The Berkeley Campus is Open

Visit the UC Berkeley COVID-19 resources website for the latest testing and access information.  Cory and Soda Halls are open but we will likely have limited in-person/on-site staffing during the first two weeks of the Spring 2022 semester.  Although most classes will be conducted remotely during this time, we anticipate in-person instruction to resume on January 31st.

New AI system allows legged robots to navigate unfamiliar terrain in real time

A new AI system, Rapid Motor Adaptation (RMA), enhances the ability of legged robots, without prior experience or calibration, to adapt to, and traverse, unfamiliar terrain in real time.  A test robot figured out how to walk on sand, mud, and tall grass, as well as piles of dirt, pebbles, and cement, in fractions of a second.  The project is part of an industry-academic collaboration with the Facebook AI Research (FAIR) group and the Berkeley AI Research (BAIR) lab that includes CS Prof. Jitendra Malik as Principal Investigator, his grad student Ashish Kumar as lead author, and alumnus Deepak Pathak (Ph.D. 2019, advisors: Trevor Darrell and Alexei Efros), now an assistant professor at Carnegie Mellon, among others.  RMA combines a base policy algorithm that uses reinforcement learning to teach the robot how to control its body, with an adaptation module that teaches the robot how to react based on how its body moves when it interacts with a new environment.  “Computer simulations are unlikely to capture everything,” said Kumar. “Our RMA-enabled robot shows strong adaptation performance to previously unseen environments and learns this adaptation entirely by interacting with its surroundings and learning from experience. That is new.”  RMA's base policy and adaptation module run asynchronously and at different frequencies so that it can operate reliably on a small onboard computer.  

Armando Fox, John DeNero, and Kathy Yelick named CDSS associate deans

Three EECS faculty have been named associate deans for the Division of Computing, Data Science, and Society (CDSS).  CS Prof. Armando Fox is the associate dean of online programs; CS Prof. John DeNero is the associate dean of undergraduate studies; and EE Prof. Katherine Yelick is transitioning from her role as CDSS’s associate dean for research to the CDSS executive associate dean.  Berkeley launched CDSS in 2018 to expand teaching and research in data science, and to bring together programs, schools, and departments across campus to tackle the technical, scientific, social, and human dimensions of urgent challenges in biomedicine and human health, climate and sustainability, and human welfare and social justice.

Pieter Abbeel wins 2022 IEEE Kiyo Tomiyasu Award

CS Prof. Pieter Abbeel has won the 2022 IEEE Kiyo Tomiyasu Award, a prestigious Technical Field Award that recognizes "outstanding early to mid-career contributions to technologies holding the promise of innovative applications."  Abbeel, who is the director of the Berkeley Robot Learning Lab, co-director of the Berkeley AI Research (BAIR) Lab, and co-founder of and Gradescope, was cited “For contributions to deep learning for robotics."  His research focuses on teaching robots reinforcement learning through their own trial and error, apprenticeship learning from people, and met-learning (learning-to-learn) to speed up skill acquisition.

Nelson Morgan wins 2022 IEEE James L. Flanagan Speech and Audio Processing Award

EE Prof. Emeritus Nelson Morgan has won the 2022 James L. Flanagan Speech and Audio Processing Award, a prestigious IEEE Technical Field Award.  Morgan and co-recipient Herve Bourlard, who are known for their seminal work in the 1990s on a hybrid system approach to speech recognition that uses neural networks probabilistically with Hidden Markov Models, were cited for "contributions to neural networks for statistical speech recognition."

Kevin Cheang and Federico Mora win 2021 Qualcomm Innovation Fellowship

EECS Ph.D. students Kevin Cheang and Federico Mora (advisor: Sanjit A. Seshia) have been awarded a 2021 Qualcomm Innovation Fellowship (QiF) for their proposed project on "Practical Lifting for Verification of Trusted Platform Software."  They are one of the sixteen winners of this year's QiF North America competition, which recognizes "innovative PhD students across a broad range of technical research areas, based on Qualcomm’s core values of innovation, execution and teamwork. QIF enables graduate students to be mentored by our engineers and supports them in their quest towards achieving their research goals."

Bin Yu awarded Honorary Doctorate from the University of Lausanne

CS Prof. Bin Yu has been awarded an Honorary Doctorate from the University of Lausanne, Switzerland (UNIL).  Honoris causa doctorates are often conferred as a way of recognizing individuals who are unaffiliated with an institution but who have contributed to a specific field or to society in general.  Yu was cited as "one of the most influential researchers of her time" for her "international reputation," "her character and her openness to others and to the world," and "the breadth and importance of her contributions" which "are far from being confined to the scientific community" and "are part of collective efforts to build a better world."  These include her recent work predicting the severity of COVID-19 in the United States.  Yu has a shared appointment in the Department of Statistics, and is affiliated with the Berkeley Institute for Data Science (BIDS) and the Berkeley Center for Computational Biology.

Ken Goldberg: Professor and Artist

CS/IEOR/Art Practice Prof. Ken Goldberg, who is also affiliated with Radiation Oncology at UCSF, is the subject of an interview with Ron Latanision and Cameron Fletcher for the National Academy of Engineering. Goldberg discusses the relationship between art and science in Western culture, the dual nature of his career trajectory, his passion for robot-related art, and why he is optimistic about the future of technology.  He also describes some of his projects, including the Telegarden installation, the African Robotics Network, and his Emmy-nominated film collaboration "Why We Love Robots."

Stuart Russell named Officer of the Most Excellent Order of the British Empire

CS Prof. Stuart Russell, has been named a 2021 Officer of the Most Excellent Order of the British Empire (OBE).  The Officer rank is the second of the order, and is bestowed by the Sovereign of the United Kingdom twice a year to reward valuable "services rendered to the United Kingdom and its people."  Russell, who co-authored the world's most popular AI textbook, Artificial Intelligence: A Modern Approach, and founded the Berkeley Center for Human-Compatible Artificial Intelligence (CHAI), was cited for "For services to artificial intelligence research."  He is an innovator in probabilistic knowledge representation, reasoning, and learning, including its application to global seismic monitoring for the Comprehensive Nuclear-Test-Ban Treaty.  He is also a powerful advocate for the creation of "safe AI" and is active in the movement to ban the manufacture and use of autonomous weapons.  His official title is now: Professor Stuart Russell OBE.

Jonathan Ragan-Kelley wins ACM SIGGRAPH 2021 Significant New Researcher Award

EECS Assistant Prof. Jonathan Ragan-Kelley is the recipient of the Association for Computing Machinery (ACM) Special Interest Group on Computer Graphics and Interactive Techniques (SIGGRAPH) 2021 Significant New Researcher Award.  The award honors researchers who are new to the field of computer graphics and who have made "recent, significant contributions to the field."  Ragan-Kelley, who was instrumental in the development of the language and compiler Halide, was cited for “outstanding contributions to systems and compilers in rendering and computational photography.” Halide is now the industry standard for providing fast, efficient and portable computation on images and tensors.  Ragan-Kelley is also an Assistant EECS professor at MIT.

Five projects led by EECS faculty win AI for Energy and Climate Security Awards

Five projects led by EECS faculty have won Digital Transformation Institute (DTI) AI for Energy and Climate Security Awards. The awards recognize projects that are using AI techniques and digital transformation to advance energy efficiency and lead the way to a lower-carbon, higher-efficiency economy that will ensure energy and climate security.  " DTI selects research proposals that inspire cooperative research and advance machine learning and other AI subdisciplines. Projects are peer-reviewed on the basis of scientific merit, prior accomplishments of the principal investigator and co-principal investigators, the use of AI, machine learning, data analytics, and cloud computing in the research project, and the suitability for testing the methods at scale." Each project was awarded $100,000 to $250,000, for an initial period of one year.  The winning proposals were:

Offline Reinforcement Learning for Energy-Efficient Power GridsSergey Levine, Assistant Professor, Electrical Engineering and Computer Sciences
We propose to develop offline RL algorithms to incorporate real-world data in training an RL agent to reduce emissions associated with running an electrical grid.

Sharing Mobile Energy Storage: Platforms and Learning Algorithms - Kameshwar Poolla, Cadence Design Systems Distinguished Professor of Mechanical Engineering
This proposal aims to design, validate, and test platforms and learning algorithms for mobile storage applications, which can simultaneously serve the role of generation (supplying energy) and distribution (reticulating energy).

Reinforcement Learning for a Resilient Electric Power SystemAlberto Sangiovanni-Vincentelli, Edgar L. and Harold H. Buttner Chair of Electrical Engineering and Computer Science
Harnessing the potential of AI techniques to make the power system resilient against such extreme cases is crucial. We propose to develop AI-based methods, and corresponding testing strategies, to achieve this goal.

Affordable Gigaton-Scale Carbon Sequestration: Navigating Autonomous Seaweed Growth Platforms by Leveraging Complex Ocean Currents and Machine LearningClaire Tomlin, Charles A. Desoer Chair in the College of Engineering
A promising approach to carbon sequestration utilizes seaweed, which fixates dissolved CO2 into biomass. Floating platforms that autonomously grow and deposit seaweed could scale this natural process to the open ocean, where the carbon is confined for millennia.

Interpretable Machine Learning Models to Improve Forecasting of Extreme-Weather-Causing Tropical Monster Storms - Da Yang, Faculty Scientist, Lawrence Berkeley National Laboratory, and Bin Yu, Chancellor's Distinguished Professor and Class of 1936 Second Chair Departments of Statistics and Electrical Engineering and Computer Sciences
We propose to develop interpretable, machine-learning (ML) models to forecast the Madden-Julian Oscillation (MJO) — the Storm King in Earth’s tropics.