PhD Fellowship Program

PhD Fellowship Program



The Microsoft Research PhD Fellowship Program has supported 122 fellows since the program was established in 2008, many of whom have gone on to work within the Microsoft Research organization. Others have gone on to perform pioneering research elsewhere within the technology industry or accept faculty appointments at leading universities. We are pleased to announce an additional 10 students have been awarded the fellowship for 2018.

Provisions of the 2018 award

  • The fellowship recipient award will cover 100 percent of the tuition and fees for two academic years (2018–19 and 2019–20).
  • A stipend is provided to help cover living expenses while in school (US$28,000 for 2018–19 and US$28,000 for 2019–20).
  • A conference and travel allowance is provided for recipients to attend professional conferences or seminars (US$4,000 for 2018–19 and US$4,000 for 2019–20).
  • The award includes an invitation to interview for one salaried internship in 2018 with leading Microsoft researchers working on cutting-edge projects related to the recipient’s field of study.
  • Fellowships are awarded to recipients for two consecutive academic years only and are not available for extension.

Eligibility criteria

  • Applicants for the Microsoft Research PhD Fellowship Program must be nominated by their universities, and their nominations must be confirmed by the office of the chair of the eligible department. Direct applications from students are not accepted.
  • Students must attend a United States or Canadian university and be enrolled in the computer science, electrical engineering, or mathematics department. If your department is within the scope of these areas but is titled differently, you are eligible.
  • The proposed research must be closely related to the research topics carried out by Microsoft Research as noted in the Research areas tab. We are particularly interested in proposals related to Systems & Networking and AI (including Machine Learning, Computer Vision, and Robotics).
  • Students must be in their second or third year of an eligible PhD program in the fall semester or quarter of 2017. The nominating university will be asked to confirm the student’s PhD program start date (month/year).
  • A maximum of three applicants per eligible department, per eligible university, will be accepted. A total of nine applications per university will be allowed.
  • Microsoft will have discretion as to how any remaining funds will be used if the student is no longer qualified to receive funding (e.g. if the student unenrolls from the program, graduates, or transfers to a different university).
  • The recipient must remain an active, full-time student in a PhD program during the two consecutive academic years of the award or forfeit the award.
  • A recipient of a Microsoft Research PhD Fellowship may not receive another fellowship from another company or institution for the same academic period. Fellows accepting multiple fellowships will become ineligible to receive continued funding from Microsoft.

Microsoft actively seeks to foster greater levels of diversity in our workforce and in our pipeline of future researchers. We are always looking for the best and brightest talent and celebrate individuality. We invite candidates to come as they are and do what they love.


How to apply

  • Applications must include:
      • Nominee’s name, email, university, and department
      • Month and year the nominee entered their PhD program (nominee must currently be in their second or third year of their PhD program and vetted by the university)
      • Nominee’s curriculum vitae
      • Nominee’s thesis proposal or research statement title
      • One-page summary of their thesis proposal or research statement
      • Their thesis proposal or research statement (short and concise is recommended—no more than five pages)
      • Name of the nominee’s advisor
      • Primary and secondary research areas (a list can be found on our webpage)

    Microsoft Research - research areas

    • Names, organizations, email addresses, and letters of reference from three established researchers familiar with the nominee’s research (at least one of which must be from their primary academic advisor/supervisor and only one letter can be from a current Microsoft employee)
  • Applications must be submitted via the online application tool by 11:59 PM (PT) on October 16, 2017, in any of the following formats: Word document, text-only file, or PDF. Email or hard-copy applications will not be considered. All application materials must be submitted by the person who is designated as the application contact by the departmental chair’s office and must not be the applicant.
  • Applications submitted to Microsoft will not be returned. Microsoft cannot assume responsibility for the confidentiality of information in submitted applications. Therefore, applications should not contain information that is confidential, restricted, or sensitive. Microsoft reserves the right to make public the information on those applications that receive awards, except those portions containing budgetary or personally identifiable information.
  • Incomplete applications cannot be considered, and notification of incompleteness will not be made.
  • Due to the volume of submissions, Microsoft Research cannot provide individual feedback on applications that do not receive fellowship awards.


Below are the answers to frequently asked questions about the 2017 Microsoft Research PhD Fellowship Program.

Selection criteria

Are international students eligible to apply?

Yes, if you are a full-time international student at an eligible US or Canadian school and pursuing your PhD in academic year 2017–18, you are eligible.

What if I’m a student attending a university outside the United States or Canada?

The Microsoft Research PhD Fellowship Program includes only US and Canadian schools. If you are a student attending a school outside the United States or Canada, you are not eligible for this fellowship. Please check out our PhD Scholarship Programme in EMEA and our Fellowships at Microsoft Research Asia.

What if I am not starting my second or third year in academic year 2017–2018?

Students must be in their second or third year in an eligible PhD program in the fall semester or quarter of 2017 to apply for this program.

Do I have to be nominated by my university or can I apply on my own?

To be considered for the program, you must be nominated by an eligible university. The application contact for your department chair must submit the application on your behalf.

Which university departments are eligible to participate?

Computer science, electrical engineering, or mathematics departments at eligible universities may each nominate up to three students. If your department falls in the broad scope of computer science, electrical engineering, or mathematics but is called something else, your department can nominate you as well. However, we prefer that your department coordinates with and sends your nomination through computer science, electrical engineering, or mathematics departments. If your department is unable to coordinate with one of these three departments at your school, please encourage your departmental chair’s office to contact us at

Fellowship review process

Who will review the nominations?

Applications will be reviewed by researchers from Microsoft Research whose expertise covers a wide range of disciplines. After the first review, a selection of applicants will be invited for in-person interviews. Award winners are chosen from the finalists.

When will I know the outcome of the review process?

Selected fellowship applicants will receive notification no later than January 31, 2018. Due to the volume of submissions, Microsoft Research cannot provide individual feedback on applications that do not receive fellowship awards.

Fellowship award details

If selected, when will my fellowship begin?

Persons awarded a fellowship in January will receive their financial awards in August/September of that year. Microsoft sends payment directly to the university, who will disperse funds according to their guidelines.

Are there any tax implications for me if I receive this fellowship?

The tax implications for your stipend, fees, and tuition are based on the policy at your university.

Will intellectual property be an issue if I am awarded a fellowship?

The Microsoft Research PhD Fellowship Program is not subject to any intellectual property (IP) restrictions unless and until the fellowship recipient also accepts the associated internship. If you accept an internship, you will be subject to the same restrictions as any other Microsoft intern.

Can I simultaneously receive fellowships from other companies?

If you accept a Microsoft Research PhD Fellowship, you may not receive another fellowship from another company or institution during the same academic period. Fellows accepting multiple fellowships will become ineligible to receive continued funding from Microsoft. Microsoft will at its sole discretion consider a joint fellowship with a government or non-profit organization. Please contact for consideration.


Microsoft Research PhD Fellows

Microsoft Research recognizes the following fellows, who represent the best and the brightest from North America.

2018 PhD Fellows

Iretiayo AkinolaIretiayo Akinola

Columbia University

As robots become more prevalent in our world, we’re challenged with programming them to learn faster and be more adaptable to different tasks and scenarios. Humans already have a high-level mastery of most of the tasks we’d like robots to learn. So Iretiayo is researching a way to map the signals from the human brain to useful reward signals through human-in-the-loop robot learning. This involves the development of techniques that use new modalities such as Brain-Computer Interfaces to allow humans to control and teach robots different skills, thus accelerating robot learning of complex tasks. To accomplish this, he is developing interactive robot learning systems that can leverage explicit human guidance through augmented reality platforms or implicit guidance signals through naturally evoked brain signals of a human to train the robot to perform different tasks.

Naama Ben-DavidNaama Ben-David

Carnegie Mellon University

Virtually all modern machines, from our laptops to our phones, have multiple cores. It is thus of the utmost importance to be able to design correct and efficient concurrent programs. While there is ample work defining and proving the correctness of concurrent algorithms, surprisingly little is known about their efficiency. Many factors, like the number of processes available, the memory access patterns of the algorithm, and the cache coherency protocols of the machine, can greatly affect the performance of a given algorithm on a given machine. Naama’s research aims to formally and rigorously characterize the costs associated with concurrency and to provide a framework with which researchers can analyze the efficiency of the algorithms they develop.

Vincent Josua HellendoornVincent Josua Hellendoorn

University of California, Davis

The world’s software is increasingly developed in globally distributed and highly decentralized settings, such as the open source domain. As a result, the main vessel for communication between developers is more often the code itself: its naming, style, and organization. Understanding this form of communication is necessary for tools that aim to intelligently support developers in creating software. Vincent’s research applies models that were originally designed for natural languages to source code to better understand the characteristics of real-world software. His work improves these (typically deep learning) models by integrating insights from software engineering research and programming language design. The resulting models are used to build tools that can assist developers in common, but taxing software engineering tasks like fault detection and API discovery.

Vikram IyerVikram Iyer

University of Washington

Insect-scale aerial robots – the size of a penny – are tiny, flying bio-inspired platforms with sensing and computation abilities uniquely suited to a host of applications benefiting from their small scale and maneuverability including disaster relief, agriculture, search and rescue, and even dynamic 3D displays. Vikram’s research focuses on addressing the fundamental wireless networking and hardware challenges for these devices including power delivery, communication, and localization.

Natalie KlcoNatalie Klco

University of Washington

Natalie is a graduate student at the University of Washington studying physics with the Institute of Nuclear Theory under adviser Dr. Martin Savage. She has been inspired by the simulation of fundamental physics and has been intrigued by a new perspective using quantum-based computation—simulating many-body quantum systems with quantum systems. Excited by recent experimental digital quantum simulations of the vacuum properties of electrodynamics as well as the great success of non-perturbative techniques developed by the nuclear and particle physics communities over recent decades for studying the interactions of quarks and gluons, Natalie plans to spend her PhD research in the pursuit of simulating field theories on quantum computers. With this research, in concert with that of Microsoft Research, she hopes to unravel an answer to the basic question: is a quantum computer sufficient to efficiently simulate our natural world?

Manish RaghavanManish Raghavan

Cornell University

Machine learning algorithms are increasingly being used in socially consequential contexts, and numerous concerns have been raised with regard to the potential adverse impacts on disadvantaged populations. Manish’s research seeks to understand and develop techniques to mitigate such effects. This involves analyzing mechanisms that address particular societal biases as well as identifying fundamental trade-offs that exist between desirable properties of algorithmic systems. Ultimately, the aim of his research is to expose the normative choices necessary in algorithmic decision-making processes and provide insights as to the impacts these choices have.

Julia RomanskiJulia Romanski

Massachusetts Institute of Technology

Julia’s research is in applied probability and algorithms. Currently, she is analyzing a model of interacting random walks. Julia initially proposed this model in order to study resource flow in an economic network, but there may be more applications of the model, for example in social science (e.g. modeling group interactions), or even in physics and chemistry. Her goal is to analyze the theoretical aspects of the model so that it can be used in applied settings. There are many theoretical questions to consider when studying the underlying Markov chain, such as phase transition, mixing time, estimation questions, and formulating an appropriate continuum version of the model.

Haijun XiaHaijun Xia

University of Toronto

In the age of digital computing, we strive to communicate with computers in order to leverage their power to augment our human intelligence and creativity. We explore creative ideas by sketching out designs, manipulating models, and conversing with others. The computing power usually comes last, where the rich and dynamic ideas are downsampled to certain supported representations, by clicking commands from the menus or filling in numbers for the properties. Haijun’s research seeks to empower us to think with computers. He invents new representations of the content, coupled with intuitive interaction techniques, to enable flexible exploration and direct expression of ideas via graphical, gestural, and verbal communication that we are naturally capable of.

Dong XieDong Xie

University of Utah

With the increasing presence of sensors, smartphones, and IoT projects, there has been an explosion in the amount of spatiotemporal data. As the data size grows beyond the storage and processing capacity of a single machine, we’re faced with the fundamental challenges of storing, processing, and analyzing large amounts of spatiotemporal data efficiently. Motivated by such observations, Dong’s research has led to building a distributed spatiotemporal data analytics engine called Simba, which features a simple yet expressive programming interface and high-performance query processing. While developing Simba as a full-fledged system, Dong explores both theory and system aspects by designing novel distributed algorithms for sophisticated spatiotemporal query and exploiting modern system techniques like full-stage code generation.

Justine ZhangJustine Zhang

Cornell University

Justine Zhang is a PhD student in Information Science at Cornell University, advised by Professor Cristian Danescu-Niculescu-Mizil. Her research aims to develop models of conversations that explicitly encode the complex interactional dynamics they entail and are applicable to the diversity and scale of large online social systems. She received her Bachelor of Science in Computer Science from Stanford University.

2017 PhD Fellows

Michael B. CohenMichael B. Cohen

Massachusetts Institute of Technology

Michael’s research is focused on obtaining faster algorithms for fundamental linear algebra and optimization problems, generally with provable guarantees. Work includes dimensionality reduction—that is, transforming a high-dimensional problem into one of much lower dimension for faster processing. In addition, Michael is working on applying graph Laplacian solvers as a faster way to obtain max flow and minimum-cost flow outputs in graphic applications.

September 2017:

The Microsoft Research family is shocked and deeply saddened by the tragic loss of our colleague and friend Michael Cohen.  Michael was a brilliant mathematician and a rising star in his field.  He spent this past summer with us as a Microsoft Research Fellow, doing what he loved most. Over the summer, he made sweeping progress in online learning and online algorithms, two fields he had just recently become acquainted with. In addition to solving five open problems in these areas, he continued his substantial progress on the k-server problem, one of the most celebrated and notoriously difficult challenges in the space of adaptive algorithms.  

Michael was a truly exceptional individual.  We will remember Michael for his infectious smile and his larger-than-life personality. We will never forget his unrelenting curiosity, his thirst for knowledge, and his deep love for mathematics and theoretical foundations of computing.  We are stunned by his loss.  We will hold onto our memories of Michael, and know that his ideas and scientific accomplishments will continue on as important advances. 

We extend our most sincere condolences to Michael’s family and friends.

Bita Darvish RouhaniBita Darvish Rouhani

University of California, San Diego

To analyze massive and densely correlated data requires solving for two main challenges: resource constraints and complexity. One set of hurdles deals with the resource and/or application constraints such as real-time requirement, available energy, or memory. The other set of challenges arises due to the complexity of the underlying learning task, which requires more than traditional linear or polynomial analytical approaches to be sufficiently accurate. Bita’s research addresses the challenges associated with big data analytics using a combination of hardware-based and software-based approaches. Her work addresses these two critical aspects of big data scenarios by designing and building solutions and tools that are both data-aware and platform-aware.

Michaelanne DyeMichaelanne Dye

Georgia Institute of Technology

Despite growing Internet access initiatives, political, economic, and social barriers continue to limit meaningful Internet engagement for individuals in resource-constrained communities. Michaelanne’s research explores how increasing access to the Internet influences lives and how one might use preexisting, local information infrastructures to design more effective services in emerging markets. Her current research is in Cuba, where, up until recently, Internet access was limited to 5 percent of the population. Through fieldwork, observation, and interviews, Michaelanne is developing a holistic understanding of how new Internet infrastructures interact with cultural values and local constraints. Her work explores how future Internet access initiatives in resource-constrained parts of the world might successfully map onto local information infrastructures.

Kira GoldnerKira Goldner

University of Washington

Developing revenue maximization models that address uncertainty and specific real-world constraints requires new approaches and algorithms. Kira’s research looks to stretch revenue maximization models beyond restricted or known distributions, complex pricing mechanisms, and risk-neutrality, as well as apply them to nonrevenue (social good) outcomes. This research has applications ranging from blockchain-based systems, to kidney exchanges, routing traffic in a network, pricing package deliveries, selling electronics or any commerce application where auctions and pricing uncertainty exist.

Aditya GroverAditya Grover

Stanford University

The major successes in machine learning during the past few decades have been restricted to supervised settings where there is access to vast quantities of labeled data. In unlabeled, unsupervised contexts with significant uncertainty, machine learning has a long way to go. Aditya’s research focuses on designing and analyzing principled, efficient algorithms for probabilistic reasoning in unsupervised settings with unlabeled data, with a special emphasis on deep generative models that can scale to large and high-dimensional datasets, such as those found in energy conservation and weather forecasting applications.

Silu HuangSilu Huang

University of Illinois at Urbana-Champaign

Silu’s research is around developing a collaborative data analytics program called DataHub. In essence, she is developing a multiuser system like GitHub, but for structured data. It features compact storage to handle large datasets as well as a rich query language for manipulations on data and versions. Early development goals include designing a compact storage engine with fast retrieval of versions, creating a unified query language for data provenance and versioning, and adapting relational databases to support versioning. This system is being built on top of PostgreSQL with a carefully designed data model and novel partitioning algorithms to efficiently support collaborative data management.

Saswat PadhiSaswat Padhi

University of California, Los Angeles

The central motivation for Saswat’s research is to help programmers build reliable software with verified guarantees. Although several formal techniques have been developed for program verification, adoption has been low, primarily because two key requirements for these techniques—a formal specification and inductive invariants—still need to be provided manually, which is a cumbersome and error-prone process. Saswat’s research focuses on developing solutions that allow users to interactively generate and refine these requirements and thus help them formally verify their programs. His approach combines 1) data-driven insights from machine learning (on data collected from programs), 2) logical insights from formal reasoning (on program structure), and 3) effective user interaction models.

Andrew QuinnAndrew Quinn

University of Michigan

The development community has designed many powerful, dynamic analyses that help programmers understand software, such as backward slicing, dynamic information flow tracking, memory checkers, invariant checkers, and data-race detectors. While these analyses are useful for debugging, developers use them only on extremely challenging bugs due to their high overhead. For example, it can take hours to calculate the backward slice of a complex program. Andrew’s research creates cluster-scale systems that allow developers to quickly understand and debug programs. The work parallelizes dynamic analyses across thousands of cores in a compute cluster, reducing analysis time from hours to seconds.

Mengting WanMengting Wan

University of California, San Diego

Mengting focuses on building scalable machine learning algorithms to process massive and heterogeneous real-world human activity datasets. In particular, her research focuses on modeling human opinions and behavior to understand the dynamics of human activities and interactions. Specifically, she is working on two problems: the relationship between macro-knowledge and micro-opinions. Regarding the relationship between what someone knows in the general to how that informs their opinions or behaviors, she aims to effectively organize and summarize both factual knowledge and subjective opinions. Regarding the inverse, how micro-opinions relate to macro-knowledge, her work aims to interpret and predict behavior, studying the connection between efficient recommendation engines and established behavioral theories in social sciences. Mengting’s work has application in opinion-oriented question answering, recommendation systems, and e-commerce.

2016 PhD Fellows

Huiwen ChangHuiwen Chang

Princeton University

Salma Hosni ElmalakiSalma Hosni Elmalaki

University of California, Los Angeles

Denae FordDenae Ford

North Carolina State University

Nika HaghtalabNika Haghtalab

Carnegie Mellon University

Sam HopkinsSam Hopkins

Cornell University

Tom HutchcroftTom Hutchcroft

University of British Columbia, Vancouver

Lukas MaasLukas Maas

Harvard University

Qing QuQing Qu

Columbia University

Caitlyn SeimCaitlyn Seim

Georgia Institute of Technology

Deepak VasishtDeepak Vasisht

Massachusetts Institute of Technology

Amir YazdanbakhshAmir Yazdanbakhsh

Georgia Institute of Technology

Venkata Ravitheja YelleswarapuVenkata Ravitheja Yelleswarapu

University of Pennsylvania

2015 PhD Fellows

Justin ChengJustin Cheng

Stanford University

Lilian de GreefLilian de Greef

University of Washington, Seattle

Pan HuPan Hu

University of Massachusetts, Amherst

Laura InozemtsevaLaura Inozemtseva

University of Waterloo

Ana KlimovicAna Klimovic

Stanford University

Andrew OwensAndrew Owens

Massachusetts Institute of Technology

Phitchaya Mangpo PhothilimthanaPhitchaya Mangpo Phothilimthana

University of California, Berkeley

Aviad RubensteinAviad Rubinstein

University of California, Berkeley

Zhaoran WangZhaoran Wang

Princeton University

Chu XuChu Xu

University of Waterloo

Irene ZhangIrene Zhang

University of Washington, Seattle

Yibo ZhuYibo Zhu

University of California, Santa Barbara

2014 PhD Fellows

  • Fadel Adib – Massachusetts Institute of Technology
  • Yoav Artzi – University of Washington, Seattle
  • Vijay Chidambaran – University of Wisconsin, Madison
  • Richard Eisenberg – University of Pennsylvania
  • Mayank Goel – University of Washington, Seattle
  • Sergey Gorbunov – Massachusetts Institute of Technology
  • Rishabh Iyer – University of Washington, Seattle
  • Albert Ng – Stanford University
  • Nihar Shah – University of California, Berkeley
  • Abhinav Shrivastava – Carnegie Mellon University
  • Niki Vazou – University of California, San Diego
  • Yanqi Zhou – Princeton University

2013 PhD Fellows

  • Bharath Hariharan – University of California, Berkeley
  • Ahmed Kirmani – Massachusetts Institute of Technology
  • Walter Lasecki – University of Rochester
  • Michael Paul – Johns Hopkins University
  • Gennady Pekhimenko – Carnegie Mellon University
  • Mert Pilanci – University of California, Berkeley
  • Jeffrey Rzeszotarski – Carnegie Mellon University
  • Rahul Sharma – Stanford University
  • Rashmi Vinayak – University of California, Berkeley
  • Mathew Weinberg – Massachusetts Institute of Technology
  • Mingchen Zhao – University of Pennsylvania
  • Yufei Zhao – Massachusetts Institute of Technology

2012 PhD Fellows

  • Utku Ozan Candogan – Massachusetts Institute of Technology
  • Gabriel Cohn – University of Washington, Seattle
  • George Dahl – University of Toronto
  • Sigal Oren – Cornell University
  • Ashish Patro – University of Wisconsin, Madison
  • Russell Power – New York University
  • Franziska Roesner – University of Washington, Seattle
  • Michael Rubinstein – Massachusetts Institute of Technology
  • Julia Schwarz – Carnegie Mellon University
  • Rishabh Singh – Massachusetts Institute of Technology
  • Richard Socher – Stanford University
  • Bangpeng Yao – Stanford University

2011 PhD Fellows

  • Michael Bernstein – Massachusetts Institute of Technology
  • Morgan Dixon – University of Washington, Seattle
  • Matthew Fredrikson – University of Wisconsin, Madison
  • Allison Lewko – University of Texas, Austin
  • Renato Paes Leme – Cornell University
  • Qiang Liu – University of California, Irvine
  • Richard Peng – Carnegie Mellon University
  • Lenin Ravindranath Sivalingam – Massachusetts Institute of Technology
  • Trang Thai – Georgia Institute of Technology
  • Yuandong Tian – Carnegie Mellon University
  • Oriol Vinyals – University of California, Berkeley
  • Chi Wang – University of Illinois, Urbana-Champaign

2010 PhD Fellows

  • Silas Boyd-Wickizer – Massachusetts Institute of Technology
  • David Erickson – Stanford University
  • Charles Han – Columbia University
  • William Harris – University of Wisconsin, Madison
  • Chris Harrison – Carnegie Mellon University
  • Dilip Krishnan – New York University
  • Jian Peng – Toyota Technological Institute at Chicago
  • Shubhangi Saraf – Massachusetts Institute of Technology
  • Dafna Shahaf – Carnegie Mellon University
  • Jonah Sherman – University of California, Berkeley

2009 PhD Fellows

  • Alekh Agarwal – University of California, Berkeley
  • Timothy Austin – University of California, Los Angeles
  • Katherine Coons – University of Texas, Austin
  • Lakshmi Ganesh – Cornell University
  • Jean-Francois Lalonde – Carnegie Mellon University
  • Edith Law – Carnegie Mellon University
  • Christopher Le Dantec – Georgia Institute of Technology
  • Huijia Lin – Cornell University
  • Kayur Patel – University of Washington
  • Snehit Prabhu – Columbia University
  • Shravan Rayanchu – University of Wisconsin
  • Sven Seuken – Harvard University
  • Yaron Singer – University of California, Berkeley
  • Ross Tate – University of California, San Diego
  • Xiang Zhang – University of North Carolina, Chapel Hill

2008 PhD Fellows

  • Aruna Balasubramanian – University of Massachusetts
  • Pravin Bhat – University of Washington
  • Michael Carbin – Massachusetts Institute of Technology
  • Eric S. Chung – Carnegie Mellon University
  • Prabal Dutta – University of California, Berkeley
  • Jon Froehlich – University of Washington
  • Vipul Goyal – University of California, Los Angeles
  • Nitin Gupta – Cornell University
  • Sasa Junuzovic – University of North Carolina at Chapel Hill
  • Ece Semiha Kamar – Harvard University
  • Dave Levin – University of Maryland
  • Rohan Narayana Murty – Harvard University
  • Zvonimir Rakamaric – University of British Columbia
  • T. Scott Saponas – University of Washington
  • John Wright – University of Illinois
  • Yisong Yue – Cornell University

2008 Social Impact Award

  • Andrea Grimes – Georgia Institute of Technology