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 alumna Hani Gomez (Ph.D. '20, advisor: Kris Pister) is the subject of a Berkeley Computing, Data Science, and Society (CDSS) profile titled "Hani Gomez, Ph.D.: Computing Pedagogy at the Nexus of Technology and Social Justice."  Gomez was born in Bolivia and earned her B.S. in EE at the University of South Carolina before coming to Berkeley for her graduate studies.  She has merged social justice and technology into a post-doc research position at Berkeley, split between EECS and the Human Contexts and Ethics (HCE) program in CDSS.  Gomez helped develop the course CS 194-100 EECS for All: Social Justice in EECS last spring, was one of three presenters in a June HCE workshop titled "Towards Social Justice in the Data Science Classroom," and serves on the EECS Anti-Racism Committee.  She says the preoccupation with perfectionism at Berkeley "doesn’t leave room [for you] to learn from your mistakes...You need to give yourself room to learn or unlearn, to grow and relearn.”

The UC Berkeley Black Engineering and Science Alumni Club (BESAC) has been selected by the Cal Alumni Association Board of Directors and the UC Berkeley Foundation Board of Trustees to receive the 2021 Loyal Company Outstanding Volunteer Group Award. This award "commends a volunteer group or alumni chapter that has maintained a meaningful relationship to Berkeley while successfully engaging its members through events, programs, and philanthropic opportunities." BESAC's mission "is to improve the opportunities and support for Cal Black alumni, students, professors, and staff in engineering and sciences and to bring UC Berkeley alumni together in organized efforts to benefit the members of the chapter and UC Berkeley."  The award will be presented during Reunion and Parents Weekend, on October 1st.

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.