Education

Graduate Portfolio Program

Robotics is emerging to be a prime technology that can greatly advance a wide variety of industries that include healthcare (e.g. surgery and rehabilitation), defense, manufacturing, transportation (e.g. autonomous driving), energy (e.g. drilling and wind turbines), smart homes, space exploration, and hazardous material handling. Due to fundamental advances across multiple disciplines, robotics will be a huge growth area over the coming years, both academically and economically.

In light of the growing importance of robotics, UT has created a Graduate Portfolio Program in Robotics starting with the 2015-2016 academic year. The robotics certification expands the opportunities for graduating students looking for academic and/or research positions related to robotics in any department. The program will highlight their interdisciplinary skills spanning multiple disciplines beyond their degreed department.

The Graduate Portfolio Program in Robotics provides graduate students the opportunity to obtain an official "certification of expertise" in robotics with their Masters or Ph.D. degree from their home departments. Students in the portfolio program will receive multidisciplinary training in robotics by completing core coursework offered by the Department of Computer Science and the Department of Mechanical Engineering. Students must also participate in research seminars and complete additional approved courses offered by participating departments (below is a list of core and approved courses). The portfolio program aims to create a student-led research community in robotics at UT Austin and to promote interdisciplinary interaction among students.

 

Graduate Portfolio Program Details

  • Admission: Admission into the program will be contingent upon the approval of the Portfolio Steering Committee. Students may apply to the program during the first or second year of their graduate program (either MS or PhD); and they must have a faculty supervisor for their robotics research prior to submitting their application. 
  • Admisssion Requirements: Formal application to the portfolio program will require students to submit the following materials to the Chair of the Portfolio Steering Committee:
    • a portfolio application form,
    • a one-page research proposal, and
    • a letter of support from the student’s research supervisor.
  • Application Deadline: The application deadline is August 1st for the fall semester, and December 1st for the spring semester. Completed applications will be evaluated by the Portfolio Steering Committee and decisions are anticipated to take less than a month.
  • Certificate Requirements: Once admitted, certification requires completion of four courses (12 semester hours) in robotics and participation in at least two semesters of a Research Seminar Series. Selected courses must have been approved by the Portfolio Steering Committee .  At least 2 of the 4 courses must be core course from different departments. Additionally, no more than 2 of the 4 courses can be from the same department. The research seminar series is a non-credit, bi-weekly seminar series. The Coursework and Research Report must be submitted in the semester a student plans to graduate and must be submitted to both their advisor and the steering committee on or before the last class day in the student's graduating semester. Please additionally fill out the official Graduate School Completion Form and send it as a separate attachment.
  • Admission and Program Administration: The program will be administered by a Portfolio Program Chair and Portfolio Steering Committee;
    • Peter Stone, Computer Science (Chair)
    • Farshid Alambeigi, Mechanical Engineering
    • Joydeep Biswas, Computer Science
    • Sandeep Chinchali, Electrical and Computer Engineering
    • Ashish Deshpande, Mechanical Engineering
    • Ann Majewicz Fey, Mechanical Engineering
    • Nick Fey, Mechanical Engineering
    • David Fridovich-Keil, Aerospace Engineering & Engineering Mechanics
    • Jose del R. Millan, Electrical and Computer Engineering
    • Scott Niekum, Computer Science
    • Mitch Pryor, Mechanical Engineering
    • Luis Sentis, Mechanical Engineering
    • James Sulzer, Mechanical Engineering
    • Andrea Thomaz, Electrical & Computer Engineering
    • Ufuk Topcu, Aerospace Engineering & Engineering Mechanics
    • Yuke Zhu, Computer Science
  • Forms: 

 

Graduate Courses in Robotics

  • Core Courses for Robotics Portfolio Program
    1. ASE 389 Decision and Control of Human Centered Robotics (Sentis)​
    2. ASE 396 (CS 395T) Verification and Synthesis for Cyberphysical Systems (Topcu)
    3. CS 393R Autonomous Robots (Stone)
    4. CS 395T Visual Recognition (Graumman)
    5. CS 395T or CS 391R Robot Learning from Demonstration and Interaction (Niekum)
    6. ECE 382V Human Robot Interaction (Thomaz)
    7. ME 397 Introduction to Robot Modeling and Control (Sulzer)
    8. ME 397 Robot Mechanism Design (Deshpande)
    9. ME 397 Algorithms for Sensor-Based Robotics (Alambeigi)
    10. CS 391R Robot Learning (Zhu)
    11. ME 397 Haptics and Teleoperated Systems (Fey)
  • Approved Courses for Robotics Portfolio Program
    1. ASE 381P-1 Linear Systems (Akella)
    2. ASE 381P-6  Statistical Estimation Theory (Humphreys)
    3. ASE 381P-7 Advanced Topics in Estimation Theory (Zanetti)
    4. ASE 381P-12 System Identification and Adaptive Control (Akella)
    5. ASE 372N Satellite-Based Navigation (Humphreys)
    6. ASE 389 Modeling Multi-Agent Systems (Fridovich-Keil)
    7. CE 397 Control Theory for Smart Infrastructure (Bartos)
    8. CE 397 Sensors and Signal Interpretation (Claudel)
    9. CS 388 Natural Language Processing (Mooney)
    10. CS 391L Machine Learning (Ballard)
    11. CS 394N Neural Networks (Miikkulainen)
    12. CS 394R Reinforcement Learning: Theory and Practice (Stone)
    13. CS 395T Advanced Geometry Processing (Huang)
    14. CS 395T Applied Natural Language Processing (Baldridge)
    15. CS 395T Deep Learning Seminar (Krahenbuhl)
    16. CS 395T Scalable Machine Learning (Dhillon)
    17. CS 395T Human Computation and Crowdsourcing (Lease)
    18. CS 395T Graphical Models (Ravikumar)
    19. CS 395T Numerical Optimization for Graphics and AI (Huang)
    20. CS 395T Structured Models for NLP (Durrett)
    21. CS 384R Geometric Modeling and Visualization (Bajaj)
    22. CS 395T Topics in Natural Language Processing (Choi)
    23. CS 395T Spoken Language Technologies (Harwath)
    24. ECE 382V Technology for Embedded IoT (Valvano)
    25. EE 381V Online Learning (Shakkottai)
    26. EE 385V Brain-Computer Interaction (Millán)
    27. EE 381V Advanced Topics in Computer Vision (Wang)
    28. EE 382V Activity Sensing and Recognition (Thomaz)
    29. GEO 391 Computational and Variational Methods for Inverse Problems (Ghattas)
    30. INF 383P Introduction to Programming (TBD)
    31. INF 385C Human-Computer Interaction (Gwizdka)
    32. INF 385Q Knowledge Management Systems (TBD)
    33. INF 385T Human-AI Interaction (Lee)
    34. M 393C Fundamentals of Predictive Machine Learning (Bajaj)
    35. ME 383Q-2 Dynamics of Mechanical Systems (Neptune)
    36. ME 384Q-2 Nonlinear Control Systems (TBD)
    37. ME 384R-4 Geometry of Mechanisms and Robots (TBD)
    38. ME 385J-22 Musculoskeletal Biomechanics (Neptune)
    39. ME 397 Brain, Body & Robotics (Despande)
    40. ME 397 Cyber-vehicle Systems (Longoria)
    41. ME 397 Digital Control (Chen)
    42. ME 397 Estimation and Control for Ground Vehicle Systems (Wang)
    43. ME 397 Medical Device Design and Manufacturing (Rylander)
    44. ME 385J Rehabilitation Engineering (Sulzer)
    45. ME 392Q 9 Mechatronics II (Zhou)
    46. ME396P Application Programming for Engineers (Pryor)

Courses in robotics and related fields will change from year to year as may their availability. Thus students (and instructors) are welcome to contact their advisor or the steering committee concerning courses not on this list, but relevant to robotics.