Faculty Fellowship

2020 Faculty Fellows

Loris D’Antoni

Assistant Professor, Department of Computer Sciences

University of Wisconsin-Madison

Loris D’Antoni is an Assistant Professor in the Department of Computer Sciences at the University of Wisconsin-Madison. There, he’s affiliated with the madPL (Madison Programming Languages) Group. He received his bachelor’s and master’s degrees in Computer Science from the University of Torino in 2008 and 2010, respectively, and his PhD in Computer Science from the University of Pennsylvania in 2015. His research is centered on building fundamental verification and synthesis techniques that help programmers write software that meets their intent. In particular, his current main focus is on building practical and predictable program synthesis techniques that can be applied to computer networks, program repair, and machine learning. He has won several awards, including the NSF CAREER Award, Google Research Award, Morris and Dorothy Rubinoff Dissertation Award, and his papers were selected for special journal issues (TOPLAS (opens in new tab), FMSD (opens in new tab)) and nominated for best paper awards (TACAS (opens in new tab)).

Christina Delimitrou

Assistant Professor, Electrical and Computer Engineering

Cornell University

Christina is an Assistant Professor and the John and Norma Balen Sesquicentennial Faculty Fellow at Cornell University, where she leads the Systems, Architecture, and Infrastructure Lab (SAIL). Her research interests are in the areas of cloud computing, computer architecture, and applied machine learning. Her recent work focuses on leveraging ML to improve the performance predictability, resource efficiency, and security of large-scale datacenters. Christina’s work has garnered significant industry impact, with several of the systems she has built being deployed in production cloud providers. Christina is the recipient of a Sloan Research Fellowship, an NSF CAREER Award, two Google Faculty Research Awards, a Facebook Faculty Research Award, four IEEE Micro Top Picks, and several best paper awards. Christina received her PhD in Electrical Engineering from Stanford University. Prior to that, she had earned an MS in Electrical Engineering, also from Stanford, and a diploma in Electrical and Computer Engineering from the National Technical University of Athens.

Chelsea Finn

Assistant Professor, Computer Science and Electrical Engineering Departments

Stanford University

Chelsea Finn is an Assistant Professor in Computer Science and Electrical Engineering at Stanford University. Her research interests lie in the ability of robots to develop broadly intelligent behavior through learning and interaction. To this end, her work spans machine learning, robotics, and computer vision, including deep learning for end-to-end robotic perception and control, meta-learning algorithms that enable flexible adaptation to new tasks and environments, and methods for self-supervised robot learning at scale. Dr. Finn received her Bachelor’s degree in Electrical Engineering and Computer Science at MIT and her PhD in Computer Science at University of California, Berkeley. Her research has been recognized through the ACM Doctoral Dissertation Award, an NSF graduate research fellowship, the C.V. Ramamoorthy Distinguished Research Award, and the MIT Technology Review 35 Innovators under 35 Award, and her work has been covered by various media outlets, including the New York Times, Wired, and Bloomberg.

Stefanie Mueller

X-Window Consortium Career Development Assistant Professor, Department of Electrical Engineering and Computer Science (MIT EECS), Computer Science and Artificial Intelligence Laboratory (MIT CSAIL)

Massachusetts Institute of Technology

Stefanie Mueller is the X-Career Development Assistant Professor in the MIT EECS department joint with MIT Mechanical Engineering and Head of the HCI Engineering Group at MIT CSAIL. In her research, she develops novel hardware and software systems that advance personal fabrication technologies. For her work, Stefanie has received multiple best paper awards at the most selective human-computer interaction venues (ACM CHI and ACM UIST), received an NSF CAREER award, and was named an Alfred P. Sloan Fellow as well as a Forbes 30 under 30 in Science. Over the last years, Stefanie has served as an ACM CHI Subcommittee Chair in 2019 and 2020 and is currently serving as the ACM UIST 2020 program chair. She has also been an invited speaker at more than 50 universities and research labs, such as MIT, Stanford University, Harvard University, University of California Berkeley, Carnegie Mellon University, and Microsoft Research.

Aaron Sidford

Assistant Professor, Management Science and Engineering

Stanford University

Aaron Sidford is an Assistant Professor of Management Science and Engineering at Stanford University, where he also has a courtesy appointment in Computer Science and an affiliation with the Institute for Computational and Mathematical Engineering (ICME). Aaron’s research interests lie in optimization, the theory of computation, and the design and analysis of algorithms, with an emphasis on work at the intersection of continuous optimization, graph theory, numerical linear algebra, and data structures. His work focuses on the design of provably efficient algorithm for solving fundamental and pervasive large-scale problems in optimization and data-analysis. He has received multiple awards for his work in these areas including a Sloan Research Fellowship, an NSF CAREER Award, an ACM Doctoral Dissertation Award honorable mention, best paper awards in FOCS (opens in new tab) and SODA (opens in new tab), and two best student paper awards in FOCS.

Mohammad Alizadeh

Assistant Professor, Electrical Engineering and Computer Science

Massachusetts Institute of Technology

Mohammad Alizadeh is the TIBCO Career Development Assistant Professor of Computer Science at MIT. His research interests are in the areas of networked computer systems and applied machine learning. His current research focuses on learning-augmented network systems, programmable networks, and network protocols and algorithms for datacenters. Mohammad’s research has garnered significant industry interest. His work on datacenter transport protocols has been implemented in Linux and Windows, and has been deployed by large network operators; his work on adaptive network load balancing algorithms has been implemented in Cisco’s flagship datacenter switching products. Mohammad received his PhD from Stanford University and then spent two years at Insieme Networks (a datacenter networking startup) and Cisco before joining MIT. He is a recipient of the NSF CAREER Award (2018), SIGCOMM Rising Star Award (2017), Alfred P. Sloan Research Fellowship (2017), and multiple best paper awards.

Stefano Ermon

Assistant Professor, Computer Science Department

Stanford University

Stefano Ermon is an assistant professor of computer science in the CS Department at Stanford University, where he is affiliated with the Artificial Intelligence Laboratory, and is a fellow of the Woods Institute for the Environment. His research is centered on techniques for probabilistic modeling of data, inference, and optimization, and is motivated by applications in the emerging field of computational sustainability. He has won several awards, including the IJCAI Computers and Thought Award, NSF CAREER Award, ONR Young Investigator Award, AFOSR Young Investigator Award, Sony Faculty Innovation Award, AWS Machine Learning Award, Hellman Faculty Fellowship, and four Best Paper Awards (AAAI, UAI and CP). Stefano earned his PhD in Computer Science at Cornell University in 2015.

Jessica Hullman

Assistant Professor, Computer Science + Journalism

Northwestern University

Jessica Hullman is an assistant professor in computer science and journalism at Northwestern. The goal of her research is to develop computational tools that improve how people reason with and make decisions from data. She is especially interested in challenges that arise in presenting data to non-expert audiences, where the need to convey a clear story often conflicts with goals of transparency and faithful presentation of uncertainty. Her current research focus is on uncertainty representation through interactive visual interfaces that enable users to articulate and reason about their prior beliefs. Jessica’s research has been supported by the NSF (CRII, CAREER), Navy, Google, Tableau, and Adobe. Prior to joining Northwestern, she was an assistant professor at the University of Washington Information School. Her PhD is from the University of Michigan, and she spent a year as a postdoctoral scholar in computer science at the University of California, Berkeley.

Yin Tat Lee

Assistant Professor, Paul G. Allen School of Computer Science & Engineering

University of Washington

Yin Tat Lee is an assistant professor in the Paul G. Allen School of Computer Science & Engineering at the University of Washington. His research interests are primarily in algorithms, and they span a wide range of topics such as convex optimization, convex geometry, spectral graph theory, and online algorithms. His primary research goal is to find algorithms for solving a general class of convex optimization problems. He has received a variety of awards for his work, including Best Paper Award and 2x Best Student Paper Awards at FOCS, Best Paper Award at SODA, Best Paper Award at NeurIPS, Sprowls Award and NSF CAREER Award, and A.W. Tucker Prize.

Raluca Ada Popa

Assistant Professor, Electrical Engineering and Computer Science

University of California, Berkeley

Raluca Ada Popa is an assistant professor of computer science at UC Berkeley working in computer security, systems, and applied cryptography. She is a co-founder and co-director of the RISELab at UC Berkeley, as well as a co-founder and CTO of a cybersecurity startup called PreVeil. Raluca received her PhD in computer science from MIT as well as her Masters and two BS degrees in computer science and in mathematics. She is the recipient of a Sloan Foundation Fellowship, a George M. Sprowls Award for best MIT CS doctoral thesis, and a Johnson Award for best CS Masters of Engineering thesis from MIT.

Yong-Yeol Ahn

Assistant Professor, School of Informatics and Computing

Indiana University Bloomington

Yong-Yeol Ahn’s research develops and leverages mathematical and computational methods to study complex systems such as cells, the brain, society, and culture. His recent contribution includes a new framework to identify pervasively overlapping modules in networks, network-based algorithms to predict viral memes, and a new computational approach to study food culture. He is currently an assistant professor at the School of Informatics and Computing at Indiana University, Bloomington. He worked as a postdoctoral research associate at Northeastern University and at the Dana-Farber Cancer Institute for three years after earning his PhD in Statistical Physics from KAIST in 2008.

Byung-Gon Chun

Assistant Professor, Department of Computer Science and Engineering

Seoul National University

Byung-Gon Chun is interested in creating new platforms for operating and distributed systems. He is currently developing a big data platform that makes it easy to implement large-scale, fault-tolerant, heterogeneous data processing applications. He has also built systems that seamlessly integrate cloud computing with mobile devices for improved performance, reliability, and security. Chun received his PhD in Computer Science from the University of California, Berkeley. Prior to joining Seoul National University, Chun was a principal scientist at Microsoft, a research scientist at Yahoo! Research and Intel Research, and a postdoctoral researcher at ICSI.

Diego Fernández Slezak

Assistant Professor, Department of Computer Science

University of Buenos Aires

Diego Fernández Slezak’s work focuses on novel methods for text analysis in massive-scale repositories to find stereotyped patterns in human thought. The goal is the development and use of machine-learning techniques to study digital text corpora associated with cognitive processes, aiming at identifying the mental operations underlying behavioral processes, with application to mental health and education. Diego Fernández Slezak received his PhD in Computer Science in 2010 from University of Buenos Aires and was recipient of the IBM PhD Fellowship.

Roxana Geambasu

Assistant Professor, Computer Science Department

Columbia University

Roxana Geambasu works at the intersection of three computer science fields: distributed systems, operating systems, and security and privacy. Her research aims to increase privacy in today’s data-driven world. Privacy has become a rare commodity in today’s world, due to users who are too eager to share their data online and Web services that aggressively collect and use that information. Roxana’s goal is to forge a new world, in which Web services are designed from the ground up with privacy in mind, and where users are more aware of the privacy implications of their online actions. Roxana obtained her Ph.D. from the University of Washington and was awarded a 2014 NSF CAREER award, an Honorable Mention for the 2013 inaugural Dennis M. Ritchie Doctoral Dissertation Award, a William Chan Memorial Dissertation Award, two best paper awards, and a 2013 Google Faculty Research Award.

Percy Liang

Assistant Professor, Computer Science Department

Stanford University

Percy Liang is an Assistant Professor of Computer Science at Stanford University (B.S. from MIT, 2004; Ph.D. from UC Berkeley, 2011). His research interests include (i) parsing natural language into semantic representations (e.g., executable code), for supporting intelligent user interfaces; and (ii) developing machine learning algorithms that infer rich latent structures (e.g., programs) from limited supervision (e.g., program output), balancing computational and statistical tradeoffs. He won a best student paper at the International Conference on Machine Learning in 2008, received the NSF, GAANN, and NDSEG fellowships, and is also a 2010 Siebel Scholar.

David Steurer

Assistant Professor, Department of Computer Science

Cornell University

David Steurer investigates the power and limitations of efficient algorithms for optimization problems that are at the heart of computer science and its applications. A focus of his work has been the Unique Games Conjectures whose resolution—no matter in which direction—promises new insights into the capabilities of efficient algorithms. As part of the research effort to resolve this conjecture, he studies provable guarantees of the sum-of-squares method, a compelling meta-algorithm that applies to a wide-range of problems and has the potential to unify the design of efficient algorithms for difficult optimization problems. Steurer received his PhD from Princeton University and was a postdoctoral researcher at Microsoft Research for two years before joining Cornell University. He is the recipient of the 2010 FOCS best paper award, the 2011 ACM Doctoral Dissertation Award Honorable Mention, an NSF CAREER Award, and an Alfred P. Sloan Research Fellowship.

Vinod Vaikuntanathan

Assistant Professor, Electrical Engineering and Computer Science Department

Massachusetts Institute of Technology

Vinod Vaikuntanathan is a Steven and Renee Finn Career Development Assistant Professor of Computer Science at MIT. His main research interest is in the theory and practice of cryptography. He works on lattice-based cryptography, building advanced cryptographic primitives using integer lattices; leakage-resilient cryptography, defining and developing algorithms resilient against adversarial information leakage; and more recently, the theory and practice of computing on encrypted data, constructing powerful cryptographic objects such as fully homomorphic encryption and functional encryption. Vinod got his Ph.D. from MIT where he received a 2009 George M. Sprowls Award for the best MIT Ph.D. thesis in Computer Science. He is also a recipient of the 2008 IBM Josef Raviv Postdoctoral Fellowship, the 2009, the 2013 Alfred P. Sloan Research Fellowship, and a 2014 NSF CAREER award.

Animashree Anandkumar

Assistant Professor, Department of Electrical Engineering and Computer Science

University of California, Irvine

Animashree Anandkumar’s research lies at the interface of theory and practice of large-scale machine learning and high dimensional statistics. Her theoretical contributions include analysis of high-dimensional estimation of graphical models and developing tensor methods for learning latent variable models. She has applied the developed algorithms to various problems in social networks and computational biology. She is currently an assistant professor at the Department of Electrical Engineering and Computer Science at the University of California, Irvine. She spent a year as a postdoctoral researcher at MIT and got her PhD from Cornell University. She has been a visiting researcher at Microsoft Research New England. She is the recipient of the ARO Young Investigator Award, NSF CAREER Award, IBM Fran Allen PhD fellowship, and several paper awards.

Katrina Ligett

Assistant Professor, Computer Science and Economics

California Institute of Technology

Katrina Ligett is an assistant professor of Computer Science and Economics at Caltech. In her research, she develops theoretical tools to address problems in data privacy and to understand individual incentives in other complex settings. She received her PhD from Carnegie Mellon University in 2009 and was a postdoctoral fellow at Cornell University before joining the California Institute of Technology in 2011. She is a recipient of the AT&T Labs Graduate Research Fellowship, the NSF Graduate Research Fellowship, the CIFellows Postdoctoral Research Fellowship, the NSF Mathematical Sciences Postdoctoral Research Fellowship, and an NSF CAREER award.

Michael Milford

Senior Lecturer, School of Electrical Engineering and Computer Science

Queensland University of Technology

Michael Milford’s research investigates how robots and biological systems map and navigate the world. He builds computational models based on experimental results and theories from the fields of neuroscience and biology and deploys them on robotic systems navigating in challenging real world environments. This novel research methodology has produced state-of-the-art results in robotics and yielded insights into how the brain may map and navigate the world. Milford received his PhD from the University of Queensland in 2006 and is the recipient of an Australian Research Council DECRA Fellowship and Discovery Project award.

Ruslan Salakhutdinov

Assistant Professor, Department of Statistics and Computer Science

University of Toronto

Ruslan Salakhutdinov received his PhD in computer science from the University of Toronto in 2009. After spending two post-doctoral years at the Massachusetts Institute of Technology Artificial Intelligence Lab, he joined the University of Toronto as an assistant professor in the departments of Statistics and Computer Science. His primary interests lie in artificial intelligence, machine learning, deep learning, and large-scale optimization. His main research goal is to understand the computational and statistical principles required for discovering structure in large amounts of data. He is an action editor of the Journal of Machine Learning Research and served on the senior programme committee of several learning conferences, including NIPS and ICML. He is an Alfred P. Sloan Research Fellow, a recipient of the Early Researcher Award and Connaught New Researcher Award, and is a Scholar of the Canadian Institute for Advanced Research.

Michael Schapira

Senior Lecturer, School of Computer Science and Engineering

Hebrew University of Jerusalem

Michael Schapira’s research draws ideas from algorithmic and economic theory to design practical Internet protocols with provable guarantees (for example, for routing and traffic management). His research aims to both “fix”’ today’s Internet protocols and to design new and improved (better performing, secure, failure-resilient, and so forth) protocols for the future Internet. Schapira also has a broad research interest in the interface of computer science, game theory, and economics. He is a recipient of the Allon Fellowship (2011) and a member of the Israeli Center of Research Excellence in Algorithms. Prior to joining Hebrew University, Schapira was a postdoctoral researcher at the University of California, Berkeley, Yale University, and Princeton University, and a visiting scientist in Google New York’s Infrastructure Networking group.

Monica Tentori

Assistant Professor, Department of Computer Science

Center for Scientific Research and Higher Education (CICESE)

Monica Tentori investigates the human experience of ubiquitous computing to inform the design of ubiquitous environments that effectively enhance humans’ interactions with their world. Her research intersecting human-computer interaction and ubiquitous computing particularly focuses on designing, developing, and evaluating natural user interfaces, self-reflection capture tools, and new interaction models for ubiquitous computing. Her work is being applied to healthcare and urban living to support the needs of urban citizens, hospital workers, elders, and individuals with autism and their caregivers. Tentori’s research demonstrates that effectively designed ubiquitous environments have the potential to promote healthy lifestyles and independence, and positively impact attention, behavior, and workload.

Ryan Williams

Assistant Professor, Computer Science Department

Stanford University

Ryan Williams works in algorithm design and complexity theory. He studies how to construct more efficient algorithms for solving computational problems, as well as how to mathematically rule out the possibility of efficient algorithms for other problems. Such impossibility results are generally perceived as very difficult; algorithms can be very clever, and it is hard to reason about all cleverness one could have. The famous P versus NP question asks about the power of efficient algorithms. Williams’ work shows how the design and analysis of algorithms for core problems in computer science can often be exploited to rule out efficient algorithms for other core problems, raising new questions about our understanding of efficient computation. Williams received his PhD from Carnegie Mellon University in 2007 under Manuel Blum. His honors include some best paper awards and an Alfred P. Sloan Fellowship.

Emma Brunskill

Assistant Professor, Department of Computer Science

Carnegie Mellon University

Emma Brunskill’s research focuses on creating automated decision systems that interact with people, a challenge that spans artificial intelligence, machine learning, and human-computer interaction. She is particularly interested in adaptive, individualized tutoring systems that learn and self-optimize. Emma also works on health applications and on using information communication technologies to address challenges in low resource settings and developing regions.

Constantinos Daskalakis

Assistant Professor, Department of Electrical Engineering and Computer Science

Massachusetts Institute of Technology

Constantinos Daskalakis is the X Consortium Assistant Professor of Computer Science at MIT. His research studies the interface of computer science and economics, with a focus on computational aspects of the Internet, online markets, and social networks. Daskalakis has been honored with the 2007 Microsoft Graduate Research Fellowship, the 2008 ACM Doctoral Dissertation Award, the 2010 Sloan Fellowship, the 2011 SIAM Outstanding Paper Prize, and the MIT Ruth and Joel Spira Award for Distinguished Teaching. His work on the complexity of the Nash equilibrium was honored by the Game Theory Society with the First Computer Science and Game Theory prize. Daskalakis received his PhD in Computer Science from UC Berkeley and was a post-doctoral researcher at Microsoft Research prior to joining MIT.

Stephen Gould

Senior Lecturer, School of Computer Science

Australian National University

Stephen Gould is a faculty member in the Research School of Computer Science at the Australian National University. He received his PhD from Stanford University in 2010. Prior to his PhD, Stephen founded and worked in a number of start-up companies. Stephen’s current research interests are in developing mathematical models that allow computers to learn how to interpret scenes from images. This involves recognizing objects and understanding how they interact with other objects and with their environment.

Andreas Krause

Assistant Professor, Department of Computer Science

ETH Zurich

Andreas Krause’s research is in learning and adaptive systems that actively acquire information; reason; and make decisions in large, distributed, and uncertain domains, such as sensor networks and the web. It spans theoretical aspects in machine learning and optimization, as well as interdisciplinary applications, ranging from community sensing to computational sustainability to social networks. He got his PhD in Computer Science from Carnegie Mellon University in 2008. He is a Kavli Frontiers Fellow of the U.S. National Academy of Sciences, and received an NSF CAREER award as well as several best paper awards.

Miriah Meyer

Assistant Professor, School of Computing

University of Utah

Miriah Meyer’s research lives at the interface of computer science and data-intensive domains, where she designs interactive visualization systems that help scientists make sense of complex data. Her current work focuses on nimble and intuitive visualization tools that support research in genomics and molecular biology. Meyer takes a user-centered, problem-driven approach to developing visualizations that target specific scientific questions, working closely with scientists in an iterative and collaborative process. Her tools are integrated into the workflow of numerous biological labs and have led to several scientific discoveries, as well as to the validation and refinement of experimental and computational methods.

Juan Carlos Niebles

Assistant Professor, Electrical and Electronic Engineering

Universidad del Norte

Juan Carlos is interested in helping computers and robots see the world. In particular, his research is focused on designing novel algorithms for automatic recognition and detailed understanding of human motions, activities, and behaviors from images and videos. This technology has the potential to enable new life-improving activity-aware systems, such as personal robots and smart homes, smart video surveillance, medical diagnosis and monitoring, automated sports analysis, and semantic video search.

Ashutosh Saxena

Assistant Professor, Department of Computer Science

Cornell University

Ashutosh Saxena works on a new generation of robots that will operate fully autonomously in human environments. His research is focused on the development of new machine-learning algorithms that enable robots to process massive amounts of sensory input data in real time and learn how to perform tasks in unstructured environments. His primary application domain is in assistive robotics, where his algorithms have enabled robots to perform tasks such as fetching items on verbal request, perform basic household chores, and identify and assist in human activities. He hopes to see such assistive robots appear in our homes, offices, and nursing homes soon.

Maria Florina Balcan

Assistant Professor, School of Computer Science

Georgia Institute of Technology

Maria Florina Balcan is an assistant professor in the School of Computer Science at Georgia Institute of Technology. She received her PhD in Computer Science from Carnegie Mellon University under the supervision of Avrim Blum. From October 2008 until July 2009, she was a postdoc at Microsoft Research, New England. Her main research interests are computational and statistical machine learning, computational aspects in economics and game theory, and algorithms. She is a recipient of the Carnegie Mellon University SCS Distinguished Dissertation Award and the National Science Foundation NSF CAREER Award.

Krishnendu Chatterjee

Assistant Professor

Institute of Science and Technology Austria

Krishnendu is interested in graph games that arise in the formal verification of systems, and has deep connections with logic and automata theory. He established many fundamental results related to stochastic games on graphs, and is currently working on quantitative graph games and its application to synthesis of correct systems. He got his PhD from University of California, Berkeley in 2007, and his thesis won the David Sakrison Memorial Prize and Ackermann Award.

Jure Leskovec

Assistant Professor, Department of Computer Science

Stanford University

Jure Leskovec is an assistant professor of Computer Science at Stanford University. His research focuses on the analysis and modeling of large social and information networks as the study of phenomena across the social, technological, and natural worlds. Problems he investigates are motivated by large scale data, the Web and Social Media. Jure received his PhD in Machine Learning from Carnegie Mellon University in 2008 and spent a year at Cornell University. His work received six best paper awards, won the ACM KDD cup and topped the Battle of the Sensor Networks competition.

Alistair McEwan

Lecturer of Computer Engineering, School of Electrical and Information Engineering

The University of Sydney

Alistair McEwan’s work aims to solve major health issues with technology, and involves research in the emerging field of bioelectronics—the interaction between electronics and biology. His current investigation of the electrode–skin interface aims to improve emergency diagnosis of heart attack and stroke as well as long-term monitoring of cardiovascular disease. He also works on related projects in electrical-impedance imaging systems, microelectronic circuits and systems, and neuromorphic engineering.

Shwetak Patel

Assistant Professor, Departments of Computer Science and Electrical Engineering

University of Washington

Shwetak Patel’s research is at the intersection of hardware, software, and human-computer interaction. His research focuses on building easy-to-deploy and practical sensing systems for the home. His work is being applied to sustainability, elder care, home safety, and the creation of new approaches for natural user interfaces. Many of his techniques use the existing utilities infrastructure as a “sensor,” thereby reducing the need for additional instrumentation. In one example, Patel has developed techniques for energy and water monitoring that provide a detailed breakdown of consumption in the home through monitoring a single point on the utility infrastructure. Through these new sensing approaches, Patel envisions the ability to instrument homes easily with smart technology for high-value applications.

Anderson de Rezende Rocha

Assistant Professor, Institute of Computing

University of Campinas

Professor Rocha’s research interests include digital image and video forensics, computer vision, pattern analysis, and machine intelligence—focused on the field of digital document forensics. He seeks solutions for problems regarding collection, organization, and classification of digital evidence that is used by law enforcement agencies in Brazil and abroad. He is investigating how to reduce the misuse of important evidence and is working on digital categorization solutions to reduce the technical effort that is required to analyze each piece of evidence. Professor Rocha’s work emphasizes tracking the source of the evidence, new techniques for establishing authenticity, and exposing possible tampering.

Keith Noah Snavely

Assistant Professor, Computer Science Department

Cornell University

Noah Snavely is interested in using massive collections of images on the web to better understand and visualize our world. His research builds new computer-vision algorithms for scalable 3-D reconstruction, new graphics techniques for experiencing places through online photos, and new ways to enable communities of photographers to capture useful image collections. His software is being used by educators, artists, and scientists across a range of disciplines.

Brent Waters

Assistant Professor, Department of Computer Sciences

University of Texas

Brent Waters studies cryptography and computer security. His research is laying the foundations for a new vision of encryption called Functional Encryption. Instead of encrypting to individual users, in a Functional Encryption system, one can embed any access predicate into the cipher text itself. In addition, he is interested in understanding the foundational underpinnings of cryptography and in developing security primitives that are both practical and provably secure.

Sinan Aral

Department of Information, Operations, and Management Sciences

New York University Stern School of Business

Doug Downey

Department of Electrical Engineering and Computer Science

Northwestern University

Raanan Fattal

School of Computer Science and Engineering

The Hebrew University of Jerusalem

abhi shelat

Department of Computer Science

University of Virginia

Haiying Shen

Department of Electrical and Computer Engineering

Clemson University

Cyrill Stachniss

Department of Computer Science

University of Freiburg, Germany

Evimaria Terzi

Computer Science Department

Boston University

Gill Bejerano

Developmental Biology and Computer Science

Stanford University

Luis Ceze

Computer Science and Engineering

University of Washington

Nicole Immorlica

Electrical Engineering and Computer Science Department

Northwestern University, McCormick School of Engineering

Svetlana Lazebnik

Department of Computer Science

University of North Carolina at Chapel Hill

Rafael Pass

Department of Computer Science

Cornell University

Kristen Grauman

Computer Sciences

University of Texas at Austin

Susan Hohenberger

Department of Computer Science

Johns Hopkins University

Robert Kleinberg

Computer Science

Cornell University

Philip Levis

Departments of Computer Science and Engineering

Stanford University

Karen Lipkow

Department of Biochemistry

University of Cambridge

Russell Tedrake

Electrical Engineering and Computer Science

Massachusetts Institute of Technology

Magdalena Balazinska

Department of Computer Science and Engineering

University of Washington

Josh Bongard

Department of Computer Science

University of Vermont

Yixin Chen

Department of Computer Science and Engineering

Washington University in St. Louis

Adam Siepel

Biological Statistics and Computational Biology

Cornell University

Luis von Ahn

Department of Computer Science

Carnegie Mellon University

Regina Barzilay

Computer Science and Artificial Intelligence

Massachusetts Institute of Technology

Aaron Hertzmann

Computer Science

University of Toronto

Scott Klemmer

Computer Science

Stanford University

Eddie Kohler

Computer Science

University of California, Los Angeles

Fei-Fei Li

Computer Science

Stanford University

Mark Rouncefield

Computing Department

University of Lancaster

Andrey Rybalchenko

Max Planck Institute for Software Systems

Ruth Baker

Centre for Mathematical Biology

University of Oxford

Frédo Durand

Computer Graphics

Massachusetts Institute of Technology

Subhash Khot

College of Computing

Georgia Institute of Technology

Dan Klein

Computer Science Division

University of California, Berkeley

Radhika Nagpal

School of Engineering and Applied Sciences

Harvard University

Wei Wang

Department of Computer Science

University of North Carolina at Chapel Hill

Klaus-Peter Zauner

School of Electronic and Computer Science

University of Southampton