Prospective: Robotics and Embedded Software

Program Requirements

All EECS MEng students should expect to complete four (4) technical courses within the EECS department at the graduate level, the Fung Institute's engineering leadership curriculum, as well as a capstone project that will be hosted by the EECS department. You must select a project from the list below.

2018-2019 Capstone Projects

For the capstone projects for Master of Engineering in Electrical Engineering and Computer Science (EECS) our department believes that the students are going to have a significantly better experience if the projects are followed closely by an EECS professor throughout the academic year. To ensure this, we have asked the faculty in each area for which the Master of Engineering is offered in our department to formulate one or more project ideas that the incoming students will have to choose from.

Project 1

TitleSmart Orthotics for Post Surgical Tracking and Assessment (advisor Prof. Ruzena Bajcsy)

DescriptionWearble sensors are rapidly becoming part of everyday life, with promises of active, healthy lifestyles. Their application to clinical challenges is currently limited due to a knowledge gab between, clinical and user needs, and the technical expertise to implement life-changing solutions. This project (in collaboration with the departments of Prosthetics & Orthothotics, Physiotherpy and Orthopedics at UCSF) aims to explore, design, test, and delpoy an innovative sensing system to track the changes in patient motion post spinal fusion.This project combines a team of clinicians at UCSF with biomechanics and robotics researchers at UCB to support an MEng team in the technological and business plan for these devices.

Project 2

Title - Impact of App Useage in Large Scale Mobility (advisor Prof. Alex Bayen)

Description - The goal of this project is to use large scale traffic data (GPS from smartphones, CDRs from cellular access providers, video data, loop data and other sources of data), at scale, to infer mobility patterns in large scale cities. With classical machine learning and optimization techniques, the team will provide analysis of the impact of app useage on congestion. The team will analyze how better routing decisions can be made to improve congestion. The analysis will rely on game theoretic analysis of selfish behavior and convex optimization. The team will have the opportunity to work with two frameworks, Flow (a platform unifying rrlab and SUMO on AWS), and the Connected Corridors, which enables one to run the microsimulator Aimsun on AWS as well. 

Project 3

Title - Berkeley DeepDrive Autonomous Vehicle Research (advisor Prof. Trevor Darrell)

Description - Teams will conduct research on autonomous perception and control relating to driving datasets and/or on-campus autonomous vehicles. Topics include deep visual learning, reinforcement learning integrated with demonstration learning, dataset collection and annotation, etc.

Technical Courses

At least THREE of your four technical courses should be chosen from the list below. The remaining technical courses should be chosen from your own or another MEng area of concentration within the EECS Department.

Fall 2018

Spring 2019

  • TBD

Note: The courses listed here are not guaranteed to be offered, and the course schedule may change without notice. Refer to the UC Berkeley Course Schedule for further enrollment information.