PhD Fellowship

PhD Fellowship


The Microsoft Research PhD Fellowship has supported hundreds of fellows over the last two decades, 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 10 students have been awarded the fellowship for 2021.


  • Nominations from the university closed August 14, 2020
  • PhD student nominees received an invitation email on August 21, 2020 to submit their proposal
  • Proposals accepted through September 21, 2020
  • Letters of recommendation accepted through September 21, 2020
  • Finalists notified in early November 2020 and interviewed virtually that month
  • Recipients announced by January 31, 2021

Provisions of the 2021 award

  • Tuition and fees are covered for two academic years (2021–22 and 2022–23).
  • A $42,000 USD stipend is provided to help with living expenses while in school for two academic years (2021-22 and 2022-23). The stipend is not expected to cover all living expenses; it can be used for expenses including, but not limited to, childcare, conference fees and travel, research equipment, meals, rent, etc.
  • An invitation to the PhD Summit: a two-day workshop in the fall hosted by Microsoft Research where fellows will meet with Microsoft researchers and other top students to share their research. We hope to offer both a virtual and in-person participation option; we will continue to monitor local and national health and safety guidance and may hold a completely virtual event if advisable.

Eligibility criteria

  • Microsoft’s mission is to empower every person and every organization on the planet to achieve more. Fellows should support this mission and embrace opportunities to foster diverse and inclusive cultures within their communities.
  • PhD students must be nominated by their university. Their nomination must be submitted by someone designated by the department chair’s office in order to ensure the number of nominations per department is not exceeded.
  • Students must be enrolled at a university in the United States, Canada, or Mexico.
  • Proposed research must be closely related to the general research areas carried out by Microsoft Research as noted in the Research areas tab above.
  • Students must be in their third year of a PhD program in the fall semester or quarter of 2020. The department chair’s office at the nominating university will need to attest that the student is considered a third year PhD student having taken into account transfers, approved leaves of absence, etc.
  • A maximum of three nominations per department will be accepted; if more than two are nominated, then at least one nominee should help us increase the opportunities for students who are underrepresented in the field of computing. This includes those who self-identify as a woman, African American, Black, Hispanic, Latinx, American Indian, Alaska Native, Native Hawaiian, Pacific Islander, and/or person with a disability.
  • 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. Fellowships are not available for extension. If you require time away for family or medical leave, this will be accommodated. If you are unsure if a particular need for time away will affect the award, you can contact us at Microsoft Research Fellowships (
  • Payment of the award, as described above, will be made directly to the university and dispersed according to the university’s policies. 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).
  • A recipient of the Microsoft Research PhD Fellowship subject to disciplinary proceedings for inappropriate behavior, including but not limited to discrimination, harassment (including sexual harassment), or plagiarism will forfeit their funding.
  • A recipient of the Microsoft Research PhD Fellowship may not receive another fellowship from another tech company during the same academic period. Fellows accepting multiple fellowships may 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.

If you do not meet the above criteria, you may be eligible for other academic research awards found on our Academic Programs page.

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 submit a nomination

Nominations for the 2021 Microsoft Research PhD Fellowship closed on August 14, 2020. Universities should inform their students once they have submitted their nomination so the nominees can begin collecting the details on the Proposal tab. Nominated students received an invitation email on August 21, 2020 to submit their proposal.

The below outlines the information necessary if you are submitting a nomination on behalf of the department chair’s office at your university. A maximum of three nominations per department will be accepted; if more than two are nominated, then at least one nominee should help us increase the opportunities for students who are underrepresented in the field of computing. This includes those who self-identify as a woman, African American, Black, Hispanic, Latinx, American Indian, Alaska Native, Native Hawaiian, Pacific Islander, and/or person with a disability.

  • Nominations must include:
    • Your name, email, job title, country, university, and department as the person submitting the nomination on behalf of the department chair’s office at your university
    • Brief description (a few sentences) of your department’s process for determining which student(s) to nominate for this fellowship
    • Attestation that the process used to determine which student(s) to nominate from your department for this fellowship was fair and non-discriminatory
    • Nominee’s name and email
    • Nominee’s primary and secondary areas of research (click on the Research areas tab at the top of the page for a full list)
    • Attestation that as of the fall semester or quarter of 2020, the university considers the nominee a third year PhD student (having taken into account transfers, approved leaves of absence, etc.)


How to submit a proposal

If you were nominated by the office of the department chair at your university, then we sent you an email from Microsoft Research Fellowships ( on Friday, August 21, 2020. Please check your junk folder if you do not see the email. The email contains a private link to submit your proposal.

If you are a nominee, the below outlines the information necessary to submit your proposal in our submission portal.

  • You will be asked to fill out the below questions in a form:
    • Your name, email, country, university, and department
    • Primary and secondary areas of research (click on the Research areas tab at the top of the page for a full list)
    • Thesis proposal or research statement title
    • Month and year entered the PhD program and expected graduation date (nominee must currently be in their third year of the PhD program and vetted by the university)
    • Attestation as to whether or not you are a doctoral student who is underrepresented in the field of computing which include those who self-identify as a woman, African American, Black, Hispanic, Latinx, American Indian, Alaska Native, Native Hawaiian, Pacific Islander, and/or person with a disability – if more than two students are nominated, then at least one should help us increase the opportunities for students who are underrepresented in the field of computing
    • URL to your professional website (optional, but strongly recommended; you are encouraged to make certain it is up-to-date)
    • Approximate cost of tuition and fees for one academic year
    • Where and when you held an internship (if applicable)
    • One to three conferences you are most likely to attend
    • Self-assessment of the Statement of Good Standing: “I declare I have never been disciplined for inappropriate behavior, including, but not limited to discrimination, harassment (including sexual harassment), or plagiarism. If I am selected to receive funding under Microsoft’s Fellowship program, during my funding time period, I agree to inform Microsoft should I be subjected to disciplinary proceedings for inappropriate behavior, including but not limited to discrimination, harassment (including sexual harassment), or plagiarism which would result in forfeiture of funding under Microsoft’s Fellowship program.”
  • You will be asked to upload 3 documents and 1 optional video:
    Your curriculum vitae, thesis statement, and one-page summary will be uploaded separately. Accepted formats are docx, doc, and pdf. The optional short video can be uploaded as mp4 and cannot exceed 1 GB. Email or hard-copy submissions will not be considered. Name the individual files using the convention indicated below. Include your first name and last name as part of your file name each separated by an underscore (e.g. Jane_Smith_cv.docx).

      • Curriculum vitae – file name: cv
      • Thesis proposal or research statement (short and concise is recommended—no more than five pages including references with font no smaller than 10-point font) – file name: thesis
      • One-page summary of the above thesis proposal or research statement with the first paragraph describing the desired impact your research will have in the field and in society, and why it is important to you – file name: summary
      • (Optional) Short video (about one minute long) describing the desired impact your research will have in the field and in society, and why it is important to you – file name: video

    Current Microsoft Research Ada Lovelace Fellow, Jazette Johnson, shares her tips for submitting a proposal. Although the PhD Fellowship is a bit different as it is for third year students and provides tuition and fees plus a stipend for two years, Jazette’s tips can still be a great resource for PhD Fellowship nominees.

  • You will be asked to request 3 letters of recommendation via the submission portal:
    • Add the contact information (name, affiliation, email) and send the email request through the submission portal to your three recommenders as soon as possible so they have ample time to provide a letter. Their deadline is the same as your proposal deadline on Monday, September 21, 2020 at 5:00 PM Pacific Daylight Time. They should be established researchers familiar with your research (at least one of which must be from your primary academic advisor/supervisor and only one letter can be from a current Microsoft employee). Once you send the request through the submission portal, they will receive a private link to upload a letter of recommendation for you. Please note that you and your recommenders may need to check your junk folders in order to find emails from our portal. As reference, here is a list of the various system email addresses you can add to allowed emails.
  • Proposals submitted to Microsoft will not be returned. Microsoft cannot assume responsibility for the confidentiality of information submitted in the proposal. Therefore, proposals should not contain information that is confidential, restricted, or sensitive. Microsoft reserves the right to make public the information on those proposals that receive awards, except those portions containing budgetary or personally identifiable information.
  • Incomplete proposals will not be considered.
  • Due to the volume of submissions, Microsoft Research cannot provide individual feedback on proposals.


Below are the answers to frequently asked questions about the 2021 Microsoft Research PhD Fellowship.

Eligibility criteria

Are international students (those who are not citizens of the United States, Canada, or Mexico) eligible?

Yes, if you are a full-time international student attending a school in the United States, Canada, or Mexico and meet the eligibility requirements.

What if I’m a student attending a university outside the United States, Canada, and Mexico?

The Microsoft Research PhD Fellowship includes only schools in the United States, Canada, and Mexico. If you are a student attending a school outside the United States, Canada, and Mexico, you are not eligible for this fellowship. You may be eligible for other academic research awards found on our Academic Programs page.

What if I am not starting my third year in a PhD program in academic year 2020–2021?

Students must be in their third year in a PhD program in the fall semester or quarter of 2020 to submit a proposal for this award. If you are starting your second year, you may be eligible for the Microsoft Research Ada Lovelace Fellowship. If you are in your fourth year or beyond of a PhD program, then you may be eligible for the Microsoft Research Dissertation Grant.

How should I determine if I’m in my third year of my PhD program? Do you consider master’s degrees when calculating eligibility?

We find that universities calculate this differently which is why we require the designated person from the department chair’s office to attest that the student is considered a third year PhD student. We kindly leave this to you and your university to determine.

Do I have to be nominated by my university or can I nominate myself?

To be considered for the award, you must be nominated by your department within your university. If you are nominated, you will be contacted to submit a proposal.

Can Microsoft employees or their families be nominated to submit a proposal?

Employees and directors of Microsoft Corporation, and its subsidiaries and affiliates are not eligible, nor are persons involved in the execution or administration of this fellowship, or the family members of each above (parents, children, siblings, spouse/domestic partners, or individuals residing in the same household).


Who is meant to submit the nomination? Does it have to be the Chair of the department or can it be my advisor?

The Department Chair’s office can designate any staff other than the student to submit the nomination. The nomination simply needs to be a coordinated effort with the Department Chair’s office. This is to ensure there are no more than three submissions from each department per fellowship and the Department Chair’s office is aware of those nominations being submitted.

Does the Department Chair have to submit a letter through the online tool as well?

No, we do not require a letter from the Department Chair.

Does whether or not a student's research is already being funded impact their eligibility for nomination and/or receiving the award?

Not from our perspective. However, should the student be chosen for both a Microsoft Research fellowship and another industry fellowship, they will be asked to choose.

Research areas

You specify very broad focus areas of research. Are there any proposals or projects that you are more interested in than others or is it up to us to choose? Is it mostly software solutions or is there any hardware interest?

There are plenty of both hardware and software projects currently in Microsoft Research. The reason the areas of research are broad is that Microsoft Research is very broad, and there are a number of people reviewing the fellowship proposals across a wide range of areas. Look at the work people in Microsoft Research are doing by clicking on the areas noted in the Research areas tab above which will give you some idea of the focus areas within the broad areas to guide your focus area choice. In the end, propose the work you are interested in doing.

How do areas of interest factor into fellowship proposal evaluations? Are there areas of interest that Microsoft Research is more focused on this year?

It depends on the individuals involved in reviewing the proposal, and it is hard to say what is going to be of more interest. The trends of the industry are probably going to be reflected in what is interesting in general. Guiding question: Imagine you succeed. Tell us how someone’s life changes as a result.

How do I determine my primary and secondary research areas? Where is the appropriate place to describe how they relate to my work (whether it's methodologically or theoretically)?

  • Your choices of primary and secondary areas help us choose who reviews your proposal.
  • Pick areas that align with conferences/journals where you would publish.
  • The one-pager is the appropriate place to describe how research areas relate to research.

How do I choose which area to pick for my research area if my research is very interdisciplinary?

Microsoft Research is interdisciplinary, so it is something we understand. What you choose as a research area is a “soft” preference and will simply help us better route your proposal. Utilize the primary and secondary research area option to help capture and communicate your research area the best you can.

Here are some suggestions and guiding questions to help you choose a research area:

  • Imagine you succeed. Tell us how someone’s life changes as a result.
  • Do you have a home conference? Are there one or two conferences you go to in a more specific area?
  • Is there a set of faculty/professors you know in a specific area?
  • Who do you want to be reading your proposal?
  • Who would you want to network with? What area of research are they in?
  • Who would be most excited about my topic? What area of research are they in?

How related does my work need to be to Microsoft Research?

Your work should be of interest to researchers at Microsoft; however, it doesn’t need to directly line up with an existing project or topic. It is important for your work to be related enough that Microsoft researchers will be able to review it and have interest in supporting it. Microsoft Research is large, interdisciplinary, and covers a broad area — use the Research areas tab above as a guideline for the areas we cover. When in doubt, we suggest you browse the webpages of researchers who look like they may be related to your area and see if they have papers in the similar topics or publish in conferences you publish in and/or attend. If you find one or more such researchers that share these connections with you, then you can feel confident that your work is related enough to submit a proposal.

Thesis proposal or research statement

Are there specific pieces of information I am required to include in my research statement (i.e. research aims, timelines, deliverables) or are you looking for more of a narrative, descriptive format of a student's plan for their doctoral research?

Your research statement should be more of a narrative format. Timelines and deliverables are not necessary. We want to see what you are interested in, where your work is going, and how you would use this fellowship to further your research and contribute to the academic community.

What sort of balance is expected between what we have done, what we are doing, and what we are planning on doing?

When reviewing a proposal, we are looking for more of a future plan. Your research papers tell us what you have done, use the research statement to tell us where you are going.

If we have just submitted a paper that is going to guide a lot of direction going forward, would you recommend submitting that as preliminary data or attaching an unpublished paper to the proposal?

If it is relevant, and all co-authors approve of you submitting the unpublished work, we recommend including this in your submission. Again, all papers should be approved by all-co-authors, for both published and unpublished works.

Should the one-page research statement be a short synopsis of the five-page research statement, or should they contain distinctively different content?

The one-page research statement and the five-page research statement should not contain different content; one should be a shorter version of the other. The purpose of the shorter version is to help us triage where proposals go to get reviewed, and its first paragraph should describe the desired impact your research will make on the field and society.

Should I be aware of formatting criteria while writing my research statement?

Your research statement should be no more than 5 pages including references with font no smaller than 10-point. The one-page summary of the aforementioned thesis proposal or research statement should also include references.

Reference letters

My undergrad was in a different field so the research was very different from what I am doing now. Who should write my reference letters?

Given you have three letters, it would be good to include a letter from one person who can speak about your current research and one person who has known you longer, even if it may not be in your current research area. The longer-term perspective is definitely important and valuable. The value of a letter is evaluating how you work, how you collaborate with people, and what your process is as a researcher. This transcends what your particular topic is. Keep in mind that one letter doesn’t have to address all things; across all three letters, we want to get a full picture of who you are over a longer term, but also insight into your recent work.

Are you more interested in learning about technical and research specific aspects of my work, or are other things, such as outreach/other university activities of interest as well?

The purpose of a letter of reference is to provide us with the bigger picture of what you are doing, how you work as a researcher, how you learn, how you approach projects, and how you collaborate with others. The letter will also provide us with insight from people who have been working with you and observing you for some amount of time.

It was suggested that recommendation letters come from established researchers. Is this limited to faculty members or would the inclusion of collaborators be acceptable as well?

At least one recommendation needs to come from an advisor, but letters of reference from collaborators are allowed. We are looking for people who can speak to you, your work as a researcher, and your character.

For the recommendation letters, is it a system where you list the people and your system will ask those people?

Those you provided as references in our system will be sent an auto-generated email with instructions to upload their recommendation letters.

Review process

Who will review the proposals?

Proposals will be reviewed by researchers at Microsoft whose expertise covers a wide range of disciplines. After their review, a selection of students will be invited to interview. Award recipients are chosen from those finalists.

When will I know the outcome of the review process?

Finalists will be contacted in early November to schedule their interviews that month. Due to the volume of submissions, Microsoft Research cannot provide individual feedback on proposals.

How many proposals were there last year?

There were over 300 proposals submitted last year.

Award details

If selected, when will my fellowship begin?

Persons awarded a fellowship in January will receive their financial awards by September of that year. Tuition and fees are covered from the fall term through the end of the spring term. Microsoft sends payment directly to the university, who will disperse funds according to their guidelines. This award will be provided as an unrestricted gift with no terms and restrictions applied to it. No portion of these funds should be applied to overhead or other indirect costs.

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

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

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

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

Is an internship included as part of the award?

No. If a fellow is interested, applying for an internship at Microsoft is strongly encouraged, but not guaranteed or required.

Can I simultaneously receive other fellowships?

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. You may not hold more than one student award at a time from Microsoft Research.

Is childcare an approved use of my stipend?

Absolutely! There is no limit to the amount of your stipend that can be used for childcare.


2021 Microsoft Research PhD Fellows

2021 PhD Fellowship recipient: Joshua BrakensiekJoshua Brakensiek

Stanford University

As society’s demands for data usage increase dramatically over time, there is a vital need for novel data-storage architectures to accommodate the vast quantities of data. In such systems, core concerns include ensuring redundancy in the face of errors corrupting the storage media as well as ensuring the privacy of data stored within the system. Joshua Brakensiek is seeking to advance the theory of modern data storage systems in areas including approximation algorithms, coding theory, and differential privacy. This work is complemented by his additional interests in automated reasoning and constraint satisfaction. He is guided on this journey by his PhD co-advisors Aviad Rubinstein and Moses Charikar in the Computer Science Department at Stanford University.

2021 PhD Fellowship recipient: Adebayo EisapeAdebayo Eisape

Johns Hopkins University

Adebayo Eisape is a PhD candidate in the Department of Electrical and Computer engineering at Johns Hopkins University and is advised by Dr. James E. West. His research explores the use of electrostatic and piezoactive materials for sensing and energy harvesting at varying scales to interrogate energy that is present in many environments: low frequency pressure oscillations. By developing devices to interact with this energy, sensing can be performed using the energy from the phenomenon being sensed, realizing systems and sensor nodes with run-times limited only by the interactions of the properties of the materials used, rather than by the amount of energy they can store. This opens up the possibility of sensing on very large spatial and temporal scales (such as atmospheric and oceanic phenomena), as well as in environments that previously would have inhibited access or maintenance efforts, such as inside a concrete pillar or under a bridge.

2021 PhD Fellowship recipient: Jordan HenkelJordan Henkel

University of Wisconsin-Madison

Jordan is a PhD Candidate at the University of Wisconsin–Madison advised by Professor Thomas Reps. His research explores the intersection of Programming Languages, Software Engineering, and Machine Learning. Recently, that perspective has led him to pursue the idea of Data Science on Code. Data Science on Code is all about finding ways to make learning from, working with, and analyzing code more intuitive and accessible by emulating the great successes of the Data Science community. In particular, Jordan has been working on notebook-driven experiences for interacting with, querying, and editing large code bases. Through a combination of traditional techniques and machine-learning-assisted methods, Data Science on Code represents an exciting paradigm for empowering developers to do more with their code.

2021 PhD Fellowship recipient: Jiaxin HuangJiaxin Huang

University of Illinois at Urbana-Champaign

Jiaxin is a PhD student in the computer science department at University of Illinois at Urbana-Champaign, advised by Prof. Jiawei Han. Her research focuses on mining structured knowledge from unstructured text data with minimal human supervision. Nowadays people are immersed with massive amount of text data, and it has been increasingly appealing to extract structured knowledge based on user’s specific needs for gaining useful insights and making critical decisions. Taxonomy is a fundamental form of knowledge representation as it organizes a set of concepts into a hierarchy, making it clear for people to understand relations between concepts. Jiaxin’s research focuses on designing frameworks to automatically organizing the unstructured text data into a multi-faceted taxonomy as hierarchical topics, which forms a concise, user-guided, and structured summary of text corpora to cope with the challenge of adapting quickly to specific domains or user interests where labels are scarce in many real-world applications.

2021 PhD Fellowship recipient: Sekwon LeeSekwon Lee

The University of Texas at Austin

Sekwon Lee is a third-year PhD student in the Department of Computer Science at the University of Texas at Austin, advised by Professor Vijay Chidambaram. His research aims at building next-generation storage systems on emerging storage technologies and architectures with a focus on high performance and crash consistency. Especially, he has been mainly working on designing high-performance persistent index structures and data stores based on Persistent Memory (PM). The emerging storage technologies have unique characteristics in terms of the level of provided performance as well as the unit of failure atomicity and addressability. Therefore, the storage systems built on them should be carefully designed while being aware of their specific properties to efficiently and reliably utilize them. His research pursues timely making an impact on the storage community by conducting studies about upcoming storage technologies in advance, thereby making them efficiently and reliably applicable to storage service layers.

2021 PhD Fellowship recipient: Lianhui QinLianhui Qin

University of Washington

Lianhui Qin is a PhD student in the Computer Science and Engineering department at the University of Washington, working with Prof Yejin Choi. Her research interests lie in natural language processing and machine learning, especially commonsense reasoning in natural language generation. She has been working on enabling pre-trained language models the ability of counterfactual reasoning and abductive reasoning. Her long-term research goal is to build AI agents with human-like commonsense reasoning capabilities to communicate with and assist humans in a reasonable, effective, and scalable way.

2021 PhD Fellowship recipient: Morgan ScheuermanMorgan Klaus Scheuerman

University of Colorado Boulder

Human characteristics are increasingly encoded into machine learning (ML) algorithms; into the datasets used to train and evaluate them, into the tasks they are trained to complete, and into the infrastructure of the algorithms themselves. A particularly salient example of algorithmic identity is computer vision (CV) technologies trained to conduct facial analysis (FA): image labeling, facial detection, facial recognition (one-to-one face matching). Morgan researches historically marginalized identities through the “eyes” of computer vision models. His work focuses on the intersection of two perspectives: (1) the technical perspective, encompassing the processes and data which enable machine learning development; and (2) the socio-historical perspective, the underlying philosophy and theory about what make up social identities. His research agenda focuses on understanding where in the pipeline social identity is embedded and how algorithmic identity representation is understood and experienced by human and technical actors.

2021 PhD Fellowship recipient: Valeri VasquezVáleri N. Vásquez

University of California, Berkeley

Váleri N. Vásquez is a PhD candidate in the Energy and Resources Group at the University of California, Berkeley, where she studies the environmental drivers and economic impacts of infectious disease. Her research is focused on the use of genetic-based interventions, including CRISPR-Cas9 systems, to control mosquito-borne illness. Váleri develops models to probe the dynamics of genetically modified organisms under realistic field conditions, and to optimize both the effectiveness and cost of their application as public health tools. She hopes for this work to help reduce scourges like Zika, dengue, and malaria, and to augment scientific understanding of novel genetic methods.

2021 PhD Fellowship recipient: Randi WilliamsRandi Williams

Massachusetts Institute of Technology

Randi Williams is a graduate research assistant in the Personal Robots Group at the MIT Media Lab. She received her Master of Science in Media Arts in Sciences from MIT in 2018 and her Bachelor of Science in Computer Engineering from UMBC in 2016. In her work, Randi seeks to empower communities through artificial intelligence (AI) education. Her research is at the intersection of human-robot interaction and primary education, with a particular focus on engaging and supporting students from underrepresented groups in technology. She has developed a number of AI education projects, including PopBots, social robot companions that teach preschoolers AI through social interaction, and How to Train Your Robot, a middle school AI and ethics curriculum used by teachers around the country. Randi is preparing the next generation to apply AI to socially meaningful problems around them.

2021 PhD Fellowship recipient: Bin YangBin Yang

University of Toronto

Bin Yang is a PhD student in the Department of Computer Science at University of Toronto, advised by Professor Raquel Urtasun. His long-term research goal is to build autonomous systems like self-driving cars that can help people achieve more by transforming future mobility with increased safety, accessibility, efficiency, and reduced cost. To achieve this, his research focuses on learning multimodal representations for robust and reliable 4D scene understanding, including real-time 3D object detection, multimodal sensor fusion as well as joint perception and prediction (motion forecasting). Currently he’s also interested in data-efficient representation learning, involving semi-supervised learning that exploits large amounts of unlabeled data and realistic simulation that facilitates closed-loop training of autonomy systems.

2020 PhD Fellows

2020 Microsoft Research PhD Fellow: Emily AlsentzerEmily Alsentzer

Harvard Medical School & Massachusetts Institute of Technology

Emily Alsentzer is a PhD student in the Harvard-MIT Health Science and Technology program where she is advised by Zak Kohane and Pete Szolovits. Emily’s research focuses on developing machine learning and natural language processing methods for healthcare. She is particularly interested in approaches that combine deep learning and symbolic biomedical knowledge. Through her research, Emily hopes to develop tools for clinicians that will facilitate better clinical decision-making and improve patient outcomes. She is currently designing methods to summarize patient electronic health records and diagnose patients with rare disease through the Undiagnosed Disease Network.

2020 Microsoft Research PhD Fellow: Poulami DasPoulami Das

Georgia Institute of Technology

Quantum computers can solve problems that are beyond the capabilities of conventional computers. Unfortunately, they suffer from extremely high error rates due to noise. Quantum computers can achieve fault-tolerance through quantum error correction, but near-term machines may be too small to afford the resource overheads required for the same and will most likely operate in the presence of noise. Poulami’s research focuses on compiler and system-level solutions to improve the reliability of near-term quantum applications and enable seamless access to quantum computers via cloud services. While, near-term quantum computers are promising, designing fault-tolerant quantum computers can power a larger number of applications. She is currently exploring efficient micro-architectural solutions to design high-performance and scalable hardware designs required to detect and correct errors in fault-tolerant quantum computers. Besides improving the reliability of quantum computers, she’s also interested in emerging technologies such as superconducting logic and conventional computer architecture.

2020 Microsoft Research PhD Fellow: Sarah FakhourySarah Fakhoury

Washington State University

By blending techniques from neurocognitive science—such as brain imaging and eye tracking—with those from empirical software engineering, Sarah’s research is breaking new grounds in the ways that we can model the cognitive processes of developers as they interact with source code. Through the precise relation of cognitive load to specific areas of source code, this framework enables the establishment of causal links between programming language features and their impact on developers. She is currently working to evaluate the impact that specific programming language design choices, such as type systems, have on developers during software development tasks. Ultimately, Sarah is excited to use her research to help companies build the next generation of language tooling, empower stakeholders to make informed decisions about the cognitive cost of different tools and languages, and support language designers to consider the user-centric evaluation of language features during the design process.

2020 Microsoft Research PhD Fellow: Zoe HitzigZoë Hitzig

Harvard University

Zoë Hitzig is a PhD candidate in the Department of Economics at Harvard University. Her current projects in microeconomic theory and political economy center on the provision of public goods and services. She is interested in understanding and expanding the ways in which economic theory mediates ethical and political issues—especially when it is used to design markets, institutions and technologies.

2020 Microsoft Research PhD Fellow: Zhiyuan LiZhiyuan Li

Princeton University

Zhiyuan Li is a third-year PhD candidate at Princeton University in the Computer Science Department, advised by Professor Sanjeev Arora. The goal of his research is to achieve better theoretical understanding for deep learning. His research involves theory on optimization and generalization of ultra-wide neural networks. Currently, he is interested in understanding the complicated interplay between different tricks in deep learning, such as batch normalization, weight decay, and learning rate decay.

2020 Microsoft Research PhD Fellow: Amine MhedhbiAmine Mhedhbi

University of Waterloo

Amine’s research is focused on understanding and developing new techniques across the graph database stack to improve execution performance of graph analytical workloads. One of his recent projects explores the interplay between worst-case optimal join algorithms and traditional join processing under a hybrid cost-model. In addition, he is working on designing flexible storage for faster graph traversal and using factorized processing to improve graph database query executors. Amine is advised by Professor Semih Salihoglu.

2020 Microsoft Research PhD Fellow: Wilson QinWilson Qin

Harvard University

Wilson’s research explores how data systems strike efficient trade-offs for performance versus cost, focusing on data movement and resource usage that their core data structures and algorithms incur. While critical performance bottlenecks are typically due to I/O’s, additional resource costs (e.g. CPU) also need to be ascertained to determine optimal designs. Today there is a proliferation of NoSQL data systems in production on cloud, handling diverse use cases, with a large design space of off-the-shelf systems, tuning options and cloud infrastructure to choose from. Wilson builds models and tools to help understand this space, starting with identifying data structure and access algorithm co-design principles that drive these trade-offs. He seeks to build a single self-designing storage engine that can tailor its data layouts and access operators to a diversity of workload needs, assuming a broader range of performance cost trade-offs than current state-of-the-art NoSQL systems.

2020 Microsoft Research PhD Fellow: Jingxian WangJingxian Wang

Carnegie Mellon University

Jingxian’s research interests lie in the area of Internet of Things (IoT) with a particular emphasis on designing low-cost, long-range and battery-free networks and services. He has designed and built a series of wireless systems that elevate today’s batteryless technologies by extending communication range, enriching sensing capabilities, and enabling novel applications, specifically in the accessibility domain. Jingxian is a PhD candidate at CMU, advised by Professor Swarun Kumar.

2020 Microsoft Research PhD Fellow: Jiyong YuJiyong Yu

University of Illinois at Urbana-Champaign

Jiyong is a PhD student in the Computer Science department at University of Illinois at Urbana-Champaign, advised by Professor Chris Fletcher. His research interest is to design high-performance hardware systems with a broad security guarantee against microarchitectural side channels. Specifically, his research consists of two parts: first, defining and implementing abstraction security specification between hardware and software and across system layers; second, designing efficient processors with succinct, strong security definition. Jiyong’s work won the best paper award of MICRO 2019.

2020 Microsoft Research PhD Fellow: Jieyu ZhaoJieyu Zhao

University of California, Los Angeles

Natural Language Processing plays an important role in many applications, including resume filtering, text analysis, and information retrieval. Despite the remarkable accuracy enabled by the advances of machine learning methods, recent studies show that these techniques also capture and generalize the societal biases in the data. For example, an automatic resume filtering system may unconsciously select candidates based on their gender and race, causing the societal disparity. Various laws have been designed to ensure societal equality and diversity. However, there is a lack of such mechanisms to restrict models from making biased predictions in sensitive applications. Jieyu’s research is to analyze potential stereotypes exhibited in various machine learning models and to develop computational approaches to enhance fairness in a wide range of NLP applications. The broader impact of her research aligns well with the goal of fairness in machine learning—in recognizing the value of diversity and under-represented groups.

2019 PhD Fellows

Constantin Dory, 2019 Microsoft Research PhD Fellowship winnerConstantin Dory

Stanford University

Constantin investigates color centers in diamond and silicon carbide to utilize them as quantum bits and single photon sources for quantum information processing, which will enable exponential speed-up in a wide range of computing applications. To integrate color centers on a chip, he’s breaking new grounds in fabrication and design techniques to develop efficiently integrated photonics. Artificial intelligence and machine learning-based algorithms design the circuits of the future by engaging the full parameter space. To realize these designs using diamond and silicon carbide (materials that are new to photonics), Constantin developed fabrication methods in the Stanford Nanofabrication Facilities. He characterizes these devices at cryogenic temperatures in quantum optics labs and utilize them as spin-photon interfaces, or to generate nonclassical light. His current efforts are on developing large-scale quantum optical experiments involving several color centers entangled in a quantum circuit. Ultimately, he hopes to make progress toward the applications of color centers in universal quantum computers, quantum repeaters, and quantum transducers.

Danielle Gonzalez, 2019 Microsoft Research PhD Fellowship winnerDanielle Gonzalez

Rochester Institute of Technology

Danielle Gonzalez is currently a PhD student in the Center for Cybersecurity and Software Engineering Department at Rochester Institute of Technology (RIT). She also received a B.S. in Software Engineering from RIT in 2016. The goal of her research is to make security testing during development easier via unit testing. She uses static code analysis, large-scale data mining, natural language processing, and machine learning techniques to learn which critical paths and conditions in security tactic implementations can and should be unit-tested and automatically generate artifacts such as test case plans and recommendations that will make the testing process easier for developers while preserving their control over what is tested.

Daehyeok Kim, 2019 Microsoft Research PhD Fellowship winnerDaehyeok Kim

Carnegie Mellon University

Daehyeok Kim is a third year PhD student in the Computer Science Department at Carnegie Mellon University, where he is advised by Professor Srinivasan Seshan and Professor Vyas Sekar. His research interests lie in the intersection of systems and networking with a current focus on making data centers faster and more efficient by designing novel network primitives with advanced networking hardware such as programmable switches and RDMA NICs.

Jayashree Mohan, 2019 Microsoft Research PhD Fellowship winnerJayashree Mohan

The University of Texas at Austin

Jayashree’s research aims at building the next generation of file systems: crash-consistent, energy-efficient, and IO-efficient. Crash consistency is the ability of a file system to ensure data and metadata consistency when a system crash occurs. It is important to build crash-consistent file systems, as applications rely on the guarantees provided by the underlying file system. In addition to crash-consistency, it is important for file systems to be energy-efficient because energy consumption is a key issue in both large-scale data centers as well as small hand-held devices. Furthermore, with the advent of storage technologies with limited write cycles, file systems have to be mindful of their IO footprint. Her work identifies the significance of each of these aspects, building tools and infrastructure to evaluate them, and understanding their impact on file system performance as a whole.

Ramakanth Pasunuru, 2019 Microsoft Research PhD Fellowship winnerRamakanth Pasunuru

University of North Carolina at Chapel Hill

Ramakanth Pasunuru is a PhD student in Computer Science at University of North Carolina Chapel Hill, advised by Professor Mohit Bansal in the UNC-NLP group. His research focuses on developing knowledgeable language generation models that incorporate generalizable semantic skills via dynamic multi-task learning and multi-reward reinforcement learning methods, as well as multimodal conversational models that condition on video-based context and continually adapt via feedback and interaction. He is the recipient of an ACL 2017 Outstanding Paper Award and a COLING 2018 Area Chair Favorites Paper Award.

Daniel Rakita, 2019 Microsoft Research PhD Fellowship winnerDaniel Rakita

University of Wisconsin-Madison

As robot platforms become increasingly common in people’s homes and workplaces over the coming years, a central challenge will be ensuring people have effective ways to specify to a robot what they want it to do, and endowing robots with effective ways to communicate back to people such that the users can confidently interpret the robot’s intent and understanding given the command at hand. Daniel’s research has centered around this robot “specification-interpretability” problem, such as presenting remote telemanipulation paradigms that affords even novice users the ability to intuitively control a robot with minimal training, creating an automatic dynamic camera method that continuously optimizes a viewpoint for a remote user, and formulating a robot bimanual shared-control method inspired by how people naturally perform bimanual manipulations. Daniel is a PhD student at the University of Wisconsin-Madison, advised by Dr. Michael Gleicher and Dr. Bilge Mutlu.

Raghuvansh R. Saxena, 2019 Microsoft Research PhD Fellowship winnerRaghuvansh R. Saxena

Princeton University

Raghuvansh R. Saxena is a graduate student with the Department of Computer Science at Princeton University. His research involves developing and studying models of communication that capture the intricacies of modern communication systems, such as wireless and radio networks. Raghuvansh is also interested in other areas of theoretical computer science, such as algorithmic game theory and complexity theory. He received his Bachelor of Technology degree in Computer Science and Engineering from IIT Delhi in 2016, with Institute Rank 1.

Joana M. F. da Trindade, 2019 Microsoft Research PhD Fellowship winnerJoana M. F. da Trindade

Massachusetts Institute of Technology

The goal of Joana’s research is to develop systems that enable data scientists and practitioners to analyze their data more efficiently and with less programming and/or cognitive effort. Despite the growing popularity and availability of graph data, existing graph analytics tools are either too slow, or too cumbersome to use; this forces upon biologists, chemists, and otherwise network scientists the choice between high-performance or usability. Existing graph databases provide accessible declarative language interfaces, yet their performance is nowhere near that of state-of-the-art graph processing libraries. Similarly, parallel graph processing libraries offer outstanding performance, but require expertise in parallel programming. So far in her PhD, Joana has identified several key challenges in this space, as well as contributed query optimization techniques that yield significant performance speedups for graph databases. Ultimately, she envisions a world where network data scientists can conduct their research without getting bogged down by cumbersome tools.

Zuxuan Wu, 2019 Microsoft Research PhD Fellowship winnerZuxuan Wu

University of Maryland

Zuxuan is a third-year PhD student in the Department of Computer Science at the University of Maryland, advised by Professor Larry S. Davis. His research interests are computer vision and machine learning. In particular, his research focuses on developing efficient frameworks through conditional computation for automated visual understanding and learning robust feature representations with limited supervision. He is also interested in large-scale video recognition, and he has developed a few systems that achieved top-notch performance in several international benchmark competitions such as the YouTube-8M Video Classification Challenge.

Katherine Ye, 2019 Microsoft Research PhD Fellowship winnerKatherine Ye

Carnegie Mellon University

People have always used tools to extend the reach of their bodies and minds. Just as the invention of the lever enabled people to build monuments, the invention of pen and paper—and then the text editor—enabled people to synthesize ideas at a massively new scale. Today, computing power is cheap, networking ubiquitous, and data abundant. Yet today’s personal tools remain much the same as those of a few decades ago. Katherine’s research reimagines personal tools as modern mediums that natively incorporate computational intelligence—that is, techniques for modeling, search, and synthesis—to augment the human ability to think and create. Currently, Katherine leads the team that builds Penrose, a platform that enables people to create beautiful diagrams just by typing mathematical notation in plain text.

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) Gaudio

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