News

Jiaheng Zhang wins 2021 Facebook Fellowship for Security & Privacy

Third-year EECS graduate student Jiaheng Zhang (advisor: Dawn Song) has won a 2021 Facebook Fellowship for Security & Privacy.   He is the only student from Berkeley this year to win one of these coveted fellowships, which are designed to support emerging scholars who are engaged in innovative research.  Zhang's focus is on computer security and cryptography, especially zero-knowledge proofs and their applications on blockchain and machine learning models.  He is a member of the RISE Lab, the Initiative for Cryptocurrencies & Contracts Lab (IC3), and the Berkeley AI Research (BAIR). 

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. 

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.

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.
 

Joe Hellerstein named Datanami 2021 Person to Watch

CS Prof. Joseph Hellerstein has been named a Datanami 2021 Person to Watch.  Hellerstein is the chief strategy officer and one of the co-founders  a Trifacta, a company which markets data preparation and interaction technology based on Data Wrangler, a data transformation and discovery tool he developed in the RISELab at Berkeley with some colleagues from Stanford.  He is the subject of a Datanami article in which he discusses the state of data science education, the next wave of data, and the secrets of his success.

Maryann Simmons and Hayley Iben win Academy Awards

CS alumnae Maryann Simmons (B.A. / M.S./ Ph.D. '01, advisor: Carlo Séquin) and Hayley Iben (M.S. '05/Ph.D. '07, advisor: James O'Brien) have won 2020 Technical Achievement Awards (SciTech Oscars) from the Academy of Motion Picture Arts and Sciences for hair simulation systems.

Simmons is now a senior staff software engineer and the technical lead for Hair & Cloth at Walt Disney Animation Studios (WDAS).  She was part of the team responsible for the WDAS Hair Simulation System, which the citation describes as "a robust, predictable, fast and highly art-directable system built on the mathematics of discrete elastic rods. This has provided Disney artists the flexibility to manipulate hair in hyper-realistic ways to create the strong silhouettes required for character animation and has enabled a wide range of complex hairstyles in animated feature films." According to The Hollywood Reporter, the WDAS System was "used in animated features such as Tangled, to manage Rapunzel’s ultra-long waves."  While at Berkeley, Simmons was a member of Phi Beta Kappa and the Golden Key Honor Society.

Iben, who is now the director of engineering at Pixar Animation Studios, was part of the team responsible for the Taz Hair Simulation System.  The citation describes Taz as "a robust, predictable and efficient mass-spring hair simulation system with novel formulations of hair shape, bending springs and hair-to-hair collisions. It has enabled Pixar artists to bring to life animated digital characters with a wide variety of stylized hair, from straight to wavy to curly."  While at Berkeley, Iben was president of Women in Computer Science and Electrical Engineering (WiCSE) from 2004-2007, and a member CSGSA.

Alvin Cheung and Jonathan Ragan-Kelley win 2020 Intel Outstanding Researcher Award

EECS Assistant Profs. Alvin Cheung and Jonathan Ragan-Kelley are among 18 winners of Intel's 2020 Outstanding Research Awards (ORA). These awards recognize exceptional contributions made through Intel university-sponsored research.  Cheung and Ragan-Kelley are developing ARION, a system for compiling programs onto heterogeneous platforms. The team will use verified lifting, which rewrites legacy code into a clean specification, stripping away optimizations that target legacy architectures. This spec, written in a DSL, can then be compiled to new platforms, sometimes with orders of magnitude of speedup in resulting code performance.