Professor Guibas heads the Geometric Computation group in the Computer Science Department of
Stanford University. He is acting director of the Artificial Intelligence Laboratory and member
of the Computer Graphics Laboratory, the Institute for Computational and Mathematical Engineering
(iCME) and the Bio-X program. His research centers on algorithms for sensing, modeling, reasoning,
rendering, and acting on the physical world. Professor Guibas' interests span computational
geometry, geometric modeling, computer graphics, computer vision, sensor networks, robotics,
and discrete algorithms --- all areas in which he has published and lectured extensively. Leonidas
Guibas obtained his Ph.D. from Stanford in 1976, under the supervision of Donald Knuth. His main
subsequent employers were Xerox PARC, MIT, and DEC/SRC. He has been at Stanford since 1984 as
Professor of Computer Science. Professor Guibas has graduated 41 Ph.D. students and has supervised
29 postdoctoral fellows, many of whom are well-known in computational geometry, in computer
graphics, in computer vision, in theoretical computer science, and in ad hoc and sensor networks.
At Stanford he has developed new courses in algorithms and data structures, geometric modeling,
geometric algorithms, computational biology, and sensor networks. Professor Guibas is a member of
the US National Academy of Engineering and the American Academy of Arts and Sciences, an ACM
Fellow, an IEEE Fellow and winner of the ACM Allen Newell award, the ICCV Helmholtz prize, and a
DoD Vennevar Bush Faculty Fellowship.
Emmanuel Abbe received his Ph.D. degree from the Department of Electrical Engineering and Computer
Science at the Massachusetts Institute of Technology in 2008, and his M.S. degree from the
Department of Mathematics at the Ecole Polytechnique Fédérale de Lausanne in 2003. He joined
Princeton University as an assistant professor in 2012 and became associate professor in 2016,
jointly in the Program for Applied and Computational Mathematics and the Department of Electrical
Engineering. He is also an associate faculty in the Department of Mathematics at Princeton
University since 2016. He is the recipient of the Foundation Latsis International Prize, the Bell
Labs Prize, the NSF CAREER Award, the Google Faculty Research Award, the Walter Curtis Johnson
Prize, and the von Neumann Fellowship from the Institute for Advanced Study.
Xin Guo joined Berkeley's IEOR department July 1, 2006 after three years at Cornell School of
Operations Research and Industrial Engineering. Prior to that, she spent four years at IBM T. J.
Watson research center at Yorktown Heights, where she was the winner of the Herman Goldstein Postdoc
Fellowship in 1999. Her primary research interests are in the general area of stochastic processes
and applications and financial engineering.
Ying Fu is currently a professor with the School of Computer Science and Technology, Beijing
Institute of Technology. She received the B.S. degree in Electronic Engineering from Xidian
University in 2009, the M.S. degree in Automation from Tsinghua University in 2012, and the Ph.D.
degree in information science and technology from the University of Tokyo in 2015. Her research
interests include computer vision, image and video processing, and computational photography.
Le Liang is a professor in the School of Information Science and Engineering, Southeast University,
Nanjing, China. His general research interests include wireless communications, signal processing,
and machine learning. He received the B.E. degree in information engineering from Southeast
University in 2012, the M.A.Sc degree in electrical engineering from the University of Victoria,
BC, Canada, in 2015, and the Ph.D. degree in electrical and computer engineering from the Georgia
Institute of Technology, Atlanta, GA, USA in 2018. From 2019 to 2021, he was a research scientist
in Intel Labs, Hillsboro, USA. Dr. Liang serves as an Editor for the IEEE Communications Letters
and as an Associate Editor for the IEEE Journal on Selected Areas in Communications Series on
Machine Learning in Communications and Networks. He received the Best Paper Award at the IEEE/CIC
ICCC in 2014 and was named an Exemplary Reviewer for the IEEE Wireless Communications Letters
Hae Young Noh is an associate professor in the Department of Civil and Environmental Engineering.
Her research introduced the new concept of “structures as sensors” to enable physical structures
(e.g., buildings and vehicle frames) to be user- and environment-aware. In particular, these
structures indirectly sense humans and surrounding environments through their structural responses
(i.e., vibrations) by inferring the desired information (e.g., human behaviors, environmental
conditions, heating and cooling system performance), instead of directly measuring the sensing
targets with additional dedicated sensors (e.g., cameras, motion sensors). This concept brought
a paradigm shift in how we view these structures and how the structures interact with us. At
Stanford University, Noh received her PhD and MS degrees in the CEE department and her second
MS degree in Electrical Engineering. Noh earned her BS in Mechanical and Aerospace Engineering at
Dr. Longbo Huang is an associate professor (with tenure) at the Institute for Interdisciplinary
Information Sciences (IIIS) at Tsinghua University, Beijing, China. He received his Ph.D. in EE
from the University of Southern California, and then worked as a postdoctoral researcher in the
EECS dept. at University of California at Berkeley before joining IIIS. Dr. Huang serves/served
on the editorial board for IEEE Transactions on Communications (TCOM), ACM Transactions on
Modeling and Performance Evaluation of Computing Systems (ToMPECS), IEEE Journal on Selected
Areas in Communications (JSAC-guest editor) and IEEE/ACM Transactions on Networking (ToN).
He is a senior member of IEEE and a member of ACM. Dr. Huang has held visiting positions at the
LIDS lab at MIT, the Chinese University of Hong Kong, Bell-labs France, and Microsoft Research
Asia (MSRA). He was a visiting scientist at the Simons Institute for the Theory of Computing at
UC Berkeley in Fall 2016. Dr. Huang received the Outstanding Teaching Award from Tsinghua
University in 2014. He received the Google Research Award (co-recipient) and the Microsoft
Research Asia Collaborative Research Award in 2014, and was selected into the MSRA StarTrack
Program in 2015. Dr. Huang won the ACM SIGMETRICS Rising Star Research Award in 2018. Dr. Huang’s
current research interests are in the areas of stochastic modeling and analysis, reinforcement
learning and control, optimization and machine learning, and big data analytics.
Ruoyu Sun is an assistant professor in UIUC ISE department, and affiliated with
coordinate science lab and department of ECE. His recent research interests are
machine learning and optimization, especially deep learning theory, generative
models and adaptive gradient methods. Previously he worked at FAIR (Facebook
Artificial Intelligence Research) as a visiting researcher, and was a postdoctoral
scholar in Dept. of Management Science & Engineering at Stanford University. He
obtained his Ph.D. in Electrical Engineering at the University of Minnesota in
2015, under the supervision of Zhi-Quan (Tom) Luo, and the B.Sc. degree in
mathematics from Peking University, Beijing, China in 2009.
Zhaoran Wang is an assistant professor in the Departments of Industrial Engineering & Management
Sciences and Computer Science (by courtesy) at Northwestern University (since 2018). He is
affiliated with the Centers for Deep Learning and Optimization & Statistical Learning.
The long-term goal of his research is to develop a new generation of data-driven decision-making
methods, theory, and systems, which tailor artificial intelligence towards addressing pressing
societal challenges. To this end, his research aims at:
making deep reinforcement learning more efficient, both computationally and statistically, in a
principled manner to enable its applications in critical domains; scaling deep reinforcement
learning to design and optimize societal-scale multi-agent systems, especially those involving
cooperation and/or competition among humans and/or robots.
With this aim in mind, his research interests span across machine learning, optimization,
statistics, game theory, and information theory.
Li YI received his Ph.D. from Stanford University, advised by Professor Leonidas J. Guibas. Prior
to joining Stanford, He got his bachelor degree in Electronic Engineering from Tsinghua University.
His recent research interests focus on 3D perception and shape analysis, with the goal of equipping
robotic agent with the ability of understanding and interacting with the 3D world.