Suggestions for Current and Prospective Graduate Students
Prof. Brian L. Evans
Department of Electrical and Computer Engineering
The University of Texas at Austin, Austin, TX 78712
09/06/24
I have been compiling this informal document while answering
questions that current and prospective graduate students have
been posing to me.
This information is not an official document
of UT Austin.
The opinions expressed in this document are my own.
I try to keep the information updated.
Please feel free to send suggestions to me at
bevans@ece.utexas.edu.
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:
My comments on both articles are quite similar.
First, I would recommend that undergraduate and graduate ECE students
take significant coursework to build breadth and depth in their field(s)
of interest.
This will help undergraduates be better prepared for graduate studies,
and graduate students be better prepared for research, and help both
be better prepared in the long term for their careers.
Second, undergraduate and graduate ECE students have opportunities to
conduct research through their courses, e.g. senior design projects for
undergraduates and semester-long projects in many graduate courses.
Conducting independent undergraduate research should not be
at the expense of building a solid foundation through courses as described
in my first point.
Third, I would recommend that undergraduate and graduate ECE students
seek significant summer internships related to their field(s) of interest.
Post-MS PhD ECE students should seek internships in corporate R&D settings.
Fourth, it is generally easier to go straight through to finish
one's degrees.
One natural place to take a break from graduate studies is right after
receiving the MS ECE degree.
However, the longer the break, the less likely the student will return
to continue one's studies and the harder the transition back to academic
studies if the student were to return.
Table of Contents
1.0 MS or Ph.D? That is the question.
2.0 Admission and financial support
3.0 Planning your Coursework
4.0 Other information
Talking with graduate students, academic advisors, faculty members,
family mentors, and friends can really help you answer this question.
Internship experiences can sometimes help.
Also, many university career assistance centers regularly help undergrads
decide about graduate school as a career path and help answer application
questions.
Through these centers, several graduate programs recruit students and
participate in recruiting events.
For example, at The University of Texas at Austin,
Texas Career Engagement
will support exploration into any discipline, and each college or school
including engineering will customize its career assistance center for
the disciplines in that college or school.
As the Texas Career Engagement does,
the University of California Riverside Career Center
also guides undergraduate students in discerning if pursuing graduate or
professional studies would match their career goals and interests.
A good reference for deciding whether a Master's degree
or Ph.D. degree is right for you and discerning what kind
of research group matches you the best is
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 BSECE students upon graduation are
generally in test engineering, technical sales,
field engineering, and basic design
- A BSECE degree is a stepping stone for graduate studies
in business, computer science, engineering, law, mathematics,
medicine, public policy, and many other fields
- An MSECE degree provides an entry to advanced design positions at
companies.
- A PhDECE 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 PhDECE 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.
2.0 Admission and financial support
Professors, as you might guess, are extremely busy.
A professor may receive hundreds of e-mail messages from
prospective graduate students each year.
It is important for your e-mail to catch the professor's attention.
Some faculty members will ask you to give your e-mail message
a certain title, as evidence that you've read their home page.
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.
If you are an undergraduate ECE student at a university with
an active graduate ECE program, then it might be worthwhile evaluating
your own graduate ECE program for possible graduate studies.
Top graduate ECE programs neither force their undergraduate students
to go somewhere else for graduate study nor coerce them to stay.
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 student 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.
With US graduate ECE admission rates being in the 8-25% range, I would recommend
applying to 7-10 US graduate ECE programs.
More than 100 PhD ECE programs and 350 MS ECE programs are available to choose
from in the US.
There are also many excellent international graduate ECE programs to
consider.
The key is to match the performance in your math, engineering and other
courses related to your intended specialization to the rank of the
graduate program to which you are applying.
Please pick ~1/3 that will be 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 and financial support.
Students with grade point averages of 3.0 or higher (on a 4.0 scale)
in the courses most related to their area of specialization for graduate
ECE studies should be able to gain admission into at least one of the
350 graduate ECE programs in the US.
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 because about half of the effort in graduate ECE
studies is reading and analyzing technical material and explaining
that material in oral and written presentations.
For graduate studies in engineering, mathematics courses are particularly
important because graduate study is generally more formal and rigorous
than undergraduate studies.
At UT Austin, GRE scores are neither required nor considered for admissions
to the graduate ECE program.
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):
- 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
- Faculty mentor for your completed senior design project
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 your primary opportunity to make your case
for admission to a particular graduate program.
It is important to express why you would like to go to graduate school.
Let the committee know what your interests are, and what you're enthusiastic
about, related to graduate study.
Admissions committees have to read a lot of them, so it is very helpful
to be concise, e.g. one page of 11pt, single-space text.
In the statement of purpose, it is generally not helpful to include
information about what you accomplished prior to college.
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.
You have the reader's best attention in the first sentence of each
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.
Other reasons for getting a PhD degree include wanting to become
a professor and wanting to start a company.
For the second sentence, you could say that you're finishing a
BSECE degree at university z with specializations in x and y.
The third sentence could express how the undergraduate specializations
are related to the specialization you'd like to pursue in graduate school.
- 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?
One of the advantages to applying to 7-10 graduate ECE programs is that you will
have a good chance of obtaining admission with financial support from at least
one of the programs.
The financial support should be roughly $20,000 in gross income for nine months
(i.e. for the fall and spring semesters) that would be in addition to full coverage
of tuition and health insurance.
In the summers, working in internships at companies or government labs would
give you complementary experience to the coursework and academic research
at the university.
With graduate ECE students making $8000 to $12000 per month (gross) in summer internships
and $2600 per month as teaching or research assistants, one could earn $47,000 to $60,000
in gross salary per year while in graduate school.
Here's a breakdown of the
cost of attendance
at UT Austin.
Financial support
comes in a variety of forms for full-time graduate ECE students:
Externally funded fellowships include the following national fellowship opportunities:
A person may apply to both NSF and NDSEG fellowships, but may not accept both.
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 would usually need to pass
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.
Exceptions are granted if you've finished a BS degree at an institution
where English is the primary lanuage of instruction except for non-English
language courses.)
- 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.)
If you have already been a TA, then you will still need to apply for
a position for each new semester.
For more information about teaching assistantships for international
students, please see
International Teaching
Assistant English Certification.
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.
On the application for graduate ECE studies, you might be to select a first
choice and second choice in your area of specialization (curriculum tracks).
If you are admitted, then you will be admitted into a specialization.
Every academic track has some overlap with communication systems.
Those interested in graduate ECE studies in communication systems
are likely to choose one of the following academic tracks:
- 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 (ECE 362K)
- Digital Communications (ECE 360K)
- Digital Image Processing (ECE 371Q)
- Digital Signal Processing (ECE 351M)
- Introduction to Networking (ECE 372N)
- Real-Time Digital Signal Processing (ECE 445S)
- Computer Engineering/Computer Science Courses
- Algorithms (ECE 360C)
- Data Science Laboratory (ECE 460J)
- Data Science Principles (ECE 461P)
- Data Structures (ECE 422C)
- Operating Systems (ECE 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 (ECE 351K)
- Real Analysis (M 365C)
- formal proofs on discrete and continuous sets
- formal derivation of convergence of iterative numeric algorithms of the form
xk+1 = f ( xk ) given an initial guess x0.
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 (ECE 351K)
- Numerical linear algebra (M 346)
- learning limitations on matrix calculations in floating point arithmetic
- characterizing matrices that are non-singular in theory but act like singular matrices
in calculations (i.e. have a high condition number)
- computing matrix decompositions, such as eigendecomposition, singular value decomposition,
and Cholesky decomposition, widely used in solving engineering problems
- strengthening understanding of linear algebra including matrix identities
that can be use in deriving numerical algorithms
- accelerating matrix computations for machine learning and other algorithms
Some applicants have been able to pick up the equivalent knowledge
at work (e.g. through short courses and hands-on experience) for some
of these courses.
It is important to point this out in your statement of purpose.
You will need a social security number to be employed at the university.
A permanent Social Security number can be obtained from the local
Social Security Office.
There is a waiting period of 10 business days for a social security card
(with the permanent social security number) to be issued.
Please arrive a couple weeks before the semester begins to allow
enough time to process your forms.
If you do not arrive far enough ahead of time, then your first paycheck
might be delayed.
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.
A graduate admissions committee should evaluate applications holistically.
One of the criteria is to determine if the applicant will be able to excel
in graduate ECE courses in the intended specialization immediately upon
enrollment.
Another criteria is the likelihood of success in completing independent
research for the PhD degree.
Many committees evaluate potential for the PhD even for MS Only students
because students can change their mind during the MS degree and decide to
the pursue the PhD degree.
There are other criteria as well.
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.
3.0 Planning your Coursework
It is very important to plan out all of your courses for your
MS or Ph.D. degree in advance.
Some graduate courses are taught every year, and some are taught
every other year.
The only graduate course that is offered every Fall and Spring semester
is ECE396K-8 Ultra Large Scale IC Fabrication Techniques.
We rarely offer ECE graduate courses or ECE undergraduate electives
in the summer.
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.
A full-time load for graduate students is nine credit hours in the
Fall and Spring semesters, and three credit hours in the Summer term.
At UT Austin, the course numbering scheme is unusual.
It is cgnl-t where c is the number of credits, g is the grade level,
nl is the subject area represented by a number and a letter, and
t is the topic number.
The grade level (middle digit) means the following:
- 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
So, ECE371Q is a three-hour, senior, elective ECE class, and
ECE381K-2 is a three-hour graduate course.
For 2021-22, ECE courses are using ECE at the graduate level and
ECE at the undergraduate level; after 2021-22, both will use ECE.
For a student, the workload for a course depends on the course and instructor,
as well as the student's pre-requisite background and interest in the
course.
At UT Austin, each formal graduate ECE course that meets for lecture and
has regular assignments takes about 15-25 hours/week of effort.
Taking three graduate ECE courses and working in a 20-hour assistantship
would take on average 80 hours/week of effort, which is an overwhelming
workload that should be avoided.
Instead, many graduate ECE students take two three-credit formal graduate
courses plus three credits of informal courses.
Here are several examples of three-credit informal courses taken
on a credit/no credit basis:
- ME 397K-1 Acoustics Seminar on Fridays 3-6pm.
Includes the weekly Acoustics Seminar on Fridays 4-5pm plus
events immediately before and after the seminar.
It is an opportunity to meet other students interested
in acoustics.
- ECE 397C Research Problems.
One-on-one independent study on a research topic with a faculty member.
- ECE 398T Supervised Teaching.
One-on-one discussions with a faculty member concerning
teaching style and approaches.
The coursework taken for graduate ECE studies is meant to provide
both breadth and depth of knowledge.
The courses contributing to the depth of knowledge is called major
work, and those contributing to the breadth of knowledge is called
supporting work.
In total, 10 courses taken for letter grade are required to
satisfy the coursework requirements for either an MSECE or PhDECE
degree.
A graduate course taken at UT Austin for letter grade could be
applied to satisfy the coursework requirements for both an MSECE
and a PhDECE degree.
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 ECE 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 ECE 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 MSECE 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
The PhD dissertation committee will decide on approval of the coursework
requirements for the PhD degree.
Formal lecture courses do not include research problem courses, conference
courses, graduate research internship, MS report or MS thesis courses.
For the MSECE 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 MSECE students currently choose the non-thesis/non-report option.
For the three MSECE 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
No more than 6 semester hours of upper-division undergraduate elective
coursework may be included on the MS ECE Program of Work.
Upper division courses have a middle digit of 2-7 and are generally
taken by undergraduates in the third or fourth year of studies.
ECE Master's Program of Work Form
Many graduate courses are only taught by one instructor.
For some graduate courses, two or three professors alternate
teaching it.
Examples of graduate courses having multiple instructors alternating
to teach them include
ECE382M-7 VLSI I (every Fall) and
ECE382N-1 Computer Architecture (every Fall and Spring).
In the Summer, it is rare that any graduate ECE courses would be offered.
Here are a few pointers for choosing courses and instructors:
- Read about the
Graduate ECE Program
and
graduate course descriptions
(scroll down to ECE 380C)
- Check the course evaluations for the instructors for a particular
course.
Numeric courses evaluation scores are on a scale of 1-5, with
1 being least favorable and 5 being most favorable.
- 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 spring 2024 to fall 2025 and
undergraduate
ECE course offerings from spring 2024 to fall 2025.
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 PhDECE coursework requirements.
The next section contains example courses for several possible graduate
ECE degree plans for different specializations, including the instructors
who are currently scheduled to teach the courses.
Graduate school gives an opportunity to choose your courses to build
depth and breadth in topics that are of interest to you.
As a graduate student, you would have access to graduate courses in
a wide variety of graduate programs across the university, except for
courses in professional degrees (e.g. Law, Medicine, MBA, and Pharmacy).
Even though you wouldn't have access to MBA courses, you would have
access to MS Business courses.
Many US Business Schools have recently started MS degrees for STEM graduates
who would like to apply their quantitative skills in business applications
and processes.
Next, I give several common choices of courses for a particular
research interest, and try to highlight both ECE and non-ECE graduate
courses.
Sometimes, it is really helpful to take an undergraduate electives
to help build breadth in a topic or build foundations for graduate
courses.
For this specialization, you would probably be enrolled in the
Decision, Information and Communication Engineering academic track as a graduate ECE student at UT Austin.
It is vital that you take graduate courses in probability, statistics,
linear algebra, optimization, information theory, and digital signal processing
theory as well as graduate courses in a variety of signal processing applications
(communication systems, image/video processing, data mining, genomics, etc.).
This sequence below assumes that you have already had undergraduate
courses in probability and random processes, digital signal processing,
digital communications, and data structures in C++.
Undergraduate Courses - Fall
Undergraduate Courses - Spring
Graduate Courses - Fall
- A graduate course on probability.
If you have taken ECE351K 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,
- ASE381P-6 Statistical Estimation Theory
(Prof. Humphreys)
- ECE381K-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)
Assumes knowledge of numerical algorithms to compute LU, QR, and Schur decompositions,
which are covered in M383E Numerical Analysis: Linear Algebra
- ORI391Q-5 Linear Programming, which is an introduction to optimization
Graduate Courses - Spring
- ASE381P-8 Stoch. Detection, Estimation and Control
(Prof. Humphreys)
- ASE389P-7 Global Navigation Satellite Systems Signal Processing
(Prof. Humphreys)
- ECE380L-10 Data Mining
(Prof. E. Thomaz)
- ECE381K-2 Digital Communications
(Prof. Andrews)
pre-requisites are ECE351K Probability and either ECE445S Real-Time DSP Lab or ECE351M DSP
offered in Spring 2020, Spring 2022, etc., and crosslisted with an undergraduate course
- ECE381K-6 Estimation Theory
(Prof. Vikalo)
offered in Spring 2021, Spring 2023, etc.
- ECE381K-16 Digital Video
(Prof. Bovik)
pre-requisite is ECE371Q Digital Image/Video Processing
- ECE381V Genomic Signal Processing
(Prof. Vikalo)
offered in Spring 2020, Spring 2022, etc.
- ECE381V 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 ECE 351K Probability
- ORI391Q-1 Nonlinear Programming
pre-requisite is ORI391Q-5 Linear Programming
- ORI391Q-4 Integer Programming
pre-requisite is ORI391Q-5 Linear Programming
For this specialization, you would probably be enrolled in the
Decision, Information and Communication Engineering academic track as a graduate ECE student at UT Austin.
This sequence below assumes that you have already had undergraduate
courses in probability and random processes, digital signal processing,
and digital communications.
Undergraduate Courses - Fall
Undergraduate Courses - Spring
Graduate Courses - Fall
- A graduate course on probability.
If you have taken ECE351K 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,
- ASE381P-6 Statistical Estimation Theory
(Prof. Humphreys)
- M383E Numerical Analysis: Linear Algebra (Prof. van de Geijn)
- M387C Numerical Analysis: Algebra and Approximation (Prof. Engquist)
Assumes knowledge of numerical algorithms to compute LU, QR, and Schur decompositions,
which are covered in M383E Numerical Analysis: Linear Algebra
- ORI391Q-5 Linear Programming, which is an introduction to optimization
Graduate Courses - Spring
- ASE381P-8 Stoch. Detection, Estimation and Control
(Prof. Humphreys)
- ASE389P-7 Global Navigation Satellite Systems Signal Processing
(Prof. Humphreys)
- ECE381K-2 Digital Communications
(Prof. Andrews)
pre-requisites are ECE351K Probability and either ECE445S Real-Time DSP Lab or ECE351M DSP
offered in Spring 2020, Spring 2022, etc., and crosslisted with an undergraduate course
- ECE381K-11 Wireless Communications
(Prof. Andrews)
pre-requisites are ECE351K Probability and either ECE445S Real-Time DSP Lab and ECE351M DSP
offered in Spring 2021, Spring 2023, etc.
- ECE381K-13 Analysis and Design of Communication Networks
(Prof. de Veciana)
pre-requisite is ECE381J Probability
- ECE381V Statistical Machine Learning
(Prof. Vikalo)
- ORI390R-5 Applied Stochastic Processes (Prof. Hasenbein)
pre-requisite an undergraduate probability course at level of our ECE 351K Probability
- ORI391Q-1 Nonlinear Programming
pre-requisite is ORI391Q-5 Linear Programming
- ORI391Q-4 Integer Programming
pre-requisite is ORI391Q-5 Linear Programming
For this specialization, you would likely be enrolled in one of the
following academic tracks:
Here is set of courses in the theory, algorithms, design, and
implementation of analog/RF/digital communication systems.
Undergraduate Courses - Fall
Undergraduate Courses - Spring
Graduate Courses - Fall
- ECE382M-7 VLSI I (Prof. Abraham)
- ECE382M-14 Analog IC Design (Prof. Sun)
- ECE382M-20 System on Chip Design (Prof. Gerstlauer)
offered in Fall 2020, Fall 2022, etc.
- ECE382N-23 Embedded System Design and Modeling (Prof. Gerstlauer)
offered in Fall 2019, Fall 2021, etc.
- ECE383L Electromagnetic Field Theory (Prof. Yilmaz)
Graduate Courses - Spring
- ECE381K-2 Digital Communications
(Prof. Andrews)
pre-requisites are ECE351K Probability and either ECE445S Real-Time DSP Lab or ECE351M DSP
offered in Spring 2020, Spring 2022, etc., and crosslisted with an undergraduate course
- ECE381K-11 Wireless Communications
(Prof. Andrews)
pre-requisites are ECE381J Probability and ECE381K-2 Digital Communications
offered in Spring 2021, Spring 2023, etc.
- ECE382M-8 VLSI II (Prof. Abraham)
pre-requisite is ECE382M-7 VLSI I
- ECE382M-24 Data Converters (Prof. Sun)
pre-requisite is ECE382M-14 Analog IC Design
- ECE382M-25 RFIC Design (Prof. Gharpurey)
pre-requisite is ECE382M-14 Analog IC Design
- ORI390R-5 Applied Stochastic Processes (Prof. Hasenbein)
pre-requisite is an undergraduate course in probability at the level of our ECE 351K Probability
For this specialization, you will need to be enrolled in either the
Decision, Information and Communication Engineering
or
Architecture, Computer Systems, and Embedded Systems
academic track as a graduate ECE student at UT Austin.
This following courses assume that you have already had an undergraduate
course in real analysis and an undergraduate course in operating
systems.
It is important not to repeat a graduate course at UT Austin that
you have taken elsewhere as a graduate course.
Instead, build on what you have learned.
Undergraduate Courses - Fall
Undergraduate Courses - Spring
Graduate Courses - Fall
- A graduate course on probability.
If you have taken ECE351K 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
ECE381J Probability and Stochastic Processes I
corequisite is M365C Real Analysis I
- else take
M362M Intro to Stochastic Processes or ORI390R-5 Applied Stochastic Processes (Spring) and then take
ECE381J in the subsequent Fall semester.
- ECE382M-7 VLSI I
(Prof. Abraham)
- ECE382N-1 Computer Architecture
(Prof. Patt)
- ECE382V Enterprise Network Security
(Prof. Tiwari)
- CS380D Distributed Computing I (Prof. Misra)
- CS386W Wireless Networking (Prof. Qiu)
- CS388H Cryptography (Prof. Waters)
Graduate Courses - Spring
- ECE380L-12 Real-Time Operating Systems
(Prof. Gerstlauer)
- ECE381K-2 Digital Communications
(Prof. Andrews)
pre-requisites are ECE351K Probability and either ECE445S Real-Time DSP Lab or ECE351M DSP
offered in Spring 2020, Spring 2022, etc., and crosslisted with an undergraduate course
- ECE381K-11 Wireless Communications
(Prof. Andrews)
pre-requisites are ECE351K Probability and either ECE445S Real-Time DSP Lab or ECE351M DSP
offered in Spring 2020, Spring 2022, etc.
- ECE381K-13 Communication Networks: Analysis and Design
(Prof. de Veciana)
- ECE381M Probability and Stochastic Processes II
(Prof. Baccelli)
pre-requisite is ECE381J Probability
- ECE381V Game Theory
(Prof. Nikolova)
- ECE381V Stochastic Geometry
(Prof. Baccelli)
pre-requisite is ECE381J Probability and ECE381K-13 Communication Networks: Analysis and Design
- ECE382N-1 Computer Architecture
(Prof. Patt)
- ECE382V Middleware Architecture and Design
(Prof. Julien)
- ECE382V Security in Hardware/Software Systems
(Prof. Tiwari)
For this specialization, you will need to be enrolled in the
Decision, Information and Communication Engineering academic track at UT Austin.
Undergraduate Courses - Fall
Undergraduate Courses - Spring
Graduate Courses - Fall
- A graduate course on probability.
If you have taken ECE351K 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,
- ECE381K-5 Advanced Telecommunication Networks
(Prof. Baccelli)
pre-requisites are ECE381J Probability and ECE381K-13 Analysis and Design of Communication Networks
- ECE382N-11 Distributed Systems I
(Prof. Garg)
- ECE382V Enterprise Network Security
(Prof. Tiwari)
- CS386W Wireless Networking (Prof. Qiu)
Graduate Courses - Spring
For this specialization, you will need to be enrolled in the
Decision, Information and Communication Engineering academic track at UT Austin.
Undergraduate Courses - Fall
Undergraduate Courses - Spring
Graduate Courses - Fall
- ASE381P-6 Statistical Estimation Theory
(Prof. Humphreys)
- A graduate course on probability.
If you have taken ECE351K 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
ECE381J Probability and Stochastic Processes I
corequisite is M365C Real Analysis I
- else take
M362M Intro to Stochastic Processes or ORI390R-5 Applied Stochastic Processes (Spring) and then take
ECE381J in the subsequent Fall semester.
- ECE381K-18 Convex Optimization
(Prof. Mokhtari)
Pre-requisites are advanced undergraduate courses on
linear algebra, optimization (linear programming), probability, signal processing, and statistics
- ECE385J-18 Biomedical Imaging
(Prof. Yankeelov)
- M383E Numerical Analysis: Linear Algebra (Prof. van de Geijn)
- M387C Numerical Analysis: Algebra and Approximation (Prof. Engquist)
Assumes knowledge of numerical algorithms to compute LU, QR, and Schur decompositions,
which are covered in M383E Numerical Analysis: Linear Algebra
- ORI391Q-5 Linear Programming, which is an introduction to optimization
- PSY387N Perceptual Systems
(Prof. Hayhoe)
Graduate Courses - Spring
- CS384G Computer Graphics
(Prof. Fussell)
- CS391L Machine Learning
(Prof. Ballard)
- ECE380L-10 Data Mining
(Prof. E. Thomaz)
- ECE381K-16 Digital Video
(Prof. Bovik)
pre-requisite is ECE371Q Digital Image/Video Processing
- ECE381V Machine Learning
(Prof. Dimakis)
pre-requisite is ECE381J Probability
- ORI391Q-1 Nonlinear Programming
pre-requisite is ORI391Q-5 Linear Programming
- PSY380E Vision Systems
(Prof. Geisler)
Machine Learning is a large field that includes linear algebra,
signal processing, image processing, probability, random
processes, statistics, estimation theory, information
theory, optimization, algorithms, and distributed computing.
For this specialization, you would most likely be enrolled in the
Decision, Information and Communication Engineering academic track as a graduate ECE 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
Undergraduate Courses - Spring
- ECE360C Algorithms
(Prof. Julien)
- ECE460J Data Science Laboratory
(Prof. Dimakis)
- ECE461P 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 ECE351K 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
ECE381J Probability and Stochastic Processes I
corequisite is M365C Real Analysis I
- else take
M362M Intro to Stochastic Processes or ORI390R-5 Applied Stochastic Processes (Spring) and then take
ECE381J in the subsequent Fall semester.
- ECE381K-18 Convex Optimization
(Prof. Mokhtari)
Pre-requisites are advanced undergraduate courses on
linear algebra, optimization (linear programming), probability, signal processing, and statistics
- ECE381V Advanced Probability: Learning, Inference and Networks
(Prof. Shakkottai)
Fall 2020, Fall 2022, etc.
- ECE381V Online Learning
(Prof. Shakkottai)
Fall 2021, Fall 2023, etc.
- ECE381V Reinforcement Learning
(Prof. Stone)
Crosslisted from a graduate course in Computer Science
- ECE382V 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)
Assumes knowledge of numerical algorithms to compute LU, QR, and Schur decompositions,
which are covered in M383E Numerical Analysis: Linear Algebra
- ORI391Q-5 Linear Programming, which is an introduction to optimization
Graduate Courses - Spring
- CS391L Machine Learning
(Prof. Ballard)
- ECE381K-16 Digital Video
(Prof. Bovik)
pre-requisite is ECE371Q Digital Image/Video Processing
- ECE381V Advanced Algorithms
(Prof. Nikolova)
- ECE381V Large-Scale Optimization
(Prof. Caramanis)
- ECE381V Statistical Machine Learning (Prof. Vikalo)
pre-requisite is ECE381J Probability and ECE351M Digital Signal Processing
- ORI391Q-1 Nonlinear Programming
pre-requisite is ORI391Q-5 Linear Programming
For this specialization, you would likely be enrolled in one of the
following academic tracks at UT Austin:
For those interested in embedded digital systems motivated by applications
signal processing and communications, here is an example set of courses.
Undergraduate Courses - Fall
Undergraduate Courses - Spring
Graduate Courses - Fall
- ASE396 Verification/Synthesis of Cyberphysical Systems
(Prof. Topcu)
- ECE382M-7 VLSI I
(Prof. Abraham)
- ECE382M-20 System on Chip Design
(Prof. Gerstlauer)
in Fall 2018, Fall 2020, etc.
- ECE382N-1 Computer Architecture
(Prof. Patt)
- ECE382N-21 Computer Performance Evaluation
(Prof. John)
- ECE382N-23 Embedded System Design and Modeling
(Prof. Gerstlauer)
in Fall 2019, Fall 2021, etc.
- ECE382V Activity, Sensing and Recognition
(Prof. E. Thomaz)
- ECE382V Security in Hardware/Software Systems
(Prof. Tiwari)
Graduate Courses - Spring
- ECE380L-12 Real-Time Operating Systems
(Prof. Gerstlauer)
- ECE382M-1 VLSI Testing
(Prof. Touba)
- ECE382M-8 VLSI II
(Prof. McDermott)
- ECE382M-11 Formal Verification
(Prof. Abraham)
- ECE382M-21 Optimization Issues in VLSI CAD
(Prof. Pan)
- ECE382M-23 Low-Power and Robust Design
(Prof. Orshansky)
- ECE382N-1 Computer Architecture
(Prof. Erez)
- ECE382N-14 High-Speed Computer Arithmetic I
(Prof. Swartzlander)
- ECE382N-19 Microarchitecture
(Prof. Patt)
For those planning to work after the MS degree, choosing your coursework
will be critical to help you prepare to be a design engineer.
Taking applied courses with design assignments, labs, and projects
will be very helpful.
These courses could be in many different graduate programs, e.g.
Aerospace Engineering for design of drone systems.
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:
Assumes knowledge of numerical algorithms to compute LU, QR, and Schur decompositions,
which are covered in M383E Numerical Analysis: Linear Algebra
- 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 and computer science.
The department regularly offers more then ten undergraduate and more than
twenty graduate
courses in signal and image processing.
4.0 Other information
Robotics at UT Austin is
a multidisciplinary research effort in robotics involving students,
faculty and
other researchers from aerospace, computer, electrical and mechanical
engineering as well as computer science and information science.
Research includes robots in a wide array of shapes, sizes and function.
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:
We also have ECE GSC faculty members who conduct research in wearables:
Also, the following faculty member conducts theoretical research in joint
controller-communication design:
Additional ECE faculty members who work in robotics and controls follow:
The UT Austin IEEE student chapter of the
Robotics and Automation Society
has several active robot teams.
In the conventional graduate ECE program as of November of 2001,
165 of the 548 graduate students (about 30%) were attending on
a part-time basis.
This path takes 5-6 years to complete an MS degree.
The alternate path is an option 3 program, which takes about two years
but very few courses are offered in this format, or the
online MS degree, which has access to all of the MS courses
offered in the traditional program.
Hence, while working full-time, you have three choices to obtain
an MSECE degree:
- MSECE degree through the conventional program (option 1).
The best route is to do an MSECE 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 MSECE degree
is handled with the applications for full-time enrollment.
- MSECE degree through the
online
MSECE Degree Program.
This program would allow you to participate via videoconferencing
to all of the MSECE courses taught in the traditional program.
- MSECE 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 MSECE program.
At the end of the two full years, you would have completed an
MS degree in Software Engineering with an MS report option.
A part-time Ph.D. is possible but extremely difficult to manage.
According to National Science Foundation statistics, it takes
about 5.5 years of full-time graduate study in engineering after
the BS degree to finish the PhD degree.
Let's assume that a full-time graduate student devotes 60 hours
per week to coursework and research, and a part-time graduate
student devotes 20 hours per week.
It would then take the part-time graduate student 16.5 years to
finish a PhD degree after the BS degree.
A lot can happen in one's life over 16.5 years.
The more aligned the person's full-time employment is with his/her
research, the faster the finish.
Last updated 09/06/24.
Mail comments about this page to
bevans@ece.utexas.edu.