Using machine-learning to reinvent cybersecurity two ways: Song and Popa

EECS Prof. and alumna Dawn Song (Ph.D. '02, advisor: Doug Tygar) and Assistant Prof. Raluca Ada Popa are featured in the cover story for the Spring 2020 issue of the Berkeley Engineer titled "Reinventing Cybersecurity."  Faced with the challenge of protecting users' personal data while recognizing that sharing access to that data "has fueled the modern-day economy" and supports scientific research, Song has proposed a paradigm that involves "controlled use" and an open source approach utilizing a new set of principles based on game theory.  Her lab is creating a platform that applies cryptographic techniques to both machine-learning models and hardware solutions, allowing users to keep their data safe while also making it accessible.  Popa's work focuses on using machine-learning algorithms to keep data encrypted in cloud computing environments instead of just surrounding the data with firewalls.  "Sharing without showing" allows sensitive data to be made available for collaboration without decryption.  This approach is made practical by the creation of a machine-learning training system that is exponentially faster than other approaches. "So instead of training a model in three months, it takes us under three hours.”

Pieter Abbeel and Sergey Levine: teaching computers to teach themselves

EECS Prof. Pieter Abbeel and Assistant Prof. Sergey Levine both appear in a New York Times article titled "Computers Already Learn From Us. But Can They Teach Themselves?" which describes the work of scientists who "are exploring approaches that would help machines develop their own sort of common sense."  Abbeel, who runs the Berkeley Robot Learning Lab, uses reinforcement-learning systems that compete against themselves to learn faster in a method called self-play.  Levine, who runs the Robotic AI & Learning Lab, is using a form of self-supervised learning in which robots explore their environment to build a base of knowledge.

Students create online "Coronavirus Tracker" to keep average Americans informed

CS major Jason (XiangJun) Li and a few friends have developed a website designed to provide clear, reliable, up-to-date numbers and trends on the COVID-19 outbreak "for average Americans," particularly those on mobile phones., which was launched last Tuesday, uses data released by the World Health Organization and official government websites, and provides "the simplest and most intuitive dashboard for people to quickly understand the trends and assess risks."  The site includes domestic and global numbers of patients confirmed/recovered/dead, simple graphics and tables, a headline feed, and text alerts using data that is updated every 3 hours.

Women In Tech at Berkeley

The 4th Annual Women In Tech Symposium, part of the Women In Tech Initiative (WITI) will be held at UC Berkeley on Friday, March 6, 2020.  The theme will be "Reimagining Cybersecurity for All."  Many members of the EECS community will be involved, including: alumna and Prof. Dawn Song (PhD '02) - opening remarks; WITI@UC co-founder and dean of Engineering Prof. Tsu-Jae King Liu - fireside chat; Prof. Raluca Ada Popa - Panel: What’s at Stake? Global and Systemic Cyber Threats;  and CITRIS Director Prof. Costas Spanos - Athena Awards presentation. Tickets will be available until Monday, March 2nd.

Keeping classified information secret in a world of quantum computing

Computer Science and Global Studies double major, Jake Tibbetts, has published an article in the Bulletin of the Atomic Scientists titled "Keeping classified information secret in a world of quantum computing."  Tibbetts, who is a research assistant at the LBNL Center for Global Security Research and a member of the Berkeley Nuclear Policy Working Group, argues that instead of worrying about winning the quantum supremacy race against China, U.S. policy makers and scholars should shift their focus to a more urgent national security problem: How to maintain the long-term security of secret information secured by existing cryptographic protections, which will fail against an attack by a future quantum computer.  Some possible avenues include deploying honeypots to misdirect and waste the resources of entities attempting to steal classified information; reducing the deployment time for new encryption schemes; and triaging cryptographic updates to systems that communicate and store sensitive and classified information.

Aditya Parameswaran and Sanjam Garg win 2020 Sloan Research Fellowships in Computer Science

Assistant Profs. Aditya Parameswaran and Sanjam Garg hav been selected 2020 Alfred P. Sloan Research Fellows in Computer Science.  These awards recognize distinguished performance by young American scientists who show "unique potential to make substantial contributions to their field."   Parameswaran develops systems for "human-in-the-loop" data analytics, and Garg's research interests are in cryptography and security.  As two of the nine UC Berkeley researchers to win the highly competitive fellowship this year, they will each receive a $75,000 award.

Alvin Cheung wins VMware Early Career Faculty Award

CS Assistant Prof. Alvin Cheung has won a VMware Early Career Faculty Award.  The award recognizes recently appointed faculty "whose research interests and accomplishments seem poised to have significant impact within the industry and academia."  Cheung's research interests include program analysis, program synthesis, improving database application performance, and building large-scale data systems in general. The award comes with a $50K grant and opportunities to collaborate with VMware.

Covariant-enabled robots go live

Pieter Abbeel, the co-founder, president and chief scientist of the start-up Covariant, is featured in a number of articles appearing in major publications this week.  The New York Times, the Wall Street Journal, Wired Magazine, the Verge, the MIT Technology Review, and the IEEE Spectrum all feature articles about robots trained using Covariant's AI technologies that will be deployed  to perform complex tasks in live warehouse environments in the next few years.  Covariant uses deep reinforcement learning techniques to train robots to distinguish between materials that are particularly difficult to discern through a lens, like highly reflective metallic surfaces, transparent plastics, and easily deformable surfaces like cloth and polypropylene, with an unparalleled 99% accuracy.

Xinyun Chen wins 2020 Facebook Fellowship

Third year CS graduate student Xinyun Chen (advisor: Dawn Song) has been awarded a 2020 Facebook Fellowship.  Chen was recognized in the Machine Learning category for her work in neural program synthesis and adversarial machine learning.  Her goal is to increase the accessibility of programming to general users, and enhance the security and trustworthiness of machine learning models.   Chen has interned at both Facebook AI Research and Google Brain.

Jake Tibbetts and SIGNAL win 2019 SGS&C Best Student Game

Computer Science and Global Studies double major, Jake Tibbetts, and the UC Berkeley Project on Nuclear Gaming (PONG) were awarded Best Student Game at the 2019 Serious Games Showcase and Challenge (SGS&C) for their work on SIGNAL.  SIGNAL is an online three-player experimental wargame in which three countries, some armed with nuclear weapons, attempt to achieve national goals through diplomacy and conflict.  It is designed to help understand the impact of emerging technologies on strategic stability and nuclear risk reduction. Tibbetts, who specializes in Peace and Conflict Studies, is a member of the Nuclear Science and Security Consortium (NSSC), a five-year program to develop a new generation of laboratory-integrated nuclear experts.  SGS&C is the premier venue for recognition of excellence in the field of Serious Games development.