Department of Electrical and Computer Engineering

1 University Station C0803

The University of Texas at Austin, Austin, TX 78712

01/20/19

For official policies concerning graduate ECE studies at UT Austin, please see information on the Graduate ECE Program Web page.

Other opinion pieces on graduate studies:

- "Reflecting on CS Graduate Admissions" by Prof. David Andersen, Dept. of Computer Science, Carnegie Mellon University (CMU), March 8, 2015.
- "Advice for Graduate Students", Professor Stephen C. Stearns, Dept. of Ecology and Evolutionary Biology, Yale University, June 6, 2011.

2.0 Admission and financial support

- 2.1 Corresponding with Professors
- 2.2 Should I Stay or Should I Go?
- 2.3 Application for Graduate ECE Studies
- 2.4 Statement of Purpose
- 2.5 Financial Support
- 2.6 Admission in Communication Systems
- 2.7 International Students
- 2.8 Holistic Evaluation

- 3.1 Load
- 3.2 Coursework Requirements
- 3.3 Choosing Courses and Instructors
- 3.4 Specializations
- 3.4.1 Digital Signal Processing
- 3.4.2 Digital Communication Systems
- 3.4.3 Analog/RF Communication Systems
- 3.4.4 Networking Protocol, Architecture, and Design
- 3.4.5 Networking Modeling, Analysis, and Design
- 3.4.6 Digital Image and Video Processing
- 3.4.7 Data Mining and Pattern Recognition
- 3.4.8 Embedded Digital Systems

- 3.5 General Suggestions
- 3.6 Students Entering who do not have an Electrical Engineering degree
- 3.7 Supporting Coursework for the M.S. and Ph.D. Degrees
- 3.8 Beneficial Courses to Take Outside of Electrical and Computer Engineering
- 3.9 Working with Me
- 3.10 Graduate EE Course Descriptions

Robert L. Peters, *Getting What You Came For:
The Smart Student's Guide to Earning a Master's
or a Ph.D.*, revised ed., Noonday Press,
ISBN 0374524777, 1997.

My personal thoughts on MS vs. PhD:

- Jobs at companies for BSEE students upon graduation are generally in test engineering, technical sales, and field engineering.
- A BSEE degree is a stepping stone for graduate studies (i.e. professional degrees) in business, law, medicine, and engineering.
- An MSEE degree provides an entry to design positions at companies.
- A PhDEE degree provides an entry into companies in managing technical aspects of design projects or in research and development, and an entry into academia as faculty and post-doctoral researchers. Most PhDEE graduates go to industry upon graduation.

If your ultimate degree objective is an MS degree, then you might consider graduate programs in cities with high-tech ecosystems in your intended specialization. Local proximity makes it very easy to interact with engineers from those companies-- they come often to campus. If your ultimate degree objective is a PhD degree, then what matters is that you can identify 5 or 6 faculty members with whom you'd like to take courses and who you would want to approach as potential PhD advisors. These 5 or 6 faculty members could be in any graduate program at the university.

When you correspond with professors, it is in your best interest to keep the correspondence short (e.g. less than 1000 characters) and customize a letter to each professor. Many professors refuse to answer form letters. One of my favorite form letters read "Dear $Professor, I am applying for graduate studies at $University." Clearly, an automated script had failed to substitute the appropriate names. I didn't respond.

To customize your e-mail message, you might spend one sentence on each of the following topics (kept the message as short as possible):

- which specialization you have applied for graduate studies
- what educational background you have, including school(s) attended and degree(s) obtained (or being completed)
- what interest you have in obtaining an MS degree, MS and PhD degrees or PhD degree only, and why
- what research topics you want to pursue in graduate school
- why you contacted the professor, esp. what research topics the professor is pursuing that are interesting to you and why
- what familiarity you have with at least one research publication by the professor that you have read
- how you would hope to contribute to the professor's current research projects

Always attach a resume (preferably in PDF format) or provide a Web address for your resume (e.g., a LinkedIn page but make sure that your settings allow public viewing of all information). I've compiled suggestions for resumes.

UT Austin graduates about 320 BS ECE students each year, and of these, about 80 go to graduate school. Each year, about 30 of the 80 stay for graduate ECE studies at UT Austin. In Spring 2006, 100 of the 570 graduate ECE students enrolled at UT Austin had received a Bachelor's degree from UT Austin.

At UT Austin, the ECE Department has about 65 tenured and tenure-track faculty members. An ECE undergraduate student would meet about 15 of them through taking undergraduate ECE courses. That leaves about 50 tenured and tenure-track faculty members unexplored for being potential research advisors and graduate course instructors. In addition, graduate ECE students can consider faculty outside of the ECE department as their research advisors. UT Austin has about 1,800 tenured/tenure-track faculty and 1,200 non-tenure-track teaching faculty.

The entire graduate study application matters.

Application evaluation committees will look carefully at the transcript. In particular, they will be looking at the grades in the courses most related to success in graduate school in general and preparation for the specialization in particular. Courses related to general success including engineering communication (formerly called technical writing), senior design project, upper division electives and mathematics courses. The mathematics courses are important because graduate study is formal and rigorous.

The key is to match the performance in your math/engineering courses related to your intended specialization to the rank of the graduate program to which you are applying. I'd recommend applying to 7-10 graduate programs. Please pick ~1/3 that will difficult to gain admission, ~1/3 for which you'll be competitive for admission, and ~1/3 for which you have high confidence in admission. Students with upper division grade point averages of 3.0 or higher (on a 4.0 scale) should be able to gain admission into at least one of the 100+ graduate ECE programs in the US.

**Letters of recommendation**
provide information that an admissions
committee cannot get elsewhere in the application.
One piece of information commonly included is the rank that the student
earned in a course in terms of the student's numeric score.
For example, was the applicant's numeric score ranked 2nd out of 40 students,
or 16th out of 80 students, enrolled in the course.

In identifying potential letter writers, I'd recommend identifying faculty members with whom you had taken math/engineering courses and in whose courses you receive the highest course ranks among all of your math/engineering courses. These would roughly correspond to the courses in which you had received the highest grades. You could contact a faculty member to determine your rank in that course. It doesn't matter if you had a lot of interaction with the faculty member or not. All of your recommendations should come from current and former faculty members who have actively published research in peer-reviewed conferences and journals in math, computing and/or engineering fields.

In the letter of recommendation, the writer will be asked to evaluate the applicant in many different categories, including analytical ability, intellectual capacity, motivation, perseverance, teaching ability, ral communication, and writing ability. In addition, the letter writer will be asked to evaluate the applicant's overall potential for success in a graduate program. Even for those students who have selected a Master's degree as their ultimate degree objective, letter writers will often evaluate the applicant's potential in a PhD program because students often change their minds after enrolling in a graduate program.

Here are faculty members to consider asking for recommendations (in order of significance):

- Faculty mentor for your senior design project
- Instructor in a third-year or fourth-year undergraduate course directly related to the specialization to which you are applying
- Instructor in a first-year or second-year undergraduate course directly related to the specialization to which you are applying

A letter writer will generally need the resume, statement of purpose and transcript from the applicant to write a letter.

Be sure to include a resume in your graduate school application whether it is asked for or not. I have posted resume suggestions.

The statement of purpose is discussed next.

- Introduction. Here are example first sentences:
- I am applying for a PhD ECE degree at The University of Texas at Austin because I would like to conduct corporate research and development after graduation.
- I am applying for an MS ECE degree at The University of Texas at Austin because I would like to be a design engineer.

- Experiences in undergraduate electives. Discuss courses you're planning to take in the spring semester.
- Experiences in senior design project course and in undergraduate research
- Experiences working in industry
- Summary. Why are you applying to this particular graduate program? Which research projects and/or research centers interest you? Which faculty would you like to work with and why (give at least three faculty). What do you plan to do with the your graduate degree after graduation?

Financial support comes in a variety of forms for full-time graduate ECE students:

- Teaching Assistantships: assigned by the ECE department
- Research Assistantship: assigned by individual faculty members
- UT Austin fellowships
- Externally funded fellowships

Externally funded fellowships include the following national fellowship opportunities:

- National Science Foundation (NSF) Graduate Research Fellowships
- Separate programs for terminal MS and PhD-bound students
- Three-year stipend of $34,000/year plus $12,000/year cost of tuition and fees (paid to the institution)
- Alex Lang's Web site on NSF fellowship applications

- National Defense Science and Engineering Graduate (NDSEG) Fellowships
- Three-year stipend of $34,000/year plus a separate amount to cover all tuition and fees

In the United States, universities may not require acceptance or rejection of a financial support offer before April 15th. This is due to an agreement among the member universities of the US Council of Graduate Schools, which has been in force for more than 20 years and recently renewed in October 2014.

Here are things to do in becoming a teaching assistant (TA) for the first time:

- Prepare a resume of 1-2 pages in 11pt or 12pt font: resume suggestions.
- Apply for a position: ECE TA application information.
- If English is not your native language, then you must take an oral TA examination of your conversational English and English reading comprehension. To schedule an appointment to take the next oral TA examination, then please contact Ms. Melanie Gulick.
- Attend TA orientations, which takes place about 10 days (5 days) before the Fall (Spring) semester begins, given by the ECE Department and also by the College of Engineering.

Most research assistantships are eligible to any full-time graduate student in good academic standing. (Good academic standing at UT Austin means a UT Austin graduate student GPA of 3.0 or higher.) Some research assistantships, however, are restricted to US citizens because they involve work a facility with classified information. Examples at UT Austin include research assistantships at the Navy-funded Applied Research Laboratories, which has about 450 full-time employees.

- Decision, Information and Communication Engineering: theory and algorithms for signal processing, image processing, communications, networking, and control systems
- Architecture, Computer Systems, and Embedded Systems: design and implementation of systems for communications and networking
- Electromagnetics and Acoustics: antenna design and wireless propagation, microphone and speaker design, and streaming audio applications
- Integrated Circuits and Systems: design and implementation of integrated circuits for communications and networking
- Solid State Electronics: optical, electronic, and optoelectronic devices to enable communication systems

As part of the holistic evaluation for admission, the DICE admissions committee would evaluate applicants with respect to their ability to take graduate DICE courses immediately upon enrolling for graduate study and perform well in them. DICE graduate courses require additional depth in mathematical analysis beyond required undergraduate courses. Taking as many of the following undergraduate courses as possible would help you do that (UT Austin course numbers are given in parenthesis):

- Electrical Engineering Courses
- Control Systems (EE 362K)
- Digital Communications (EE 360K)
- Digital Image Processing (EE 371R)
- Digital Signal Processing (EE 351M)
- Introduction to Networking (EE 372N)
- Real-Time Digital Signal Processing (EE 445S)
- Wireless Communications (EE 471C)

- Computer Engineering/Computer Science Courses
- Algorithms (EE 360C)
- Data Science Laboratory (EE 460J)
- Data Science Principles (EE 461P)
- Data Structures (EE 422C)
- Operating Systems (EE 461S)

- Mathematics Courses
- Introduction to Stochastic Processes (M 362M)
- modeling and analysis of a time-varying random signal, e.g. noise and interference
- course pre-requisite is an undergraduate course in probability (EE 351K)

- Real Analysis (M 365C)
- formal proofs on discrete and continuous sets
- formal derivation of convergence of iterative numeric algorithms of the form
x
_{k+1}= f ( x_{k}) given an initial guess x_{0}. This is known as the fixed-point theorem. A fixed point x* occurs when x* = f( x* ); i.e., the algorthm has converged. - formal definition of vector spaces
- course pre-requisites are discrete math, linear algebra, and number theory

- Mathematical Statistics (M 378K)
- estimating a statistical distribution from measured data
- what is the confidence in the statistical fit?
- how good is the statistical fit vs. the distribution obtained from the measured data itself? This is known as Kullback-Leibler divergence.
- course pre-requisite is an undergraduate course in probability (EE 351K)

- Numerical linear algebra (M 346)

- Introduction to Stochastic Processes (M 362M)

Newly enrolling UT Austin graduate students should see Ms. Melanie Gulick in the ECE Graduate Office (ENS 101) and a counselor in the International Office. After being in the US for at least 10 days, newly enrolling UT Austin international graduate students should go to the Federal Building located near Cameron Road and Interstate 35 to apply for a social security number. There is a city bus that goes from the university campus to the area near the federal building. Please see Ms. Melanie Gulick for more details.

With this mind, the cumulative GPA is not very informative for graduate admission. Instead, a graduate admissions committee would hopefully look at the particular courses that are directly related to the intended specialization in graduate study. Those courses should build a solid foundation for graduate work in the intended specialization. And, yes, the graduate admissions committee will look closely at the grades in those foundation courses for the intended specialization.

About half of graduate ECE studies is engineering communication-- understanding deeply technical material through reading and other means, and communicating that understanding in reports and presentations. To this end, grades in technical writing, engineering communication and senior design project courses are important, as well as in engineering courses with significant open-ended projects in them.

When trying to evaluate a student's potential to successfully complete independent research for the PhD, we look at the technical design work in the senior design project sequence and other courses with open-ended projects in them. Sometimes, a student will excel in a local or national design competition. About 2% of applicants publish a peer-reviewed publication as undergraduate students-- this is rare and of course stands out because of its rarity.

The entire application is helpful to the graduate admissions committee performing the evaluation-- transcript, resume, letters of recommendation, statement of purpose, etc.

Planning ahead will be important in making sure that you are able
to fit in all the courses you want to take.
For those who are planning to do a Ph.D., the choice of courses
to take is strongly related to one's research topic.
*In order to transfer a graduate course to UT Austin, you would
not be able to take a course that is essentially the same and apply
both to a graduate degree.*

- 0 = first-year
- 1 = sophomore
- 2-3 = usually a required undergraduate course
- 4-6 = usually an elective undergraduate course
- 7 = elective undergraduate course
- 8-9 = graduate level

You may transfer up to six credit hours of graduate coursework taken at another university (provided that the coursework was not applied to a degree) towards an MSEE degree. For the PhD coursework requirements, up to 18 semester credit hours of formal graduate-level coursework (which excludes research problems, conference course, MS report and MS thesis hours) taken at another university may be transferred, even if those hours had been applied toward a graduate degree. However, if you retake the same graduate course at UT Austin that you took at another university, then the course will not generally transfer. UT Austin requires that you take at least 18 hours of coursework on site. The Graduate ECE program requires that you take at least 12 hours of formal coursework, although this requirement might be higher depending on the academic track in which you are enrolled.

To satisfy the Ph.D. course requirements, you will need to take

- 10 formal lecture graduate courses for letter grade (30 credit hours).
- Of the 10 formal lecture graduate courses,
- At least 6 courses must be in primary coursework, which are usually in your enrolled academic track of study
- At least 2 courses must be in supporting work that are either (1) graduate ECE courses not in your enrolled academic track of study, and (2) outside the ECE department.

For the MSEE degree, there are three options: MS thesis, MS report, and MS non-thesis/non-report. The best option is somewhat dependent on your research direction. MSEE students at UT Austin are roughly divided equally among these three options.

For the three MSEE options, the coursework requirements vary:

- Thesis: 24 credit hours of formal lecture courses plus 6 credit hours of MS thesis
- Report: 27 credit hours of formal lecture courses plus 3 credit hours of MS report
- Non-Thesis/Non-Report: 30 credit hours of formal lecture coursework

ECE Master's Program of Work Form

Here are a few pointers for choosing courses and instructors:

- Read about the Graduate ECE Program, which includes course descriptions.
- Check the course evaluations for the instructors for a particular
course.
- Partial numerical results are available to the public -- after entering the faculty member's or course name, click on the "Find" link under "Course Instructor Survey".
- Complete numerical results are available with a UT electronic ID at

- Ask other students.
- Talk to the faculty members who are scheduled to teach the courses in which you are interested.
- Talk to the faculty member who is the academic advisor for the academic track in which you are enrolled
- Plan out all courses for your degree objective to make sure that you schedule everything correctly, e.g. to take the appropriate the pre-requisites. Scheduling 10 graduate courses distributed properly between major and supporting work will simultaneously satisfy the coursework requirements for both an MS non-thesis/non-report option and the PhD coursework requirements. I have compiled graduate ECE course offerings from fall 2018 to spring 2020 and undergraduate ECE course offerings from fall 2016 to spring 2018. Some graduate courses are only offered every other year. Develop the plan with your research advisor, or the academic academic track advisor if you do not have a research advisor. Taking the right distribution of the 10 graduate-level courses will also satisfy the PhDEE coursework requirements.

Undergraduate Courses - Fall

- EE445S Real-Time Digital Signal Processing Laboratory (Prof. Evans)
- EE360C Algorithms (Prof. Soloveichik)
- EE371R Digital Image and Video Processing (Prof. Bovik)
- M365C Real Analysis I

- EE445S Real-Time Digital Signal Processing Laboratory (Prof. Evans)
- EE351M Digital Signal Processing
(Prof. Vikalo)

- EE360C Algorithms (Prof. Julien)
- M365C Real Analysis I

- A graduate course on probability.
If you have taken EE351K Probability (or its equivalent)
**and**M362M Intro to Stochastic Processes (or its equivalent),**and**you have either taken or will be taking concurrently M365C Real Analysis I,**then take**EE381J Probability and Stochastic Processes I (Prof. Shakkottai)**else take**M362M Intro to Stochastic Processess and take EE381J in the subsequent fall semester.

- EE381K-17 Wireless Communications Lab
(Prof. Heath)

*prerequisite is an undergraduate course in either DSP or digital communications* - EE381K-6 Estimation Theory
(Prof. Vikalo)
in Fall 2019, Fall 2021, etc.

*pre-requisites are EE381J Probability, EE362K Introduction to Automatic Control, and EE351M Digital Signal Processing* - EE381V Genomic Signal Processing (Prof. Vikalo) in Fall 2018, Fall 2020, etc.
- M383E Numerical Analysis: Linear Algebra (Prof. Dhillon)

- ORI391Q-5 Linear Programming, which is an introduction to optimization

- ASE381P-8 Stoch. Detection, Estimation and Control (Prof. Humphreys)
- ASE389P-7 Global Navigation Satellite Systems Signal Processing (Prof. Humphreys)
- EE380L-10 Data Mining (Prof. Ghosh)
- EE381K-2 Digital Communications
(Prof. Andrews)

*pre-requisites are EE381J Probability and EE351M DSP* - EE381K-7 Information Theory
(Prof. Dimakis)

*pre-requisite is EE381J Probability* - EE381K-16 Digital Video
(Prof. Bovik)

*pre-requisite is EE371R Digital Image/Video Processing* - M383F Numerical Analysis: Interpolation/Approximation

*pre-requisite is M383E Numerical Analysis: Linear Algebra*

- ORI390R-5 Applied Stochastic Processes (Prof. Hasenbein)

*pre-requisite is an undergraduate probability at the level of EE 351K Probability* - ORI391Q-1 Nonlinear Programming

*pre-requisite is ORI391Q-5 Linear Programming* - ORI391Q-4 Integer Programming

*pre-requisite is ORI391Q-5 Linear Programming*

Undergraduate Courses - Fall

- EE445S Real-Time Digital Signal Processing Laboratory (Prof. Evans)
- M365C Real Analysis I

- EE445S Real-Time Digital Signal Processing Laboratory (Prof. Evans)
- EE351M Digital Signal Processing
(Prof. Vikalo)

- EE360C Algorithms (Prof. Julien)
- M365C Real Analysis I

- A graduate course on probability.
If you have taken EE351K Probability (or its equivalent)
**and**M362M Intro to Stochastic Processes (or its equivalent),**and**you have either taken or will be taking concurrently M365C Real Analysis I,**then take**EE381J Probability and Stochastic Processes I (Prof. Shakkottai)**else take**M362M Intro to Stochastic Processes and take EE381J in the subsequent fall semester.

- EE381K-5 Advanced Telecommunication Networks
(Prof. Francois Baccelli)

*pre-requisites are EE381J Probability and EE381K-13 Analysis and Design of Communication Networks* - EE381K-6 Estimation Theory
(Prof. Vikalo)

*pre-requisites are EE381J Probability, EE362K Introduction to Automatic Control, and EE351M Digital Signal Processing* - EE381K-17 Wireless Communications Lab
(Prof. Heath)

*prerequisite is an undergraduate course in either DSP or digital communications* - M383E Numerical Analysis: Linear Algebra (Prof. Dhillon)

- ORI391Q-5 Linear Programming, which is an introduction to optimization

- ASE381P-8 Stoch. Detection, Estimation and Control (Prof. Humphreys)
- ASE389P-7 Global Navigation Satellite Systems Signal Processing (Prof. Humphreys)
- EE381K-2 Digital Communications
(Prof. Andrews)

*pre-requisites are EE381J Probability and EE351M DSP* - EE381K-7 Information Theory
(Prof. Dimakis)

*pre-requisite is EE381J Probability* - EE381K-11 Wireless Communications
(Prof. Andrews)

*pre-requisites are EE381J Probability and EE381K-2 Digital Communications* - EE381K-13 Analysis and Design of Communication Networks
(Prof. de Veciana)

*pre-requisite is EE381J Probability* - EE381S Space-Time Communications
(Prof. Heath)

*pre-requisites are EE381J Probability, a graduate course on communication systems, and EE351M Digital Signal Processing* - M383F Numerical Analysis: Interpolation/Approximation

*pre-requisite is M383E Numerical Analysis: Linear Algebra*

- ORI390R-5 Applied Stochastic Processes (Prof. Hasenbein)

*pre-requisite an undergraduate probability course at level of our EE 351K Probability* - ORI391Q-1 Nonlinear Programming

*pre-requisite is ORI391Q-5 Linear Programming* - ORI391Q-4 Integer Programming

*pre-requisite is ORI391Q-5 Linear Programming*

Undergraduate Courses - Fall

- EE325K Antennas and Wireless Propagation

- EE445S
Real-Time Digital Signal Processing Laboratory
(Prof. Evans)

- M362M Intro to Stochastic Processess

Undergraduate Courses - Spring

- EE438K Analog Electronics (Prof. Becker)
- EE445S
Real-Time Digital Signal Processing Laboratory
(Prof. Evans)

- EE351M Digital Signal Processing (Prof. Vikalo)
- EE363M Microwave and RF Engineering (Prof. Alu)

Graduate Courses - Fall

- EE381K-17 Wireless Communications Laboratory
(Prof. Heath)

*prerequisite is an undergraduate course in either DSP or digital communications* - EE382M-7 VLSI I (Prof. Abraham)
- EE382M-14 Analog IC Design (Prof. Akinwande)
- EE382M-20 System on Chip Design (Prof. Gerstlauer) in Fall 2018, Fall 2020, etc.
- EE382N-23 Embedded System Design and Modeling (Prof. Gerstlauer) in Fall 2019, Fall 2021, etc.
- EE383L Electromagnetic Field Theory (Prof. Yilmaz)

- EE381K-2 Digital Communications
(Prof. Andrews)

*pre-requisites are EE381J Probability and EE351M DSP* - EE381K-11 Wireless Communications
(Prof. Andrews)

*pre-requisites are EE381J Probability and EE381K-2 Digital Communications* - EE381S Space-Time Communication
(Prof. Heath)

*pre-requisites are EE381J Probability, EE381K-2 Digital Communications, and EE351M DSP* - EE382M-8 VLSI II (Prof. Abraham)

*pre-requisite is EE382M-7 VLSI I* - EE382M-24 Data Converters (Prof. Sun)

*pre-requisite is EE382M-14 Analog IC Design* - EE382M-25 RFIC Design (Prof. Gharpurey)

*pre-requisite is EE382M-14 Analog IC Design* - EE392K Antenna Theory and Practice (Prof. Ling)

*pre-requisite is EE383L Electromagnetic Field Theory* - EE396K-24 Microwave Devices (Prof. Neikirk)
- ORI390R-5 Applied Stochastic Processes (Prof. Hasenbein)

*pre-requisite is an undergraduate course in probability at the level of our EE 351K Probability*

Undergraduate Courses - Fall

- EE360C Algorithms (Prof. Soloveichik)
- M365C Real Analysis I

- EE360C Algorithms (Prof. Julien)
- M365C Real Analysis I

- A graduate course on probability.
If you have taken EE351K Probability (or its equivalent)
**and**M362M Introduction to Stochastic Processes (or its equivalent),**and**you have either taken or will be taking concurrently M365C Real Analysis I,**then take**EE381J Probability and Stochastic Processes I (Prof. Shakkottai)

*corequisite is M365C Real Analysis I*

**else take**M362M Intro to Stochastic Processes and take EE381J in the subsequent fall semester.

- EE380N-11 Optimization in Engineering Systems
(Prof. Baldick)

- EE381K-5 Advanced Telecommunication Networks
(Prof. Baccelli)

*pre-requisites are EE381J Probability and EE381K-13 Analysis and Design of Communication Networks* - EE381K-17 Wireless Communications Lab
(Prof. Heath)

*prerequisite is an undergraduate course in either DSP or digital communications* - EE382M-7 VLSI I
(Prof. Abraham)

- EE382N-1 Computer Architecture
(Prof. Patt)

- CS380D Distributed Computing I (Prof. Misra)
- CS386W Wireless Networking (Prof. Qiu)
- CS388H Cryptography (Prof. Waters)
- CS396M Advanced Networking Protocols (Prof. Lam)

- EE380L-6 Interfacing to Operating Systems (Prof. Valvano)
- EE381K-2 Digital Communications
(Prof. Andrews)

*pre-requisites are EE381J Probability and EE351M DSP*

- EE381K-7 Information Theory
(Prof. Vishwanath)

*pre-requisite is EE381J Probability* - EE381K-11 Wireless Communications
(Prof. Andrews)

*pre-requisites are EE381J Probability and EE381K-2 Digital Communications* - EE381K-13 Communication Networks: Analysis and Design
(Prof. de Veciana)

- EE381M Probability and Stochastic Processes II
(Prof. Baccelli)

*pre-requisite is EE381J Probability* - EE381S Space-Time Communications
(Prof. Heath)

*pre-requisites are EE381J Probability, EE381K-2 Digital Communications, and EE351M Digital Signal Processing* - EE381V Game Theory (Prof. Nikolova)
- EE381V Stochastic Geometry
(Prof. Baccelli)

*pre-requisite is EE381J Probability and EE381K-13 Communication Networks: Analysis and Design* - EE382N-1 Computer Architecture
(Prof. Patt)

- EE382V Middleware Architecture and Design (Prof. Julien)
- EE382V Security in Hardware/Software Systems (Prof. Tiwari)

Undergraduate Courses - Fall

- M365C Real Analysis I

- M365C Real Analysis I

- A graduate course on probability.
If you have taken EE351K Probability (or its equivalent)
**and**M362M Intro to Stochastic Processes (or its equivalent),**and**you have either taken or will be taking concurrently M365C Real Analysis I,**then take**EE381J Probability and Stochastic Processes I (Prof. Shakkottai)

*corequisite is M365C Real Analysis I*

**else take**M362M Intro to Stochastic Processes and take EE381J in the subsequent fall semester.

- EE380N-11 Optimization in Engineering Systems
(Prof. Baldick)

- EE381K-5 Advanced Telecommunication Networks
(Prof. Baccelli)

*pre-requisites are EE381J Probability and EE381K-13 Analysis and Design of Communication Networks* - EE381K-17 Wireless Communications Lab
(Prof. Heath)

*prerequisite is an undergraduate course in either DSP or digital communications* - EE382N-11 Distributed Systems I (Prof. Garg)
- CS386W Wireless Networking (Prof. Qiu)
- CS396M Advanced Networking Protocols (Prof. Lam)

- EE381K-2 Digital Communications
(Prof. Andrews)

*pre-requisites are EE381J Probability and EE351M DSP*

- EE381K-7 Information Theory
(Prof. Vishwanath)

*pre-requisite is EE381J Probability* - EE381K-11 Wireless Communications
(Prof. Andrews)

*pre-requisites are EE381J Probability and EE381K-2 Digital Communications* - EE381K-13 Communication Networks: Analysis and Design
(Prof. de Veciana)

- EE381M Probability and Stochastic Processes II
(Prof. Baccelli)

*pre-requisite is EE381J Probability* - EE381S Space-Time Communications
(Prof. Heath)

*pre-requisites are EE381J Probability, EE381K-2 Digital Communications, and EE351M Digital Signal Processing* - EE381V Game Theory (Prof. Nikolova)
- EE381V Stochastic Geometry
(Prof. Baccelli)

*pre-requisite is EE381J Probability and EE381K-13 Communication Networks: Analysis and Design* - EE382V Formal Methods in Distributed Systems
(Prof. Julien)

*may fulfill a requirement in Software Engineering* - M394C Stochastic Processes and Applications
(Prof. Zariphopoulou)

*pre-requisites are EE381J and M381C Real Analysis*

Undergraduate Courses - Fall

- EE445S Real-Time Digital Signal Processing Laboratory (Prof. Evans)
- EE360C Algorithms (Prof. Soloveichik)
- EE461P Data Science Principles (Prof. Ghosh)
- EE371R Digital Image and Video Processing (Prof. Bovik)
- M365C Real Analysis I

- EE445S Real-Time Digital Signal Processing Laboratory (Prof. Evans)
- EE351M Digital Signal Processing
(Prof. Vikalo)

- EE360C Algorithms (Prof. Julien)
- M365C Real Analysis I

- CS381V Visual Recognition (Prof. Grauman)
- A graduate course on probability.
If you have taken EE351K Probability (or its equivalent)
**and**M362M Intro to Stochastic Processes (or its equivalent),**and**you have either taken or will be taking concurrently M365C Real Analysis I,**then take**EE381J Probability and Stochastic Processes I (Prof. Shakkottai)

*corequisite is M365C Real Analysis I***else take**M362M Intro to Stochastic Processes and take EE381J in the subsequent fall semester.

- EE381K-6 Estimation Theory (Prof. Vikalo)
in Fall 2019, Fall 2021, etc.

*pre-requisite is EE381J Probability, EE362K Introduction to Automatic Control, and EE351M Digital Signal Processing* - EE381K-18 Convex Optimization Theory
(Prof, Caramanis)

*pre-requisites include (1) a graduate course in optimization such as EE380N-11 Optimization in Engineering Systems or ORI391Q-5 Linear Programming, (2) a graduate course in linear algebra such as EE380K System Theory or M383E Numerical Analysis: Linear Algebra, and (3) Matlab programming* - EE385J-18 Biomedical Imaging (Prof. Yankeelov)
- M383E Numerical Analysis: Linear Algebra (Prof. Dhillon)

- ORI391Q-5 Linear Programming, which is an introduction to optimization
- PSY387N Perceptual Systems (Prof. Hayhoe)

- CS384G Computer Graphics (Prof. Fussell)
- CS391L Machine Learning (Prof. Ballard)
- EE380L-10 Data Mining (Prof. Ghosh)
- EE381K-7 Information Theory
(Prof. Dimakis)

*pre-requisite is EE381J Probability* - EE381K-16 Digital Video
(Prof. Bovik)

*pre-requisite is EE371R Digital Image/Video Processing* - EE381V Machine Learning
(Prof. Dimakis)

*pre-requisite is EE381J Probability* - ORI391Q-1 Nonlinear Programming

*pre-requisite is ORI391Q-5 Linear Programming* - PSY380E Vision Systems (Prof. Geisler)

For this specialization, you would most likely be enrolled in the Decision, Information and Communication Engineering academic track as a graduate EE student at UT Austin. For those interested in pattern recognition and data mining, especially as applied to image and video processing, here is a set of courses in the area.

Undergraduate Courses - Fall

- EE360C Algorithms (Prof. Soloveichik)
- EE471P Data Sciences Principles (Prof. Ghosh)
- EE371R Digital Image and Video Processing (Prof. Bovik)
- M365C Real Analysis I

Undergraduate Courses - Spring

- EE351M Digital Signal Processing
(Prof. Vikalo)

- EE360C Algorithms (Prof. Julien)
- M365C Real Analysis I

Graduate Courses - Fall

- CS381V Visual Recognition (Prof. Grauman)
- CS394N Neural Networks (Prof. Miikkulainen)
- CS394R Reinforcement Learning (Prof. Stone)
- CS395T Robot Learning (Prof. Niekum)
- A graduate course on probability.
If you have taken EE351K Probability (or its equivalent)
**and**M362M Intro to Stochastic Processes (or its equivalent),**and**you have either taken or will be taking concurrently M365C Real Analysis I,**then take**EE381J Probability and Stochastic Processes I (Prof. Shakkottai)

*corequisite is M365C Real Analysis I***else take**M362M Intro to Stochastic Processes and take EE381J in the subsequent fall semester.

- EE381K-6 Estimation Theory (Prof. Vikalo)
in Fall 2019, Fall 2021, etc.

*pre-requisite is EE381J Probability, EE362K Introduction to Automatic Control, and EE351M Digital Signal Processing* - EE381K-18 Convex Optimization Theory
(Prof, Caramanis)

*pre-requisites include (1) a graduate course in optimization such as EE380N-11 Optimization in Engineering Systems or ORI391Q-5 Linear Programming, (2) a graduate course in linear algebra such as EE380K System Theory or M383E Numerical Analysis: Linear Algebra, and (3) Matlab programming* - EE382V Human Robot Interaction (Prof. A. Thomaz)
- INF 384H Concepts of Information Retrieval (Prof. Lease)
- M383E Numerical Analysis: Linear Algebra (Prof. Dhillon)

- ORI391Q-5 Linear Programming, which is an introduction to optimization

Graduate Courses - Spring

- CS391L Machine Learning (Prof. Ballard)
- EE380L-10 Data Mining (Prof. Ghosh)
- EE380N-11 Optimization in Engineering Systems
(Prof. Baldick)

*pre-requisite is M365C Real Analysis I* - EE381K-7 Information Theory
(Prof. Dimakis)

*pre-requisite is EE381J Probability* - EE381K-16 Digital Video
(Prof. Bovik)

*pre-requisite is EE371R Digital Image/Video Processing* - EE381V Machine Learning
(Prof. Dimakis)

*pre-requisite is EE381J Probability* - ORI391Q-1 Nonlinear Programming

*pre-requisite is ORI391Q-5 Linear Programming*

- Decision, Information and Communication Engineering
- Computer Architecture and Embedded Processors
- Integrated Circuits and Systems

Undergraduate Courses - Fall

- EE445L Embedded System Design Laboratory (Prof. Valvano)
- EE445S Real-Time Digital Signal Processing Laboratory
(Prof. Evans)

- EE360C Algorithms (Prof. Soloveichik)
- EE460M Digital System Design Using HDL (Prof. Touba)

Undergraduate Courses - Spring

- EE445S
Real-Time Digital Signal Processing Laboratory
(Prof. Evans)

- EE360C Algorithms (Prof. Julien)
- EE460M Digital System Design Using HDL (Prof. John)

Graduate Courses - Fall

- ASE396 Verification/Synthesis of Cyberphysical Systems (Prof. Topcu)
- EE382M-7 VLSI I (Prof. Abraham)
- EE382M-20 System on Chip Design (Prof. Gerstlauer) in Fall 2018, Fall 2020, etc.
- EE382N-1 Computer Architecture (Prof. Patt)
- EE382N-21 Computer Performance Evaluation (Prof. John)
- EE382N-23 Embedded System Design and Modeling (Prof. Gerstlauer) in Fall 2019, Fall 2021, etc.
- EE382V Activity, Sensing and Recognition (Prof. E. Thomaz)
- EE382V Security in Hardware/Software Systems (Prof. Tiwari)

Graduate Courses - Spring

- EE380L-6 Operating Systems (Prof. Gerstlauer)
- EE382M-1 VLSI Testing (Prof. Touba)
- EE382M-8 VLSI II (Prof. McDermott)
- EE382M-11 Formal Verification (Prof. Abraham)
- EE382M-21 Optimization Issues in VLSI CAD (Prof. Pan)
- EE382M-23 Low-Power and Robust Design (Prof. Orshansky)
- EE382N-1 Computer Architecture (Prof. Erez)
- EE382N-14 High-Speed Computer Arithmetic I (Prof. Swartzlander)
- EE382N-19 Microarchitecture
(Prof. Patt)

If you are coming to UT Austin with a B.S.E.E. degree, then the following schedule will satisfy your coursework requirements for both the Architecture, Computer Systems, and Embedded Systems (ACSES) academic track and the DICE academic track, and give you a good background in theory, algorithms, design, and implementation of signal processing systems. At the end of four Fall/Spring semesters, you could have accumulated 30 semester credit hours of formal graduate coursework to fulfill the coursework requirements for the PhDEE degree, and satisfied the PhDEE coursework requirements for either the DICE or ACSES academic track. The fifth or sixth Fall/Spring semester might be a great semester to take the Ph.D. Qualifying Examination. After the qualifying examination, the only remaining requirement would be the successful defense your written Ph.D. dissertation.

When you arrive, be sure to verify your course requirements with your research advisor and the academic track academic advisor. If you are enrolled in DICE, then you would take at least 4 DICE graduate courses, and 2 additional graduate courses. The outside area courses are generally in mathematics or computer science, and you do not have to take the two outside area courses in the same department. One of the "additional ECE courses" and one of the "outside ECE courses" may be undergraduate courses. Check the details with your academic track academic advisor. If you are unsure, then check with the Graduate Advisor, Prof. Frank Register.

If you do not have an Electrical Engineering degree or a Computer Engineering degree, then you might consider taking a couple of core undergraduate ECE courses to help you become better prepared for graduate ECE courses. The specific undergraduate courses will depend on the curriculum track in which you are enrolled. Please consult the academic advisor for your curriculum track.

In order to obtain a Master's degree, you must complete 30 credit hours. The MS Thesis counts six credit hours and the MS Report counts three credit hours. The remaining credit hours (24 for MS Thesis, 27 for MS Report and 30 for non-Thesis/non-Report option) must be fulfilled by formal courses. Formal courses do not include independent study courses and seminars.

For the formal courses, at least 18 credit hours must be in major work (in your academic track) and at least 6 credit hours in supporting work (not in your academic track). Supporting work could be in another department. For the Master's degree, you can apply up to six credit hours of formal undergraduate upper-division elective courses.

Supporting coursework is meant to give students breadth of knowledge to complement the depth of knowledge in their major work. Supporting coursework should be complementary to your primary academic track of study without duplicating the courses you have taken in your primary academic track of study. This supporting work rule applies to the sum total of ALL of the graduate courses you have ever taken, including those taken at schools other than UT.

Please see the Academic Advisor for your academic track for more information about supporting coursework.

There are many excellent courses in mathematics and computer science that are relevant to research in electrical and computer engineering. Some of the useful undergraduate courses are:

- CS345 Programming Languages
- CS378 Mathematical Methodologies
- CS378 Advanced Networking and Implementation
- M325K Discrete Mathematics
- M343M Error Correcting Codes
*M362M Stochastic Processes*(esp. for commmunications, networks, and systems)- M364L Vector and Tensor Analysis I
- M364L Vector and Tensor Analysis II
*M365C Real Analysis*(esp. for commmunications, networks, and systems)*M368K Numerical Analysis**M378K Mathematical Statistics*- ME366M Operation Research Methods

Some of the useful graduate courses are:

- ASE381P-8 Stoch. Detection, Estimation and Control
- ASE389P-7 Global Navigation Satellite Systems Signal Processing
- CS386L Programming Languages
- CS388S Formal Semantics and Verification
- CS392C Methods and Techniques for Parallel Programming
- CS393D Topics in Numerical Analysis
- CS395T Real-Time Systems
*M381C Real Analysis*measure theory and Lebesgue integration*M381E Functional Analysis*introduction to Hilbert and Banach spaces*M383C Methods of Applied Mathematics I*applications of Hilbert spaces (Fall)*M383D Methods of Applied Mathematics II*covers calculus of variations (Spring)*M383E Numerical Analysis: Linear Algebra*- M383F Numerical Analysis: Interpolation/Approximation
- ORI391Q Heuristic Search Methods and Mathematical Optimization
- ORI391Q Mixed Integer Programming
- ORI391Q Stochastic Optimization
*PSY380E Vision Systems*

If you are interested in working with me, I suggest that you apply to either the Decision, Information and Communication Engineering academic track or the Architecture, Computer Systems, and Embedded Systems academic track or the in the graduate program in Electrical and Computer Engineering. If you were admitted in the Architecture, Computer Systems, and Embedded Systems (ACSES) track then I would recommend that you take your ECE supporting work in DICE. If you were admitted in the DICE academic track, then I would recommend that you take your ECE supporting work in ACSES. In either case, I recommend that you take as many signal/image processing and embedded systems courses as you can, and that you take your outside department supporting work in mathematics, computer science, and computational psychology. The department regularly offers more then ten undergraduate and more than twenty graduate courses in signal and image processing.

Any faculty member on our ECE Graduate Studies Committee (GSC) could serve as the primary advisor, sole advisor, or co-advisor for the PhD dissertation of a graduate ECE student. The following faculty members on the above list of Robotics faculty members are on the ECE GSC:

- Prof. Kristen Grauman (ECE GSC; CS home dept.) grauman@cs.utexas.edu
- Prof. Todd Humphreys (ECE GSC; Aeropsace Eng. home dept.) todd.humphreys@mail.utexas.edu
- Prof. Peter Stone (ECE GSC; CS home dept.) pstone@cs.utexas.edu
- Prof. Andrea Thomaz (ECE GSC; ECE home dept.) athomaz@ece.utexas.edu
- Prof. Ufuk Topcu (ECE GSC; Aerospace Eng. home dept.) utopcu@utexas.edu

We also have ECE GSC faculty members who conduct research in wearables:

- Prof. Edison Thomaz (ECE GSC; ECE home dept.) ethomaz@utexas.edu
- Prof. Nanshu Lu (ECE GSC; Aerospace Eng. home dept.) nanshulu@utexas.edu

- Prof. Takashi Tanaka (Aerospace Eng. Dept.) ttanaka@utexas.edu

- Prof. Ari Arapostathis, ari@ece.utexas.edu, stochastic control
- Prof. Constantine Caramanis, cmcaram@ece.utexas.edu, stochastic optimization and system theory
- Prof. Andreas Gerstlauer gerstl@ece.utexas.edu design automation for cyberphysical systems
- Prof. Jonathan Valvano, valvano@mail.utexas.edu, embedded control systems

- Prof. Joe Beaman, Dept. of Mechanical Eng., jbeaman@mail.utexas.edu
- Prof. Tess Moon, Dept. of Mechanical Eng., tmoon@mail.utexas.edu

- MSEE degree through the conventional program (option 1).
The best route is to do an MSEE degree with a report option.
You would take nine formal lecture-style courses, plus do an
MS report and register for an MS report course.
The MS report is a description of an implementation,
and does not have to represent new original research.
In Fall and Spring semesters, we offer a wide variety of
graduate courses in the evening, esp. in the circuit design,
computer engineering, and communications academic tracks.
We offer few if any graduate ECE courses in Summer.
By taking one course per Fall/Spring semester, you could finish
in nine semesters (i.e., four years and one semester).
Although ECE doesn't offer any graduate ECE courses or
undergraduate ECE electives in the summer, there are
graduate courses and undergraduate electives in mathematics
and computer science taught each summer.
By taking one course in each Fall, Spring and Summer semester,
you could finish in three years.
Admissions for part-time enrollment for the MSEE degree
is handled with the applications for full-time enrollment.
- MSEE degree through the Software Engineering Program (Option 3). This option meets one Friday/Saturday each month all year long. You would take two formal courses each Fall/Spring semester, and one course each summer. The key drawback is that only four or five courses are offered each Fall/Spring semester in this format. The good news is that the courses and instructors are the generally same as the ones in the conventional MSEE program. At the end of the two full years, you would have completed an MS degree in Software Engineering with an MS report option.

Last updated 01/20/19. Mail comments about this page to bevans@ece.utexas.edu.