The Berkeley Blue team, which includes EECS undergraduates Ethan Guo and James Shi, and CS/Math undergraduate Justin Yokota, has won a silver medal at the 2020 ACM International Collegiate Programming Contest (ICPC) North America West Division Championship.  If the team does well in the North American Division (NADC) Championship this August, they will be eligible to compete in the the world’s most prestigious competition of young talents in the field of IT, the 2022 ICPC World Finals, which will be held in Moscow in 2022.   UCSD placed first, followed by Berkeley Blue, and teams from UCLA, UWash, Stanford, UBC, and the Berkeley Gold team, which includes students Ajit Kadaveru,  Samuel Lee, and Jonathan Guo.

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. 

EECS graduate student Wenshuo Guo (advisor: Michael I. Jordan) has won a 2021 Google PhD Fellowship in Algorithms, Optimization and Markets.  This award acknowledges and supports exemplary PhD students in computer science and related fields who are making contributions to their areas of specialty.   Guo studies robustness guarantees in algorithms and machine learning foundations, as well as their impact on society.  She is also interested in the intersection of CS and economics, and is currently focused on mechanism design, causal inference, and statistical questions in reinforcement learning. The award, which will cover full tuition, fees, and a stipend for the 2021-22 school year, will be presented at the Global Fellowship Summit over the summer.
 

CS alumnae Maryann Simmons (B.A. / M.S./ Ph.D. '01, advisor: Carlo Séquin) and Hayley Iben (M.S. '05/Ph.D. '07, advisor: James O'Brien) have won 2020 Technical Achievement Awards (SciTech Oscars) from the Academy of Motion Picture Arts and Sciences for hair simulation systems.

Simmons is now a senior staff software engineer and the technical lead for Hair & Cloth at Walt Disney Animation Studios (WDAS).  She was part of the team responsible for the WDAS Hair Simulation System, which the citation describes as "a robust, predictable, fast and highly art-directable system built on the mathematics of discrete elastic rods. This has provided Disney artists the flexibility to manipulate hair in hyper-realistic ways to create the strong silhouettes required for character animation and has enabled a wide range of complex hairstyles in animated feature films." According to The Hollywood Reporter, the WDAS System was "used in animated features such as Tangled, to manage Rapunzel’s ultra-long waves."  While at Berkeley, Simmons was a member of Phi Beta Kappa and the Golden Key Honor Society.

Iben, who is now the director of engineering at Pixar Animation Studios, was part of the team responsible for the Taz Hair Simulation System.  The citation describes Taz as "a robust, predictable and efficient mass-spring hair simulation system with novel formulations of hair shape, bending springs and hair-to-hair collisions. It has enabled Pixar artists to bring to life animated digital characters with a wide variety of stylized hair, from straight to wavy to curly."  While at Berkeley, Iben was president of Women in Computer Science and Electrical Engineering (WiCSE) from 2004-2007, and a member CSGSA.

In an effort to facilitate the conversation about diversity and inclusion in the field of EECS, undergraduate students Neha Hudait and Prachi Deo have put together a web page and calendar of events for March 2021 and beyond.  The web page will feature a series of profiles, the first of which is of EECS graduate student Xinyun Chen, who is working with Prof. Dawn Song at the intersection of deep learning, programming languages, and security.  Their events are organized around a different theme every week, and will encompass community building, the tech industry, academia, personal projects, and achievements in tech.  They will also host daily giveaways and social media challenges, and encourage everyone in the community to join in the celebration.

EECS 5th Year Master's student Gloria Tumushabe is the subject of a Berkeley News article titled "COVID Stories: The chance to teach young Ugandan women to code."  A MasterCard Foundation Scholar from Uganda, Tumushabe had always wanted to teach.   When the Coronavirus pandemic hit in early 2020, she realized that many Ugandan schools were closed, remote learning opportunities would be scarce, and there would be even fewer programs to motivate and empower young women to learn code in her native country.  So she created a pilot program called Afro Femm Coders, which targeted promising 19- and 20-year-olds who had finished high school but whose educational opportunities had evaporated because of the pandemic.   Overcoming challenges, like a shortage of laptops and poor Wi-Fi connectivity, and drafting other graduate students to help as tutors, she began teaching 13 young women the skills that would allow them to create computer software, apps and websites, free from the intimidation and danger that they would usually have to face.

Liaowang (Zoey) Zou, an EECS Master of Engineering (MEng) candidate with a concentration in Data Science and Systems, is the subject of a Coleman Fung Institute interview.  Zou, who grew up in China, describes how she became interested in STEM as a child, what drove her to EECS, her experience working as a consultant for a tech company after graduating from Duke, why she decided to come back to school, and her capstone project on detecting incipient disease using artificial intelligence (AI) models.

A team of researchers, including EECS graduate students Ali Moin, Andy Zhou, Alisha Menon, George Alexandrov, Jonathan Ting and Yasser Khan, Profs. Ana Arias and Jan Rabaey, postdocs Abbas Rahimi and Natasha Yamamoto, visiting scholar Simone Benatti, and BWRC research engineer Fred Burghardt, have created a new flexible armband that combines wearable biosensors with artificial intelligence software to help recognize what hand gesture a person intends to make based on electrical signal patterns in the forearm.  The device, which was described in a paper published in Nature Electronics in December, can read the electrical signals at 64 different points on the forearm.  These signals are then fed into an electrical chip, which is programmed with an AI algorithm capable of associating these signal patterns in the forearm with 21 specific hand gestures, including a thumbs-up, a fist, a flat hand, holding up individual fingers and counting numbers. The device paves the way for better prosthetic control and seamless interaction with electronic devices.

Senior EECS student Steven Cao has won a Computing Research Association (CRA) 2021 Outstanding Undergraduate Researcher Award, and senior Stephen Tian was named runner-up.    The award recognizes significant contributions to computing research projects.  Cao (nominated by Prof. Dan Klein)  worked with the Berkeley Natural Language Processing group, where he developed new methods in syntactic parsing for one project, and contributed to the development and testing of new methods to provide more accurate translations between languages in another.  He also worked on developing new and provably correct blockchain protocols and on several projects related to medical imaging.  He co-authored seven papers, including first authorship on papers at three conferences.  He served as Teaching Assistant for two courses while also acting as a research mentor for the group.  Tian (nominated by Prof. Sergey Levine) demonstrated how a robotic finger with a touch sensor could perform myriad tasks using the same reinforcement learning algorithm in one project, and proposed a novel algorithm to allow a robot to achieve a variety of goals indicated as goal images in another.  He co-authored several papers at at least three conferences,  and served as a TA, while also volunteering at events for local high school students.   Ryan Lehmkuhl  (nominated by Prof. Raluca Ada Popa)a was a finalist, and Joey Hejna (nominated by Prof. Pieter Abbeel) received an honorable mention.

CS Prof. Ken Goldberg is the co-author of a study published in Science Robotics which describes the creation of a new artificial intelligence software that gives robots the speed and skill to grasp and smoothly move objects, making it feasible for them to soon assist humans in warehouse environments.  He and postdoc Jeffrey Ichnowski had previously created a Grasp-Optimized Motion Planner that could compute both how a robot should pick up an object and how it should move to transfer the object from one location to another, but the motions it generated were jerky.  Then they, along with EECS graduate student Yahav Avigal and undergraduate (3rd year MS) student Vishal Satish, integrated a deep learning neural network into the motion planner, cutting the average computation time from 29 seconds to 80 milliseconds, or less than one-tenth of a second.  Goldberg predicts that, with this and other advances in robotic technology, robots could be assisting in warehouse environments in the next few years.