News

Jelani Nelson to participate in event celebrating statistician David Blackwell

EECS Prof. Jelani Nelson will participate on a panel discussing Berkeley's first Black full professor, statistician David Blackwell, on Thursday, April 29, 2021.  Blackwell made seminal contributions to game theory, probability theory, information theory, and Bayesian statistics. He was the first African American inducted into the National Academy of Sciences, and the seventh African American to receive a PhD in Mathematics.  The panel discussion brings together colleagues, students, and friends of Professor Blackwell, who will discuss his invaluable and lasting contributions to the field of Statistics, as well as the role he played in their careers and lives.  They will also explore life in the early days of the Berkeley Department of Statistics.

Berkeley Blue team takes silver medal at ACM programming championship

The Berkeley Blue team, which includes EECS undergraduates Ethan Guo and James Shi, and CS/Math undergraduate Justin Yokota, has won a silver medal at the 2020 ACM International Collegiate Programming Contest (ICPC) North America West Division Championship.  If the team does well in the North American Division (NADC) Championship this August, they will be eligible to compete in the the world’s most prestigious competition of young talents in the field of IT, the 2022 ICPC World Finals, which will be held in Moscow in 2022.   UCSD placed first, followed by Berkeley Blue, and teams from UCLA, UWash, Stanford, UBC, and the Berkeley Gold team, which includes students Ajit Kadaveru,  Samuel Lee, and Jonathan Guo.

Anthony Joseph named Director of Fung Institute

EECS Prof. Anthony Joseph has been name the next Director of the Coleman Fung Institute for Engineering Leadership.  After earning his degrees at MIT, Joseph was hired as a professor of Computer Science at Berkeley in 1998. His primary research interests are in Genomics, Secure Machine Learning, Datacenters, mobile/distributed computing, and wireless communications (networking and telephony). His research also includes adaptive techniques for cloud computing, distributed network monitoring and triggering, cybersecurity, and datacenter architectures. He is the former Director of Berkeley Intel Lab, the co-founder of two startup companies, and a committed teacher who has experience developing and teaching five successful massive, open online courses (MOOC) on Big Data and Machine Learning offered through the BerkeleyX platform. Joseph is noted for his commitment to access and inclusion, and has worked to recruit and mentor a diversity of students at the undergraduate and graduate levels.  He will begin his directorship on July 1st.

Ranade, Shrivastava, Monga, Yang, Rampure and Shen win Extraordinary Teaching in Extraordinary Times Awards

EECS alumna and Assistant Teaching Prof. Gireeja Ranade (M.S. '09/Ph.D. '14, advisor: Anant Sahai), and Graduate Student Instructors (GSIs) Ritika Shrivastava, Jay Monga, Maxson Yang, Suraj Rampure and Allen Shen have won UC Berkeley Extraordinary Teaching in Extraordinary Times awards.  They are among 59 people from of pool of over 500 nominees honored at Berkeley by the Academic Senate’s Committee on Teaching for embracing the challenges posed by the 2020 COVID-19 pandemic, and engaging in or supporting excellent teaching. "These instructors and staff used innovative methods and worked beyond their traditional roles to ensure that students remained engaged and supported, and were challenged to do meaningful work under extraordinary circumstances."

Shrivastava, a fall GSI for EECS C106A/206A Introduction to Robotics, provided a warm, supportive, and positive environment for her students, developed new materials, and used tools to promote inclusiveness and overcome technological differences.  Jay Monga, also a fall GSI and lab TA for EECS 106A/206A, helped students with their lab-focused robotics class by creating a video walkthrough and slides demonstrating procedures and assignments, recording a presentation to promote asynchronous instruction, helping to design a more accessible lab, and creating a Discord server for better virtual learning. Yang, who was a summer GSI for CS 10 The Beauty and Joy of Computing,  released a comprehensive student survey to guide course policy and focused on  reducing common stressors (like deadlines), implementing weekly check-ins, and creating ways to improve the students' virtual experience (like memes).  Rampure, who was a fall GSI and summer instructor for Data C100 Principals & Techniques of Data Science, and Shen, who was a fall GSI and summer instructor for CS 186 Introduction to Data Systems, won the award together for teaching two of Berkeley’s flagship undergraduate data science courses.  They introduced new applications of course material, prioritized accessibility in lectures, designed assessments, and used real-world examples to promote engagement. 

Tiny wireless implant detects oxygen deep within the body

CS Prof. and Chan Zuckerberg Biohub investigator Michel Maharbiz is the senior author of a paper in Nature Biotechnology titled "Monitoring deep-tissue oxygenation with a millimeter-scale ultrasonic implant," which describes a tiny wireless implant that can provide real-time measurements of tissue oxygen levels deep underneath the skin. The device, which is smaller than the average ladybug and powered by ultrasound waves, could help doctors monitor the health of transplanted organs or tissue and provide an early warning of potential transplant failure.  “It’s very difficult to measure things deep inside the body,” said Maharbiz. “The device demonstrates how, using ultrasound technology coupled with very clever integrated circuit design, you can create sophisticated implants that go very deep into tissue to take data from organs.”

Yang You makes Forbes 30 Under 30 2021 Asia for Healthcare and Science

EECS alumnus Yang You (Ph.D. '20, advisor: James Demmel) has been named in the Forbes 30 Under 30 2021 Asia list for Healthcare and Science.  Yang, who is now a Presidential Young Professor of Computer Science at the National University of Singapore, studies Machine Learning, Parallel/Distributed Algorithms, and High-Performance Computing. The focus of his research is scaling up deep neural networks training on distributed systems or supercomputers.  He has broken two world records for AI training speed: one in 2017 for ImageNet and the other in 2019 for Boundless Electrical Resistivity Tomography (BERT).  Yang has won numerous best paper awards as well as the inaugural Berkeley EECS Lotfi A. Zadeh Prize for outstanding contributions to soft computing and its applications by a graduate student.

Rediet Abebe co-chairing ACM Conference on Equity and Access in Algorithms, Mechanisms, & Optimization

CS Assistant Prof. Rediet Abebe is co-chairing the inaugural ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization (EAAMO ’21) in October 2021.  This conference will highlight work where techniques from algorithms, optimization, and mechanism design, along with insights from other disciplines, can help improve equity and access to opportunity for historically disadvantaged and underserved communities.  Launched by the Mechanism Design for Social Good (MD4SG) initiative, it will feature keynote talks and panels, and contributed presentations of research papers, surveys, problem pitches, datasets, and software demonstrations.   The submission deadline is June 3, 2021.

Andreea Bobu named 2021 Apple Scholar in AI/ML

EECS graduate student Andreea Bobu (advisor: Anca Dragan) has been named a 2021 Apple Scholar in AI and Machine Learning (AI/ML).  The scholarship was created by Apple to "celebrate the contributions of students pursuing cutting-edge fundamental and applied machine learning research worldwide."  Bobu's research interests lie at the intersection of machine learning, robotics, and human-robot interaction, with a focus in robot learning with uncertainty. She is particularly interested in the ways in which autonomous systems’ models of the world and of other agents (e.g. humans) can go wrong, and is devising ways to enhance interaction between people and robots.  She earned her BS in Computer Science and Engineering at MIT in 2017, where she worked on probabilistic models for medical image analysis.  She is currently associated with the Berkeley Artificial Intelligence Research (BAIR) lab.

Wenshuo Guo wins 2021 Google PhD Fellowship

EECS graduate student Wenshuo Guo (advisor: Michael I. Jordan) has won a 2021 Google PhD Fellowship in Algorithms, Optimization and Markets.  This award acknowledges and supports exemplary PhD students in computer science and related fields who are making contributions to their areas of specialty.   Guo studies robustness guarantees in algorithms and machine learning foundations, as well as their impact on society.  She is also interested in the intersection of CS and economics, and is currently focused on mechanism design, causal inference, and statistical questions in reinforcement learning. The award, which will cover full tuition, fees, and a stipend for the 2021-22 school year, will be presented at the Global Fellowship Summit over the summer.
 

Michael Jordan explains why today’s AI systems aren’t actually intelligent

CS Prof. Michael I. Jordan is the subject of an IEEE Spectrum article which describes his life, research, and philosophy.  A computer science pioneer, Jordan blended CS, statistics, and applied mathematics, to help transform unsupervised machine learning into a powerful algorithmic tool for solving problems in fields like natural language processing, computational biology, and signal processing.  He explains that machine learning is, in essence, a new field of engineering focused on the interface between people and technology.  The optimal goal of machine learning should not be artificial imitation of human thinking since that is something human beings can already do for themselves.  Instead, AI should be focused on helping humanity solve the problems that it has created.  “While the science-fiction discussions about AI and super intelligence are fun, they are a distraction,” Jordan says. “There’s not been enough focus on the real problem, which is building planetary-scale machine learning–based systems that actually work, deliver value to humans, and do not amplify inequities.