and its societal implications, including foundational results on issues of algorithmic bias and fairness.

Algorithmic fairness is an area within AI that has generated a great deal of public and media interest. Despite being at a very early stage of his career, Raghavan has been one of the leading figures shaping the direction and focus of this line of research.

Raghavan is a Postdoctoral Fellow at the Harvard Center for Research on Computation and Society. His primary interests lie in the application of computational techniques to domains of social concern, including algorithmic fairness and behavioral economics, with a particular focus on the use of algorithmic tools in the hiring pipeline. Raghavan received a BS degree in Electrical Engineering and Computer Science from the University of California, Berkeley, and MS and PhD degrees in Computer Science from Cornell University.

Honorable Mentions for the 2021 ACM Doctoral Dissertation Award go to Dimitris Tsipras of Stanford University, Pratul Srinivasan of Google Research and Benjamin Mildenhall of Google Research.

Dimitris Tsipras’ dissertation, “ Learning Through the Lens of Robustness ,” was recognized for foundational contributions to the study of adversarially robust machine learning (ML) and building effective tools for training reliable machine learning models. Tsipras made several pathbreaking contributions to one of the biggest challenges in ML today: making ML truly ready for real-world deployment.

Tsipras is a Postdoctoral Scholar at Stanford University. His research is focused on understanding and improving the reliability of machine learning systems when faced with the real world. Tsipras received a Diploma in Electrical and Computer Engineering from the National Technical University of Athens, as well as SM and PhD degrees in computer science from the Massachusetts Institute of Technology (MIT).

Pratul Srinivasan and Benjamin Mildenhall are awarded Honorable Mentions for their co-invention of the Neural Radiance Field (NeRF) representation, associated algorithms and theory, and their successful application to the view synthesis problem. Srinivasan’s dissertation, " Scene Representations for View Synthesis with Deep Learning ," and Mildenhall’s dissertation, “ Neural Scene Representations for View Synthesis ,” addressed a long-standing open problem in computer vision and computer graphics. That problem, called “view synthesis” in vision and “unstructured light field rendering” in graphics, involves taking just a handful of photographs of a scene and predicting new images from any intermediate viewpoint. NeRF has already inspired a remarkable volume of follow-on research, and the associated publications have received some of the fastest rates of citation in computer graphics literature—hundreds in the first year of post-publication.

Srinivasan is a Research Scientist at Google Research, where he focuses on problems at the intersection of computer vision, computer graphics, and machine learning. He received a BSE degree in Biomedical Engineering and BA in Computer Science from Duke University and a PhD in Computer Science from the University of California, Berkeley.

Mildenhall is a Research Scientist at Google Research, where he works on problems in computer vision and graphics. He received a BS degree in Computer Science and Mathematics from Stanford University and a PhD in Computer Science from the University of California, Berkeley.

2020 ACM Doctoral Dissertation Award

Chuchu Fan is the recipient of the 2020 ACM Doctoral Dissertation Award for her dissertation, “ Formal Methods for Safe Autonomy: Data-Driven Verification, Synthesis, and Applications .” The dissertation makes foundational contributions to verification of embedded and cyber-physical systems, and demonstrates applicability of the developed verification technologies in industrial-scale systems.

Fan’s dissertation also advances the theory for sensitivity analysis and symbolic reachability; develops verification algorithms and software tools (DryVR, Realsyn); and demonstrates applications in industrial-scale autonomous systems.

Key contributions of her dissertation include the first data-driven algorithms for bounded verification of nonlinear hybrid systems using sensitivity analysis. A groundbreaking demonstration of this work on an industrial-scale problem showed that verification can scale. Her sensitivity analysis technique was patented, and a startup based at the University of Illinois at Urbana-Champaign has been formed to commercialize this approach.

Fan also developed the first verification algorithm for “black box” systems with incomplete models combining probably approximately correct (PAC) learning with simulation relations and fixed point analyses. DryVR, a tool that resulted from this work, has been applied to dozens of systems, including advanced driver assist systems, neural network-based controllers, distributed robotics, and medical devices.

Additionally, Fan’s algorithms for synthesizing controllers for nonlinear vehicle model systems have been demonstrated to be broadly applicable. The RealSyn approach presented in the dissertation outperforms existing tools and is paving the way for new real-time motion planning algorithms for autonomous vehicles.

Fan is the Wilson Assistant Professor of Aeronautics and Astronautics at the Massachusetts Institute of Technology, where she leads the Reliable Autonomous Systems Lab. Her group uses rigorous mathematics including formal methods, machine learning, and control theory for the design, analysis, and verification of safe autonomous systems. Fan received a BA in Automation from Tsinghua University. She earned her PhD in Electrical and Computer Engineering from the University of Illinois at Urbana-Champaign.

Honorable Mentions for the 2020 ACM Doctoral Dissertation Award go to Henry Corrigan-Gibbs and Ralf Jung .

Corrigan-Gibbs’s dissertation, “ Protecting Privacy by Splitting Trust ,” improved user privacy on the internet using techniques that combine theory and practice. Corrigan-Gibbs first develops a new type of probabilistically checkable proof (PCP), and then applies this technique to develop the Prio system, an elegant and scalable system that addresses a real industry need. Prio is being deployed at several large companies, including Mozilla, where it has been shipping in the nightly version of the Firefox browser since late 2019, the largest-ever deployment of PCPs.

Corrigan-Gibbs’s dissertation studies how to robustly compute aggregate statistics about a user population without learning anything else about the users. For example, his dissertation introduces a tool enabling Mozilla to measure how many Firefox users encountered a particular web tracker without learning which users encountered that tracker or why. The thesis develops a new system of probabilistically checkable proofs that lets every browser send a short zero-knowledge proof that its encrypted contribution to the aggregate statistics is well formed. The key innovation is that verifying the proof is extremely fast.

Corrigan-Gibbs is an Assistant Professor in the Electrical Engineering and Computer Science Department at the Massachusetts Institute of Technology, where he is also a member of the Computer Science and Artificial Intelligence Lab. His research focuses on computer security, cryptography, and computer systems. Corrigan-Gibbs received his PhD in Computer Science from Stanford University.

Ralf Jung’s dissertation, “ Understanding and Evolving the Rust Programming Language ,” established the first formal foundations for safe systems programming in the innovative programming language Rust. In development at Mozilla since 2010, and increasingly popular throughout the industry, Rust addresses a longstanding problem in language design: how to balance safety and control. Like C++, Rust gives programmers low-level control over system resources. Unlike C++, Rust also employs a strong “ownership-based” system to statically ensure safety, so that security vulnerabilities like memory access errors and data races cannot occur. Prior to Jung’s work, however, there had been no rigorous investigation of whether Rust’s safety claims actually hold, and due to the extensive use of “unsafe escape hatches” in Rust libraries, these claims were difficult to assess.

In his dissertation, Jung tackles this challenge by developing semantic foundations for Rust that account directly for the interplay between safe and unsafe code. Building upon these foundations, Jung provides a proof of safety for a significant subset of Rust. Moreover, the proof is formalized within the automated proof assistant Coq and therefore its correctness is guaranteed. In addition, Jung provides a platform for formally verifying powerful type-based optimizations, even in the presence of unsafe code.

Through Jung's leadership and active engagement with the Rust Unsafe Code Guidelines working group, his work has already had profound impact on the design of Rust and laid essential foundations for its future.

Jung is a post-doctoral researcher at the Max Planck Institute for Software Systems and a research affiliate of the Parallel and Distributed Operating Systems Group at the Massachusetts Institute of Technology. His research interests include programming languages, verification, semantics, and type systems. He conducted his doctoral research at the Max Planck Institute for Software Systems, and received his PhD, Master's, and Bachelor's degrees in Computer Science from Saarland University.

Chuchu Fan is the recipient of the 2020 ACM Doctoral Dissertation Award for her dissertation, “ Formal Methods for Safe Autonomy: Data-Driven Verification, Synthesis, and Applications .” Honorable Mentions go to Henry Corrigan-Gibbs of the Massachusetts Institute of Technology and Ralf Jung of the Max Planck Institute for Software Systems and MIT.

Fan’s dissertation makes foundational contributions to verification of embedded and cyber-physical systems, and demonstrates applicability of the developed verification technologies in industrial-scale systems. Her dissertation also advances the theory for sensitivity analysis and symbolic reachability; develops verification algorithms and software tools (DryVR, Realsyn); and demonstrates applications in industrial-scale autonomous systems.

2020 ACM Doctoral Dissertation Award Honorable Mention

2019 acm doctoral dissertation award.

Dor Minzer of Tel Aviv University is the recipient of the 2019 ACM Doctoral Dissertation Award for his dissertation, “ On Monotonicity Testing and the 2-to-2-Games Conjecture .” Honorable Mentions go to Jakub Tarnawski of École polytechnique fédérale de Lausanne (EPFL) and JiaJun Wu of Massachusetts Institute of Technology.

Dor Minzer's dissertation, “ On Monotonicity Testing and the 2-to-2-Games Conjecture ,” settles the complexity of testing monotonicity of Boolean functions and makes a significant advance toward resolving the Unique Games Conjecture, one of the most central problems in approximation algorithms and complexity theory.

Property-testers are extremely efficient randomized algorithms that check whether an object satisfies a certain property, when the data is too large to examine. For example, one may want to check that the distance between any two computers in the internet network does not exceed a given bound. In the first part of his thesis, Minzer settled a famous open problem in the field by introducing an optimal tester that checks whether a given Boolean function (voting scheme) is monotonic.

The holy grail of complexity theory is to classify computational problems to those that are feasible and those that are infeasible. The PCP theorem (for probabilistically checkable proofs) establishes the framework that enables classifying approximation problems as infeasible, showing they are NP-hard. In 2002, Subhash Khot proposed the Unique Games Conjecture (UGC), asserting that a very strong version of the PCP theorem should still hold. The conjecture has inspired a flurry of research and has had far-reaching implications. If proven true, the conjecture would explain the complexity of a whole family of algorithmic problems. In contrast to other conjectures, UGC has been controversial, splitting the community into believers and skeptics. While progress toward validating the conjecture has stalled, evidence against it had been piling up, involving new algorithmic techniques.

In the second part of his dissertation, Minzer went halfway toward establishing the conjecture, and in the process nullified the strongest known evidence against UGC. Even if UGC is not resolved in the immediate future, Minzer’s dissertation makes significant advances toward solving research problems that have previously appeared out of reach.

Minzer is a postdoctoral researcher at the Institute for Advanced Study (IAS) in Princeton, New Jersey, and will be joining MIT as an Assistant Professor in the fall of 2020. His main research interests are in computational complexity theory, PCP, and analysis of Boolean functions. Minzer received a BA in Mathematics, as well as an MSc and PhD in Computer Science from Tel Aviv University.

Dor Minzer of Tel Aviv University is the recipient of the 2019 ACM Doctoral Dissertation Award for his dissertation, “ On Monotonicity Testing and the 2-to-2-Games Conjecture .” The key contributions of Minzer’s dissertation are settling the complexity of testing monotonicity of Boolean functions and making a significant advance toward resolving the Unique Games Conjecture, one of the most central problems in approximation algorithms and complexity theory.

Honorable Mentions for the 2019 ACM Doctoral Dissertation Award go to Jakub Tarnawski , École polytechnique fédérale de Lausanne (EPFL) and JiaJun Wu , Massachusetts Institute of Technology (MIT).

Jakub Tarnawski’s dissertation “ New Graph Algorithms via Polyhedral Techniques ” made groundbreaking algorithmic progress on two of the most central problems in combinatorial optimization: the matching problem and the traveling salesman problem. Work on deterministic parallel algorithms for the matching problem is motivated by one of the unsolved mysteries in computer science: does randomness help in speeding up algorithms? Tarnawski’s dissertation makes significant progress on this question by almost completely derandomizing a three-decade-old randomized parallel matching algorithm by Ketan Mulmuley, Umesh Vaziriani, and Vijay Vazirani.

The second major result of Tarnawski’s dissertation relates to the traveling salesman problem: find the shortest tour of n given cities. Already in 1956, George Dantzig et al. used a linear program to solve a special instance of the problem. Since then the strength of their linear program has become one of the main open problems in combinatorial optimization. Tarnawski’s dissertation resolves this question asymptotically and gives the first constant-factor approximation algorithm for the asymmetric traveling salesman problem.

Tarnawski is a researcher at Microsoft Research. He is broadly interested in theoretical computer science and combinatorial optimization, particularly in graph algorithms and approximation algorithms. He received his PhD from EPFL and an MSc in Mathematics and Computer Science from the University of Wrocław, Poland.

JiaJun Wu’s dissertation, “ Learning to See the Physical World ,” has advanced AI for perceiving the physical world by integrating bottom-up recognition in neural networks with top-down simulation engines, graphical models, and probabilistic programs. Despite phenomenal progress in the past decade, current artificial intelligence methods tackle only specific problems, require large amounts of training data, and easily break when generalizing to new tasks or environments. Human intelligence reveals how far we need to go: from a single image, humans can explain what we see, reconstruct the scene in 3D, predict what’s going to happen, and plan our actions accordingly.

Wu addresses the problem of physical scene understanding—how to build efficient and versatile machines that learn to see, reason about, and interact with the physical world. The key insight is to exploit the causal structure of the world, using simulation engines for computer graphics, physics, and language, and to integrate them with deep learning. His dissertation spans perception, physics and reasoning, with the goal of seeing and reasoning about the physical world as humans do. The work bridges the various disciplines of artificial intelligence, addressing key problems in perception, dynamics modeling, and cognitive reasoning.

Wu is an Assistant Professor of Computer Science at Stanford University. His research interests include physical scene understanding, dynamics models, and multi-modal perception. He received his PhD and SM degree in Electrical Engineering and Computer Science from MIT, and Bachelor’s degrees in Computer Science and Economics from Tsinghua University in Beijing, China.

2019 ACM Doctoral Dissertation Award Honorable Mention

2018 acm doctoral dissertation award.

Chelsea Finn of the University of California, Berkeley is the recipient of the 2018 ACM Doctoral Dissertation Award for her dissertation, “ Learning to Learn with Gradients .” In her thesis, Finn introduced algorithms for meta-learning that enable deep networks to solve new tasks from small datasets, and demonstrated how her algorithms can be applied in areas including computer vision, reinforcement learning and robotics.

Deep learning has transformed the artificial intelligence field and has led to significant advances in areas including speech recognition, computer vision and robotics. However, deep learning methods require large datasets, which aren’t readily available in areas such as medical imaging and robotics.

Meta-learning is a recent innovation that holds promise to allow machines to learn with smaller datasets. Meta-learning algorithms “learn to learn” by using past data to learn how to adapt quickly to new tasks. However, much of the initial work in meta-learning focused on designing increasingly complex neural network architectures. In her dissertation, Finn introduced a class of methods called model-agnostic meta-learning (MAML) methods, which don’t require computer scientists to manually design complex architectures. Finn’s MAML methods have had tremendous impact on the field and have been widely adopted in reinforcement learning, computer vision and other fields of machine learning.

At a young age, Finn has become one of the most recognized experts in the field of robotic learning. She has developed some of the most effective methods to teach robots skills to control and manipulate objects. In one instance highlighted in her dissertation, she used her MAML methods to teach a robot reaching and placing skills, using raw camera pixels from just a single human demonstration.

Finn is a Research Scientist at Google Brain and a postdoctoral researcher at the Berkeley AI Research Lab (BAIR). In the fall of 2019, she will start a full-time appointment as an Assistant Professor at Stanford University. Finn received her PhD in Electrical Engineering and Computer Science from the University of California, Berkeley and a BS in Electrical Engineering and Computer Science from the Massachusetts Institute of Technology.

Honorable Mentions for the 2018 ACM Doctoral Dissertation Award go to Ryan Beckett and Tengyu Ma , who both received PhD degrees in Computer Science from Princeton University.

Ryan Beckett developed new, general and efficient algorithms for creating and validating network control plane configurations in his dissertation, “ Network Control Plane Synthesis and Verification .” Computer networks connect key components of the world’s critical infrastructure. When such networks are misconfigured, several systems people rely on are interrupted—airplanes are grounded, banks go offline, etc. Beckett’s dissertation describes new principles, algorithms and tools for substantially improving the reliability of modern networks. In the first half of his thesis, Beckett shows that it is unnecessary to simulate the distributed algorithms that traditional routers implement—a process that is simply too costly—and that instead, one can directly verify the stable states to which such algorithms will eventually converge. In the second half of his thesis, he shows how to generate correct configurations from surprisingly compact high-level specifications.

Beckett is a researcher in the mobility and networking group at Microsoft Research. He received his PhD and MA in Computer Science from Princeton University, and both a BS in Computer Science and a BA in Mathematics from the University of Virginia.

Tengyu Ma’s dissertation, " Non-convex Optimization for Machine Learning: Design, Analysis, and Understanding ,” develops novel theory to support new trends in machine learning. He introduces significant advances in proving convergence of nonconvex optimization algorithms in machine learning, and outlines properties of machine learning models trained via such methods. In the first part of his thesis, Ma studies a range of problems, such as matrix completion, sparse coding, simplified neural networks, and learning linear dynamical systems, and formalizes clear and natural conditions under which one can design provable correct and efficient optimization algorithms. In the second part of his thesis, Ma shows how to understand and interpret the properties of embedding models for natural languages, which were learned using nonconvex optimization.

Ma is an Assistant Professor of Computer Science and Statistics at Stanford University. He received a PhD in Computer Science from Princeton University and a BS in Computer Science from Tsinghua University.

2018 ACM Doctoral Dissertation Award Honorable Mention

Chelsea Finn of the University of California, Berkeley is the recipient of the 2018 ACM Doctoral Dissertation Award for her dissertation, “ Learning to Learn with Gradients .” Honorable Mentions go to Ryan Beckett and Tengyu Ma , who both received PhD degrees in Computer Science from Princeton University.

Beckett developed new, general and efficient algorithms for creating and validating network control plane configurations in his dissertation, “ Network Control Plane Synthesis and Verification .” Computer networks connect key components of the world’s critical infrastructure. When such networks are misconfigured, several systems people rely on are interrupted—airplanes are grounded, banks go offline, etc. Beckett’s dissertation describes new principles, algorithms and tools for substantially improving the reliability of modern networks. In the first half of his thesis, Beckett shows that it is unnecessary to simulate the distributed algorithms that traditional routers implement—a process that is simply too costly—and that instead, one can directly verify the stable states to which such algorithms will eventually converge. In the second half of his thesis, he shows how to generate correct configurations from surprisingly compact high-level specifications.

Ma’s dissertation, " Non-convex Optimization for Machine Learning: Design, Analysis, and Understanding ,” develops novel theory to support new trends in machine learning. He introduces significant advances in proving convergence of nonconvex optimization algorithms in machine learning, and outlines properties of machine learning models trained via such methods. In the first part of his thesis, Ma studies a range of problems, such as matrix completion, sparse coding, simplified neural networks, and learning linear dynamical systems, and formalizes clear and natural conditions under which one can design provable correct and efficient optimization algorithms. In the second part of his thesis, Ma shows how to understand and interpret the properties of embedding models for natural languages, which were learned using nonconvex optimization.

2017 ACM Doctoral Dissertation Award

Aviad Rubinstein is the recipient of the Association for Computing Machinery (ACM) 2017 Doctoral Dissertation Award for his dissertation “ Hardness of Approximation Between P and NP .” In his thesis, Rubinstein established the intractability of the approximate Nash equilibrium problem and several other important problems between P and NP-completeness—an enduring problem in theoretical computer science.

For several decades, researchers in areas including economics and game theory have developed mathematical equilibria models to predict how people in a game or economic environment might act given certain conditions.

When applying computational approaches to equilibria models, important questions arise, including how long it would take a computer to calculate an equilibrium. In theoretical computer science, a problem that can be solved in theory (given finite resources, such as time) but for which, in practice, any solution takes too many resources (that is, too much time) to be useful is known as an intractable problem. In 2008, Daskalakis, Goldberg and Papadimitriou demonstrated the intractability of the Nash equilibrium, an often-examined scenario in game theory and economics where no player in the game would take a different action as long as every other player in the game remains the same. But a very large question remained in theoretical computer science as to whether an approximate Nash equilibrium (a variation of the Nash equilibrium that allows the possibility that a player may have a small incentive to do something different) is also intractable.

Rubinstein’s dissertation introduced brilliant new ideas and novel mathematical techniques to demonstrate that the approximate Nash equilibrium is also intractable. Beyond solving this important question, Rubinstein’s thesis also insightfully addressed other problems around P and NP completeness, the most important question in theoretical computer science. Rubinstein is a postdoctoral researcher at Harvard University and will be starting an appointment as an Assistant Professor at Stanford University in the fall of 2018. He received a PhD in Computer Science from the University of California, Berkeley, an MSc in Computer Science from Tel Aviv University (Israel) and a BSc in Mathematics and Computer Science from Technion (Israel).

Honorable Mentions for the 2017 ACM Doctoral Dissertation Award went to Mohsen Ghaffari , who received his PhD from the Massachusetts Institute of Technology’s Department of Electrical Engineering and Computer Science (MIT EECS) and Stefanie Mueller , who received her PhD from the Hasso Plattner Institute (Germany). 

In Ghaffari’s dissertation, “ Improved Distributed Algorithms for Fundamental Graph Problems ,” he presents novel distributed algorithms that significantly lower the costs of solving fundamental graph problems in networks, including structuring problems, connectivity problems, and scheduling problems. Ghaffari’s dissertation includes both breakthrough algorithmic contributions and interesting methodology. The first part of the dissertation presents a new maximal independent set (MIS) algorithm, which is a breakthrough because it achieves a better time bound than previous algorithms for this three-decades-old problem. The second part of the dissertation contains a collection of related results about vertex connectivity decompositions. Finally, in the third part of his dissertation, Ghaffari introduces a time-efficient algorithm for concurrent scheduling of multiple distributed algorithms. Ghaffari is an Assistant Professor of Computer Science at ETH Zurich. He received a PhD and SM in Electrical Engineering and Computer Science from the Massachusetts Institute of Technology and received a double major in Computer Science and Electrical Engineering from Sharif University (Iran).

Mueller’s dissertation, “ Interacting with Personal Fabrication Devices ,” demonstrates how to make personal fabrication machines interactive. Her approach involves two steps: speeding of batch processing and turn taking, and real-time interaction.  Her software systems faBrickator, WirePrint and Platener allow users to fabricate 10 times faster, a process she calls low-fidelity fabrication or low-fab. In her dissertation she also outlines how to add interactivity. Constructable, a tool she developed, allows workers to fabricate by sketching directly on the workpiece, causing a laser cutter to implement these sketches when the user stops drawing. Another of Mueller’s tools, LaserOrigami, extends this work to 3D.  Mueller is an Assistant Professor of Computer Science at MIT EECS and MIT CSAIL. She received a PhD in Computer Science as well as an MSc in IT-Systems Engineering from the Hasso Plattner Institute (Germany). Earlier, she received a BSc in Computer Science and Media from the University of Applied Science Harz (Germany).

Honorable Mentions for the 2017 ACM Doctoral Dissertation Award went to Mohsen Ghaffari , who received his PhD from the Massachusetts Institute of Technology’s Department of Electrical Engineering and Computer Science (MIT EECS) and Stefanie Mueller , who received her PhD from the Hasso Plattner Institute (Germany).

2017 ACM Doctoral Dissertation Award Honorable Mention

Aviad Rubinstein is the recipient of the  Association for Computing Machinery (ACM) 2017 Doctoral Dissertation Award for his dissertation “ Hardness of Approximation Between P and NP .” Honorable Mentions for the award went to Mohsen Ghaffari , who received his PhD from the Massachusetts Institute of Technology’s Department of Electrical Engineering and Computer Science (MIT EECS) and Stefanie Mueller , who received her PhD from the Hasso Plattner Institute (Germany).

2017 ACM Doctoral Dissertation Award Award Honorable Mention

Aviad Rubinstein is the recipient of the Association for Computing Machinery (ACM) 2017 Doctoral Dissertation Award for his dissertation “ Hardness of Approximation Between P and NP .” Honorable Mentions for the award went to Mohsen Ghaffari , who received his PhD from the Massachusetts Institute of Technology’s Department of Electrical Engineering and Computer Science (MIT EECS) and Stefanie Mueller , who received her PhD from the Hasso Plattner Institute (Germany).

2016 ACM Doctoral Dissertation Award

Haitham Hassanieh is the recipient of the ACM 2016 Doctoral Dissertation Award . Hassanieh developed highly efficient algorithms for computing the Sparse Fourier Transform, and demonstrated their applicability in many domains including networks, graphics, medical imaging and biochemistry.  In his dissertation,  The Sparse Fourier Transform: Theory and Practice , he presented a new way to decrease the amount of computation needed to process data, thus increasing the efficiency of programs in several areas of computing.

In computer science, the Fourier transform is a fundamental tool for processing streams of data. It identifies frequency patterns in the data, a task that has a broad array of applications. For many years, the Fast Fourier Transform (FFT) was considered the most efficient algorithm in this area. With the growth of Big Data, however, the FFT cannot keep up with the massive increase in datasets. In his doctoral dissertation Hassanieh presents the theoretical foundation of the Sparse Fourier Transform (SFT), an algorithm that is more efficient than FFT for data with a limited number of frequencies. He then shows how this new algorithm can be used to build practical systems to solve key problems in six different applications including wireless networks, mobile systems, computer graphics, medical imaging, biochemistry and digital circuits. Hassanieh’s Sparse Fourier Transform can process data at a rate that is 10 to 100 times faster than was possible before, thus greatly increasing the power of networks and devices.

Hassanieh is an Assistant Professor in the Department of Electrical and Computer Engineering and the Department of Computer Science at the University of Illinois at Urbana-Champaign. He received his MS and PhD in Electrical Engineering and Computer Science at the Massachusetts Institute of Technology (MIT). A native of Lebanon, he earned a BE in Computer and Communications Engineering from the American University of Beirut. Hassanieh’s Sparse Fourier Transform algorithm was chosen by  MIT Technology Review as one of the top 10 breakthrough technologies of 2012. He has also been recognized with the Sprowls Award for Best Dissertation in Computer Science, and the SIGCOMM Best Paper Award.

Honorable Mention for the 2016 ACM Doctoral Dissertation Award went to Peter Bailis of Stanford University and Veselin Raychev of ETH Zurich.

In Bailis’s dissertation, Coordination Avoidance in Distributed Databases , he addresses a perennial problem in a network of multiple computers working together to achieve a common goal: Is it possible to build systems that scale efficiently (process ever-increasing amounts of data) while ensuring that application data remains provably correct and consistent? These concerns are especially timely as Internet services such as Google and Facebook have led to a vast increase in the global distribution of data. In addressing this problem, Bailis introduces a new framework, invariant confluence, that mitigates the fundamental tradeoffs between coordination and consistency. His dissertation breaks new conceptual ground in the areas of transaction processing and distributed consistency—two areas thought to be fully understood. Bailis is an Assistant Professor of Computer Science at Stanford University. He received a PhD in Computer Science from the University of California, Berkeley and his AB in Computer Science from Harvard College.

Raychev’s dissertation, Learning from Large Codebases , introduces new methods for creating programming tools based on probabilistic models of code that can solve tasks beyond the reach of current methods. As the size of publicly available codebases has grown dramatically in recent years, so has interest in developing programming tools that solve software tasks by learning from these codebases. Raychev’s dissertation takes a novel approach to addressing this challenge that combines advanced techniques in programming languages with machine learning practices. In the thesis, Raychev lays out four separate methods that detail how machine learning approaches can be applied to program analysis in order to produce useful programming tools. These include: code completion with statistical language models; predicting program properties from big code; learning program from noisy data; and learning statistical code completion systems. Raychev’s work is regarded as having the potential to open up several promising new avenues of research in the years to come. Raychev is currently a co-founder and Chief Technology Officer of DeepCode, a company developing artificial intelligence-based programming tools. He received a PhD in Computer Science from ETH Zurich. A native of Bulgaria, he received MS and BS degrees from Sofia University.

2016 ACM Doctoral Dissertation Honorable Mention Award

Haitham Hassanieh is the recipient of the ACM 2016 Doctoral Dissertation Award .  Honorable Mention for the 2016 ACM Doctoral Dissertation Award went to Peter Bailis of Stanford University and Veselin Raychev of ETH Zurich.

Haitham Hassanieh  is the recipient of the ACM 2016  Doctoral Dissertation Award . Hassanieh developed highly efficient algorithms for computing the Sparse Fourier Transform, and demonstrated their applicability in many domains including networks, graphics, medical imaging and biochemistry.  In his dissertation,  The Sparse Fourier Transform: Theory and Practice , he presented a new way to decrease the amount of computation needed to process data, thus increasing the efficiency of programs in several areas of computing.

Hassanieh is an Assistant Professor in the Department of Electrical and Computer Engineering and the Department of Computer Science at the University of Illinois at Urbana-Champaign. He received his MS and PhD in Electrical Engineering and Computer Science at the Massachusetts Institute of Technology (MIT). A native of Lebanon, he earned a BE in Computer and Communications Engineering from the American University of Beirut. Hassanieh’s Sparse Fourier Transform algorithm was chosen by  MIT Technology Review  as one of the top 10 breakthrough technologies of 2012. He has also been recognized with the Sprowls Award for Best Dissertation in Computer Science, and the SIGCOMM Best Paper Award.

Honorable Mention for the 2016 ACM Doctoral Dissertation Award went to  Peter Bailis  of Stanford University and  Veselin Raychev  of ETH Zurich.

In Bailis’s dissertation,  Coordination Avoidance in Distributed Databases , he addresses a perennial problem in a network of multiple computers working together to achieve a common goal: Is it possible to build systems that scale efficiently (process ever-increasing amounts of data) while ensuring that application data remains provably correct and consistent? These concerns are especially timely as Internet services such as Google and Facebook have led to a vast increase in the global distribution of data. In addressing this problem, Bailis introduces a new framework, invariant confluence, that mitigates the fundamental tradeoffs between coordination and consistency. His dissertation breaks new conceptual ground in the areas of transaction processing and distributed consistency—two areas thought to be fully understood. Bailis is an Assistant Professor of Computer Science at Stanford University. He received a PhD in Computer Science from the University of California, Berkeley and his AB in Computer Science from Harvard College.

Carnegie Mellon Graduate Earns ACM Doctoral Dissertation Award

Julian Shun has won the 2015 ACM Doctoral Dissertation Award presented by ACM for providing evidence that, with appropriate programming techniques, frameworks and algorithms, shared-memory programs can be simple, fast and scalable. In his dissertation Shared-Memory Parallelism Can Be Simple, Fast, and Scalable , he proposes new techniques for writing scalable parallel programs that run efficiently both in theory and in practice.

While parallelism is essential to achieving high performance in computing, writing efficient and scalable programs can be very difficult. Shun’s three-pronged approach to writing parallel programs that he outlines in his thesis includes:

• proposing tools and techniques for deterministic parallel programming;
• the introduction of Ligra, the first high-level shared-memory framework for parallel graph traversal algorithms; and
• presenting new algorithms for a variety of important problems on graphs and strings that are both efficient in theory and practice.

Shun is a post-doctoral researcher at the University of California, Berkeley, where he was awarded a Miller Research Fellowship. He earned his Ph.D. at Carnegie Mellon University, which nominated him for the ACM Doctoral Dissertation Award. He earned a B.A. in Computer Science from the University of California, Berkeley, where he was ranked first in the 2008 graduating class of computer science students. During the 2013-2014 academic year, he was the recipient of a Facebook Graduate Fellowship.

He will receive the Doctoral Dissertation Award and its $20,000 prize at the annual ACM Awards Banquet on June 11 in San Francisco. Financial sponsorship of the award is provided by Google Inc.

Honorable Mention

Honorable mention for the 2015 ACM Doctoral Dissertation Award went to Aaron Sidford of the Massachusetts Institute of Technology, and Siavash Mirarab of the University of Texas at Austin. They will share a $10,000 prize, with financial sponsorship provided by Google Inc.

In Sidford’s dissertation, Iterative Methods, Combinatorial Optimization, and Linear Programming Beyond the Universal Barrier , he considers the fundamental problems in continuous and combinatorial optimization that occur pervasively in practice, and shows how to improve upon the best-known theoretical running times for solving these problems across a broad range of parameters. Sidford uses and improves techniques from diverse disciplines including spectral graph theory, numerical analysis, data structures, and convex optimization to provide the first theoretical improvements in decades for multiple classic problems ranging from linear programming to linear system solving to maximum flow. Sidford is presently a postdoctoral researcher at Microsoft New England. He received a Ph.D. in Computer Science from the Massachusetts Institute of Technology, which nominated him for this award.

Mirarab’s dissertation, Novel Scalable Approaches for Multiple Sequence Alignment and Phylogenomic Reconstruction , addresses the growing need to analyze large-scale biological sequence data efficiently and accurately. To address this challenge, Mirarab introduces several methods: PASTA, a scalable and accurate algorithm that can align data sets up to one million sequences; statistical binning, a novel technique for reducing noise in estimation of evolutionary trees for individual parts of the genome; and ASTRAL, a new summary method that can run on 1,000 species in one day and has outstanding accuracy. These methods were essential in analyzing very large genomic datasets of birds and plants. Mirarab is currently an Assistant Professor of Electrical and Computer Engineering at the University of California, San Diego. He obtained a Ph.D. in Computer Science from the University of Texas at Austin, which nominated him for this award.

Creator Of Advanced Data Processing Architecture Wins 2014 Doctoral Dissertation Award

Matei Zaharia  won the 2014 Doctoral Dissertation Award for his innovative solution to tackling the surge in data processing workloads, and accommodating the speed and sophistication of complex multi-stage applications and more interactive ad-hoc queries. His work proposed a new architecture for cluster computing systems, achieving best-in-class performance in a variety of workloads while providing a simple programming model that lets users easily and efficiently combine them.

To address the limited processing capabilities of single machines in an age of growing data volumes and stalling process speeds, Zaharia developed Resilient Distributed Datasets (RDDs). As described in his dissertation “An Architecture for Fast and General Data Processing on Large Clusters,” RDDs are a distributed memory abstraction that lets programmers perform computations on large clusters in a faulttolerant manner. He implements RDDs in the open source Apache Spark system, which matches or exceeds the performance of specialized systems in many application domains, achieving up to speeds 100 times faster for certain applications. It also offers stronger fault tolerance guarantees and allows these workloads to be combined.

Zaharia, an assistant professor at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL), completed his dissertation at the University of California, Berkeley, which nominated him. A graduate of the University of Waterloo, where he won a gold medal at the ACM International Collegiate Programming Contest (ICPC) in 2005, he earned a Bachelor of Mathematics (B. Math) degree. He is a co-founder and Chief Technology Officer of Databricks, the company that is commercializing Apache Spark.

He will receive the Doctoral Dissertation Award and its $20,000 prize at the annual ACM Awards Banquet on June 20 in San Francisco, CA. Financial sponsorship of the award is provided by Google Inc.

Honorable Mention for the 2014 ACM Doctoral Dissertation Award went to  John Criswell  of the University of Rochester, and  John C. Duchi  of Stanford University. They will share a $10,000 prize, with financial sponsorship provided by Google Inc.

Criswell’s dissertation, “Secure Virtual Architecture: Security for Commodity Software Systems,” describes a compiler-based infrastructure designed to address the challenges of securing systems that use commodity operating systems like UNIX or Linux. This Secure Virtual Architecture (SVA) can protect both operating system and application code through compiler instrumentation techniques. He completed a Ph.D. degree in Computer Science from the University of Illinois at Urbana-Champaign, which nominated him for this award.

Duchi’s dissertation, “Multiple Optimality Guarantees in Statistical Learning,” explores tradeoffs that occur in modern statistical and machine learning applications. The criteria for these tradeoffs – computation, communication, privacy – must be optimized to maintain statistical performance. He explores examples from optimization, and shows some of the practical benefits that a focus on multiple optimality criteria can bring about. A graduate of the University of California, Berkeley with an M.A. degree in Statistics and a Ph.D. degree in Computer Science, he was also an undergraduate and masters student at Stanford University. He was nominated by UC Berkeley for this award.

ACM will present these and other awards at the ACM Awards Banquet on June 20, 2015 in San Francisco, CA.

Press Release

Doctoral Dissertation Award Recognizes Young Researchers

Nivedita Arora  is the recipient of the ACM Doctoral Dissertation Award for demonstrating wireless and batteryless sensor nodes using novel materials and radio backscatter in her dissertation “Sustainable Interactive Wireless Stickers: From Materials to Devices to Applications.” Honorable Mentions for the ACM Doctoral Dissertation Award go to Gabriele Farina , whose PhD was earned at Carnegie Mellon University, for his dissertation “Game-Theoretic Decision Making in Imperfect-Information Games”; and William Kuszmaul , whose PhD was earned at MIT, for his dissertation “Randomized Data Structures: New Perspectives and Hidden Surprises.”

Full List of ACM Awards

Acm awards by category, career-long contributions, early-to-mid-career contributions, specific types of contributions, student contributions, regional awards, how awards are proposed.

The Office of Undergraduate Research and Major Awards

The Research for Aspiring Coogs in the Humanities (REACH) Program at the University of Houston started as a collaborative effort supported by the Cougar Initiative to Engage and the Office of Undergraduate Research and Major Awards. REACH will provide a year-long introductory research experience for students in humanities disciplines by connecting participants to existing undergraduate research projects at the University of Houston. From projects in collaboration with UH Libraries Special Collections, to the digital humanities, to individual faculty research projects in humanities disciplines, the REACH program provides an entry-point to hands-on scholarly inquiry. Check out the exciting projects below!

REACH participants will develop their research skills through their work on a mentored research project and through their participation in OURMA undergraduate research programming. Students work closely with the project mentor to contribute to the existing project from October through May. Students will also learn how to apply for future research opportunities such as SURF, PURS and the Mellon Research Scholars Program. REACH participants receive a $1,500 scholarship split between the fall and spring semesters in the program and three points towards the Honors in Co-Curricular Engagement transcript designation .

The 2024-2025 REACH application is now open! Apply by September 4, 2024.

Current REACH Projects

• View Current REACH Projects
• View Past REACH Projects

La Louisiane en Tejas

The Gulf Coast Sound

Recovering the US Hispanic Literary Heritage

Sharing Stories from 1977

Storymapping Houston

Triumph and Tragedy in the Bayou City

The Year 1771

Reach spotlights.

REACH Scholar Curated Zindler Exhibit

REACH Scholar Valeria González Featured in Provost Profiles

Exhibit Highlights the Genesis of UH African American Studies

New Exhibit Features Houston GLBT Political Caucus

UH Students Documenting U.S. Latino Experience Through Research, Archival Initiative

University of Houston Launches a New Undergraduate Research Program

Learn more about the reach program.

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About the Program

REACH will provide a year-long introductory research experience for students in humanities disciplines by connecting participants to existing undergraduate research projects at the University of Houston.  REACH participants will develop their research skills through their work on a mentored research project and through their participation in OURMA undergraduate research programming. Students will also learn how to apply for future research opportunities such as SURF, PURS and the Mellon Research Scholars Program. REACH participants receive a $1,500 scholarship split between the fall and spring semesters in the program.

Eligibility

• Sophomore, junior, senior and transfer students at UH main campus enrolled for the entire academic year they apply.
• Students outside of a humanities major but pursuing a minor in a humanities-related discipline will need to articulate how research in the selected subject area will play an integral role in their future trajectory.
• Are interested in contributing to one of the projects facilitated by our campus partners (no prior research experience required).
• Students should be in good academic standing.

Project Selection

When students apply to the REACH program, they are asked to identify projects they would be interested in working on and why. Selection for the REACH program aims to match campus partner needs with student interests. To find descriptions of REACH projects, go to our Current Projects page . For descriptions of previous REACH projects, go to our Past Projects page .

Expectations/Workload

Selected students will be expected to:

• Devote a minimum of 6-7 hours of research activity per week for the academic year
• Contribute directly to the existing research project and produce a research project deliverable by the end of the academic year in coordination with the project mentor
• Attend OURMA Undergraduate Research webinars and bi-monthly check-ins
• Complete pre- and post-surveys
• Present their research findings through a research poster at UH Undergraduate Research Day

HCCE Points

REACH participants will receive 3 points towards the Honors in Co-Curricular Engagement (HCCE) transcript designation . The HCCE transcript designation is awarded to students upon graduation who pursue a broad range of experiential learning opportunities from freshman to senior year. Students can pursue undergraduate research, internships, learning away and abroad, service learning, leadership experience and other academic enrichment opportunities.

Please contact Dr. Rikki Bettinger in the Office of Undergraduate Research and Major Awards if you have any questions or if you require additional information.

Interested but Not Yet Eligible?

No problem! Consider applying for the REACH program the following year. We encourage you to reach out to the Office of Undergraduate Research and Major Awards to get started in undergraduate research.

• News & Stories

News in Brief: EHD Alumna, Researcher Wins Outstanding Dissertation Award

Now a researcher at Youth-Nex, Meredith Franco was awarded the 2024 APA Division 16 Outstanding Dissertation Award for her work as an EHD doctoral student.

Leslie Booren

August 30, 2024

Meredith P. Franco, currently a research scientist at Youth-Nex, was recently recognized for her outstanding dissertation while a Ph.D. student at the UVA School of Education and Human Development. A 2023 graduate of the clinical and school psychology program, Franco’s dissertation, “Culturally Responsive Practice in PK-12 Classrooms: Identification and Validation of Discrete Indicators,” was honored by the American Psychological Association (APA) Division 16 this month.

The honor is given to an individual who recently completed a dissertation that merits special recognition and has the potential to contribute to the science and practice of school psychology. An article stemming from Franco’s dissertation work was published in the high-impact journal, Review of Educational Research, and is now freely available .

“We are grateful that the APA and the review committee selected Meredith for this prestigious award,” said Professor Catherine Bradshaw, senior associate dean for research and co-chair of Franco’s dissertation committee. “Her dissertation work is exceptional in many ways. Meredith is skillful in conceptualizing and addressing key challenges in the field by employing rigorous quantitative methodologies with a critical lens.”

In her dissertation, Franco encourages school researchers and practitioners to recognize the positive impact of culturally responsive practice (CRP) on classroom climate and how to understand different measurement approaches for CRP-related interventions. She also encourages them to find ways to create new, innovative measures that can be used across developmental stages and contexts.

Now a Nationally Certified School Psychologist and researcher, Franco’s scholarship continues to explore how teachers’ use of culturally responsive educational practices can promote student equity and, ultimately, inform the design of just classrooms, schools, and systems.

“I am incredibly thankful to the Division 16 Award Committee for this recognition.” Franco said. “The encouragement I received from my EHD mentors—including professors Catherine Bradshaw, Jessika Bottiani, and Jason Downer—to take on ambitious projects and aim for publishing in high-impact journals has laid the groundwork for my path as an early career researcher.”

Franco and other Division 16 award winners were recognized at the annual APA Convention this summer on August 10 in Seattle, WA.  

News Information

Research center or department, featured faculty.

• Meredith Powers Franco
• Catherine P. Bradshaw

News Topics

• Teaching & Learning

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• Join The Division

Ralph Alexander Best Dissertation Award

The Ralph Alexander Dissertation Award is given to the author of the best doctoral dissertation in the field of human resource management. In order to be eligible for this award, a dissertation must address a phenomenon that is of importance to the human resources field and have been completed with 24 months prior to the submission deadline. The award winner will be announced at the Academy meeting during the HR Division Awards Ceremony and receive a plaque and a $1000 honorarium.

In support of the HR Division's goal of increasing member involvement and its international reach, we strongly encourage nominations that reflect the rich diversity, backgrounds, and perspectives of our membership.

• The significance and importance of the problem to human resources.
• The extent to which the design, findings, or orientation advances research or theory.
• Given the length allotment, special attention will be paid to the conceptual development of the paper.

Nominations should adhere to the following procedures:

• An entrant should submit an electronic copy of their paper. Papers are limited to a maximum of 50 double-spaced pages (including title page, abstract, text, figures, tables, references, footnotes, appendices, etc.).
• The name of the submitter, his/her institutional affiliation, current mailing address, and phone number should appear only on the title page.
• A submitter must provide a letter from his/her dissertation chair specifying (a) that the paper submitted adequately represents the completed dissertation, and (b) the date the dissertation was accepted by the university.
• A paper may be submitted only once.
• Please  combine all required materials (paper, nomination letter, etc.) into a single PDF document (less than 100 MB)   and use the “nominate” button to complete an official nomination for this award. The nomination deadline is February 1, 2024.  

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This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.

This project examined the role of non-governmental organizations (NGOs) in the development of regulatory policy for nanomaterials in the European Union (EU) and the United States (US). Participation of environmental NGOs, trade unions, and other civil society groups in discussions about regulatory policy can make such procedures more democratic and more representative. However, s tructural differences between the EU and US political systems create distinctive political opportunities for NGOs to participate in debates about nanomaterials. The study considered the implications of each system for public participation in complex technoscientific policy debates. Anna Lamprou, a PhD candidate in the Science and Technology Studies Department at Rensselaer Polytechnic Institute, conducted interviews with stakeholders involved in nanotechnology and chemical policymaking in the EU and the US. In addition, she observed conferences and meetings concerning nanotechnology regulation and analyzed documents relevant to nanotechnology policymaking. 

In both the EU and the US, NGOs have been included as members of “expert” groups (such as the group responsible for developing guidelines for the regulation of nanomaterials under existing frameworks in the EU and the group that developed the voluntary program for reporting nanomaterials in the US) and they comment on draft regulations. However, there are important contrasts between the two systems. In the EU, capacity building projects aimed at increasing NGO knowledge about nanotechnology have made their participation more meaningful. In addition, NGO participation and influence is more pronounced in the EU because of the parliamentary system, and more importantly, the presence of a Green Party that is responsive to environmental NGOs. However, despite the relative prominence of NGOs in EU deliberations about nanotechnology, decisions are not necessarily made more democratically. NGOs struggle to exert influence in expert panels, because of their limited resources. Furthermore, “non-expert” members of the public have very few opportunities to have their voices heard in debates about nanotechnology. A key conclusion of this research, therefore, is that simply creating opportunities for NGOs to weigh in on important regulatory issues is not sufficient to ensure that decisions are made in the public interest. Capacity building initiatives to provide NGOs with sufficient knowledge to make informed contributions can deepen the level of public engagement; however, in systems where industry lobby groups have significant influence and the general public is not broadly involved in discussions about risky new technologies, formal NGO participation can only have a limited effect on policy outcomes.

Last Modified: 12/14/2012 Modified by: Abby Kinchy

Please report errors in award information by writing to: [email protected] .