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 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.”

EECS graduate student Shiekh Zia Uddin (advisor: Ali Javey) has won a Materials Research Society (MRS) 2022 Graduate Student Gold Award.  These awards recognize "students of exceptional ability who show promise for significant future achievement in materials research."  Uddin works in the areas of photophysics and optoelectronics of low dimensional semiconductors, with a focus on the photophysics of low-dimensional excitonic materials.  He was honored for research which demonstrated that two-dimensional monolayer semiconductors can be defective yet perfectly bright.   The award, which comes with comes with a $400 prize, will be presented at the 2022 MRS Fall Meeting in November.

Four EECS students in Robert Pilawa-Podgurski's lab have won best presentation awards for papers they presented at the 2022 IEEE Applied Power Electronics Conference (APEC) in March.  Three Technical Lecture Awards were won by:  undergraduate EECS student Amanda Jackson for "A Capacitively-Isolated Dual Extended LC-Tank Converter with 50% Two-Phase Operation at Even Conversion Ratios;" graduate student Samantha Coday for "Design and Implementation of a (Flying) Flying Capacitor Multilevel Converter;" and graduate student Kelly Fernandez for "A Charge Injection Loss Compensation Method for a Series-Stacked Buffer to Reduce Current and Voltage Ripple in Single-Phase Systems."  Graduate student Rose Abramson won a Technical Dialogue Award for "Core Size Scaling Law of Two-Phase Coupled Inductors — Demonstration in a 48-to-1.8 V MLB-Pol Converter."   The Technical Sessions showcased the best, peer-reviewed papers that described "new design ideas" and "innovative solutions" in "all areas of technical interest for the practicing power electronics professional." The dialogue sessions concentrated on papers "with a more specialized focus."  APEC is the premier conference in the field of power electronics.

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.

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.

EECS Ph.D. candidate Alisha Menon (M.S. '20, advisor: Jan Rabaey) has won a 2022 Outstanding Graduate Peer Mentor Award.  This award, presented by The Graduate Assembly, honors four Berkeley graduate and professional students annually "who have shown an outstanding commitment to mentoring, advising, and generally supporting either undergraduate students or their fellow graduate students."  Menon's research is in the area of neural engineering, an interdisciplinary field centered on the interface between humans and computers.  Her focus is on digital integrated circuits and systems for biomedical applications, specifically the intersection of hardware-efficient machine learning algorithms, physiological sensor fusion, gesture recognition, and closed-loop neural prosthetic feedback.  Menon won an NSF Graduate Research Fellowship and UC Berkeley Fellowship in 2018.  She is also an accomplished theater actress and Indian Classical dancer.

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.”

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.