Prospective: Data Science and Systems

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 here.

2020-2021 Capstone Projects*

For the capstone projects for Master of Engineering in Electrical Engineering and Computer Science (EECS) our department believes that our 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.

Back to M.Eng. Admissions.

*we will continue to update this list as more proposals are received from our faculty for the upcoming year

Project 1 & 2

Title Optimizing traffic in Fremont (advisor Prof. Alexandre M.Bayen)

Description The goal of the project is to visualize the impact navigational apps have on road traffic in the Mission San José district (in Fremont) by upgrading a microsimulation traffic model of the area. The microsimulation results will be used to evaluate transportation network performance.


Title - BISTRO (advisor Prof. Alexandre M.Bayen)

Description The goal of this project is to design, implement, and test multii-objective optimization techniques applied to an agent-based simulation of transportation in the City of San Francisco. BISTRO is an open-source optimization and agent-based simulation platform that enables development of optimal transportation system interventions based on realistic stakeholder, policy, and operation objectives. In the San Francisco BISTRO scenario, a sample population of 25,000 agents is synthesized, each with assigned sociodemographic attributes and a daily plan to travel to and from activities (e.g., work, school, groceries) throughout a day. Agents' transportation mode choices are sensitive to realized travel time and cost parameters of each available mode (e.g., car, transit, ridehail, etc.) at the time of each trip. Several congestion pricing strategies will be optimized over the course of the project, necessitating that the team develop and adapt Bayesian optimization techniques (e.g., hyperameter tuning, acquitision functions, metamodeling, etc.) to produce realistic and interpretable results with efficient use of cloud computing resources.

Project 3

Title - Oz Vision (advisor Prof. Ren Ng)

Description - The goal of this project is to prototype next-generation color display technology and probe the limits of human vision. The technology is based on a new concept of creating visual percepts by laser stimulation of individual retinal photoreceptors. In principle, the system has the potential to display new colors impossible in the real world, or enable a color blind person to differentiate red and green for the first time. This underlying research is in collaboration with the School of Optometry. The scope of the project can be adjusted based on the expertise of the project members. Helpful background includes some of: systems engineering, software engineering, embedded programming, computer graphics, computer vision, vision science, computational imaging, signal processing, machine learning, optical engineering, physical electronics, biomedical devices, and precision engineering.

Project 4

TitleReal-Time Natural Language Processing on Edge Devices (advisor Prof. Sophia Shao)

DescriptionThe goal of this project is to design and prototype efficient edge devices for natural langurage processing (NLP). Recent advances in deep learning have made significant improvement in the domain of NLP. However, the large computational and storage requirements have significantly limit the capability of real-time inference and fine-tuning on edge devices. This project will systematically characterize the computational and storage behaviors of NLP applications and design custom hardware to execute these applications efficiently.

Projects 5 & 6

Title - California Water + Fire: Understanding Community Vulnerability and Resilience (advisors Profs. David Culler, Meredith Lee)

Description - Every year, up to 1 million Californians lack access to clean, safe drinking water. Increasingly, communities are facing challenges at the intersection of water resources and fire hazards. How might we prepare for a more resilient future? With recent legislation such as the Open and Transparent Water Data Act and momentum from 3 years of community-led data projects, this capstone project will produce new analysis from existing and emerging State data and sensor data.  


Title - Driver Video Privacy Challenge(advisors Profs. David Culler, Meredith Lee)

Description - Why do some vehicle drivers crash? How might we extract value from in-cabin driver video data, while preserving privacy? This project will build on a national-scale Driver Video Privacy research effort with the U.S. Department of Transportation and the National Science Foundation, investigating video data de-identification and re-identification techniques alongside behavioral analysis.   

Projects 7 & 8

Title - Vision Correcting Display (advisor Prof. Brian Barsky)

Description - Vision problems such as near-sightedness, far-sightedness, as well as others, are due to optical aberrations in the human eye. These conditions are prevalent, and the number of people who have these hardships is growing rapidly. Correcting optical aberrations in the human eye is traditionally done optically using eyeglasses, contact lenses, or refractive surgeries; these are sometime not convenient or not always available to everyone. Furthermore, higher order aberrations are not correctable with eyeglasses. This research is investigating a novel approach which involves a new computation based aberration-correcting light field display: by incorporating the person’’s own optical aberrations into the computation, content shown on the display is modified such that the viewer will be able to see the display in sharp focus without using corrective eyewear. Our research involves the analysis of image formation models; through the retinal light field projection, it is possible to compensate for the optical blurring on the target image by a process of prefiltering with the inverse blur. As part of this project, we are building a light field display prototype that supports our desired inverse light field prefiltering. We are working towards extending the capability to correct for higher order aberrations. This latter aspect is particularly exciting since it would enable people for whom it is not possible to see displays in sharp focus using eyeglasses to be able to do so using no corrective eyewear. This is a broad project that incorporates many different aspects. A variety of different backgrounds for students is welcome. Students are free to choose what is the most interesting part for them.


Title - Assistive Technology for Navigation, Selection, Pointing, and Clicking in a Mouse-free Environment by Capturing Hand Movements (advisor Prof. Brian Barsky)

Description -This project is concerned with assistive technology that enables users with fine motor control difficulties to navigate, select, point, and click without physically manipulating a mouse. The idea is to use a camera to capture the user’s hand movements. There are many individuals who do not have the ability to control the pointer easily by moving a physical mouse. Unfortunately, there no viable alternatives due to the fact that the mouse has become an essential input device for all modern computers. Inability to control the mouse could be caused by impaired sensation from a wide variety of conditions and illnesses. This project aims to help those with impaired sensation by developing a computer-vision based input system with a camera as its input device. The current project is developing a system that comprises three modules of detection, tracking, and response: (1) The detection stage extracts the hand and recognizes its gesture which can then be used to alter the users' control; for example, a specific movement could correspond to a click of the mouse. (2) The hand is traced by a tracker and its movement is filtered with an anti-shake filter to perform a more stable movement. (3) In the response stage, the granularity (e.g., dots per inch) of the cursor is adjusted according to the user’s speed of hand movement.

Project 8

TitleRobustness in Implicit Deep Learning (advisor Prof. Laurent El Ghaoui)

Description - Implicit deep learning is a generalization of deep neural networks that use a fixed-point equation to generate the prediction, as opposed to the more standard feedforward pass. The formalism allows for rigorous robustness analyses to be made on existing networks. The project will investigate the approach in the context of autonomous driving (images) and NLP (text) applications.


Project 11

Title - High Performance Genome Data Analysis (advisor Prof. Kathy Yelick & Aydin Buluc)

Description - Genomic data for human health and the environment are growing exponentially due to improvements in sequencing technology. The goal of this project is to develop analysis algorithms and tools of genomic data using parallel computing of various types, including graphics processing units and high performance parallel machines. The project may including genome alignment and assembly of raw sequence data, as well as the use of machine learning for clustering, classification, and functional annotation. See

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 2019 (Updated: 7/10/19)

Spring 2020 (Updated: 10/7/2019)

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.