CS/IEOR Prof. Ken Goldberg, director of the AUTOLAB, and his EE graduate student Jeff Mahler, are profiled in a  New York Times article titled "In the Future, Warehouse Robots Will Learn on Their Own," about researchers who are using neural networks and machine learning to teach robots to grab things they have never encountered before.   The AUTOLAB robot was trained by being shown hundreds of purely digital objects, after which it could pick up items that weren’t represented in its digital data set.  “We’re learning from simulated models and then applying that to real work,” said Goldberg,

EECS sophomore Alex Montanez is part of the inaugural class of Hallac Scholars.  The program, sponsored by the global asset management firm BlackRock, combines scholarship, mentorship and internship to help students learn how engineers can use their skills to develop innovative tech for delivering financial services.  Although Montanez was fascinated by computers, his junior high and high school didn’t offer any computer science or engineering classes, and had no computer club.  He had to learn almost everything on his own. As a BlackRock intern next summer, he’ll serve on the science team that works on Aladdin as well as on developing apps used by the firm’s clients. “I wanted to know how computers and electronics worked because they were everywhere. I’m interested in the impact computers have in helping people,” he says.

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.”

CS Profs. Pieter Abbeel and Michael Jordan, two of the best-known experts in machine learning, have been appointed as joint faculty in the department of Industrial Engineering and Operations Research (IEOR) in addition to their primary appointments in EECS (and Statistics for Jordan).  "Profs. Abbeel and Jordan are terrific colleagues that bring extremely valuable perspectives to our interests in robotics, automation, machine learning, and data science," states Ken Goldberg, Chair of IEOR.  Abbeel's work has been featured in many popular press outlets, including BBC, New York Times, MIT Technology Review, Discovery Channel, SmartPlanet and Wired.  In a recent article in Science, Jordan was named the currently most influential computer scientist in the world.

CS Assistant Prof. Sergey Levine explores how deep learning will unleash robotics in an NVIDIA AI Podcast which first aired on Sept 1st.  “One of the most important things is that you have to somehow communicate to the robot what it means to succeed,” Levine said in a conversation with AI Podcast host Michael Copeland. “That’s one of the most basic things …You need to tell it what it should be doing.”  He points out that it’s important that the robots don’t just repeat what they learn in training, but understand why a task requires certain actions. “If you want to get a robot to do interesting things, you kind of need it to learn on its own,” Levine said

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.

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.”

CS alumnus Andrew Ng (Ph.D. '02), one of the world's leading authorities on AI, interviews EE Prof. Pieter Abbeel for Heroes of Deep Learning, an interview series from Ng's cousera course, Deep learning AI.  “Work in Artificial Intelligence in the EECS department at Berkeley involves foundational research in core areas of knowledge representation, reasoning, learning, planning, decision-making, vision, robotics, speech and language processing," Abbeel says. "There are also significant efforts aimed at applying algorithmic advances to applied problems in a range of areas, including bioinformatics, networking and systems, search and information retrieval. There are active collaborations with several groups on campus, including the campus-wide vision sciences group, the information retrieval group at the I-School and the campus-wide computational biology program. There are also connections to a range of research activities in the cognitive sciences, including aspects of psychology, linguistics, and philosophy. Work in this area also involves techniques and tools from statistics, neuroscience, control, optimization, and operations research. Berkeley Artificial Intelligence Research Lab (BAIR)."

CS alumnus Andrew Ng (Ph.D. '02, adviser: Michael Jordan) has been singled out by NewsCenter.io as one of 7 leaders shaping the AI revolution.  Ng founded the “Google Brain” project, which developed massive-scale deep learning algorithms.  He led the AI group at Baidu, China’s largest search engine company, which directed research into advertising, maps, take-out delivery, voice and internet searching, security, consumer finance, among others. Ng also co-founded Coursera, an online education company that has raised more than $200 million venture capital funding.  He is also currently an adjuct professor at Stanford.

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.