Department of Electrical and Computer Engineering

The University of Texas at Austin, Austin, TX 78712

04/25/21

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.
- "Some Modest Advice for Graduate Students", Prof. Stephen C. Stearns, Dept. of Ecology and Evolutionary Biology, Yale University.

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 Machine Learning
- 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 in business, computer science, engineering, law, mathematics, medicine, and many other fields
- 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.
- Many faculty members will gain 2-4 years of post-doctoral research or corporate R&D experience before becoming faculty members
- In the US, fewer than one-fourth of PhDEE graduates ultimately become tenure-track or tenured faculty members

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. 200 words) and customize a letter to each professor. Many professors will not respond to 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 resisted the urge to respond with "Dear $Student" out of respect for the student. Nonetheless, the episode makes for a great story.

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.

In spring 2018, UT Austin graduated about 340 BS ECE students, and of these, about 80 went directly 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 75 tenured and tenure-track faculty members. An ECE undergraduate student would only have met 15 as instructors for their undergraduate ECE courses. As a graduate ECE student, the department would still largely be unexplored. Moreover, a graduate ECE students has wide access to the courses and faculty in the 160 other graduate programs. UT Austin has about 1,850 tenured/tenure-track faculty and 1,850 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 engineering, mathematics and other courses most related to success in graduate school in general and preparation for the specialization in particular. They will also look at courses in engineering communication and senior design projects. For graduate studies in engineering, mathematics courses are particularly important because graduate study is generally more formal and rigorous than undergraduate studies.

**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 5th out of 40 students,
or 21st out of 80 students, enrolled in the course.
The course rank is more helpful than a letter grade because there is
such a wide variety in how instructors assign letter grades.

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 advised graduate students in research and published research in peer-reviewed conference papers and journal articles in STEM fields.

In the letter of recommendation, the writer will be asked to evaluate the applicant in many different categories, such as analytical ability, intellectual capacity, motivation, perseverance, teaching ability, oral 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. For this reason, I would recommend that all of your references come from faculty members who have had experience advising PhD students in research. That would be true with any faculty member who holds the rank of Assistant Professor, Associate Professor or Professor.

Here are faculty members to consider asking for recommendations (in order of decreasing 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
- Faculty mentor from a summer research program such as the NSF Research Experience for Undergraduates
- 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 in a timely manner 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 a professional statement-- it indicates

- what you have learned in the profession so far (through courses, internships, student organizations, and professional hobbies) and
- what you would like to learn next (through courses, individual research efforts, team research efforts, and summer internships)
- how going to graduate school would help you reach your career goals (start a company, or join a business unit at company, or join a corporate R&D lab, or become a professor, or some combination thereof)

Here is an outline of an effective statement of purpose by paragraph:

- 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 courses. What were your most inspiring courses? What electives have you taken to help you prepare for graduate studies in the particular specialization? Describe the courses you're planning to take in the spring semester. Mention any special circumstances (e.g. the COVID-19 pandemic) and how you responded to them (e.g. switching a grade to pass/fail or dropping a course).
- Experiences in senior design project course, hobbies, extra-curricular student organizations, and undergraduate research (if any). You do not have to conduct undergraduate research to be admitted for graduate studies. It is more important to build the depth and breadth of your knowledge for graduate study through courses, hobbies and extra-curricular activities. After pursuing these opportunities and you have about 10 hours/week to spare, you could decide to pursue undergraduate research. Some undergraduate programs have undergraduate research built into required courses (such as senior design projects) and electives.
- Experiences working in industry (if any)
- Contributions to broader impacts of engineering. For example, broadening the participation of people from underrepresented groups or dissemination of science to the public.
- 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 an individual faculty member
- Externally funded fellowships
- UT Austin fellowships

Externally funded fellowships include the following national fellowship opportunities:

- National Consortium for Graduate Degrees for Minorities in Engineering and Science GEM Fellowship
- Separate programs for terminal MS engineering, PhD engineering and PhD science students
- Five-year stipend of $16,000/year for PhD-bound students plus supplemental living expenses from GEM Member University
- A condition of acceptance of the fellowship is to work at a GEM Employer in the first summer of graduate studies
- Submission deadline in 2020 in Nov. 13.

- 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
- Submission deadline in 2020 is Oct. 19-22, depending on research area.

- National Defense Science and Engineering Graduate (NDSEG) Fellowships
- Three-year stipend of $38,400/year plus a separate amount to cover all tuition and fees
- Submission deadline in 2020 is Nov. 2nd.

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. (At UT Austin, here's the ECE TA application information.)
- Contact faculty members concerning openings.
- If English is not your native language, then you must take an oral TA examination of your conversational English and English reading comprehension. (At UT Austin, to schedule an appointment to take the next oral TA examination, then please contact Ms. Melanie Gulick.)
- Attend TA orientations. (At UT Austin, they take place about 10 days (5 days) before the Fall (Spring) semester begins, and are given by the College of Engineering.)

Responsibilities and duties for a research assistantship can vary quite a bit depending on the source of funds:

**Contract with a company**. The research assistantship would likely come with specific and regular deadlines to reach milestones. The work might not be very well aligned with your PhD dissertation research.**Grant**from the federal government, state government, or non-profit institution. Examples include funding from the US National Science Foundation and the US National Institutes of Health. These grants encourage widespread dissemination of research results via publications, software releases, reports, blog postings, etc. These grants might require an end-of-project report, and possibly annual reports.**Gift funding.**This funding comes without any reporting requirements or other strings attached. This source generally provides the widest latitude on what to research and how to conduct the research, and where to publish it.**Internal university funding.**Examples include funding from an endowment held by a faculty member, such as a Professorship or Endowed Chair. Generally comes in the form of discretionary funds similar to gift funds.

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 at 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, RF, 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 371Q)
- Digital Signal Processing (EE 351M)
- Introduction to Networking (EE 372N)
- Real-Time Digital Signal Processing (EE 445S)

- 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 algorithm 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 contact Ms. Melanie Gulick in the ECE Graduate Office and a counselor in International Student and Scholar Services.

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

All 10 courses taken for a graduate ECE degree are electives that are categorized into major and supporting work. This allows each student to create their own curriculum by taking courses in ECE, Computer Science, Mathematics, Physics, other STEM courses and even non-STEM courses. For the PhD degree, all ten courses must be graduate-level courses, whereas for the MS degree, up to two courses could be upper division (third-year/fourth-year) undergraduate courses.

In order to apply a course toward a graduate EE degree, a grade of A, A-, B+, B, or B- should be received in that course. No more than one course with a grade of C or C+ may be applied towards a graduate EE degree. There is an exception for courses taken in the spring 2020 semester due to the impact of the COVID-19 pandemic on the campus community, including the sudden shift to online learning in all courses on March 13, 2020.

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 30 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 credit hours of coursework on site, and PhD dissertation courses and other independent study courses taken at UT Austin will indeed count toward the 18 credit hours. That is, the 18 credit hours do not necessarily have to be formal courses.

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 major coursework -- these are strongly related to your PhD dissertation topic and as a result, some might be non-ECE courses.
- At least 2 courses must be in supporting work that provide breadth of knowledge and that could include ECE and non-ECE courses
- You could split the 10 formal lecture graduate courses into 8/2, 7/3, or 6/4 in terms of major/supporting coursework.
- You would need to have a 3.5 cumulative GPA in your major work courses as well as your supporting work courses

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. A majority of MSEE students currently choose the non-thesis/non-report option.

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 and graduate 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 username and password

- 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 2021 to spring 2023 and undergraduate ECE course offerings from fall 2021 to spring 2023. Some graduate courses are only offered every other year. Develop the plan with your academic track advisor, or your research advisor if you have one. 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)
- EE351M Digital Signal Processing
(Prof. Vikalo)

- EE360C Algorithms (Prof. Soloveichik)
- EE371Q Digital Image Processing (Prof. Bovik)
- M365C Real Analysis I
- M378K Intro to Mathematical Statistics

- EE445S Real-Time Digital Signal Processing Laboratory (Prof. Evans)
- EE360C Algorithms (Prof. Julien)
- M365C Real Analysis I
- M378K Intro to Mathematical Statistics

- 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.

- ASE381P-6 Statistical Estimation Theory (Prof. Humphreys)
- EE381K-18 Convex Optimization
(Prof. Mokhtari)

Pre-requisites are advanced undergraduate courses on linear algebra, optimization (linear programming), probability, signal processing, and statistics - M383E Numerical Analysis: Linear Algebra (Prof. van de Geijn)

- M387C Numerical Analysis: Algebra and Approximation (Prof. Engquist)

- 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. E. Thomaz)
- EE381K-2 Digital Communications
(Prof. Andrews)

*pre-requisites are EE381J Probability and EE351M DSP*

offered in Spring 2020, Spring 2022, etc. - EE381K-6 Estimation Theory (Prof. Vikalo) offered in Spring 2021, Spring 2023, etc.
- EE381K-16 Digital Video
(Prof. Bovik)

*pre-requisite is EE371Q Digital Image/Video Processing* - EE381V Genomic Signal Processing (Prof. Vikalo) offered in Spring 2020, Spring 2022, etc.
- EE381V Statistical Machine Learning (Prof. Vikalo)
- 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)
- EE351M Digital Signal Processing (Prof. Vikalo)
- EE360C Algorithms (Prof. Julien)
- EE460J Data Science Lab (Prof. Caramanis or Prof. Dimakis)
- EE461P Data Science Principles (Prof. Sanghavi or Prof. Ghosh)
- M362M Intro to Stochastic Processes
- M365C Real Analysis I
- M378K Intro to Mathematical Statistics

- EE445S Real-Time Digital Signal Processing Laboratory (Prof. Evans)
- EE360C Algorithms (Prof. Julien)
- EE460J Data Science Lab (Prof. Caramanis or Prof. Dimakis)
- EE461P Data Science Principles (Prof. Sanghavi or Prof. Ghosh)
- M362M Intro to Stochastic Processes
- M365C Real Analysis I
- M378K Intro to Mathematical Statistics

- 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.

- ASE381P-6 Statistical Estimation Theory (Prof. Humphreys)
- M383E Numerical Analysis: Linear Algebra (Prof. van de Geijn)

- M387C Numerical Analysis: Algebra and Approximation (Prof. Engquist)

- 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*

offered in Spring 2020, Spring 2022, etc. - EE381K-11 Wireless Communications
(Prof. Andrews)

*pre-requisites are EE381J Probability and EE381K-2 Digital Communications*

offered in Spring 2021, Spring 2023, etc. - EE381K-13 Analysis and Design of Communication Networks
(Prof. de Veciana)

*pre-requisite is EE381J Probability* - EE381V Statistical Machine Learning (Prof. Vikalo)
- 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 (offered next in Fall 2020)

*pre-requisite is EE 325 Electromagnetic Engineering* - EE445S
Real-Time Digital Signal Processing Laboratory
(Prof. Evans)

- EE351M Digital Signal Processing (Prof. Vikalo)
- M362M Intro to Stochastic Processes

Undergraduate Courses - Spring

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

- EE363M Microwave and RF Engineering (Prof. Neikirk)

*pre-requisite is EE 325 Electromagnetic Engineering*

Graduate Courses - Fall

- EE382M-7 VLSI I (Prof. Abraham)
- EE382M-14 Analog IC Design (Prof. Sun)
- EE382M-20 System on Chip Design (Prof. Gerstlauer)

offered in Fall 2020, Fall 2022, etc. - EE382N-23 Embedded System Design and Modeling (Prof. Gerstlauer)

offered 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*

offered in Spring 2020, Spring 2022, etc. - EE381K-11 Wireless Communications
(Prof. Andrews)

*pre-requisites are EE381J Probability and EE381K-2 Digital Communications*

offered in Spring 2021, Spring 2023, etc. - 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* - 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.

- EE382M-7 VLSI I
(Prof. Abraham)

- EE382N-1 Computer Architecture (Prof. Patt)
- EE382V Enterprise Network Security (Prof. Tiwari)
- CS380D Distributed Computing I (Prof. Misra)
- CS386W Wireless Networking (Prof. Qiu)
- CS388H Cryptography (Prof. Waters)

- EE380L-12 Real-Time Operating Systems (Prof. Gerstlauer)
- EE381K-2 Digital Communications
(Prof. Andrews)

*pre-requisites are EE381J Probability and EE351M DSP*

offered in Spring 2020, Spring 2022, etc. - EE381K-11 Wireless Communications
(Prof. Andrews)

*pre-requisites are EE381J Probability and EE381K-2 Digital Communications*

offered in Spring 2021, Spring 2023, etc. - EE381K-13 Communication Networks: Analysis and Design
(Prof. de Veciana)

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

*pre-requisite is EE381J Probability* - 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.

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

*pre-requisites are EE381J Probability and EE381K-13 Analysis and Design of Communication Networks* - EE382N-11 Distributed Systems I (Prof. Garg)
- EE382V Enterprise Network Security (Prof. Tiwari)
- CS386W Wireless Networking (Prof. Qiu)

- EE381K-2 Digital Communications
(Prof. Andrews)

*pre-requisites are EE381J Probability and EE351M DSP*

offered in Spring 2020, Spring 2022, etc. - EE381K-11 Wireless Communications
(Prof. Andrews)

*pre-requisites are EE381J Probability and EE381K-2 Digital Communications*

offered in Spring 2021, Spring 2023, etc. - EE381K-13 Communication Networks: Analysis and Design
(Prof. de Veciana)

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

*pre-requisite is EE381J Probability* - 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)
- EE351M Digital Signal Processing (Prof. Vikalo)
- EE360C Algorithms (Prof. Soloveichik)
- EE461P Data Science Principles (Prof. Ghosh)
- EE371Q Digital Image Processing (Prof. Bovik)
- M365C Real Analysis I

- EE445S Real-Time Digital Signal Processing Laboratory (Prof. Evans)
- EE360C Algorithms (Prof. Julien)
- M365C Real Analysis I

- ASE381P-6 Statistical Estimation Theory (Prof. Humphreys)
- 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. de Veciana)

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

- EE381K-18 Convex Optimization
(Prof. Mokhtari)

Pre-requisites are advanced undergraduate courses on linear algebra, optimization (linear programming), probability, signal processing, and statistics - EE385J-18 Biomedical Imaging (Prof. Yankeelov)
- M383E Numerical Analysis: Linear Algebra (Prof. van de Geijn)

- M387C Numerical Analysis: Algebra and Approximation (Prof. Engquist)

- 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. E. Thomaz)
- EE381K-16 Digital Video
(Prof. Bovik)

*pre-requisite is EE371Q 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 machine learning, including applications in image and video processing, here is a set of courses in the area.

Undergraduate Courses - Fall

- EE351M Digital Signal Processing
(Prof. Vikalo)

- EE360C Algorithms (Prof. Soloveichik)
- EE460J Data Science Laboratory
(Prof. Caramanis)

- EE461P Data Sciences Principles (Prof. Xu)
- EE371Q Digital Image Processing (Prof. Bovik)
- M365C Real Analysis I
- M378K Intro to Mathematical Statistics

Undergraduate Courses - Spring

- EE360C Algorithms (Prof. Julien)
- EE460J Data Science Laboratory
(Prof. Dimakis)

- EE461P Data Sciences Principles (Prof. Ghosh)
- M365C Real Analysis I
- M378K Intro to Mathematical Statistics

Graduate Courses - Fall

- CS391L Machine Learning (Prof. Klivans)
- CS394N Neural Networks (Prof. Miikkulainen)
- CS395T Deep Learning Seminar (Prof. Kraehenbuehl)
- 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. de Veciana)

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

- EE381K-18 Convex Optimization
(Prof. Mokhtari)

Pre-requisites are advanced undergraduate courses on linear algebra, optimization (linear programming), probability, signal processing, and statistics - EE381V Advanced Probability: Learning, Inference and Networks
(Prof. Shakkottai)

Fall 2020, Fall 2022, etc. - EE381V Online Learning
(Prof. Shakkottai)

Fall 2021, Fall 2023, etc. - EE381V Reinforcement Learning
(Prof. Stone)

Crosslisted from a graduate course in Computer Science - EE382V Scalable Machine Learning (Prof. Dimakis)
- INF385T-3 Human Computing and Crowdsourcing (Prof. Lease)
- M383E Numerical Analysis: Linear Algebra (Prof. van de Geijn)

- M387C Numerical Analysis: Algebra and Approximation (Prof. Engquist)

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

Graduate Courses - Spring

- CS391L Machine Learning (Prof. Ballard)
- EE381K-16 Digital Video
(Prof. Bovik)

*pre-requisite is EE371Q Digital Image/Video Processing* - EE381V Advanced Algorithms (Prof. Nikolova)
- EE381V Large-Scale Optimization (Prof. Caramanis)
- EE381V Statistical Machine Learning (Prof. Vikalo)

*pre-requisite is EE381J Probability and EE351M Digital Signal Processing* - ORI391Q-1 Nonlinear Programming

*pre-requisite is ORI391Q-5 Linear Programming*

- Architecture, Computer Systems, and Embedded Systems
- Decision, Information and Communication Engineering
- 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-12 Real-Time 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)

For those who are intending to complete a PhD degree, the coursework is to provide an opportunity to search for a PhD topic areas, To this end, choosing one course each semester with an open-ended research project is helpful. Choosing two such courses might be overwhelming. Once the topic area is chosen, subsequent courses are to provide depth and breadth for the topic area. For breadth, and sometimes depth, graduate courses in Mathematics or Computer Science can be particularly helpful.

At UT Austin, be sure to verify your course requirements with your academic track academic advisor. For your MS degree, up to two graduate courses can be transferred from another institution as long as the courses were not applied to another degree. For the PhD degree, all of the graduate courses taken at another institution can be in general transferred, even if they had been applied to an MS degree at that institution. The approval of the coursework for the PhD degree comes from the PhD committee and the ECE Graduate Adviser.

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 taken for letter grade. The MS Thesis counts as six credit hours and the MS Report counts as 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 PhD degree, a student must complete at least 30 credit hours of formal graduate courses taken for letter grade. The same graduate course taken at UT Austin for letter grade can be applied towards satisfying the coursework requirements for an MS degree as well as the PhD degree.

For a graduate ECE degree, the courses would need to be divided between a major field of study and supporting work. The courses in the major field of study are courses strongly related to the student's research area of interest. Hence, the courses under the major field of study could include ECE and non-ECE courses, and the same also holds for supporting work coursework:

- For a PhD ECE degree, the major field of study is a cluster of graduate courses strongly related to the student's PhD dissertation topic that has been approved by the PhD dissertation committee. The courses under the major field of study must have an average GPA of 3.5 or higher, and also the courses under supporting work.
- For an MS ECE degree, the courses in the major field of study would by default only consist of course listed under the ECE curriculum track of enrollment. More generally, an MS ECE student can propose alternative courses for inclusion under the major field of study to the faculty member who serves as the Academic Advisor for the curriculum track. For the Master's degree, you can apply up to six credit hours of formal undergraduate upper-division elective courses, with approval of the Academic Advisor for the student's curriculum track.

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 major academic track of study without duplicating the courses you have taken in your primary academic track of study. 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 follow, with italicized courses directly related to graduate studies in electrical engineering:

- CS345 Programming Languages
- CS378 Mathematical Methodologies
- CS378 Advanced Networking and Implementation
- M325K Discrete Mathematics
- M343M Error Correcting Codes
*M346 Applied Linear Algebra*for analysis and design of matrix-based algorithms such solving a linear system of equations and computing eigendecompositions*M362M Stochastic Processes*for modeling and processing of random signals such as thermal noise and additive interference- M364L Vector and Tensor Analysis I
- M364L Vector and Tensor Analysis II
- M365C Real Analysis for analysis of convergence for iterative numerical algorithms
*M368K Numerical Methods of Applications*for splines, data smoothing, eigenvalue approximation, signal processing, optimization, and Monte Carlo simulation methods*M378K Mathematical Statistics*for statistical distributions, estimation of distribution parameters, and hypothesis testing- ME366L 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*- M387C Numerical Analysis: Algebra and 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 (DICE) or Architecture, Computer Systems, and Embedded Systems (ACSES) academic tracks in the graduate program in Electrical and Computer Engineering. If you were admitted on the ACSES track, then I would recommend that you take your ECE supporting work in DICE. If you were admitted on the DICE 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.

A graduate student at UT Austin may select any of the 1850 tenured and tenure-track faculty members at UT Austin as their research supervisor. If the research supervisor is not a member of the ECE Graduate Studies Committee, then the graduate student would also select a member of the ECE Graduate Studies Committee to serve as their co-advisor.

The following faculty members on the ECE Graduate Studies Committee conduct research in robotics:

- Prof. Sandeep Chinchali (ECE GSC; ECE home dept.) csandeep@stanford.edu.
- 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. Jose del R. Millan (ECE GSC; ECE home dept.) jose.millan@austin.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

- 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

- 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
online
MSEE Degree Program.
This program would allow you to participate via videoconferencing
to all of the MSEE courses taught in the traditional program.
- 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 04/25/21. Mail comments about this page to bevans@ece.utexas.edu.