Sumit Gulwani (Ph.D. '05, advisor: George Necula), now a Partner Research Manager at Microsoft Research in Redmond, Washington, has been selected to receive the 2021 Max Planck-Humboldt Medal for "automatic programming and computational education."  Gulwani, who won the ACM SIGPLAN Doctoral Dissertation award and the MSR Ph.d. Fellowship while at Berkeley, is an expert in program analysis and artificial intelligence.  He shaped the field of program synthesis, which emerged around 2010, by developing algorithms that can efficiently generate computer programs from very few input-output examples, natural-language-based specification, or from just the code and data context. His work made it possible for non-programmers to program tedious, repetitive spreadsheet tasks, and enabled productivity improvements for data scientists and developers for data wrangling and software engineering tasks. Recently, Gulwani has also been using the tools of program synthesis for computer-aided education of pupils and students. Starting from the automatic correction of learners' work in programming education, he further evolved this line of work to detect misunderstandings and give learning feedback and grades, also in subjects like mathematics and language learning. He is also the inventor of the popular Flash Fill feature in Microsoft Excel.  The award will be presented during a ceremony in Berlin on November 3, 2022.

CS Prof. Katherine Yelick has been named UC Berkeley's next vice chancellor for research.  She will take over the role from EECS Prof. Randy Katz on January 1, 2022.  Yelick is an expert in the field of parallel computing and currently serves as executive associate dean in the Division of Computing, Data Science, and Society (CDSS).  “Kathy Yelick is one of the most talented leaders I have ever worked with — she listens, sees the big picture, and co-creates and implements phenomenal solutions,” said Jennifer Chayes, the CDSS Associate Provost. “I cannot imagine a better vice chancellor for research, and we at CDSS look forward to working with Kathy in her new role.” Yelick spent 11 years in leadership and management roles at Berkeley Lab (LBNL), where she oversaw a variety of initiatives, including the opening of new computing facility Shyh Wang Hall, the founding of the Berkeley Quantum collaboration, the formation of the lab’s machine learning for science initiative, and the launch of the U.S. Department of Energy’s Exascale Computing Project.  “UC Berkeley’s research community is uniquely positioned to tackle some of the world’s most important social and scientific problems, from climate change and public health to equity and social justice,” Yelick said. “I think it’s important to bring together diverse expertise and perspectives, and I look forward to collaborating with my colleagues across academic disciplines, from the humanities and social sciences to the physical and biological sciences, engineering, professional schools and beyond.”

Graduate students Sagnik Bhattacharya (B.A. CS and Statistics '21) and Jay Shenoy (B.A. CS '21) are recipients of the 2022 Siebel Scholars award.  The Siebel Scholars program annually recognizes "exceptional students from the world’s leading graduate schools of business, computer science, and bioengineering."  Bhattacharya, a 5th Year Masters student and TA for CS 70 (Discrete Math and Probability), is interested in machine learning theory and its applications in data science.  He is currently working with Prof. Jonathan Shewchuk on the theory behind deep linear neural networks.  Shenoy is working on computational imaging with Prof. Ren Ng, as well as problems in autonomous vehicle simulation in the Industrial Cyber-Physical Systems (iCyPhy) group.  Siebel Scholars receive a $35,000 award for their final year of studies. "On average, Siebel Scholars rank in the top five percent of their class, many within the top one percent."

CS Prof. Ali Ghodsi will be the inaugural speaker for "The Tale of a Success" entrepreneurship series, hosted by the Iranian Students of California (ISC) in collaboration with the Berkeley Iranian Students Association in America (ISAA).  Ghodsi is a co-founder and the CEO of enterprise software company Databricks, a start-up which grew out of the AMPLab project that is now valued at $38B. He was one of the original creators of the open source project Apache Spark, and "the ideas from his academic research in resource management and scheduling and data caching have been applied to Apache Mesos and Apache Hadoop."  The lecture series features stories by successful Iranian-American entrepreneurs "who have all built category-defining tech companies."  Ghodsi will give his presentation via Zooom webinar on October 14th.

EECS Associate Prof. Boubacar Kanté is among the authors of a paper published in the journal Science titled "Plasmonic topological metasurface by encircling an exceptional point."  The paper introduces "an additional degree of freedom to address optical phase engineering by exploiting the topological features of non-Hermitian matrices operating near [the] singular points".   The novel phase, which was shown to be topologically protected, enables the construction of novel polarization dependent and chiral phased arrays and holograms. The ease of implementation together with its compatibility with other phase-addressing mechanisms will enable information multiplexing with antenna arrays.

CS Prof. Joseph Hellerstein, and Assistant Profs. Aditya Parmeswaran and Sarah Chasins, are among the principal investigators of a new lab that has just received a $2M grant from the National Science Foundation to make big datasets used by the criminal justice system more accessible to non-technical researchers.  The Effective Programming, Interaction, and Computation with Data (EPIC) Lab will create tools that utilize machine learning, program synthesis, and human-centered design, to improve the ability of public defenders, investigators and paralegals to research police misconduct, judicial decision-making, and related issues, for their cases.  The tools, which will initially be used in San Francisco, Alameda and Sacramento, are designed to address systemic power and resource disparities in California by helping under-resourced practitioners better defend their clients.

EECS Assistant Prof. Gopala Anumanchipalli has been selected for the Rose Hills Innovator Program which supports distinguished early-career UC Berkley faculty who are "interested in developing highly innovative research programs" in STEM fields.  The program will provide discretionary research support of up to $85,000 per year for "projects with an exceptionally high scientific promise that may generate significant follow-on funding."   Anumanchipalli's project, titled "Multimodal Intelligent Interfaces for Assistive Communication," proposes to "improve the current state of assistive communication technologies by integrating multiple neural and behavioral sensing modalities, and tightly integrating the graphical interfaces, and personalizing them to the user’s context."  His team will use "state-of-the-art neural engineering and artificial intelligence to develop novel communication interfaces" including Electrocorticography, non-invsive in-ear Electroencephalography sensors and functional near infrared spectroscopy.  They will also use on-device speech recognition and dialog management to incorporate the acoustic context of the user.

EECS Prof. Sanjit Seshia was a recipient of the CAV Award at the 2021 International Conference on Computer-Aided Verification (CAV) earlier this month.  This award is presented annually "for fundamental contributions to the field of Computer-Aided Verification," and comes with a cash prize of $10K that is shared equally among recipients.  This year's award specifically recognizes “pioneering contributions to the foundations of the theory and practice of satisfiability modulo theories (SMT).”  Seshia's Ph.D. thesis work on the UCLID verifier and decision procedure helped lay the groundwork for this field.  SMT solvers are critical to verification of software and hardware model checking, symbolic execution, program verification, compiler verification, verifying cyber-physical systems, and program synthesis. Other applications include planning, biological modeling, database integrity, network security, scheduling, and automatic exploit generation.  CAV is the premier international conference on computer-aided verification and  provides a forum for a broad range of advanced research in areas ranging from model checking and automated theorem proving to testing, synthesis and related fields.

A team of researchers from UC Berkeley, Georgia Tech, and USC, have been awarded $20M by the National Science Foundation (NSF) to launch an institute which will deploy AI to tackle massive optimization challenges.  The researchers hope the new National Artificial Intelligence (AI) Institute for Advances in Optimization will deliver a paradigm shift in automated decision-making by fusing AI and optimization to address grand challenges in highly constrained settings, such as logistics and supply chains, energy and sustainability, and circuit design and control.  EECS/IEOR Prof. Pieter Abbeel will lead the Reinforcement Learning Team, and EECS/IEOR Prof. Laurent El Ghaoui will be on both the End to End Optimization and the New Learning Methods Teams.  EECS Profs. Borivoje Nikolic and Vladimir Stojanovic will also be participating.  The group intends to integrate ethics and values into their complex systems design, from inception through operation, to ensure that all scientific advances will ultimately serve the interests of society.  The institute also plans to partner with historically Black colleges and universities (HBCUs) in Georgia, and Hispanic-serving community colleges in California, to build longitudinal education and workforce development programs.  Partners include Clark Atlanta University, Spelman College, and the University of Texas at Arlington.

EECS alumnus Yang You (Ph.D. '20, advisor: James Demmel)  was named as one of two honorable mentions for the 2020 ACM Special Interest Group in High Performance Computing (SIGHPC) Dissertation Award.  You was selected for developing LARS (Layer-wise Adaptive Rate Scaling) and LAMB (Layer-wise Adaptive Moments for Batch training) to accelerate machine learning on HPC platforms. His thesis, “Fast and Accurate Machine Learning on Distributed Systems and Supercomputers,” focuses on improving the speed and accuracy of Machine Learning training to optimize the use of parallel programming on supercomputers.  You made the Forbes 30 Under 30 2021 Asia list for Healthcare and Science in April and is now a Presidential Young Professor of Computer Science at the National University of Singapore.