Reports from the Heart of the Machine
Editors: Zhang Qian, Xiao Zhou
Congratulations to Chen Danqi, Fang Fei, Gu Quanquan, Li Bo and other 20 winners in the field of computer science.
Just now, the Sloan Foundation announced the winners of the 2022 Sloan Research Awards. Established in 1955 and awarded annually to support and reward distinguished scientists and scholars in the early stages of their careers, the awards this year in subject areas such as chemistry, computer science, earth system science, economics, mathematics, neuroscience, and physics. The winner will receive a $75,000 prize that can be used to support any of their research within two years.
Among them, there are twenty winners in the field of computer science, including Chen Danqi, an assistant professor in the Department of Computer Science at Princeton University, who is familiar to us, David K. Duvenaud, one of the best authors of NeurIPS 2018 papers, and Matei Zaharia, a developer of Apache Spark. Here are the details of the winners:
Mark Bun, Boston University
Mark Bun, an assistant professor at Boston University, received his Ph.D. in computer science from Harvard University in 2016 under the tutelage of Salil Vadhan. He has a broad interest in theoretical computer science, including data privacy, computational complexity, cryptography, and machine learning fundamentals.
Personal homepage: http://cs-people.bu.edu/mbun/
Danqi Chen, Princeton University
Danqi Chen, an assistant professor in the Department of Computer Science at Princeton University, was a visiting scientist at Facebook AI Research (FAIR), has made a series of research achievements in the field of natural language processing (NLP), and is one of the authors of RoBERTa.
Danqi Chen graduated from Yao Ban of Tsinghua University in 2012 and received his Ph.D. in Computer Science from Stanford University in 2018 under the tutelage of Christopher Manning, professor of linguistics and computer science at Stanford University. In 2019, her doctoral dissertation was uploaded thousands of times in just four days, making it one of Stanford's hottest graduation papers in the past decade. Her mentor commented, "Tan Danqi Chen is a pioneer in using neural network methods to solve natural language understanding problems. Her simple, clean, high-success model attracted everyone's attention... Her thesis focused on neural network reading comprehension and Q&A, emerging technologies that are enabling better access to information — that allow computer systems to actually answer your actual questions, rather than simply returning document search results."
Personal homepage: https://www.cs.princeton.edu/~danqic/
David K. Duvenaud, University of Toronto
David K. Duvenaud is an assistant professor at the University of Toronto, one of the founders of the University of Toronto Vector School, and co-founder of Invenia, an energy forecasting and trading company.
He received his PhD from the University of Cambridge and later completed his post-Doctoral work at the Intelligent Probabilistic Systems Laboratory at Harvard University. Currently, he teaches courses in probability learning and reasoning, machine learning statistical methods, differential inference, and generative models at the University of Toronto. In 2018, his paper Neural Ordinary Differential Equations, of which he was the corresponding author, won the NeruIPS 2018 Best Paper Award, for which Chen Tianqi was the author.
Personal homepage: http://www.cs.toronto.edu/~duvenaud/
Fei Fang, Carnegie Mellon University
Fei Fang, Assistant Professor, Institute of Software, School of Computer Science, Carnegie Mellon University (CMU), winner of the 2021 IJCAI Award for Computers and Ideas. Prior to joining CMU, she was a postdoctoral fellow at Harvard University, where she received her Ph.D. from the University of Southern California (USC) in 2016. Fang Fei's research direction is artificial intelligence and multi-agent systems, and he is committed to combining machine learning with game theory.
Her research has received numerous awards from top AI conferences, including the IJCAI-ECAI'18 Distinguished Paper Award, the IAAI'16 Innovative Application Award, and the IJCAI'15 CompSust Track Outstanding Paper Award. Her paper has been the runner-up of the IFAAMAS-16 Victor Lesser Distinguished Dissertation Award, the William F. Ballhaus, Jr. Prize, and the University of Southern California Computer Science Best Paper Award. Her research has been successfully deployed in the application of securing ferry lines and anti-poaching, contributing to building a better social environment.
Personal homepage: https://feifang.info/
Manya Ghobadi, Massachusetts Institute of Technology
Manya Ghobadi, Assistant Professor of Career Development at TIBCO, Laboratory of Computer Science and Artificial Intelligence (CSAIL), Department of Electronic Engineering and Computer Science, Massachusetts Institute of Technology, focuses on large-scale reconfigurable networks, high-performance cloud infrastructure, machine learning networks, software and hardware collaborative design, data center networks, network optimization, and optical fiber networks.
Personal homepage: http://people.csail.mit.edu/ghobadi/
Gu Quanquan, UCLA
Gu Quanquan is an assistant professor in the Department of Computer Science at UCLA. He received his Ph.D. in Computer Science from the University of Illinois at Urbana-Champaign in 2014 with a focus on statistical machine learning, with a focus on developing and analyzing non-convex optimization algorithms for machine learning to understand large-scale, dynamic, complex, and heterogeneous data and lay the theoretical foundation for deep learning.
Personal homepage: http://web.cs.ucla.edu/~qgu/
Josiah Hester, Northwestern University, USA
Josiah Hester, assistant professor in the Department of Electrical and Computer Engineering at Northwestern University, has research interests focused on: large-scale sustainable sensing (powering sensors from the soil and building sustainable network infrastructure); wearable health devices (smart masks, wearable cameras, and devices that record eating); low/no-power interactive systems (a battery-free Game Boy). He designs and deploys microcomputers that can be used for decades, supporting applications in sustainability and healthcare. Not long ago, the mask he designed to measure heart rate and respiratory rate was widely reported by the media.
Personal homepage: https://josiahhester.com/cv/
Phillip Isola, Massachusetts Institute of Technology
Phillip Isola is an assistant professor in the Department of Electrical Engineering and Computer Science at the Massachusetts Institute of Technology. He was a visiting research scientist at OpenAI and a postdoctoral scholar in the Department of Electrical Engineering and Computer Science at the University of California, Berkeley. Phillip Isola's research interests include computer vision and machine learning, and he is committed to building more general-purpose agents.
Personal homepage: http://web.mit.edu/phillipi/
Alec Jacobson, University of Toronto
Alec Jacobson, Assistant Professor in the Department of Computer Science and Mathematics at the University of Toronto and Senior Research Scientist at the Adobe Institute, received his Ph.D. in Computer Science from ETH Zurich under the tutelage of Professor Olga Sorkine-Hornung. His work spans all aspects of geometric processing, including machine learning for 2D/3D geometry, physics-based simulation, computational manufacturing, numerical methods for geometric energies and partial differential equations, and interaction design tools for 2D and 3D. He led the development of the widely used geometry processing library libigl, which won the 2015 SGP Software Award. In 2020, he received the ACM SIGGRAPH Significant New Researcher Award.
Personal homepage: http://www.cs.toronto.edu/~jacobson/
Pravesh K. Kothari, Carnegie Mellon University
Pravesh K. Kothari is an assistant professor in the Department of Computer Science at Carnegie Mellon University. His research aims to design effective algorithms to underpin theoretical computer science, which has improved efficiency for a series of papers on algorithms used to learn high-dimensional Gaussian mixtures.
Personal homepage: https://www.cs.cmu.edu/~praveshk/
Li Bo, University of Illinois at Urbana-Champaign
Professor Li Bo currently works in the Department of Computer Science at the University of Illinois at Urbana-Champaign. She has received numerous academic awards, including MIT Technology Review TR-35, Alfred P. Sloan Sloan Research Award, NSF CAREER Award, Intel Rising Star Award, Symantec Research Lab Fellowship, and academic research awards from technology companies such as Amazon, Facebook, Google, Intel, and IBM. Her paper has won best paper awards at several top machine learning and security conferences; her research is in the permanent collection of the UK Science and Technology Museum.
Li Bo's research focuses on theoretical research and practical analysis of trusted machine learning, computer security, machine learning, privacy, and game theory. She has designed multiple robust machine learning algorithms and privacy-preserving data publishing systems. Her work has been featured in major media outlets such as Nature, Wired, Fortune, and The New York Times.
Pedro Lopes, University of Chicago
Pedro Lopes is an assistant professor in the Department of Computer Science at the University of Chicago. Pedro Lopes' research aims to design interactive devices that integrate directly with the user's body, including wearables. Pedro Lopes' research not only makes the device very small by borrowing a part of the user's body as input/output hardware, but also enables a novel interaction model that allows the device to integrate directly with the user's body.
Personal homepage: http://plopes.org/
Nicolas Papernot, University of Toronto
Nicolas Papernot, Assistant Professor in the Department of Electrical and Computer Engineering at the University of Toronto, Ph.D., graduated from Pennsylvania State University with a degree in Computer Science and Engineering. His research interests are at the intersection of security, privacy, and machine learning. Nicolas Papernot also serves as Vice Chair of the IEEE Security and Privacy Symposium (S&P).
Personal homepage: https://www.papernot.fr/
Dorsa Sadigh, Stanford University
Dorsa Sadigh, an assistant professor in the Department of Computer Science at Stanford University, holds a Ph.D. degree in electrical engineering and computer science from the University of California, Berkeley. Dorsa Sadigh's research interests are at the intersection of robotics, machine learning, and control theory, working to develop efficient algorithms for safe, reliable, and adaptive human-machine interactions and multi-agent interactions.
Personal homepage: https://dorsa.fyi/
Shuran Song, Columbia University
Shuran Song is an assistant professor in the Department of Computer Science at Columbia University. She received her B.S. in Computer Science from the Hong Kong University of Science and Technology in 2013 and her Ph.D. in Computer Science from Princeton University in 2018 with research interests at the intersection of computer vision and robotics. Shuran Song's research team won the Amazon Robot Challenge in 2017.
Personal homepage: https://datascience.columbia.edu/people/shuran-song/
Deian Stefan, University of California, San Diego
Deian Stefan, assistant professor in the Department of Computer Science and Engineering at the University of California, San Diego, co-founder and chief scientist of cybersecurity startup Intrinsic (acquired by VMWare). Previously, he received his Ph.D. in Computer Science from Stanford University. He is primarily interested in research spanning security, programming languages, and systems.
Personal homepage: https://cseweb.ucsd.edu/~dstefan/
Avishay Tal, University of California, Berkeley
Avishay Tal, an assistant professor in the Department of Electrical Engineering and Computer Science at the University of California, Berkeley, received his Ph.D. from the Weizmann Institute of Science in 2015. His research interests are mainly focused on computational complexity, Boolean function analysis, pseudo-randomness, and quantum computing.
Personal homepage: https://www.avishaytal.org/
Yulia Tsvetkov, University of Washington
Yulia Tsvetkov, an assistant professor in the Paul G. Allen School of Computer Science and Engineering at the University of Washington, received her Ph.D. from CMU. She focuses on natural language processing, especially mixed solutions where machine learning and the intersection of theory or sociolinguistics intersect.
Personal homepage: https://homes.cs.washington.edu/~yuliats/
Henry Yuen, Columbia University
Henry Yuen, an assistant professor of computer science at Columbia University, has research interests focused on quantum computing, complexity theory, cryptography, and the interaction between information theory.
Personal homepage: https://www.henryyuen.net/
Matei Zaharia, Stanford University
Matei Zaharia is an assistant professor at Stanford University's School of Computer Science and co-founder and chief technologist at Databricks, a data and artificial intelligence platform startup. He is interested in computer systems for emerging large-scale workloads such as machine learning, big data analytics, and cloud computing, and has received a Ph.D. in Computer Science and ACM PhD Dissertation Award from the University of California, Berkeley for his research on large-scale computer systems.
During his PhD, he launched a project called Apache Spark, which is now one of the most widely used frameworks for distributed data processing. In addition, he was involved in launching other widely used data center software such as Apache Mesos, Alluxio, and Spark Streaming.
At Stanford University, he co-developed DAWNBench, a machine learning performance competition that attracted top industry groups to submit and influenced the industry standard MLPerf. He and his team are also continuing to develop open source software such as Weld, NoScope, FlexFlow, and ColBERT.
Personal homepage: https://cs.stanford.edu/~matei/
Reference link: https://sloan.org/fellowships/2022-Fellows