9 papers co-authored by 6 EECS faculty, 13 students,  3 post docs, and 3 alumni have made it into the Top 10 research papers ranked by TOPBOTS in four categories of AI Research. TOPBOTS is the largest publication, community, and educational resource for business leaders applying AI to their enterprises.  3 papers co-authored by Sergey Levine made the #1, #3, and #9 spots in "What Are Major Reinforcement Learning Achievements & Papers From 2018?"  A paper co-authored by Moritz Hardt ranked #5 in "Top 2018 AI research papers" and #3 in  "Recent Breakthrough Research Papers In AI Ethics." A paper co-authored by Jitendra Malik ranked #7 in the Top 2018 papers and #5 in "10 Cutting Edge Research Papers In Computer Vision & Image Generation."  The #2 Top 2018 paper was co-authored by David Wagner, and a paper co-authored by Alexei Efros ranked #9 in the Computer Vision category.

The EECS department is celebrating Women’s History Month (WHM) this March by recognizing and sharing stories about women, both past and present, in the fields of electrical engineering and computer science. The goal of Berkeley EECS WHM, a student-led department-backed initiative created by 4th year EECS major Olivia Hsu, is to facilitate the conversation about diversity and inclusion in the field through a series of events and newsletters.  A kickoff event will take place on Friday, March 1st at 9:30 am in the Woz.

A research team from Lab11, led by Associate Prof. Prabal Dutta and PhD student Noah Klugman, have created a new suite of technologies called GridWatch that uses the sensors on smartphones to monitor an electrical grid and measure outages, grid frequency, and voltage sags and spikes.   They launched an app in Ghana last year called DumsorWatch, that uses a variety of data from phone sensors (power charging, movement, WiFi signals, etc) to determine probabilistically whether a nearby electrical grid is working.  The team also includes PhD student Joshua Adkins, research scientist Matt Podolsky, and Professor Jay Taneja from the University of Massachusetts, Amherst.

Haas Scholars Jasmine Jan and Andre Lai, who are both Bioengineering majors minoring in EECS, made presentations at the 2019 Haas Scholars Spring Research Conference last week titled "Disrupting: Daring to Reimagine."  Jan spoke about her research on a "Solution Processable Point-of-care Optoelectronic Device for Procalcitonin Sensing." Lai spoke about his research on a "A High-Throughput Microfluidic Device for Single Cell Isolation and Analysis."

The 2nd Annual Women in Data Science (WiDS) 2019 Datathon will be held on Saturday, February 2, 2019 in Soda Hall.  The challenge will be to create a model that can detect oil palm plantations in high-resolution satellite imagery to help build awareness about deforestation and oil palm plantations.  The Datathon is a chance for women to meet other participants, form teams, learn the basics of participating in Kaggle competitions, and get a jump start on Datathon submissions with the help of technical mentors and domain experts.  Mentors who have some knowledge about deforestation, data science, image analysis, or have experience with technical project management, Kaggle competitions, or hackathons in general are welcome.  Tickets required.

A team of Berkeley undergraduates that includes CS major Gan Tu,  EECS majors Philipp Wu (EE/ME), Malhar Patel, and Bradley Qu, and EECS minor Travis Brashears (Engineering Physics major), are building autonomous backpack-sized mobile robots for a project called Autonomous Motion at Cal (AMAC).  Their aim is to create autonomous vehicles that will be able to navigate the densely populated UC Berkeley campus.

CS grad student Lydia Liu (advisers: Michael Jordan and Moritz Hardt) has won the inaugural Microsoft Research Ada Lovelace Fellowship.  The new three-year fellowship is for PhD students at North American universities who are members of groups underrepresented in computing and pursuing research aligned to the topics carried out by Microsoft Research.  Liu's research aims to establish theoretical foundations for machine learning algorithms to achieve reliable and robust performance. The fellowship comes with a $42K stipend, tuition for three years, and an invitation to the PhD Summit, a two-day workshop where fellows will meet with Microsoft researchers and other top students to share their research.

A group of researchers at UC Berkeley (including EE Prof. Sergey Levine, grad student Tuomas Haarnoja and undergraduate researcher Aurick Zhou) and Google Brain have used maximum-entropy reinforcement learning to make a quadrupedal robot teach itself to walk.   It taught iself through trial and error in a mere two hours before researchers introduced the machine to new environments, like inclines and obstacles, where it adapted with ease.