1/16/2019 - In a new paper, Berkeley engineers build on 35 years of research with new algorithms that compute robust robot pick points, enabling robot grasping of a diverse range of products without training.
1/14/2019 - For the first time ever, an international team of researchers imaged the microscopic state of negative capacitance. This novel result could have far-reaching consequences for energy-efficient electronics.
12/19/2018 California Magazine - Unlike most robots, the ones in the Berkeley Artificial Intelligence Research (BAIR) Lab haven’t been programmed to perform a specific task. Instead, they’ve been programmed to learn new stuff by observation or through physical trial and error.
12/11/2018 - EECS professor Connie Chang-Hasnain has been named a fellow of the National Academy of Inventors, an organization that champions the societal benefits of university research.
12/3/2018 - Researchers from Intel Corp. and UC Berkeley's MSE are looking beyond current transistor technology and preparing the way for a new type of memory and logic circuit that could someday be in every computer on the planet.
11/27/2018 - EECS professor Katherine Yelick and CEE professor Allen Goldstein, both faculty scientists at Berkeley Lab, were elected to the American Association for the Advancement of Science.
11/1/2018 - UC Berkeley is forming a new academic division, provisionally referred to as the Division of Data Science and Information, to facilitate interactions among researchers, students and faculty in a wide variety of disciplines.
10/29/2018 - Berkeley transportation researchers are addressing the emerging era of smart vehicles with a project that uses machine learning to manage traffic where autonomous, semi-autonomous and manned vehicles share the road. They presented their project, called Flow, at the Conference on Robotic Learning.
10/30/2018 - Berkeley computer theorists have shown that there is merit behind a method of verifying quantum supremacy, a term that describes a quantum computer's ability to solve a problem that is prohibitively difficult for any classical algorithm.
10/24/2018 - Berkeley engineers, led by computer sciences professor Dawn Song, are part of the new Center for Trustworthy Machine Learning funded by the National Science Foundation. The NSF center, led by Pennsylvania State University and announced today, will focus on developing secure systems in the era of machine learning models. The center will receive $10 million over five years.