Information for Prospective M.Eng. Students

Admission Timeline Costs and Financial Aid Frequently Asked Questions

Admission

Who Should Apply

The Master of Engineering (M.Eng.) is designed for students who plan to join the engineering profession following graduation. The accelerated program is designed to develop professional engineering leaders of the future who understand the technical, economic, and social issues of technology. More information about this degree program can be found at the M.Eng. Program Description and the College of Engineering Fung Institute websites.

Application Requirements

admchecklist

Please consult the M.Eng. Admissions Checklist for complete admissions requirements.

Admissions decisions will be announced by e-mail in early March.

The Fall 2016 Admissions Application Deadline is January 6.


Suggestions for Applicants Without a CS Degree

Although we do not require applicants to our computer science programs to have a degree in computer science, we do expect them to have a strong technical background equivalent to a computer science bachelors degree. Admission to these programs requires experience in programming, algorithms, data structures, and theory at or above the undergraduate level.

Info Sessions

For the MEng program, the Fung Institute holds online info sessions. Learn more and register

Some Admissions Information

The admissions rate for the EECS MEng program has been around 12% for the past two years.

We do not have minimum or cutoff scores for any MEng student, but typical scores of MEng admits across all departments are:

GRE: >90% in Quantitative, >70% Verbal, >3.5 score Analytical
TOEFL: >100 score; iBT: 90 minimum (or 7 out of 9 on the IELTS)
GPA: average of 3.7, minimum 3.0

Contact Us

Email: gradadmissions at eecs.berkeley.edu

Program Requirements by Area

  Data Science and Systems

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.

2016-2017 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.

Project 1 Title - Scaling up Deep Learning on Clusters (Advisor: Prof. John Canny)
Description - This project is about building the next generation of machine learning and deep learning systems, and improving speed and scale by about 2 orders of magnitude over the present state-of-the-art. We will be using state-of-the-art tools including BIDMach, Spark, Caffe and Google's Tensorflow, and developing easy-to-use APIs for large-scale model development. The goal is to build large-scale learning system for clusters of cloud machines with GPUs. We use a novel design that decouples single-machine learning from inter-machine coordination, and leads to a particularly simple shared-nothing design. Currently the work is on EC2, but we will be working also with Microsoft Azure next year. We are currently collaborating with Databricks and Yahoo on this project, and industry participation will be part of the project.
Project 2 Title - Understanding Deep Learning through Visualization (Advisor: Prof. John Canny)
Description - Deep learning, i.e. deeply-layered neural models, is leading a revolution in machine learning in industry. While these models are very powerful, they are often extremely expensive in time and resources to train. The training of models is currently a black art, but it is clear that there are orders of magnitude difference between good and poor training schedules. This project is about visualizing features of the training process to better illuminate the factors that influence training performance. It will also involve reinforcement learning to train optimization policies. In other words its about "learning to learn". The project will cover interactive, on-the-fly visualization and data collection from neural networks, using BIDMach, Caffe and Tensorflow. This is a new project but we expect to acquire some industry partners as it starts up.
Project 3 Title - Classifying All Technology: Improving the Patent Office's Patenting Schema (Advisor: Prof. Lee Fleming)
Description - The US patent corpora numbers almost 6 million and counting while hundreds of thousands of applications accumulate every year. Classifying those patents accurately and quickly remains a large challenge and the lack of effective solutions contributes to bad patents being issued every year. The team will investigate a variety of clustering algorithms and newly available data to re-classify at least the US patent system, and possibly the European, Japanese, and Chinese as well. The USPTO will support this project and provide input, data, and feedback. The team may also choose to investigate commercialization of their work or present the results visually. A trip to the United States Patent Office in Washington DC is likely.
Project 4 Title - Predicting Bad Patents: Applying Machine Learning to Improve US Patent Accuracy (Advisor: Prof. Lee Fleming)
Description - Bad patents cost society time in litigation and invalidation and ultimately hurt the innovation ecosystem patents were designed to support. One 2007 estimate put the annual cost of bad patents at $25B. Using newly available data and algorithms that the team will select and code, this project is of interest and will be supported by Google and possibly IBM. The team may also choose to investigate commercialization of their work or present the results visually. The team will probably need to travel to Google in Mountain View on a regular basis. The USPTO has also expressed interest in the results.
Project 5 Title - Machine Learning and Predictive Financial Models (Advisor: Prof. Lee Fleming)
Description - Quant funds are increasingly using machine learning and predictive models to develop trading strategies. This Capstone will develop such models, using data only available (to the best of our knowledge) to the Fung Institute Patent Lab and other data that can be developed from public sources. Strategies will be simulated and compared. It is likely this project will be in collaboration with Haas Masters in Financial Engineering students. Team members will also survey the competitive landscape and consider models for commercialization.

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 2016

Spring 2017

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.

  Physical Electronics and Integrated Circuits

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.

2016-2017 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. The faculty have not yet formulated capstone projects for 2016-2017, but you can refer to the 2015-2016 projects to get some idea what next year's projects might be like.

Project 1 Title - Modern High-Speed Link Design (Advisor: Prof Vladimir Stojanovic)
Description - This project aims to create a design infrastructure for fast prototyping of high-speed serial interfaces for a variety of channel and modulation conditions (chip-to-chip, backplane, etc). The work will comprise creation of a library of standard building blocks for high-speed links (serializers, deserializers, transmit and receive equalizers, clock and data recovery, etc) for both PAM2 and PAM4 modulation formats. Building blocks will be designed at the behavioral modeling level (Verilog and Verilog A), mixed-signal and digital circuit and scripted layout level for accelerated design automation, targeting sub-40nm process nodes. We will target link designs in the 10-50Gb/s speed range. The project will sharpen the following design skills: system level and component modeling of high-speed links (timing, equalization, modulation); digital design of link back-ends in Verilog and synthesis, place and route flow; analog and mixed-signal design (DLL/PLL, driver and receiver circuits).

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 2016

Spring 2017


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.

  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.

2016-2017 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 Title - Controls for Assistive Robots (Advisor: Prof. Anca Dragan)
Description - Design and implement a low-level control architecture for an assistive robot, meant to support people with activities of daily living. This project requires combining software engineering and design skills with robot control. The project will start with a needs assessment, survey the control literature for solutions, decide on what solution fits the needs best, and then implement that solution on real robot hardware.

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 2016

Spring 2017

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.

  Signal Processing and Communications

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.

2016-2017 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 Title - (Simulating) Spectrum Access Systems (Advisor: Prof. Anant Sahai)
Description - The wireless world is on the brink of a revolution. Traditionally, radio spectrum was statically cut into different bands that were then allocated to different uses, and within any band, channels were assigned to individual entities (like AT&T, Verizon, Sprint, etc.). This was done for a practical reason --- to limit radio interference. One set of bands, the unlicensed bands, is special in that everyone is allowed to use them, and devices are individually responsible for managing interference. Crudely speaking, the traditional static approach corresponds to having dedicated wires while the traditional unlicensed approach corresponds to purely packet-based networking. The advance of information technology and the congestion of radio spectrum have brought us to a point where we need to basically bring software defined networking (SDN) ideas to radio spectrum. Instead of having static allocations (done by lawyers), we will have Spectrum Access Systems (SAS) that manage wireless use across different systems. This involves bringing in modern techniques involving databases, wireless signal processing, learning, security, mechanism-design, and networking. It also touches on law and economics. Because SASs change the competitive landscape, they also create all sorts of interesting business-model questions that need to be explored. To support this exploration, we envision in this project to work towards building a toy SAS simulator building on our existing Python research codebase. The exact direction that the project will take will depend on the interests of the students who join it.
Project 2 Title - Portable Computational Imagers (Advisor: Prof. Laura Waller)
Description - We are building inexpensive and simple imaging systems that can fit in your hand and have no fancy lenses, but achieve good imaging performance through computational refocusing. Useful skills for this project will be Fourier image analysis, 3D printing and maker skills and knowledge of basic optics and camera systems. Photographers welcome.
Project 3 Title - System Design for a 94GHz FMCW Radar/Sensor for Mobile Phones (Advisor: Prof. Ali Niknejad)
Description - Our team at the Berkeley Wireless Research Center (BWRC) has designed a 94 GHz FMCW radar transceiver array chip in a 130nm SiGe process. The chip packaged in a package/module containing 8 antenna elements for beam forming. This chip has reduced the power consumption of the radar to allow integration into a mobile phone platform. The goal of this project is to design a complete radar system using this chip in addition to analog-to-digital converters, a DSP and/or FPGA, and PMU circuitry, and the necessary software to perform signal processing.

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 2016

Spring 2017

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.

  Visual Computing and Computer Graphics

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.

2016-2017 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 Title - Vision Correcting Displays (Advisor: 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.
Project 2 Title - Design Tools for Maker Movement (Advisor: Prof. Bjoern Hartmann)
Description - In this capstone you will create new design software and hardware tools to empower everyday users to create a variety of useful and functional objects through digital fabrication (e.g., 3D printing). Possible applications include: design of DIY Prosthetics, augmented power tools that teach fabrication skills, and making 3D scanning more useful and accessible for makers. Students should have a solid background in computer graphics and computer vision; and some prior experience with 3D printing. You will have access to digital fabrication equipment in the CITRIS Invention Lab and the Jacobs Institute for Design Innovation.
Project 3 Title - User Interface and Data Visualization for Environmental Assessment (Advisor: Prof. Bjoern Hartmann)
Description - UI design can make a greener world. Life-cycle assessment (LCA) software is the most rigorous and credible way to measure environmental impacts of products, services, buildings, and companies. However, few companies use LCA because existing software's user interfaces and data visualization are so poor. Allowing users to build models faster and more intuitively, and creating visualizations that are both more engaging and more informative, could greatly increase adoption of LCA software in industry. Increasing LCA adoption can greatly enhance industry's ability to produce greener products and reduce systemic impacts. A successful project could become a startup company after graduation.
Project 4 Title - Forensic Methods for Detecting Image Manipulation (Advisor: Prof. James O'Brien)
Description - Sophisticated photo editing software has made it increasingly easy to manipulate digital images. Often visual inspection cannot definitively distinguish the resulting forgeries from authentic photographs. When manipulated images are passed off as authentic they facilitate hoaxes, fraud, and misinformation. Techniques in image forensics aim to detect the geometric or statistical inconsistencies that result from specific forms of photo manipulation, and definitively distinguish forgeries from authentic photographs. The relative focal length and center of projection (COP) of a camera may be computed from known objects in the image. These parameters may be recovered even when object geometry is only partially known, e.g., only knowing that a building's sides are orthogonal or that wheels are round. Objects that do not provide enough information to completely determine the parameters still impose constraints on the possible values. If parameters estimated from different parts of the image are mutually inconsistent, it indicates that objects may have been combined from multiple images or otherwise manipulated. This project will investigate how the geometry of human faces and other objects can be used to estimate the projection parameters for invalidating images.

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 2016

Spring 2017

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.

Timeline

Various Times and Locations Info Sessions through the Fung Institute Register Here
September 2015 (TBD) Info Session with EECS Department
January 6, 2016 11:59pm Online Application and Supplemental Materials Deadline
January - February 2016 Interviews (only if admissions committee has additional questions)
March 2016 Admissions Decisions, Via email
April 15, 2016 Statement of Intent to Register Deadline
June 2016 Capstone Project Matching, emails through EECS
August 2016 Mandatory Orientations, UC Berkeley Campus
August 2016 Classes Begin

Costs and Financial Aid

Tuition & Fees

Tuition and fees for the Master of Engineering program 2015-2016 academic year are $51,024.50 for California Residents and $54,215.50 for Nonresidents. Rates for 2016-2017 may be higher.

To check the most up-to-date fee information at any time, visit the Office of the Registrar. (Fees on the Office of the Registrar website are shown per semester.)

College of Engineering Awards

Applying to the MEng program with EECS automatically makes you a candidate for the college's merit-based grant (also called Fung Fellows). There is also an Opportunity Grant awarded to applicants who enhance educational diversity and demonstrate financial need and who have completed the grant financial and essay portions of the application.

Other Financial Aid Options

There are also a variety of scholarships, grants, and fellowships that you can apply to through the following websites:

Fellowships available for MEng Applicants applying for Fall 2016 Admission:


National Defense Science and Engineering Graduate (NDSEG) Fellowships

American Society for Engineering Education
1818 N Street NW, Suite 600
Washington, DC 20036

Phone: (202) 331-3516
Fax: (202) 265-8504
E-mail:
Homepage: http://www.asee.org/ndseg
ASEE Science & Engineering Fellowships List: http://www.asee.org/fellowship-programs/graduate

Deadline: various


The Paul and Daisy Soros Fellowships for New Americans

400 West 59th Street, 4th Floor
New York, NY 10019

Phone: 212-547-6926
E-mail: pdsoros_fellows@sorosny.org
Homepage: http://www.pdsoros.org

Deadline: November 1, 2015


National Physical Science Consortium Fellowships

National Physical Science Consortium
USC - RAN
3716 S. Hope, Suite 348
Los Angeles, CA 90007-4344

Phone: (800) 854-NPSC or (213) 743-2409
E-mail:
Fax:
(213) 743-2407
Homepage: http://www.npsc.org

Deadline: November 30, 2015


Natural Sciences and Engineering Research Council (NSERC)

Postgraduate Scholarships (PGS D or M)

Scholarships and Fellowships Division
350 Albert St.
Ottawa ON
CANADA K1A 1H5

Phone: (613) 995-4273
E-mail:
Homepage: http://www.nserc-crsng.gc.ca/Students-Etudiants/PG-CS/BellandPostgrad-BelletSuperieures_eng.asp

Deadline: October 15, 2015

Frequently Asked Questions

I did not major in electrical engineering or computer science as an undergraduate student. Will I be eligible for admission into your program?

We have numerous applicants who are "career changers" and our best advice for you is to show within your application that you have the requisite knowledge and skills to succeed in this challenging program. This can include coursework, projects, industry experience, publications, letters of recommendation, and more. Succeeding in courses at UC Berkeley Extension (or another institution) could be a useful addition to your application.


Is there a faculty member I can contact to ask questions about the program?

Our department receives thousands of admissions applications per year, so our faculty members are unable to respond to admissions inquiries. Our Masters Student Services Advisor (see Contacts) will be happy to field any of your admissions questions.


My official TOEFL or GRE scores have not yet been received according to my application. What should I do?

As long as you have entered your self-reported scores into the application, then we can use those for the review process. Once decisions are sent out, we will contact admitted applicants whose official scores we still need.


What kind of financial assistance is offered for this program?

The College of Engineering offers two kinds of grants to ~30% of the M.Eng. student population: merit-based and need-based grants. Most students will be responsible for the entire cost of attendance for the program, and should plan carefully to ensure adequate financial resources.

Consideration for our merit-based grants (also called Fung Fellows Award) and need-based grants (also called Opportunity Grants) is based upon the information provided on the admissions application.

All applications are automatically considered for the merit grants, and a portion of top candidates are awarded grants that cover about 1/3 of the tuition. Applicants who have completed the financial portions of the application will be considered for the Opportunity Grant.


Please visit the Fung Institute FAQ for additional information.