The Microsoft Research Faculty Fellowship recognizes innovative, promising new faculty, whose exceptional talent for research and innovation identifies them as emerging leaders in their fields. This year, 185 individuals were nominated. Microsoft Research is proud to announce the 2020 recipients for these awards.
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The five fellowship recipients cover a broad spectrum of directions in their work: learning and intelligence in robotics, tools to help programmers write software that meets their intent, machine learning applied to large-scale data centers, optimization and computation theory, and systems to advance personal fabrication technologies.
The stories of the researchers are equally as inspiring as their work. Each of the fellows credited many others that have helped them along the way—from family to colleagues, from students to advisors—all mentioning mentorship as a reason they made it to where they are today. Questions posed to the researchers also revealed insights into challenges that lie ahead in their respective disciplines.
On program verification and synthesis, recipient Dr. Loris D’Antoni of the University of Wisconsin-Madison says, “In particular, if we can understand what makes a problem hard, we can design new abstractions that help us attack that problem in practice and hide the complexity from the end users of our tools.”
This sentiment, one of optimism in working to overcome significant challenges in their research areas, is another common thread among recipients, which speaks to both the possibilities of their research and the power of a collective positive mindset to contribute to broader advancements.
“My research…requires knowledge in mechanical engineering, electrical engineering, computer graphics, material science, and optical engineering,” says recipient Dr. Stefanie Mueller, of the Massachusetts Institute of Technology, on her work in human-computer interaction, “and my research team has PhD students and postdocs with all these different backgrounds. It’s strongly interdisciplinary research that requires bringing together engineers from many different areas.”
Loris D’Antoni
Assistant Professor, Department of Computer Sciences at 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)).
“The main goal of my research is to build tools that can help programmers write software that meets their intent—i.e., it does the thing they want it to do. To progress towards [this] I am designing techniques that allow programmers to specify what their code should do using high level logical specifications and algorithms that can transform these specifications into executable programs.”
Christina Delimitrou
Assistant Professor, Electrical and Computer Engineering at 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 machine learning to improve the performance predictability, resource efficiency, and security of large-scale data centers. 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.
“Fellowships like this are unique, especially for junior faculty, as they allow us to explore risky, but potentially highly impactful approaches. Specifically, in my group we have recently started a large undertaking to leverage machine learning in the design of a cloud-edge synergistic system. The system consists of a cluster of cloud servers and a swarm of edge devices, drones in our case, with the machine learning controller determining how work should be partitioned across cloud and edge to guarantee fault tolerant, responsive, and power-efficient computation. Not only will this fellowship provide us the funds to evaluate larger systems and their implications, but also explore more ambitious applications of our system, such as digital agriculture and disaster recovery scenarios.”
Chelsea Finn
Assistant Professor, Computer Science and Electrical Engineering Departments at 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.
“I wasn’t planning to become a professor, but have always enjoyed puzzles, math, and problem solving, as well as mentoring and helping others. I find that academia merges all of these things, which I love. I also wouldn’t be where I am today without the infectious passion for research of my undergraduate adviser, Seth Teller, nor without all that I have learned from my PhD advisers, Pieter Abbeel and Sergey Levine.”
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) at 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.
“My research is at the intersection of human-computer interaction and digital manufacturing. My long-term vision is to enable a future in which the physical objects in our daily lives are customized to each individual user’s personal needs. All of the devices we use in our daily lives today are standardized, that is, a few items of the same size, shape, appearance, and function are sold in large volumes. Since a standardized product must cater to all its potential users, it consequently cannot address each individual user’s personal needs…In my research, I investigate how we can facilitate the development of such individualized physical products and how we can enable products to adapt themselves as users’ preferences and needs change over time.”
Aaron Sidford
Assistant Professor, Management Science and Engineering at 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 algorithms 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.
“I believe it is an exciting time to be an algorithms and optimization researcher, and I am thrilled to be advancing the theoretical foundations of these areas. By combining classic techniques with modern tools for processing massive data sets and new mathematical insights, my research has helped resolve decades-old problems in optimization and algorithm design. I believe these results are just the beginning of a new modern toolkit for efficient computation.”
Microsoft Research is excited to empower these fellows as they foster diverse and inclusive cultures within their communities. You can learn more about the fellowship at https://aka.ms/Faculty-Fellowship (opens in new tab).
Microsoft Research congratulates the 2020 Faculty Fellows!
Algorithms 
Human-computer interaction 
Programming languages and software engineering 
Systems and networking