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CS Grad Xin Lyu

Xin Lyu wins CCC 2022 Best Student Paper Award

CS graduate student Xin Lyu (advisors: Jelani Nelson and Avishay Tal) has won the Best Student Paper Award at the Computational Complexity Conference (CCC) 2022. The solo-authored paper titled “Improve Pseudorandom Generators for AC^0 Circuits” was one of two co-winners of the Best Student Paper Award at CCC, which is an annual conference on the inherent difficulty of computational problems in terms of the resources they require. Organized by the Computational Complexity Foundation, CCC is the premier specialized publication venue for research in complexity theory.

Rod Bayliss and Vivek Nair win 2022 Hertz Fellowships

EECS graduate students Roderick Bayliss III (advisor: Robert Pilawa-Podgurski) and Vivek Nair (advisor: Dawn Song) have been selected to receive 2022 Hertz Fellowships.  One of the most prestigious awards of its kind, Hertz Fellowships support PhD students whose research show "the greatest potential to tackle society's most urgent problems." Bayliss is developing more efficient and power-dense types of power converters—devices that change the current, voltage or frequency of electrical energy—and inductors, which store energy, to help reduce the world’s dependence on fossil fuels. He earned his B.S. and M.Eng. in Electrical Engineering from MIT.  Nair is developing cutting-edge cryptographic techniques to defend digital infrastructure against sophisticated cyberthreats. He was the youngest-ever recipient of a B.A. and Master's in computer science from the University of Illinois Urbana-Champaign, and is the founder of Multifactor.com.  Their fellowships will fund up to five years of graduate research with "the freedom to pursue innovative ideas wherever they may lead."  Hertz Fellows also receive lifelong professional support, including mentoring and networking with a powerful community of more than 1,200 researchers.

BAIR Climate Initiative creates partnerships to fight climate change

Berkeley Artificial Intelligence researchers are joining forces with climate experts, government agencies, and industry, as part of the new Berkeley AI Research (BAIR) Climate Initiative, a multi-disciplinary student-led hub dedicated to fighting climate change.  The effort is being led by co-founding director CS Prof. Trevor Darrel and organized by three of his graduate students, Colorado Reed (co-advised by Kurt Keutzer), who will help lead the initiative, Medhini Narasimhan, and Ritwik Gupta (co-advised by Shankar Sastry).  Their objective is to develop AI techniques that address problems with data processing, particularly involving massive data sets. To maximize the benefit to other researchers studying the same problems around the world, all work done by the initiative will be openly published and available without exclusive or proprietary licensing.  One of their first projects, “The Fate of Snow,” will be a collaboration between BAIR Climate Initiative researchers and other scientists and policy experts on the Berkeley campus, Berkeley Lab (LBNL), Meta AI (which belongs to Meta Platforms, Inc.) and the Center for Western Weather and Water Extremes. The researchers plan to apply AI methods to a multitude of openly available weather and satellite data sources to estimate how much water is in the Sierra Nevada snowpack and forecast what that will mean for streadmflow in the region.

Pratul Srinivasan and Benjamin Mildenhall jointly awarded honorable mention for 2021 ACM Doctoral Dissertation Award

Two of EECS Prof. Ren Ng's former graduate students, Pratul Srinivasan and Benjamin Mildenhall, jointly received an honorable mention for the 2021 Association for Computing Machinery (ACM) Doctoral Dissertation Award.  This award is presented annually to the "author(s) of the best doctoral dissertation(s) in computer science and engineering."  Srinivasan and Mildenhall, who both currently work at Google Research,  were recognized "for their co-invention of the Neural Radiance Field (NeRF) representation, associated algorithms and theory, and their successful application to the view synthesis problem."  Srinivasan’s dissertation, "Scene Representations for View Synthesis with Deep Learning," and Mildenhall’s dissertation, “Neural Scene Representations for View Synthesis,” addressed a long-standing open problem in computer vision and computer graphics called the "view synthesis" problem:  If you provide a computer with just a few of photographs of a scene, how can you get it to predict new images from any intermediate viewpoint?  "NeRF has already inspired a remarkable volume of follow-on research, and the associated publications have received some of the fastest rates of citation in computer graphics literature—hundreds in the first year of post-publication."

EECS faculty applaud graduates’ resilience

EECS Assistant Prof. Nika Haghtalab and CS Assistant Prof. and Associate Prof. in the School of Information, David Bamman, are quoted in a Computing, Data Science, and Society (CDSS) article about the resiliency and determination of the 2022 graduating class, particularly during the pandemic. “This generation of students has persevered, despite these global challenges, to forge a real community with their peers,” said Bamman. They also anticipated the ways the graduates will use their new skills to shape our collective future. “We need graduates who understand the technical methods of data science, their limitations and sources of bias, and the broader context in which information is used to drive policy, inform decision-making, and shape opinion,” Bamman said.  Haghtalab noted that “this is a great time to enter the workforce and contribute to the shaping of data science and computing for the advancement and betterment of the world.”

Chase Norman selected to participate in the Heidelberg Laureate Forum

CS undergraduate student Chase Norman is among 200 young mathematics and computer science researchers selected from across the globe to attend the 9th Heidelberg Laureate Forum (HLF) in Germany this September. During the week-long conference, participants will share ideas with some of the "most exceptional mathematicians and computer scientists of their generations," namely the recipients of some of the field’s most prestigious awards: the Abel Prize, ACM A.M. Turing Award, ACM Prize in Computing (won this year by Berkeley CS Prof. Pieter Abbeel), Fields Medal, and Nevanlinna Prize. Participants and laureates will interact through a blend of scientific and social activities that are designed to foster a relaxed atmosphere and encourage scientific exchange.  Participants are selected by a panel of international reviewers on the basis of their research experience, social engagement skills, and letter of motivation.”. Norman is a CS and Math double major who was admitted to the EECS Honors Program in the breadth area of Mathematical Logic and Foundations.  He is also the president of the CS honor society Upsilon Pi Epsilon, was course staff for CS 170 and CS 61A, and was a percussionist with UC Jazz and the UCB Symphony Orchestra.

Dave Epstein wins 2022 Paul & Daisy Soros Fellowship

CS graduate student Dave Epstein (advisor: Alexei Efros) has won a 2022 Paul & Daisy Soros Graduate Fellowship for New Americans.  This fellowship recognizes outstanding graduate students who are immigrants and children of immigrants in the United States, and "who are poised to make significant contributions to US society, culture or their academic field."  Epstein is affiliated with the Berkeley AI Research (BAIR) Lab where he is teaching machines to solve visual problems without labels, and enabling a creative understanding of the real world to emerge. He is also interested in language, machine learning, synthesis, and interaction. Paul & Daisy Fellowships come with a $90K award.

Aviral Kumar, Serena Wang and Eric Wallace win 2022 Apple Scholars in AI/ML PhD fellowships

Three EECS graduate students, Aviral Kumar (advisor: Sergey Levine), Serena Wang (advisors: Rediet Abebe and Michael Jordan), and Eric Wallace (advisors: Dan Klein and Dawn Song) have been named 2022 recipients of the Apple Scholars in AI/ML PhD fellowship.  This fellowship recognizes graduate and postgraduate students in the field of Artificial Intelligence and Machine Learning who are "emerging leaders in computer science and engineering" as demonstrated by their "innovative research, record as thought leaders and collaborators, and commitment to advance their respective fields."  Kumar is working in the area of "Fundamentals of Machine Learning" to develop "reinforcement learning algorithms and tools that enable learning policies by effectively leveraging historical interaction data and understanding and addressing challenges in using RL with deep neural nets." Wang is working in the area of "AI for Ethics and Fairness" to "foster positive long-term societal impact of ML by rethinking ML algorithms and practices, employing tools from robust optimization, constrained optimization, and statistical learning theory."  Wallace is working in the area of "Privacy Preserving Machine Learning," to make "NLP models more secure, private, and robust." Apple Scholars receive support for their research, internship opportunities, and a two-year mentorship with an Apple researcher in their field.

‘Off label’ use of imaging databases could lead to bias in AI algorithms, study finds

A paper with lead author EECS postdoc Efrat Shimron and co-authors EECS graduate student Ke Wang, UT Austin professor Jonathan Tamir (EECS PhD ’18), and EECS Prof. Michael Lustig shows that algorithms trained using "off-label" or misapplied massive, open-source datasets are subject to integrity-compromising biases.  The study, which was published in the Proceedings of the National Academy of Sciences (PNAS), highlight some of the problems that can arise when data published for one task are used to train algorithms for a different one.  For example, medical imaging studies which use preprocessed images may result in skewed findings that cannot be replicated by others working with the raw data.  The researchers coined the term “implicit data crimes” to describe research results that are biased because algorithms are developed using faulty methodology. “It’s an easy mistake to make because data processing pipelines are applied by the data curators before the data is stored online, and these pipelines are not always described. So, it’s not always clear which images are processed, and which are raw,” said Shimron. “That leads to a problematic mix-and-match approach when developing AI algorithms.”

Chandan Singh is 2022 Berkeley Grad Slam Competition semi-finalist

CS graduate student Chandan Singh (advisor: Bin Yu) has made it to the semi-finals of the 2022 Berkeley Grad Slam Competition, a UC showcase for graduate student research presented in three-minute talks for a general audience, likened to short Ted Talks.  In "Unlocking Scientific Secrets by Distilling Neural Networks," Singh hopes to build on recent advances in machine learning to improve the world of healthcare.   His research focuses on how to build trustworthy machine-learning systems by making them more interpretable through partnerships with domain experts (e.g. medical doctors and cell biologists). These collaborations give rise to useful methodology that both build more transparent models as well as improve the trustworthiness of black-box models. He hopes to help bridge the gap between both types of models so that they can be reliably used to improve real-world healthcare.