EECS Prof. Jelani Nelson will participate on a panel discussing Berkeley's first Black full professor, statistician David Blackwell, on Thursday, April 29, 2021.  Blackwell made seminal contributions to game theory, probability theory, information theory, and Bayesian statistics. He was the first African American inducted into the National Academy of Sciences, and the seventh African American to receive a PhD in Mathematics.  The panel discussion brings together colleagues, students, and friends of Professor Blackwell, who will discuss his invaluable and lasting contributions to the field of Statistics, as well as the role he played in their careers and lives.  They will also explore life in the early days of the Berkeley Department of Statistics.

EECS Prof. Anthony Joseph has been name the next Director of the Coleman Fung Institute for Engineering Leadership.  After earning his degrees at MIT, Joseph was hired as a professor of Computer Science at Berkeley in 1998. His primary research interests are in Genomics, Secure Machine Learning, Datacenters, mobile/distributed computing, and wireless communications (networking and telephony). His research also includes adaptive techniques for cloud computing, distributed network monitoring and triggering, cybersecurity, and datacenter architectures. He is the former Director of Berkeley Intel Lab, the co-founder of two startup companies, and a committed teacher who has experience developing and teaching five successful massive, open online courses (MOOC) on Big Data and Machine Learning offered through the BerkeleyX platform. Joseph is noted for his commitment to access and inclusion, and has worked to recruit and mentor a diversity of students at the undergraduate and graduate levels.  He will begin his directorship on July 1st.

EECS alumna and Assistant Teaching Prof. Gireeja Ranade (M.S. '09/Ph.D. '14, advisor: Anant Sahai), and Graduate Student Instructors (GSIs) Ritika Shrivastava, Jay Monga, Maxson Yang, Suraj Rampure and Allen Shen have won UC Berkeley Extraordinary Teaching in Extraordinary Times awards.  They are among 59 people from of pool of over 500 nominees honored at Berkeley by the Academic Senate’s Committee on Teaching for embracing the challenges posed by the 2020 COVID-19 pandemic, and engaging in or supporting excellent teaching. "These instructors and staff used innovative methods and worked beyond their traditional roles to ensure that students remained engaged and supported, and were challenged to do meaningful work under extraordinary circumstances."

Shrivastava, a fall GSI for EECS C106A/206A Introduction to Robotics, provided a warm, supportive, and positive environment for her students, developed new materials, and used tools to promote inclusiveness and overcome technological differences.  Jay Monga, also a fall GSI and lab TA for EECS 106A/206A, helped students with their lab-focused robotics class by creating a video walkthrough and slides demonstrating procedures and assignments, recording a presentation to promote asynchronous instruction, helping to design a more accessible lab, and creating a Discord server for better virtual learning. Yang, who was a summer GSI for CS 10 The Beauty and Joy of Computing,  released a comprehensive student survey to guide course policy and focused on  reducing common stressors (like deadlines), implementing weekly check-ins, and creating ways to improve the students' virtual experience (like memes).  Rampure, who was a fall GSI and summer instructor for Data C100 Principals & Techniques of Data Science, and Shen, who was a fall GSI and summer instructor for CS 186 Introduction to Data Systems, won the award together for teaching two of Berkeley’s flagship undergraduate data science courses.  They introduced new applications of course material, prioritized accessibility in lectures, designed assessments, and used real-world examples to promote engagement. 

CS Prof. and Chan Zuckerberg Biohub investigator Michel Maharbiz is the senior author of a paper in Nature Biotechnology titled "Monitoring deep-tissue oxygenation with a millimeter-scale ultrasonic implant," which describes a tiny wireless implant that can provide real-time measurements of tissue oxygen levels deep underneath the skin. The device, which is smaller than the average ladybug and powered by ultrasound waves, could help doctors monitor the health of transplanted organs or tissue and provide an early warning of potential transplant failure.  “It’s very difficult to measure things deep inside the body,” said Maharbiz. “The device demonstrates how, using ultrasound technology coupled with very clever integrated circuit design, you can create sophisticated implants that go very deep into tissue to take data from organs.”

CS Assistant Prof. Rediet Abebe is co-chairing the inaugural ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization (EAAMO ’21) in October 2021.  This conference will highlight work where techniques from algorithms, optimization, and mechanism design, along with insights from other disciplines, can help improve equity and access to opportunity for historically disadvantaged and underserved communities.  Launched by the Mechanism Design for Social Good (MD4SG) initiative, it will feature keynote talks and panels, and contributed presentations of research papers, surveys, problem pitches, datasets, and software demonstrations.   The submission deadline is June 3, 2021.

CS Prof. Michael I. Jordan is the subject of an IEEE Spectrum article which describes his life, research, and philosophy.  A computer science pioneer, Jordan blended CS, statistics, and applied mathematics, to help transform unsupervised machine learning into a powerful algorithmic tool for solving problems in fields like natural language processing, computational biology, and signal processing.  He explains that machine learning is, in essence, a new field of engineering focused on the interface between people and technology.  The optimal goal of machine learning should not be artificial imitation of human thinking since that is something human beings can already do for themselves.  Instead, AI should be focused on helping humanity solve the problems that it has created.  “While the science-fiction discussions about AI and super intelligence are fun, they are a distraction,” Jordan says. “There’s not been enough focus on the real problem, which is building planetary-scale machine learning–based systems that actually work, deliver value to humans, and do not amplify inequities.

CS Assistant Prof. Rediet Abebe is the subject of a profile in Quanta Magazine which describes how she uses the tools of theoretical computer science to understand pressing social problems -- and try to fix them.   Abebe, who is from Ethiopia, earned a B.A. in mathematics from Harvard, attended a one-year intensive math program at Cambridge, and switched to Computer Science at Cornell where she earned her Ph.D.   She was drawn to CS because it allowed her to apply mathematical thinking to social problems like discrimination, inequity and access to opportunity.  Abebe has co-founded two organizations: Black in AI, a community of Black researchers working in artificial intelligence, and Mechanism Design for Social Good, which brings together researchers from different disciplines to address social problems. The Q&A interview discusses her life and career choices, as well as her research and its applications.

CS Prof. Joseph Hellerstein has been named a Datanami 2021 Person to Watch.  Hellerstein is the chief strategy officer and one of the co-founders  a Trifacta, a company which markets data preparation and interaction technology based on Data Wrangler, a data transformation and discovery tool he developed in the RISELab at Berkeley with some colleagues from Stanford.  He is the subject of a Datanami article in which he discusses the state of data science education, the next wave of data, and the secrets of his success.

CS Associate Prof. Nir Yosef has joined with colleagues in Bioengineering to write an algorithm called totalVI that uses deep learning to integrate gene and protein data about single cells, and which will allow collaborative experiments to be more accurate and efficient.   TotalVI will help to manage, analyze, and distribute gene and protein data about single cells that were gathered from different tissues and donors, and that were processed in different labs, into a single organizational system.  “The combination of CITE-seq (an RNA sequencing technique) and totalVI allows us to estimate, from the same cell, not only its gene expression but also the expression of the cell membrane proteins,” said Yosef.  “Those tell us a lot about the biology of the cells, since working with these proteins is kind of the standard in immunology.”  The new algorithm will enable researchers to integrate single-cell datasets from labs around the world, and will aid the progression of global knowledge bases.

EECS Prof. Boubacar Kanté and his team have found a new way to harness properties of light waves that can radically increase the amount of data they carry. They demonstrated the emission of discrete twisting laser beams from antennas made up of concentric rings roughly equal to the diameter of a human hair, small enough to be placed on computer chips.  The new work, reported in a paper published Thursday, February 25, 2021, in the journal Nature Physics, throws wide open the amount of information that can be multiplexed, or simultaneously transmitted, by a coherent light source.  “It’s the first time that lasers producing twisted light have been directly multiplexed,” said Kanté. “We’ve been experiencing an explosion of data in our world, and the communication channels we have now will soon be insufficient for what we need. The technology we are reporting overcomes current data capacity limits through a characteristic of light called the orbital angular momentum. It is a game-changer with applications in biological imaging, quantum cryptography, high-capacity communications, and sensors.”