diff --git a/--verbose/commonmarker b/--verbose/commonmarker new file mode 100755 index 00000000..4aa1f9bc --- /dev/null +++ b/--verbose/commonmarker @@ -0,0 +1,27 @@ +#!/System/Library/Frameworks/Ruby.framework/Versions/2.6/usr/bin/ruby +# +# This file was generated by RubyGems. +# +# The application 'commonmarker' is installed as part of a gem, and +# this file is here to facilitate running it. +# + +require 'rubygems' + +version = ">= 0.a" + +str = ARGV.first +if str + str = str.b[/\A_(.*)_\z/, 1] + if str and Gem::Version.correct?(str) + version = str + ARGV.shift + end +end + +if Gem.respond_to?(:activate_bin_path) +load Gem.activate_bin_path('commonmarker', 'commonmarker', version) +else +gem "commonmarker", version +load Gem.bin_path("commonmarker", "commonmarker", version) +end diff --git a/--verbose/listen b/--verbose/listen new file mode 100755 index 00000000..ad54fd5c --- /dev/null +++ b/--verbose/listen @@ -0,0 +1,27 @@ +#!/System/Library/Frameworks/Ruby.framework/Versions/2.6/usr/bin/ruby +# +# This file was generated by RubyGems. +# +# The application 'listen' is installed as part of a gem, and +# this file is here to facilitate running it. +# + +require 'rubygems' + +version = ">= 0.a" + +str = ARGV.first +if str + str = str.b[/\A_(.*)_\z/, 1] + if str and Gem::Version.correct?(str) + version = str + ARGV.shift + end +end + +if Gem.respond_to?(:activate_bin_path) +load Gem.activate_bin_path('listen', 'listen', version) +else +gem "listen", version +load Gem.bin_path("listen", "listen", version) +end diff --git a/--verbose/racc b/--verbose/racc new file mode 100755 index 00000000..89167b45 --- /dev/null +++ b/--verbose/racc @@ -0,0 +1,27 @@ +#!/System/Library/Frameworks/Ruby.framework/Versions/2.6/usr/bin/ruby +# +# This file was generated by RubyGems. +# +# The application 'racc' is installed as part of a gem, and +# this file is here to facilitate running it. +# + +require 'rubygems' + +version = ">= 0.a" + +str = ARGV.first +if str + str = str.b[/\A_(.*)_\z/, 1] + if str and Gem::Version.correct?(str) + version = str + ARGV.shift + end +end + +if Gem.respond_to?(:activate_bin_path) +load Gem.activate_bin_path('racc', 'racc', version) +else +gem "racc", version +load Gem.bin_path("racc", "racc", version) +end diff --git a/--verbose/rake b/--verbose/rake new file mode 100755 index 00000000..8b69076f --- /dev/null +++ b/--verbose/rake @@ -0,0 +1,27 @@ +#!/System/Library/Frameworks/Ruby.framework/Versions/2.6/usr/bin/ruby +# +# This file was generated by RubyGems. +# +# The application 'rake' is installed as part of a gem, and +# this file is here to facilitate running it. +# + +require 'rubygems' + +version = ">= 0.a" + +str = ARGV.first +if str + str = str.b[/\A_(.*)_\z/, 1] + if str and Gem::Version.correct?(str) + version = str + ARGV.shift + end +end + +if Gem.respond_to?(:activate_bin_path) +load Gem.activate_bin_path('rake', 'rake', version) +else +gem "rake", version +load Gem.bin_path("rake", "rake", version) +end diff --git a/404.html b/404.html new file mode 100644 index 00000000..f2374f12 --- /dev/null +++ b/404.html @@ -0,0 +1,142 @@ + + + +
+ + +Oops! This page doesn’t exist.
+ +D. Fridovich-Keil is a recipient of a CAREER Award from the NSF’s CPS Program to study models and algorithms for large-scale, multi-agent, and uncertain cyber-physical systems.
+ +The long-term goal of this project is to build flexible models and efficient algorithms for large-scale, multi-agent, and uncertain cyber-physical systems. In settings such as traffic management, for example, practitioners face fundamental challenges due to complex dynamics, hierarchical influence, noncooperative actors, and hard-to-model uncertainty. Strong simplifying assumptions have become essential: for instance, many theoretical models of road networks take the form of static, deterministic, and/or aggregative games. In these instances, static assumptions make it possible to predict the aggregate impact of decisions such as tolling on traffic patterns. However, neglecting temporal dynamics and feedback effects can lead city planners to make myopic decisions, which may have unintended consequences as drivers adapt to one another’s behavior over time. This project develops theoretical and algorithmic techniques to address some of the underlying challenges and will also support mentoring of graduate and undergraduate researchers, development of undergraduate course material, and outreach to local underrepresented communities.
+ +This NSF CAREER project aims to develop a sound algorithmic basis for game-theoretic inference and design in dynamic and multi-agent cyber-physical systems. The specific goals of this project are threefold. The first goal is to formalize and solve a set of structural inference problems in noncooperative games that arise in transportation. For example, one such problem is to discover hierarchies of influence among decision-makers from observations of their actions. The second goal of this project is to design dynamic, time-varying mechanisms which influence agents’ decisions and induce desired outcomes. In transportation systems, these mechanisms correspond to tolls, bus routes, timetables, etc. The third and final goal considers stochastic variants of the aforementioned games and aims to develop a computationally-tractable theory of time-varying, feedback decision-making in these settings. This project will enable the analysis and design of cyber-physical systems which interact with one another in complex hierarchies and enable planners and regulators to guide these systems toward desired outcomes. Theory and algorithms will be validated in a physical laboratory testbed which emulates urban driving, via large-scale simulation of traffic in the city of Austin and using French air traffic management data.
+ +Most prior work in multi-agent cyber-physical systems is restricted to highly structured settings, such as static routing games in which rational actors possess full information. Yet, with the advent of (semi)autonomous traffic and app-informed route guidance, these assumptions have become brittle. This project emphasizes the dynamic, time-varying nature of cyber-physical interactions as a first-class citizen, and considers settings in which critical aspects of agents’ decision-making processes are unknown, open to external influence, and/or stochastic. This necessitates a careful analysis of time-varying incentive structures, feedback effects, and uncertainty representations, as well as an investigation of efficient numerical algorithms. This project will build fundamental frameworks for modeling, control, and mechanism design applicable to a wide range of cyber-physical systems, including transportation networks and smart grids, and ultimately pave the way for new and exciting inquiries into, e.g., multi-agent cyber-physical systems’ sensitivity to active deception or varied structures of hierarchical influence.
+ +This project has the potential to reduce fossil fuel consumption and improve transportation safety by improving the reliable flow of ground and air traffic. The innovations of this project can also extend beyond transportation, e.g., by making our energy grid more robust to environmental shocks. An educational objective of this project is to expose undergraduate and graduate students at UT Austin to the subtle and often unanticipated consequences of multi-agent interactions, ranging from pilot-induced oscillations to traffic jams. To that end, this project aims to build interdisciplinary connections between students and officials at the city of Austin’s expanded public transit network, Project Connect.
+ +This Q&A is divided into four main categories: Research, Advising, Funding, and Lab Culture. They are tailored to PhD students but can be relevant to Master’s students as well.
+ +What type of research do you like to do? Theoretical vs applied? Software vs hardware?
+ +I like to stay somewhere in the middle on the “theory vs application” spectrum. I have a hard time getting excited about something unless I can see some (mathematical) abstraction that explains why it works that way, but I also have a hard time staying excited for a long time unless I can see it working in a realistic application. I expect that each student will have their own preference for where to be on these spectra. The main thing from my perspective as an advisor is that regardless of your specific interests, you should have a strong background in mathematics and be comfortable connecting (your own and others’) abstract research ideas to (your own and others’) more concrete applications.
+ +What types of classes should I be thinking about taking? Any other topics you’d recommend I read up on in my first year?
+ +You are in the driver’s seat here, so at the end of the day it’s up to you! That said, I will strongly recommend that you become proficient in the following material, either by taking classes or by self-study: optimization (not just convex!), probability and stochastic processes, linear and nonlinear system theory. If you are interested in multi-agent systems, you should plan to take my graduate course on game theory in your second year (it presumes familiarity with optimization).
+ +Note that I have very explicitly not recommended that you jump right into a course on machine learning or reinforcement learning. My experience is that taking these courses before nailing down your fundamentals in optimization and control theory can be inefficient. If you are interested in these topics, I certainly encourage you to take courses on them, but you will get more out of those courses if you are already familiar with the fundamentals. Walk before you run.
+ +Besides classes, there are a lot of other really great resources out there you should be familiar with. Some particular favorites of mine: the matrix cookbook, MIT OpenCourseWare, 5 minutes with Cyrill, and Maths for Intelligent Systems.
+ +What kind of progress do you expect from me?
+ +Once you have (essentially) finished courses in your second year, you should be able to spend full cycles on research. In your first year, I expect that you will focus on taking classes, building your ability to read and present academic papers, and start to get your feet wet with research by working on an existing (or new!) research project in the lab. The first year will look different for different people, but the bottom line is that I expect that by the end of your first year, you should have mastered most of the essential skills you need to engage with research and passed your written qualifying exam. A good goal for all first year students is to submit a first-authored conference paper in the fall of second year (e.g., ICRA, ACC).
+ +In subsequent years, I expect that you focus almost all work-related effort on research and at any given time maintain a mix of projects you are “leading” and those you are “helping out with.” Every student’s journey will be different, though, so if you are not sure how things are going or how to plan your schedule, let’s talk.
+ +How many papers you expect from your students? What is the typical lifecycle of a project/paper?
+ +1-2 a year is a decent number. You should be able to hit that with absolutely no problem by your third year. More is better, if they are high quality. It is worse if they are getting in the way of you being able to think deeply about more significant research problems. By your third year, you should be able to go from “rough idea” to “submitted conference paper” in roughly 6-9 months. Obviously, this is something that you get more comfortable with over time and as you get more experienced. So, for example, I expect most people’s first paper to be based on work they start by the end of the first semester, and not get submitted until the end of the following summer. By your fifth year, you should probably be able to work on 3-4 projects every year, or more.
+ +What kind of projects do you see me working on?
+ +I want them to be mostly coming from you, and adapted based on our discussions. But you will be the driver. I hope that your projects will teach me new things!
+ +When working on new ideas, how do you know when it looks like a dead end, and how do you determine that?
+ +I think the secret is to just have multiple projects going on at any one time. Your first project is usually going to be something that I help suggest… and therefore it’ll almost certainly be “low risk” from a technical perspective. In future, you should have multiple of “your own” things going on at once so that you can bounce between them as you feel one of them hitting a snag, then come back when you want to.
+ +How do you recommend that students stay motivated and disciplined?
+ +Keep a normal 8-5 schedule. Don’t work too hard on the weekend (maybe just 4 hrs on a typical weekend once you have finished taking all your classes and are in “research-only” mode, more before then). Make sure you have multiple projects, with multiple different collaborators. That helps keep things exciting.
+ +How do you recommend balancing research with other commitments such as classes, teaching, and quals?
+ +Research should be your highest priority at the end of the day. The whole point of a PhD is to learn to be an independent technical thinker, and research is the way you build that skill. At times some commitments will take temporary priority, e.g., you need to make sure you study hard for quals a couple weeks before the exam in order to make sure you pass. However, it is important to keep your research progress in mind. For example, do not fall into the trap of automatically prioritizing homework assignments because they have a due date and research does not.
+ +Grades are a bit less meaningful in graduate classes than in undergraduate classes; typically, grad classes are graded very generously and are not intended to eat too much time that could be used for research. As a rough guideline, anything lower than an A- should start to sound some alarm bells.
+ +That said, you should absolutely be attending all of your classes and spending the time you need with the material in order to understand it. After all, the point of classes is to learn the material, not to spend the minimum amount of time in order to get a decent grade. In your first year, this will mean you have a bit less time for research. However, you should make sure that you regularly still carve out at least some significant blocks of time to do research.
+ +A final note: if you have difficulty balancing research with other commitments such as classes, do not be surprised if I recommend that you TA at least part time while you are finishing your classes. This will allow you to gain valuable experience presenting technical material during the semesters in which classes are impeding your ability to prioritize research. In this situation, we will discuss expectations regarding research productivity on a case-by-case basis.
+ +I just got reviews back on my paper. Now what should I do?
+ +First things first: it is very tempting to look at a negative review and think “Oh man, those reviewers were totally out to lunch and they missed the whole point of the paper because they were so incompetent. I can totally disregard what they have to say.” NO! Do not do this! Reviewers are doing a public service by reading our papers, and should always be thought of—not just treated—with respect. Even when you get a review that you don’t agree with or that demonstrates an egregious misunderstanding of the paper, you should think to yourself: “Ok. The reviewer is a very smart person who might have been reading this on a plane or in a hurry. They misunderstood something important about my ideas. How can I present the ideas more clearly to minimize the chance of a reader doing the same thing.” Of course, sometimes reviewers point out serious technical flaws; again, take these seriously and do not ignore or whitewash these issues. If it means removing an improper result or even retracting the submission, do it—better to find out before publication than afterward.
+ +Another perspective: when I review papers, part of me is trying to learn something new about the topic at hand, but part of me is also trying to save the authors from potential future embarassment, e.g., from incorrect results, poor literature review, theoretical issues, bad writing, …. Interpret reviewer comments accordingly.
+ +Now, if you need to prepare a rebuttal, a few pieces of advice:
+ +How do you prefer students to communicate with you?
+ +Effective communication is absolutely essential for us to work together well. Typically, we will have a 1-1 meeting for 30 minutes every week, although this may vary on a case-by-case basis. I expect that during this meeting we will check in on technical, administrative, and general life updates. When I was a PhD student, this was usually the high point of my week, and I hope it can be for you as well.
+ +A few other things to note: (1) I try to be very responsive on email and on Slack, but I find that these media are not particularly efficient for conveying technical ideas and are best used for quick messages like “I can’t make our meeting tomorrow” or “Can you take a look at my latest paper draft?” (2) While communication is good, too much communication can become inefficient and tiring. Please try to keep our communication channels clear so that each message remains important. (3) Relatedly, “ghosting” is never appropriate. I will not ghost you, and I expect you not to ghost me.
+ +What do you do when students are struggling? How do you know when to step in and help more vs. letting the student figure it out?
+ +I err on the side of letting students figure things out themselves, with only minimal technical pointers from me. A PhD is not about being taught by an advisor, at least not in the sense of learning lots of technical details from him/her. It’s about being advised to investigate interesting questions, and learning to find the resources and technical capabilities you need to figure them out. An advisor can help point, but is not the person who teaches those capabilities, at least not most of the time.
+ +Put another way: if a student will always be struggling without their advisor’s support, then the advisor has failed to train a self-reliant student who will be capable of success in the future. You are in charge of your own education, and I expect you to take the lead!
+ +How do you approach the writing process with students?
+ +Communication is probably the most important part of science and engineering. I expect students to develop excellent communication skills, both in writing and in speaking. I will happily look at (a bounded number of reasonably polished) drafts and give feedback, but you need to be proactive in writing early, soliciting feedback, revising, and repeating the loop as much as needed until you have a manuscript you are proud of. Please consult the lab writing guide for more details. For advice on giving talks, I strongly recommend watching Talks that Don’t Suck from Cyrill Stachniss.
+ +For collaborative projects, what is the expected level of contribution from everyone for the collaboration to work?
+ +It varies from project to project, and should be organic in my opinion. People should contribute as much as they want to, and if no one wants to contribute enough to make the project work, then the project was always going to be doomed. If someone is not contributing meaningfully, then they should politely tell their collaborators that they are either uninterested or do not have bandwidth to be a good collaborator. On the other hand, there can be tremendous value to having a collaborator who only contributes periodically but whose input helps direct the project in useful directions. Contributions come in many forms.
+ +The lab seems to be growing larger pretty quickly. How do you support everyone?
+ +With difficulty and much proposal writing. Also, I expect every student to TA at least two semesters during the PhD. This will vary depending upon funding needs, but TAing is a really important educational experience and provides a great way to practice technical communication. I found it essential as a graduate student.
+ +What do you see me doing during summer? Internships?
+ +Whatever you want, for the most part. There is a chance I might prefer you to intern certain summers if I am short $$. The sooner we talk about things the better.
+ +Would you help students enter the job market?
+ +Definitely!
+ +Do you anticipate funding to change during my time as a student?
+ +Yes, the source will definitely change. I will do my best to make sure it does not affect your research at all. However, I may ask you to attend meetings and present your work to sponsors; I did this many times as a PhD student and it can be a great way to get some face time with important people, meet potential collaborators, etc.
+ +Should I apply for fellowships?
+ +If you are eligible and have good grades, yes. A (partial) list of fellowships is: NSF GRFP, NASA NSTGRO, DOD NDSEG, DOD SMART, Hertz, Siebel, Microsoft, Google, NVIDIA, Apple. For a more comprehensive list, you can check out the one maintained by CMU.
+ +The lab is still relatively new; what are your plans for the lab for the future?
+ +I want to build a vibrant lab where ~6-10 PhD students are intellectually curious and self-motivated to study questions they find exciting at the intersection of optimization, control, games, etc. I want to learn new things from my students, and I want everyone to enjoy working together.
+ +How much are we expected to work on papers alone vs. with other colleagues/students? How would we compartmentalize tasks?
+ +You should have something — a “story” — that is your own, primarily. This will be your thesis. On the other hand, that “story” can and should intersect with others’ and you should collaborate to your heart’s content. For example, my “stor” was fast algorithms for N-player smooth dynamic games, with applications to human-robot interaction. I was the primary driver behind the algorithmic part for games, but literally every single paper had at least one other student coauthor, and in half/most I was not the first author. This was great. I would have quit the PhD if it had all been solo.
+ +What about travel and vacation?
+ +Vacation is important, and I trust you to take vacations when you need them. That said, remember that doing a PhD is about research, and research does not necessarily align with the university’s academic calendar. Be aware of upcoming project deadlines and plan your vacations accordingly. Also, I find that “working on vacation” is rarely as productive as anyone thinks it is going to be… so please think carefully when planning trips. Last, please remember to let me know when you will be out of town and cancel any 1-1 meetings that are scheduled in your absence.
+ +We are a group of scientists and engineers working at the intersection between robotics, control theory, machine learning, and game theory to design high performance, interactive autonomous systems. Our lab is based in the Department of Aerospace Engineering and Engineering Mechanics and is affiliated with Texas Robotics, the Center for Autonomy, and the Oden Institute for Computational Engineering and Sciences.
+ +Some of our lab’s core research interests are:
+ +Our lab aims to make a positive impact beyond research. We participate in a broad program of academic outreach organized through the Center for Autonomy, run hands-on events for local high school students, and mentor interested high school and undergraduate students.
+ +Our group started in August 2021. Visit our people page for profiles of lab members.
+ +Visit the Q&A page to learn more about what research is like in our lab.
+ +If you are a prospective graduate student, please feel free to reach out to David by email to express your interest. In your note, please identify one recent publication from the lab that you find interesting, and explain the technical nugget you found most exciting. Please also mention your favorite math class and the most exciting topic you learned in that class.
+ +Our lab also supports highly motivated undergraduate researchers. If you are an undergraduate student and would like to get involved, please understand that the vast majority of work we do is mathematically-oriented. To see if you might be a good fit, please do the following: (i) read the first two chapters of this textbook, and if you find the concepts exciting then (ii) email David to set up a time to chat about how you might get involved in the lab. At the meeting, be ready to explain key technical ideas you learned from your reading.
+ +This monograph accompanies the graduate class ASE 389: Game-Theoretic Modeling of Multi-Agent Systems at UT Austin. It provides a general overview of both static and dynamic games, with a focus on developing an optimization-oriented perspective for defining—and solving for—equilibria of games in which players have differentiable objectives and constraints.
+ +A preprint is available here, and below. If you find this monograph useful in your work, please cite it as follows:
+@book{SmoothGameTheory,
+ title={Smooth Game Theory},
+ author={David Fridovich-Keil},
+ year={2024},
+ url={https://clearoboticslab.github.io/documents/smooth_game_theory.pdf},
+}
+
+ Department of Aerospace Engineering and Engineering Mechanics
+ Oden Institute for Computational Engineering and Sciences
+ Center for Autonomy
+ Texas Robotics
+ The University of Texas at Austin
+
+
+
+ CLeAR Lab on GitHub
+ CLeAR Lab on YouTube
+ dfk@utexas.edu
+
caderade.a@gmail.com
+ CV (pdf)
+ (214) 918-7029
Office
+Anna Hiss Gym 2.204
+2501 Wichita St,
+Austin, TX 78712
Cade Armstrong is a third-year undergraduate student at The University of Texas at Austin. He is studying Aerospace Engineering with a minor in Computer Science. Cade’s main areas of interest include Orbital Mechanics, Optimization, Dynamics and Modeling, Control Systems, Computational Methods, and Game Theory. After his undergrad, Cade plans to pursue a master’s degree in Aerospace Engineering as well.
+ + leedh0124@utexas.edu
Office
+POB 5.200
+201 E 24th St,
+Austin, TX 78712
Dong Ho Lee is a PhD student at the University of Texas at Austin. Dong Ho’s research interests lie at the intersection between optimization, control theory and learning for autonomous multi-agent systems.
+ +Prior to UT Austin, Dong Ho served as a First Lieutenant (Research Officer) in the ROK army for 3 years in Daejeon, South Korea. He received his B.S. and M.S. in Aerospace Engineering at the Korea Advanced Institute of Science and Technology (KAIST).
+ + jake.levy@utexas.edu
Office
+Anna Hiss Gym 2.204
+2501 Wichita St,
+Austin, TX 78712
Jacob Levy is a Master’s and PhD student at the University of Texas at Austin. Jake is interested in developing algorithms for robotic systems which learn from past experiences to adapt to changing dynamics and unseen environments. This includes research in machine learning (ML), reinforcement learning (RL), and adaptation techniques which will enable effective real-world performance where only uncertain dynamics models of the system and environment are available.
+ +Prior to enrolling at UT Austin, Jake worked for 10 years at Parker Aerospace in Fort Worth, TX. His previous roles include Engineering Test Lab Manager and Test Engineer. Jake completed his B.S. in Aerospace Engineering at the University of Texas at Arlington.
+ + ryanjpark03@utexas.edu
+ CV (pdf)
+ (469)-661-6030
Office
+Anna Hiss Gym 2.204
+2101 Rio Grande St,
+Apt 8001
+Austin, TX 78705
Ryan Park is an undergraduate student studying Computer Science in the College of Natural Sciences as a Polymathic Scholar as well as Aerospace Engineering in the Cockrell College of Engineering. Ryan’s main interests lie in theory development; currnetly in using kernel methods in place of neural ODE’s.
+ +Besides helping with research, Ryan is also a part of the Undergraduate Computational Finance Club, a competitive club dedicated to learning and implementing trading strategies. Ryan has previously worked at Boeing as a software engineer, focusing primarily on improving sensor fusion algorithms.
+ +In his free time, Ryan enjoys rock climbing, cooking Korean food, and light reading.
+ + + yz009@utexas.edu
+ CV
Bryant Zhou was a Master’s student at the University of Texas at Austin in the Mechanical Engineering department, co-advised by Prof. David Fridovich-Keil and Prof. Takashi Tanaka. Bryant’s research interests include robotics, data-driven control, learning for control, control theory, and game theory. His motivations stem from the goal to achieve safety guaranteed autonomous systems.
+ +Prior to working with David, Bryant worked on ground vehicle dynamics and control, lane detection and following, and electric motor speed control for a year at UT. Bryant completed his undergraduate degree in Mechanical Engineering at Bucknell University in Pennsylvania. Bryant is now pursuing a PhD at Princeton in Mechanical and Aerospace Engineering.
+ + andriy_malyshchak@utexas.edu
+ CV (pdf)
+ (817)-808-0706
Office
+Anna Hiss Gym 2.204
+2501 Wichita St,
+Austin, TX 78712
Andriy Malyshchak is an undergraduate student studying Aerospace Engineering in the Engineering Honors College as well as minors in computer science, and business at the University of Texas at Austin. Andriy’s main interests lie in autonomous robot navigation/perception, numerical optimization for path planning, manipulating artifical intelligence/ML techniques to create control methods, and effective societal integration for new robot technology.
+ +Besides helping with research, Andriy is also a part of Antler Venture Capital’s fellowship program helping grow the next generation of U.S. startups, 1/35 Microsoft Campus Leads across the U.S., founding member of Texas Guadaloop’s business/engineering teams, and former software engineer for Tuk-Tuk, an Austin startup. Andriy hopes to pursue a Master’s in engineering as well as an MBA with the hope of working at the intersection between deep-tech and venture capital.
+ +In his free time, Andriy enjoys working out, volunteering in the community, and listening to great music.
+ + alopezz@utexas.edu
+ CV (pdf)
Office
+Anna Hiss Gym 2.204
+2501 Wichita St,
+Austin, TX 78712
Antonio Lopez is a Fulbright scholar from Mexico pursuing a Master’s degree at University of Texas at Austin. His interests include exploring the use of optimal control, control theroy and machine learning on autonomous systems, robot safety and spacecraft applications. +Antonio completed his BS in Mechatronics at National Autonomous University of Mexico (UNAM) where he worked in two nanosatellite projects called K’OTO and KuauhtliSat.
+ +In his free time, Antonio enjoys playing chess, hiking and visit museums.
+ + bbarkley@utexas.edu
+ CV (pdf)
Office
+Gates Dell Complex 3.504C
+2317 Speedway,
+Austin, TX 78712
Brett Barkley is a Computer Science PhD student at the University of Texas at Austin co-advised by David Fridovich-Keil and Amy Zhang. Brett’s research interest focuses on methods that promote waste minimization in the lifecycle of deep reinforcement learning algorithms, specifically the 3 Rs: reduce, reuse, recycle.
+ +Before enrolling at UT, Brett was an employee of the Johns Hopkins Applied Physics Laboratory where he was the sub and full-scale aircraft red team autonomy lead for DARPA ACE. Brett holds a BS and MS in Aerospace Engineering from the University of Maryland and a BS in Engineering Physics from Elon University. Outside of research, Brett enjoys being a hobbyist in brazilian jiu jitsu, playing video games with friends, and eating entirely too much H-E-B queso.
+ + dfk@utexas.edu
+ CV (pdf)
Office
+ASE 3.232, 2617 Wichita St
+Austin, TX 78712
Teaching
+ASE 330M: Linear System Analysis (Spring 2022, Spring 2023)
+ASE 389: Game-Theoretic Modeling of Multi-Agent Systems (Fall 2021, Fall 2022, Fall 2023)
David Fridovich-Keil is an assistant professor at the University of Texas at Austin. David’s research spans optimal control, dynamic game theory, learning for control, and robot safety. While he has also worked on problems in distributed control, reinforcement learning, and active search, he is currently investigating the role of dynamic game theory in multi-agent interactive settings such as traffic. David’s work also focuses on the interplay between machine learning and classical ideas from robust, adaptive, and geometric control theory. David completed his PhD under the supervision of Claire Tomlin at UC Berkeley and did a postdoc at Stanford University with Mac Schwager. During his PhD, David interned at Nuro, where he worked on motion planning and prediction. David is the recipient of an NSF Graduate Research Fellowship and an NSF CAREER Award.
+ + fernandopalafox@utexas.edu
+ Website
Fernando Palafox is a Ph.D. student at the Department of Aerospace Engineering at UT Austin.
+He is interested in building artificial intelligence that can reason about multi-agent interactions.
+His research leverages control theory, game theory, and machine learning.
+Learn more about his research here.
Fernando was born and raised in Mexico City and holds a B.S. and M.S. in Aerospace Engineering from the University of Colorado Boulder. +Outside of research, he enjoys reading and exercising. +Read more about him here.
+ + hamzah@utexas.edu
Hamzah Khan is a Master’s and Ph.D. student at the University of Texas at Austin in the Aerospace Engineering department and is advised by Professor David Fridovich-Keil. His interests span distributed control and planning, game theory, interpretability in learned systems, robot safety, and autonomous vehicles. He worked for three years in the self-driving vehicle industry at Uber ATG and subsequently Aurora Innovation. Hamzah completed his undergraduate degree at Harvey Mudd College in Southern California (Class of 2018).
+ ++ + + + David Fridovich-Keil +
++ + + + Xinjie Liu +
++ + + + Dong Ho Lee +
++ + + + Jacob Levy +
++ + + + Brett Barkley +
++ + + + Fernando Palafox +
++ + + + Hamzah Khan +
++ + + + Nick Strohmeyer +
++ + + + Tianyu Qiu +
++ + + + Jaehan Im +
++ + + + Kushagra Gupta +
++ + + + Ryan Park +
++ + + + Cade Armstrong +
+Who are they | +When were they here | +Where they went | +
---|---|---|
Antonio Lopez | +MS student in ASE (2021-2023) | +Pacific Northwest National Lab | +
Andriy Malyshchak | +Undergraduate student in ECE (2022-2023) | +Texas Robotics | +
Jonathan Salfity | +PhD student in ME (2021-2023) | +PhD student with NRG | +
Junette Hsin | +MS student in ASE (2022-2023) | +PhD student in HCRL | +
Vincent Spada | +Undergraduate student in ASE (2023) | +Flight dynamics, NASA Langley | +
Tyler Westenbroek | +Postdoc in Oden Institute (2023) | +Postdoc in CS at UW | +
Bryant Zhou | +MS student in ME (2021-2022) | +PhD student in MAE at Princeton | +
jaehan.im@utexas.edu
+ CV (pdf)
Office
+POB 5.204
+2317 Speedway,
+Austin, TX 78712
Jaehan Im is a PhD student at the University of Texas at Austin. His main research interest includes decentralized control of a multi-agent system, Interaction between humans and the autonomous system with special emphasis on the safety-critical system.
+ + j.salfity@utexas.edu
+ CV (pdf)
Office
+Anna Hiss Gym 2.204
+2501 Wichita St,
+Austin, TX 78712
Jonathan Salfity is a PhD student at the University of Texas at Austin, co-advised by Mitch Pryor in the Nuclear and Applied Robotics Group. Jonathan’s research covers robotic behaviors, control theory, and as it relates, Generative AI.
+ +Prior to enrolling at UT Austin, Jonathan worked for 4 years at HP Labs in Palo Alto, CA, where his research focused on mobile robots and manipulation for post-processing of 3D printed parts. Prior to HP Labs, Jonathan worked for 2 years in HP 3D-Print R&D on low-level control engineering in San Diego, CA. Jonathan completed his M.S. and B.S. in Mechanical Engineering at UCLA.
+ + jhsin@utexas.edu
+ CV (pdf)
Junette Hsin is a PhD student at the University of Texas at Austin. Junette’s research interests span orbital mechanics, estimation, optimization, and learning. After graduating from UC Davis with a Bachelors in Aerospace and Mechanical Engineering, Junette worked for 6 years on satellites at Maxar Technologies (formerly Space Systems/Loral). Her previous roles include Flight Engineer in the Mission Control Center and Dynamics and Controls Analyst where she designed control algorithms and simulated orbital trajectories for spacecraft.
+ + kushagrag@utexas.edu
+ CV (pdf)
Office
+POB 5.204
+2317 Speedway,
+Austin, TX 78712
Kushagra’s main goal is to work towards making robots boring - as ubiquitous as refrigerators! His main research interests lie at the intersection of learning, control and games for robotics. He is an ECE PhD student coadvised by David Fridovich-Keil, Ufuk Topcu and Sandeep Chinchali.
+ + nstrohmeyer@utexas.edu
+ CV (pdf)
Office
+Anna Hiss Gym
+2501 Wichita St,
+Austin, TX 78712
Nick Strohmeyer is a PhD student co-advised by Sriram Vishwanath in the WNCG Group. His research interests lie in the use of optimal control, machine learning, and game theory for modeling dynamical systems and the design of behaviorial planning algorithms. Prior to attending UT Austin, Nick worked as a Data Analyst in digital marketing and telcommunications for 3 years. He graduated with a B.S. in Mathematics from the University of San Francisco. In his free time, Nick enjoys playing recreational sports such as ice hockey, softball, and tennis and getting outdoors to hike or explore.
+ + tianyuqiu@utexas.edu
+ Personal Website
Office
+POB 5.200
+201 E 24th St,
+Austin, TX 78712
Tianyu Qiu is a first-year Ph.D. student in the Department of Aerospace Engineering and Engineering Mechanics, Cockrell School of Engineering at The University of Texas at Austin. He is advised by Prof. David Fridovich-Keil. His research interests lie in game theory, reinforcement learning and decision-making for multi-agent systems and robots.
+ +Prior to UT Austin, Tianyu received his M.S. degree in Electronic Information from the Department of Automation, School of Electronic Information and Electrical Engineering at Shanghai Jiao Tong University. His master thesis was on Social Navigation for Mobile Robots based on Inverse Dynamic Games. Tianyu obtained his B.S. degree in Electrical and Computer Engineering from Shanghai Jiao Tong University.
+ + vinspada@utexas.edu
+ Resume (pdf)
Office
+Anna Hiss Gym 2.204
+2501 Wichita St,
+Austin, TX 78712
Vincent Spada is a fourth year undergraduate student at the University of Texas at Austin studying aerospace engineering. As a student Vincent has interned at NASA Langley Research Center where he has developed multidisciplinary interests relating to wind tunnel and flight test experimentation with atmospheric vehicles. Specific interests include vehicle dynamics and modeling, system and sub-system controls, computational tool validation, system idendification, and experiment methodology development. Vincent plans on continuing to work at NASA Langley after graduating and plans on pursuing an M.S. degree in aerospace engineering with a focus in controls/robotics and a specialty in structures.
+ + xinjie-liu@utexas.edu
+ Website
Office
The Oden Institute, Peter O’Donnell Jr. Building (POB), 5.104
+201 E. 24th St,
+Austin, TX 78712
Xinjie is a first-year Ph.D. student in the Department of Electrical and Computer Engineering, Cockrell School of Engineering at The University of Texas at Austin. He is very fortunate to be co-advised by Prof. David Fridovich-Keil and Prof. Ufuk Topcu. His research interests lie in theoretical foundations and deployable practical decision-making and control strategies for autonomous systems in dynamic and uncertain environments. He is currently focused on intelligent, safe interactions of robots with other agents and efficient robot control policy learning.
+ +Xinjie obtained a master’s degree (the highest cum laude distinction) in Robotics from the Department of Cognitive Robotics (CoR) at the Delft University of Technology, Netherlands, where he was very fortunate to be advised by Prof. Javier Alonso-Mora. His master’s thesis was on game-theoretic motion planning for multi-agent systems. Before that, He received his bachelor’s degree in Automotive Engineering from Tongji University, Shanghai. During his senior year, he studied as a visiting undergraduate student at the Graz Univerisity of Technology, Austria.
+ +Keywords: robotics, optimization, control theory, dynamic game theory, reinforcement learning
+ +For metrics and citations, please refer to David’s Google Scholar profile.
+ +