Classes usually take place on Fridays and Saturdays with morning classes from 8:00 a.m. to 12:00 p.m. and afternoon classes from 1:00 p.m. to 5:00 p.m. Recorded lectures are available for those unable to join in person.

$34,000 Total Tuition

A $1,000 non-refundable deposit is required to hold your spot upon admission into the program. which will be applied to your first tuition payment. Please note that the tuition rate is subject to change and does not include any additional fees students may incur.

Tuition reimbursement is an employee benefit through which an employer pays for a pre-determined amount of continuing education credits or college coursework to be applied toward a degree as it relates to their current career track. Prospective students are encouraged to contact their employer to determine eligibility for tuition assistance.

Financial Aid

Students are eligible to apply for the following financial aid options through the University Office of Scholarships and Financial Aid:

• Direct Subsidized/Unsubsidized
• Direct PLUS
• Private Loans
• G.I. Bill or V.A. Tuition Assistance (Veterans Only)

This program is classified as Option III and does not qualify for financial assistance through state loans or benefits programs like The Hazlewood Exemption.

Learn more about financial aid and loan options Texas One Stop.

Admission Criteria

Applicants must meet the following criterion to be considered for program admission.

• Earned a Bachelor of Science in Software Engineering; Electric and Computer Engineering; Computer Science; or related degree.
• Demonstrate proficiency in Data Structures, Algorithms, and Java or C++
• An upper-division GPA of 3.0 is recommended.
• 12 months of industry-related experience and an upper-division GPA of 3.0 is recommended but not required.

Applicants who do not meet the criteria may be recommended for conditional admission pending approval by the graduate dean.

Application Requirements

Applicants must submit the following materials by the application deadline to be considered for program admission:

• GRE scores are no longer required nor considered for admission to the TxEEE Master of Science in Software Engineering program
• Complete online application
• Official transcripts from all senior colleges
• Current Resume
• Statement of Purpose
• 3 professional letters of recommendation

Application Deadlines

Applicants are required to submit all application materials by the application deadline to be considered for program admission. Any materials received after the deadline has passed will not be accepted. Applicants are encouraged to apply as soon as possible as official documents and test scores may take up to three weeks to transmit to UT Austin.

Once all application requirements have been submitted, applicants will receive a notice of an admission decision around one month after completing the application process.

International prospective students

Due to the structure of this professional master's degree program, it is not possible for students to register for more than 6 credit hours per semester, which does not meet the federal government minimum 9 credit-hour requirement to be eligible for an F-1 VISA. The university must abide by these regulations. Please note that the application fee is non-refundable.

International students on eligible non-student VISA are welcome to apply.

Courses cover a wide range of software topics on a rotating basis each semester to offer students the flexibility to customize their program of work to their educational needs.

ECE 381V: Statistical Machine Learning
Introduction to the theoretical and conceptual underpinnings of machine learning, and the practical algorithms that emerge from those foundations. Topics include the probably approximately correct (PAC) learning framework; generalization and the Vapnik- Chervonenkis dimension; the bias-variance tradeoff; linear classification and regression; logistic regression and gradient descent; overfitting and regularization; boosting; support vector machines and kernel methods; decision trees; neural networks; clustering; dimensionality reduction; feature selection/generation; generative models; and variational Bayesian methods.

ECE 382V: Algorithmic Foundations for Software Systems
This course studies combinatorial algorithms. Students will learn proof-based reasoning about algorithms; asymptotic complexity analysis; algorithm design principles; common types of algorithms and their applications; and the nature, impact, and handling of intractability. Please see the separate Course Plan for the tentative schedule of topics covered in the course. The principle focus of the lectures will be theoretical, in the style of the Algorithm Design text listed below. There will also be a number of programming assignments, in which you will be required to implement algorithms.

ECE 382C.11 Requirements Engineering
This course will address theoretical and practical methods for acquiring and modeling requirements for various systems stakeholders. Topics will include methods and techniques for managing the acquisition process among distributed team members and distributed stakeholders, eliciting and verifying requirements as a function of the type of stakeholder, the types of requirements, and system development maturity, managing the requirements artifacts, constructing model-based representations of requirements, synthesizing requirements for various stakeholders, and analyzing and evolving model-based requirements.

ECE 382C Software Architectures
The course will teach students about software architectures, architectural model specification techniques and analysis techniques offered by the research community as well as those architectures, model specifications and analytical methods commonly used in industry.

ECE 382C.16 Distributed Information System Security
Intended to acquaint the student with the analysis and engineering techniques employed in securing today's networked information system environment. Emphasis is placed on examination of practical security threats, exposures in distributed systems and the technology that is being applied and developed as countermeasures.

ECE 382C.3 Verification and Validation
This course covers various traditional and state-of-the-art techniques for software validation, a process that includes reasoning about (the correctness of) programs and testing programs. The course content will include both techniques for dynamic analysis, such as glass-box and black-box testing, equivalence partitioning, test strategy and automation, regression testing and debugging, and techniques for static analysis, such as symbolic execution, and also techniques for software model checking including those that employ artificial intelligence based heuristics.

ECE 382C Mobile Computing
As mobile computing devices like laptops, PDAs, cellular phones, and even miniature sensors become increasingly pervasive, the demand for applications for this novel environment escalates. This course explores the effects of mobile computing on software design and development. The approach taken uses current research projects in the field of mobile computing to highlight the key aspects that complicate software engineering. We will focus on these concerns in the context of application development.

ECE 382V Parallel Algorithms
This is an introductory graduate course in parallel algorithms. It assume undergraduate knowledge of sequential algorithms. The following topics will be covered in the course:

• Basic Techniques: Reduce, Parallel Prefix Scan, Pointer Jumping, Partitioning, Cascading
• Breaking Symmetry: Maximal Independent Set Problem
• Sorting Algorithms: Odd-Even Sort, Bitonic Sort, Parallel Mergesort, Parallel Radix Sort
• Randomization: Markov's inequality, Chernoff Bound
• Parallel Graph Algorithms: BFS, Ear Decomposition, Spanning Tree, Shortest Path
• Parallel Matrix Algorithms: Matrix multiplication, Matrix inversion
• Miscellaneous: Parallel FFT, String Matching
• Cuda: Implementing Parallel Algorithms on GPU using CUDA
• MPI: Implementing Parallel Algorithms using MPI

ECE 382V Systems Programming
This is a computer systems course with an emphasis in software. The course will start with looking at tools like compliers, linkers, loaders, and debuggers that an operating system provides and how they work. We will explore the POSIX System-Call API that all modern operating systems implement with focus on processes, threads, i/o and inter-process-communication. The second part of the class is on the design and implementation of an operating system with focus on process, memory virtualization, and concurrency.

ECE 382V Formal Methods in Distributed Systems
This course gives an introduction to the use of formal methods within the software design process. Specifically, this class will cover the application of models to distributed and concurrent systems. Modern software systems are commonly highly distributed, and this added sophistication further complicates software design. The rigor offered by formal methods aims to make the process more precise.

ECE 382V Advanced Programming Tools
Programming is difficult - some of the problem developers face include.

• How can a project be structured so that developers can work on it concurrently?
• How can the building of a project be automated?
• How can a program be written to make it portable?
• How can a program be prototyped efficiently?
• How can a program be tested and debugged efficiently?
• How can the performance of a program be increased?

Using the right tools can solve these problems. Examples include tools for version control, documentation, program building and configuration, automatic testing, program analysis, and integrated development.

Our approach will be to introduce a specific problem, show how a tool can solve the problem, and then develop the technical principles underlying the tool. We will have written homework problems as well as coding exercises for each concept. The class will have a major design project that will begin at the start of the term. Use of the tools will be a required part of the project. We will use open-source tools to illustrate these concepts. The specific tool stack is described in the lectures section of this document. I selected these tools based on my experience at Google; they also power many state-of-the-art commercial projects.

ECE 382V Data Engineering
Course Description – Data Engineering is concerned with the role of data in the design, development, management, and utilization of complex computing/information systems. Issues of interest include database design; meta knowledge of the data and its processing; languages to describe data, define access, and manipulate databases; strategies and mechanisms for data access, security, and integrity control.

ECE 382V Social Computing
This is an introductory course on social networks, markets and Internet computing. The emphasis of this course is on algorithms where multiple agents interact with each other. The following topics will be covered in the course: Matching Bipartite Matching, online matching, Hungarian algorithm, Auction-based algorithm Stable Matching: Optimal matching, enumerating all matchings, Kidney-Exchange Auctions: First-price, Second-Price Auctions, VCG Mechanisms Games Mixed strategies, Nash Equilibrium, Pareto Optimality, Social Optimality Voting: majority rules, positional voting, Arrow's Theorem Experts Algorithm: Multiplicative Updates Method P2P Computing: Consistent Hashing Streaming Algorithms: Sketches, Bloom Filter, Heavy Hitters Privacy and Authentication: Public Key Cryptography.

ECE 382V Software Testing
This course first introduces the basics of software testing theory and practice, and then presents some recently developed techniques for systematically finding bugs in programs and improving their reliability. Learning the techniques and tools presented in this course is likely to significantly increase the students’ productivity as software developers and testers, and improve the quality of the code they develop.

ECE 382V Large-scale Machine Learning
Linear classifiers and logistic regression. Feedforward neural networks. Convolutional neural networks. Deep learning training by backpropagation. Interpretability of deep neural networks. Prediction and overfitting. Statistical Learning theory. Unsupervised machine learning. Deep generative unsupervised models. Generative Adversarial networks and Autoencoders. Applied deep learning using Python, Tensorflow and Keras.

ECE 380L Data Mining
Basic concepts of data mining, in parallel with a practical track involving hands-on experience with industrial strength software and a term project will be covered.

ECE 382N Communication Networks: Tech/Arch/Protocol
This is an introductory course in Computer Networking. It covers all basic components of modern networks, including: link level technologies such as Ethernet, token rings, and wireless Ethernet; switching technologies such as bridges and ATM; internetworking including IP; the transport layer, including TCP and RPC; and congestion control. Time permitting we will also consider security, quality of service, high-performance networks, and/or multimedia. Although IP and TCP are primary examples used in the course, it is NOT a course on TCP/IP!

ECE 382N.11 Distributed Systems
This course will expose students to the theoretical and practical aspects of designing distributed systems such as: Datagram Sockets, TCP sockets, Java RMI, Map Reduce Abstraction, Models of Distributed Computation, Logical clocks, vector clocks, Resource Allocation, Drinking Philosophers, Global Property Evaluation, Snapshots, Unstable properties, Ordering of Messages, Elections, Spanning Trees, Synchronizers, Consensus, Byzantine Agreement, and Self-stabilizing algorithms.

ECE 382N.4 Advanced Embedded Microcontroller Systems
Hardware and software design of microcontroller systems; applications, including communication systems; object-oriented and operating systems approaches to interfacing and resource management.

ECE 382C.16 Distributed Information System Security
Intended to acquaint the student with the analysis and engineering techniques employed in securing today's networked information system environment. Emphasis is placed on examination of practical security threats, exposures in distributed systems and the technology that is being applied and developed as countermeasures.

ECE 382C System Engineering Program Management and Evaluation
Management, engineering, and evaluation approaches applicable to a spectrum of software development programs is taught. General guidelines, metrics, program artifacts, and processes will be discussed in conjunction with case studies.

ECE 382C.12 Multicore Computing
This course will expose students to the theoretical and practical aspects of designing multicore software systems such as: programming constructs for concurrent computation, openMP, sequential consistency, linearizability, lock-based synchronization, lock-free synchronization, wait-free synchronization, consensus number, software transactional memory, testing and debugging parallel programs, race detection, concurrent data structures such as stacks, queues, linked lists, hash tables and skiplists, and model checking of concurrent programs.

ECE 382 Computer Graphics
This is an introductory course on the major topics in computer graphics including image synthesis, interactive techniques, geometric modeling, and computer-based animation. Covered material includes: OpenGL programming, principles of operation of raster graphics systems, sampling and antialiasing, homogeneous coordinate transformation techniques, parallel and central projection and perspective transformations, hidden surface removal, light and reflectance models for local and global illumination, shading techniques, ray tracing, basic object modeling techniques, visual perception and basic color theory, hierarchical modeling, and basic animation.

ECE 382 Middleware
This course is a graduate level course introducing and investigating middleware at all levels, largely from a software engineering perspective. Students are introduced to various types of middleware (from object-oriented middleware to message-oriented middleware and beyond) both through lecture materials and through active "mini-projects" through which the students build complex applications using existing middleware solutions. The course also offers lectures on "trends" in middleware, including how middleware addresses challenges related to mobile computing, sensor networks, real-time computing, "green computing," etc.

ECE 382 Algorithmic Foundations for Software Systems
We will begin by reviewing foundations of discrete mathematics. We will then study measuring program performance using the big-O notation. Following this, we will study fundamental data structures and their associated algorithms; specifically, we will cover lists, arrays, queues, stacks, hash tables, sets, binary trees, and graphs. We will then focus on general algorithm design principles, such as greedy approaches and dynamic programming. Our last topic will be matrix algorithms. The principle focus of the lectures will be on theoretical aspects, in the style of the CLRS Algorithms text listed below. There will also be a number of programming assignments that will require implementing and testing algorithms. In addition, there will be a team project that either evaluates some textbook algorithm(s) in real-world settings, or explores how to specialize and enhance some textbook algorithm(s) under specific conditions.

ECE 381V Introduction to Optimization
This course will serve as an introduction to modeling, applications and algorithms of discrete and continuous optimization. The students will learn how to model the real world within the paradigms of linear programming, mixed integer linear programming, and more general convex optimization. We will emphasize interesting applications where these classes have had impact in industry, including applications in data mining and machine learning (no prior knowledge of Machine Learning is required).

ECE 379K Engineering Dynamic Program Analysis
Dynamic analysis is commonly used to detect errors in software, including memory errors, concurrency errors (e.g., data races), performance issues, etc. Although valuable, dynamic analysis can be costly because the program execution needs to be (continuously) monitored to collect necessary data for the analysis. Additionally, naively engineered dynamic analysis can interfere with the program being analyzed, which can impact the conclusions of the analysis. The main goal for this course is to provide motivation for various dynamic analysis techniques, introduce popular tools that are frequently used to implement an efficient and effective dynamic analysis, and provide hands-on experience in developing dynamic analysis techniques.

ECE 382V: Advanced Algorithms
This course introduces students to advanced techniques for the design and analysis of algorithms. It is intended to be a follow-up to the course “Algorithmic Foundations for Software Systems,” students must be comfortable with the topics covered in that course. The topics and applications tentatively planned to cover include: approximation algorithms (set cover, steiner tree and TSP, multiway cut, knapsack, minimum makespan scheduling), lattice-theoretic algorithms, LP-Based algorithms (LP Duality, set cover via dual fitting, LP rounding techniques, facility location), randomized algorithms (min-cut, maximal independent set, leader election, graph coloring) and game theory.

ECE 379K: Programming Paradigms
There are hundreds of programming languages out there and many of them bring several unique language features that should help developers to write correct software faster. This course will study several programming languages (including C, C++, Java, OCaml, etc.) and some of the unique features that are introduced to increase developers’ productivity. The course will also explore how software engineering practices differ across languages and the set of design patterns for each language. Moreover, unique language features have impact on how developers organize, write, test, and analyze code; we will introduce and discuss several of these aspects.

ECE 398R Master’s Report
Completion of report in the last semester enrolled in the program to fulfill the requirement for the master's degree. Offered on the credit/no credit basis only. Prerequisite: Graduate standing in electrical engineering and consent of the graduate adviser.

Fall Semester Enrollment

Fall 1 - 2 courses
Spring 1 - 2 courses
Summer 1 - 1 course

Fall 2 - 2 courses
Spring 2 - 2 courses
Summer 2 - Master's Report OR 1 course

Spring Semester Enrollment

Spring 1 - 2 courses
Summer 1 - 1 course
Fall 1 - 2 courses

Spring 2 - 2 courses
Summer 2 - 1 course
Fall 2 - 2 courses, OR 1 course & Master's Report

Master's Report

Software Engineering graduate students can complete 10 courses to attain their degree, or 9 courses and a Master's Report. For more information, please review the following:

Report Deadlines

This document lists all of the important deadlines students will need to meet in order to graduate (graduation application deadline, project dates): Dates & Deadlines

Quick Reference Sheet

This document has links to the graduation application, formatting templates, formatting guidelines, and other important forms: Quick Reference Sheet(PDF)

Choosing Committee Members

Information on how to select a supervisor and reader for the report/thesis committee: Quick Reference Sheet(PDF)

Director - Vijay Garg, Ph.D. | Distributed Systems
Prof. Garg's current research interests are in the areas of distributed systems, discrete event systems and software engineering. He has published more than 130 refereed research articles in these areas. His research has been supported by NSF, IBM, Texas Advanced Research Program, TRW, and Compaq among others.

Suzanne Barber, Ph.D. | Requirements & Software Arch.
Dr. Suzanne Barber serves our nation's citizens, industrial, governmental and academic institutions by aggressively combatting current and emerging identity management threats and fraud. She delivers the highest quality identity management discoveries, applications and outreach available.

Bill Bard, M.S. | Com. Networks: Tech/Arch/Protocol, Distributed System Security
Bill Bard has been employed continuously by the University of Texas since 1966 in positions including teaching assistant, lead operating system development specialist, digital system design engineer, manager of computer maintenance, and associate director for technical affairs.

Constantine Caramanis, Ph.D. | Data Mining
Dr. Constantine Caramanis is an Associate Professor in the Department of Electrical & Computer Engineering at The University of Texas at Austin. His research interests center on decision-making in large-scale complex systems, with a focus on learning and computation.

Alex Dimakis, Ph.D. | Large-scale Machine Learning
Alex Dimakis is an Associate Professor at the Electrical and Computer Engineering department, University of Texas at Austin. From 2009 until 2012 he was with the Viterbi School of Engineering, University of Southern California. He received his Ph.D. in 2008 and M.S. degree in 2005 in electrical engineering and computer sciences from UC Berkeley and the Diploma degree from the National Technical University of Athens in 2003. During 2009 he was a CMI postdoctoral scholar at Caltech.

Christine Julien, Ph.D. | Advanced Topics I & II summer course
Dr. Julien's research interests lie in the realm of software engineering, specifically for mobile computing. Much of her previous work has focused on software engineering for ad hoc mobile networks and includes the development of algorithms for mobile computing, and middleware for simplifying the software development process.

Sarfraz Khursid, Ph.D. | Validation and Verification
Dr. Sarfraz Khurshid is an Associate Professor of Electrical and Computer Engineering at The University of Texas at Austin. His current research focuses on software testing, specification languages, code conformance, model checking, and applications of heuristics in program analysis.

Bruce McCann, Ph.D. | System Engineering Prog. Mgmt. & Eval.
Dr. McCann holds BS, MS and PhD degrees in Electrical Engineering from The University of Texas at Austin. During twenty five years with Schlumberger he held positions in the US and France: Engineering management positions as Section Manager, Project Manager, Department Manager and VP Engineering.

Daniel Miranker, Ph.D. | Data Engineering
Daniel P. Miranker is a Full Professor, in the Department of Computer Science at the University of Texas at Austin, where he has taught since 1986. Starting with his Ph.D. dissertation, a consistent theme in Miranker's research is a focus on parallel and distributed processing of large-scale databases in concert with applications in knowledge-base systems and machine learning.

Mohit Tiwari, Ph.D. | Distributed Information System Security
Mohit Tiwari researches computer architecture and security at UT Austin (ECE), following a PhD at UC Santa Barbara in 2011 and an NSF post-doc fellowship at UC Berkeley (2011—13). His current work protects data while it is being processed in untrusted applications and cloud infrastructure, and has received best paper awards at ASPLOS'15, PACT'09, HOST'18 (runner-up), and HOST'19 (nominee); IEEE Micro Top Picks in architecture in 2010 and 2014 (Honorable Mention); CSAW best applied cybersecurity paper finalist in 2013 and 2018, the Qualcomm Faculty Award in 2017 and 2018, and the NSF CAREER award in 2015.

Ramesh Yerraballi, Ph.D. | Communication Networks
Dr. Yerraballi's teaching interests and experience span a broad swath of the Computing curriculum from, Theory of Computing, Algorithms and Data Structures, Introductory, Object-Oriented and Systems Programming, Operating Systems, Real-Time Systems, Distributed Systems, Computer Architecture and Performance Analysis of Computer Systems.

Our program is unique given the in-person format conveniently offered one weekend per month. The classroom experiences, conversations and relationships inspired by taking our courses in-person enrich the value of your educational investment ten-fold. The communication that happens between our award-winning professors and students and also student to student leads to worthwhile and lucrative relationships that will last throughout your career.

The following explanation is provided to clarify the unique qualities of a master’s in software engineering in comparison to a master’s in computer science. Software engineering can be viewed as a subdiscipline of computer-science, a discipline that is more tactical rather than theoretical in nature. Many computer science graduates these days work in positions that could easily be labeled "software engineer" though the training is different. As software engineers, we take a look at the system, from an engineering perspective. We look at the lifecycle of that system, from requirements elicitation, working with stakeholders, through design, architecture, implementation, testing, and maintenance. Along the way we use or integrate all kinds of tools and skills from computer science, but the curriculum constantly revisits the engineering mindset. That mindset is skilled and ready to analyze the framing of software engineering challenges and create solutions through constructing, testing and maintaining the entire system.

What is the application fee?
There is a $65 fee for domestic applicants and a $90 fee for international applicants, paid at the time of application submission. There is no application fee for non-degree seeking applicants.

Can I apply to enter the master’s degree program in the summer semester?
No, students are only accepted into the program in the fall and spring semesters.

How do I check my application status?
Students can view their application status by monitoring MyStatus.

Is the GRE required for admission?
No, the GRE is not required at this time, although it may be used to strengthen an application. UT Austin’s institution code is 6882. Please note that all application materials are due by the deadline and test scores may take up to three weeks to transmit to UT Austin.

Can I enroll in the master’s degree program full time?
No, this program is a part-time program.

How do I register for courses?
Prior to upcoming semesters, students will be asked via email to provide their course preferences by responding to a Registration Survey. Our staff will register students based on their responses, and priority will be given to students nearing the end of their studies. Students who fail to submit their responses before the registration deadline will be charged a late registration fee in addition to their tuition costs.

How are online courses delivered?
Online courses are delivered through Canvas. Students can click here to log into Canvas and access 24/7 support.

Are the courses asynchronous?
No, the courses in this program are not asynchronous.

Does this program have a thesis requirement?
There is no thesis requirement, however, Software Engineering graduate students can complete 10 courses to attain their degree, or 9 courses and a Master's Report.

How long does it take to earn a master’s degree in Software Engineering?
The master’s degree program can be completed in as little as two years. However, students have up to five years to complete the program. Most students complete the program within two to three years.

Can students reach out to professors for assistance?
Yes! In addition to providing feedback, professors are available to students when needed. Professors and teacher assistants can be reached during their office hours and are enthusiastic to answer questions.

Can students take a leave of absence?
Yes, all students are granted two leaves of absence, which excuses them from either a fall or spring semester with no penalty. A leave of absence must be approved prior to the first day of class. Leave of absences are not required to skip a summer semester enrollment.

What online services are available to students?
UT Austin students have access to numerous online resources, including academic coaching through the Sanger Learning Center and writing appointments through the University Writing Center.

Do I have access to UT Austin’s library?
Yes, students can access databases, digital collections, e-journals and e-books online at the University of Texas Libraries.

How do I access the student handbook?
Students can click here to view the student handbook.

How do I get help with technical issues?
Technical assistance is available 24/7 through Canvas. Students can also request tech support by visiting IT@UT or the Cockrell School of Engineering’s IT site.

What financial aid options are available?
Master’s degree students are not eligible for financial aid, but they are eligible for federal loans through the Office of Scholarships and Financial Aid.

Can I apply my veterans or military benefits toward tuition?
Yes, students enrolled in either the master’s degree or graduate certificate program are eligible to receive veterans benefits from the G.I. Bill or V.A. Tuition Assistance. Please refer to the Veteran Education Benefits page on Texas One Stop for more information.

How do I make a payment?
Students can pay their tuition online at My Tuition Bill. Students can pay online using a credit card, eCheck, electronic funds transfer, installment plan, tuition loan or eProxy.

How can I access my transcripts?
Unofficial transcripts are available to current and former students with a financial bar. Students can email This email address is being protected from spambots. You need JavaScript enabled to view it. for instructions on how to order and have a transcript mailed to them. Unofficial transcripts are $20 per copy, and students will need their UT EID, if known, and full name on record. Unofficial transcripts cannot be sent to a third party.

For the same cost as an unofficial transcript, students without a financial bar can order an official transcript. Students can order an official transcript online through the secure online transcript order system using their EID. Official transcripts can be sent electronically or by mail to students or third parties. Official transcripts can also be ordered by submitting a signed transcript order form 891 via one of the following:

• Email
  This email address is being protected from spambots. You need JavaScript enabled to view it.
• Fax
  512-475-7681
• Mail
  The University of Texas at Austin
  Office of the Registrar
  Transcript Services
  P.O. Box 7216
  Austin, TX 78713-7216

What is the graduation procedure?
During their final semester, master’s degree students must apply to graduate by submitting the Master’s Graduation Application form online. Forms are valid for a single semester, and students who fail to meet the deadline will be unable to receive their degree until the following semester. For more information, students can visit the Graduate School’s Deadlines and Submission Instructions page.

Does this program offer networking opportunities?
Yes! Students and graduates of the Cockrell School of Engineering have access to the Engineering Career Assistance Center (ECAC), which offers career counseling and workshops to help students navigate the recruitment process and find high-paying careers. Texas Engineering also offers numerous ways for graduates to stay connected with their 70,000 fellow alumni.

What does option 3 mean?
Option 3 is the designation used by UT Austin for self-funded programs with atypical hours and adjusted modes of instruction.