EECS senior Armen Chouldjian was one of 11 engineering interns, selected from more than 200 college applicants around the nation, to work in the Bay Area Rapid Transit (BART) Maintenance and Engineering Department.  His summer project was to take information-dense reports generated from various BART computer systems and make them more readable and accessible.  He and his partner, Anuj Shah, went far beyond that, creating an internal web application that is already being used for greater efficiency and quicker diagnosis and resolution of problems.  “It’s a win-win,” said Chouldjian. “I get to play with cool technology and it ends up helping people.”

For the second year in a row, U.C. Berkeley has ranked No. 2 among the 50 undergraduate programs that produce the most venture capital-backed entrepreneurs, according to PitchBook’s 2017-18 report.  The report distinguishes undergraduate and MBA programs, compares Ivy League colleges to other universities and analyzes numbers such as companies per sector, female founders and total capital raised by founders’ companies. This year, UC Berkeley produced 1,089 entrepreneurs and 961 companies.

Graduate student Yang You (advisor: James Demmel) has won a 2017 ACM IEEE Computer Society George Michael Memorial Fellowship for his work on designing accurate, fast, and scalable machine learning algorithms on distributed systems.   The award, which was named in honor of George Michael, one of the founding fathers of the Supercomputing (SC) Conference series, is given in recognition of overall potential for research excellence in subjects of interest to the High Performance Computing (HPC) community.  In You's most recent work, “Scaling Deep Learning on GPU and Knights Landing Clusters,” his goal is to scale up the speed of training neural networks so that networks which are relatively slow to train can be redesigned for high performance clusters. This approach has reduced the percentage of communication from 87% to 14% and resulted in a five-fold increase in speed.

Enrollment in CS 61A, The Structure & Interpretation of Computer Programs,  has increased from 1,568 students last fall to 1,762 students this semester.  CS 61A is a popular introductory coding class--a requirement for EECS majors--co-taught by Assistant Teaching Professor Jon DeNero and Prof. Paul Hilfinger.  The live lecture attendance is expected to drop as students discover that lectures are being webcasted three different times for about 600 students each time.  “We have enough funding and enough TAs [over 50] and, as of yesterday, I think we have enough rooms,” DeNero said.  Additional student support is provided by discussion sections, expanded small group-mentoring sections, and pilot online versions of discussions and labs.  Last fall, 60 percent of the students rated their class experience 5/5.

Undergraduate students Jian Lu (EECS junior), Walt Leung (CS sophomore), Jiayi Chen (CS junior), and Malhar Patel (EECS junior) placed 3rd at the Greylock Hackfest in July.  Their platform, BeAR, allows multiple users to connect to the same #AR (augmented reality) session.  The Hackfest, sponsored by Greylock Partners, allows 45 teams of up to four university students the opportunity to show what they can build to a panel of tech industry  judges.  Hacks are judged based on five different criteria: level of difficulty, aesthetics, originality, usefulness, and your project’s “WOW factor.”

U.C. Berkeley made StudySoup's list of the top 20 female-friendly computer science programs in the country.  The graduate student group WICSE (Women in Computer Science and Engineering) is credited for the ranking because they are working to "build a more inclusive environment in the industry. In addition to outreach programs for younger students, the organization partners with research institutions and corporate partners to host workshops and network events."

CS graduate student Rebecca Portnoff (adviser: David Wagner) has developed the first algorithm to identify adult ads tied to human trafficking rings by linking the ads to public information from Bitcoin — the primary payment method for online sex ads.  “The technology we’ve built finds connections between ads,” says Portnoff.  “Is the pimp behind that post for Backpage also behind this post in Craigslist? Is he the same man who keeps receiving Bitcoin for trafficked girls? Questions like these are answerable only through more sophisticated technological tools – exactly what we’ve built in this work – that link ads together using payment mechanisms and the language in the ads themselves.”  Her team will present their findings this month at the Association for Computing Machinery’s SIGKDD Conference on Knowledge Discovery and Data Mining.

CS graduate student Grant Ho, Aashish Sharma (LBNL),  CS alumna Mobin Javed (Ph.D. 2016), and CS Profs. Vern Paxson and David Wagner have won the 2017 Internet Defense Prize, worth $100,000, for their paper "Detecting Credential Spearphishing in Enterprise Settings."  CS graduate student Thurston Dang,  Petros Maniatis (Google Brain), and Prof. David Wagner, were finalists for their paper "Oscar: A Practical Page-Permissions-Based Scheme for Thwarting Dangling Pointers."  The award, which is funded by Facebook and offered in partnership with USENIX, recognizes research that meaningfully makes the internet more secure.

Assistant Prof. Sergey Levine, Prof. Pieter Abbeel, and graduate student Chelsea Finn are featured in a CSO article highlighting research they presented at the International Conference on Machine Learning (ICML).  The research was done in partnership with the NVIDIA AI Labs (NVAIL) programme.  Levine’s team wants to help intelligent agents learn faster and require less training by teaching deep neural networks to learn more like humans.  “Look at how people do it,” said Levine. “We never learn things entirely from scratch. We draw on our past experience to help us learn new skills quickly. So we’re trying to get our learning algorithms to do the same.”  Levine and his team have been using an NVIDIA DGX-1 system to train their algorithms how to coordinate movement and visual perception.

NVAIL helps keep AI pioneers ahead of the curve with support for students, assistance from researchers and engineers, and gives them access to the industry’s most advanced GPU computing power.

CS Prof. Alexei Efros (also alumnus, Ph.D. '03) and his team have developed a new technique, leveraging deep neural networks and AI, to allow novices--even those with limited artistic ability--to quickly add realistic color to black and white images.  "The goal of our previous project was to just get a single, plausible colorization," says Richard Zhang, a coauthor and PhD candidate, advised by Efros. "If the user didn't like the result, or wanted to change something, they were out of luck. We realized that empowering the user and adding them in the loop was actually a necessary component for obtaining desirable results."  They will present their research into "Real-Time User Guided Colorization with Learned Deep Priors" at SIGGRAPH 2017 in August.