Berkeley EECS is once again ranked as the #1 Electrical/Electronic/Communications Engineering graduate program in the country for 2023, tied with MIT and Stanford. The Berkeley Computer Engineering graduate program ranked #2 (tied with Stanford), as did the Computer Science graduate program (tied with Carnegie Mellon and Stanford). Berkeley Engineering, as a whole, again ranked #3.
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.
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.”
EECS Prof. Pieter Abbeel launched “The Robot Brains Podcast” in the spring of 2021. In each episode, he is joined by leading experts in AI Robotics from around the world to explore how far humanity has come in its mission to create conscious computers, mindful machines and rational robots. Abbeel sits down for a Q&A with Berkeley Engineering, in which he discusses his experience with podcasting and how it has shaped his own thinking about communicating AI to a broader audience.
UC Davis Chancellor and EECS alumnus Gary S. May (M.S. '88/Ph.D. '91, advisor: Costas Spanos) took the stage with UC President Michael V. Drake and Presidents Emeriti Janet S. Napolitano and Mark G. Yudof for the UCD Chancellor's Colloquium on March 8th. The four discussed the challenges they faced and lessons learned during their tenures in office. Topics included the impact of the pandemic on campus communities, the importance of public health, and the efficacy of remote learning; the university's federal lawsuit over the Deferred Action for Childhood Arrivals (DACA) program; approaches to managing UC funding cuts, including maintaining access to retirement plans and student aid; and America's cultural and democratic future, including ways that universities might help shape it.
A new type of high-resolution LiDAR chip developed by EECS Prof. Ming Wu could lead to a new generation of powerful, low-cost 3D sensors for autonomous cars, drones, robots, and smartphones. The paper, which appeared in the journal Nature, was co-authored by his former graduate students Xiaosheng Zhang (Ph.D. '21) and Johannes Henriksson (Ph.D. '21), current graduate student Jianheng Luo, and postdoc Kyungmok Kwon, in the Berkeley Sensor and Actuator Center (BSAC). Their new, smaller, more efficient, and less expensive LiDAR design is based on a focal plane switch array (FPSA) with a resolution of 16,384 pixels per 1-centimeter square chip, which dwarfs the 512 pixels or less currently found on FPSA. The design is scalable to megapixel sizes using the same complementary metal-oxide-semiconductor (CMOS) technology used to produce computer processors. Additionally, large, slow and inefficient thermo-optic switches are replaced by microelectromechanical system (MEMS) switches, which are traditionally used to route light in communications networks. If the resolution and range of the new system can be improved, conventional CMOS production technology can be used to produce the new, inexpensive chip-sized LiDAR.
EECS Prof. Alberto Sangiovanni-Vincentelli will receive an Honorary Doctorate, or Doktor Honoris Causa, from AGH University of Science and Technology in Krakow, Poland on March 18th. AGH UST includes engineering disciplines, exact sciences, Earth sciences, and social sciences, with an emphasis on current priorities of economy and business, and it regularly ranks first among Polish technical universities in international rankings. Sangiovanni-Vincentelli, an expert in electronic design automation, co-founded both Cadence Design Systems and Synopsys, Inc. He has also been awarded Honorary Doctorates by the combined EE and CS departments of the University of Aalborg in Denmark (2009) and from KTH in Sweden (2012).
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.
CS graduate student Lucas Spangher (advisor: Costas Spanos) gathered musicians from all over the Bay Area to perform a benefit concert in support of Ukraine on March 13th. Opera and gospel singers, violists, pianists and harpists, were among the more than one dozen volunteers to participate in the Benefit Concert for Humanitarian Aid for Ukraine at Herbst Hall in San Francisco. Spangher, who plays the cello, reached out to other local musicians on social media to ask if anyone would be interested in participating in an informal, online musical performance in honor of Ukraine, and it expanded from there. “It turned into this amazing professional operation,” said Spangher, “which I think just speaks to the energy and communal desire to do something. This is more than just a fundraiser. It’s a political statement and a way to honor Ukraine’s amazing contributions to classical music that can’t be erased by a vicious autocrat.” Spangher is a committed climate change activist whose research focuses on how to make artificial intelligence become more flexible for a transition to green energy. All proceeds from the performance have been donated to Nova Ukraine.
EECS and Bioengineering Prof. Steven Conolly has been awarded the 2022 U.C. Berkeley Bakar Prize. This prize is given annually to former Bakar Program Fellows whose technological innovations promise to deliver solutions to some of the world’s most pressing problems. Funds are provided to help new technologies transition from an academic setting to industrial applications. The objective of Conolly's project, titled Rapid in vivo optimization of solid tumor CAR-T cell therapies using advanced magnetic particle imaging (MPI), is to determine whether a particular CAR-T cell cancer immunotherapy is working in hours rather than months. CAR-T cells are tagged with safe magnetic nanoparticles before a treatment is administered so that oncologists can view how well they are targeting cancer cells using high resolution imaging technology.
