Join the Research Group
Join my new research group at Oklahoma State University–Tulsa working at the intersection of optimal control, machine learning, and autonomous aerospace systems.
If you would like to be considered for current or future opportunities, please use the form below rather than sending a general PhD inquiry by email.
Explore the Group
Research Opportunities
I welcome expressions of interest from prospective PhD students who would like to conduct research in the School of Mechanical and Aerospace Engineering at Oklahoma State University. My group is based at the OSU-Tulsa campus and focuses on trustworthy autonomy for aerospace systems.
PhD research in the group is shaped around the student’s background, interests, and the projects that are active when they join. I therefore encourage strong prospective students to introduce themselves even when I am not advertising a specific position.
Our central research question is:
How can autonomous aerospace systems learn, adapt, and make intelligent decisions while remaining safe, predictable, and worthy of our trust?
My group approaches this problem by combining the mathematical structure and guarantees of optimal control, optimization, and model-based guidance and control with the adaptability of machine learning and artificial intelligence. Rather than treating learning and classical control as competing approaches, we develop hybrid architectures in which they complement one another.
As a PhD student in my group, you will have opportunities to work across the full research pipeline:
Research Areas
Research in the group is intentionally not limited to a single predetermined project. A PhD student may work across one or more of the following interconnected themes, depending on their background, interests, and the projects active in the group.
1. Optimal & Uncertainty-Aware Guidance and Control
We develop computationally efficient methods for planning and control of aerospace systems operating under nonlinear dynamics, constraints, and uncertainty. A major goal is to move optimization from an offline design tool toward a capability that can support real-time autonomous decision-making.
Research directions include optimal control, trajectory optimization, lossless and successive convexification, Model Predictive Control (MPC), stochastic optimal control, covariance control, chance-constrained planning, and risk-aware guidance.
Potential applications span UAV navigation, advanced air mobility, spacecraft proximity operations, planetary and rocket landing, launch vehicles, and other safety-critical autonomous systems.
2. Learning-Enabled & Trustworthy Autonomy
Machine learning can give autonomous systems capabilities that are difficult to obtain from fixed analytical models, but black-box learning alone is often poorly suited to safety-critical aerospace applications. We investigate ways to integrate learning inside structured, model-based control architectures so that adaptability does not come at the expense of safety, interpretability, or constraint awareness.
Topics may include reinforcement-learning-enhanced MPC, physics-informed neural networks, imitation learning, learning-assisted trajectory optimization, safe learning, transfer and meta-learning, online adaptation, multi-agent autonomy, formal verification, and runtime assurance.
A recurring theme is to use learning where it is most valuable (e.g., to adapt optimization parameters, identify model mismatch, generate high-quality warm starts, or extract information from data) while retaining rigorous structure in the final decision-making process.
3. Experimental Autonomous Aerospace Systems
A major objective of the group is to take new autonomy and control methods beyond simulation and evaluate them on real experimental platforms.
One particularly exciting direction will be laboratory experiments that recreate spacecraft maneuvers and operations. The group plans to develop a spacecraft test platform that moves over an almost frictionless surface, allowing us to reproduce aspects of orbital motion inside the laboratory and test autonomous guidance and control algorithms on real hardware. These experiments will support research on spacecraft rendezvous, proximity operations, docking, formation flight, on-orbit servicing, and other autonomous space missions.
The group will also develop autonomous UAV platforms equipped with onboard computing and sensing for research in learning-based control, navigation, multi-vehicle coordination, and real-time autonomy. Planned capabilities include indoor motion-capture experiments, outdoor flight testing, and hardware-in-the-loop validation.
Selected work related to these directions
The following papers provide examples of research directions that I plan to expand in the group:
- Convex Optimization of Launch Vehicle Ascent Trajectory with Heat-Flux and Splash-Down Constraints — real-time-oriented convex trajectory optimization.
- Convex Approach to Covariance Control with Application to Stochastic Low-Thrust Trajectory Optimization — uncertainty-aware trajectory and feedback-policy design.
- Deep Learning Techniques for Autonomous Spacecraft Guidance During Proximity Operations — imitation learning and reinforcement learning for autonomous guidance.
- Reinforcement-Learning-Enhanced Model Predictive Control with Application to Autonomous Planetary Landing — hybrid learning and model-based control under uncertainty.
- Physics-Informed Pontryagin Neural Networks for Path-Constrained Optimal Control Problems — physics-informed learning for constrained optimal control.
More information about my broader research program is available on the Research and Publications pages.
From Theory to Experimental Validation
The laboratory is currently being established at OSU-Tulsa. Its planned capabilities will support research from computational development and simulation to experiments on real autonomous systems:
Computing & Simulation
High-performance computing for machine learning, numerical optimization, Monte Carlo analysis, and high-fidelity simulation of autonomous aerospace systems.
Autonomous UAVs & Flight Testing
UAV platforms with onboard computing and sensing for autonomous flight, learning-based control, navigation, and multi-vehicle coordination, supported by indoor motion-capture experiments and outdoor flight testing.
Spacecraft Maneuver Simulation
Laboratory platforms designed to recreate key aspects of spacecraft motion and operations on Earth, enabling experimental research in autonomous rendezvous, proximity operations, docking, formation flight, on-orbit servicing, and spacecraft guidance and control.
Because these capabilities are being developed as the group grows, students will have opportunities to help design, build, and test the platforms that support their research, under my supervision. This gives students the chance to shape the experimental infrastructure around their own theoretical and computational work and to carry new ideas from simulation to real-world validation.
Why OSU-Tulsa?
The positions are located at Oklahoma State University–Tulsa, not the Stillwater campus. The group is based in the Helmerich Research Center, near downtown Tulsa.
A major advantage of being based in Tulsa is the opportunity to connect the group’s research with OSU’s broader aerospace and advanced-air-mobility ecosystem. Through the Oklahoma Aerospace Institute for Research and Education (OAIRE), OSU brings together university researchers, government organizations, and industry partners across Oklahoma’s aerospace sector. Within this ecosystem, the LaunchPad Center supports advanced air mobility research, technology development, and entrepreneurship in the Tulsa region, while the Skyway Range provides infrastructure and expertise for research and testing of uncrewed systems.
For students working in autonomous aerospace systems, this creates opportunities to connect fundamental research with real platforms, flight testing, and industry-relevant problems.
Who Is a Good Fit?
I am interested in students who are curious, mathematically and technically strong, motivated to do research, and interested in developing into independent researchers.
There is no single ideal academic background. Relevant preparation may come from:
- Aerospace Engineering
- Mechanical Engineering
- Electrical or Computer Engineering
- Robotics and Autonomous Systems
- Computer Science
- Applied Mathematics
- Controls, Optimization, or related quantitative fields
An M.S. degree is preferred but not required. I also encourage strong bachelor’s-level students to get in touch if they have developed relevant experience through research, industry, internships, independent projects, or other technical work. A student who already brings a strong combination of skills in areas such as controls, optimization, machine learning, robotics, or autonomous systems may be an excellent fit even without a master’s degree.
You are not expected to have experience in every research area listed above. I am interested in building a group with complementary strengths. If your background is outside traditional aerospace engineering, I am especially interested in understanding how your expertise could contribute to autonomous aerospace systems. Strong candidates should not hesitate to express interest based solely on differences between their academic background and the traditional aerospace engineering path.
Particularly useful preparation
Any subset of the following can be valuable:
- dynamics and control;
- optimal control and numerical optimization;
- probability, estimation, or stochastic systems;
- machine learning and reinforcement learning;
- robotics or autonomous systems;
- numerical methods and scientific computing;
- programming in Python, MATLAB, C/C++, or related languages;
- simulation, embedded systems, sensors, or experimental hardware.
What matters most is not checking every box, but demonstrating strong fundamentals, research potential, intellectual curiosity, and the ability to learn independently.
Funding & Availability
Funding for PhD students in the group is typically provided through graduate research assistantships (GRAs) and/or graduate teaching assistantships (GTAs) when appointments are available. Assistantship packages and university benefits are determined by the specific appointment and may change from year to year.
Because this is a standing expression-of-interest page, submitting the form does not imply that a funded position is currently open or guarantee an assistantship. Availability depends on research funding, teaching needs, project timing, and the semester of entry.
If I see a strong potential match and there is a realistic opportunity to move forward, we will discuss the expected funding arrangement before you proceed with the formal OSU application.
Mentoring & Group Culture
Choosing a PhD advisor is about more than choosing a research topic. I want to build a group where students can pursue ambitious research while feeling supported, comfortable asking questions, and progressively confident in developing ideas of their own.
My mentoring philosophy emphasizes guided independence, freedom to explore, and work-life balance. I care about strong research and meaningful progress, but I also want students to enjoy this stage of their lives and develop into independent researchers without feeling that they have to navigate difficult problems alone.
Learn More About Mentoring & Group Culture
Express Interest in Joining the Group
If you are interested in joining the group in a future semester, I would be happy to learn more about you. The form below is my preferred way for prospective PhD students to contact me about research opportunities, because it allows me to keep expressions of interest organized and review them consistently.
This form is simply a way to introduce yourself, tell me about your background and research interests, and share your CV or other relevant material. It is not a formal application to Oklahoma State University, and it is not an application to a guaranteed open position.
I review submissions on a rolling basis. If your background and interests appear to align well with the group and there is a realistic current or upcoming opportunity, I may contact you to schedule an informal research conversation. Because the form remains open throughout the year, I may not be able to respond individually to every submission.
If we both feel that moving forward makes sense, I will then guide you toward the formal OSU PhD application and discuss the anticipated research direction, timing, and funding arrangement.
What to share
To help me get to know you, please include:
- CV or résumé — required.
- A short research-interest statement — entered directly in the form below. Introduce yourself, explain what interests you about the group, and describe how your background connects to one or more of the research directions above.
- Anything else you think may be helpful — optional. This could include an unofficial transcript, publications or preprints, a thesis or thesis abstract, project documentation, a portfolio, or other material that gives me a better sense of your experience.
There is no need to provide recommendation letters at this stage. Please do not include sensitive personal information that is not relevant to evaluating your academic and research background.
Tell Me About Yourself
Next Step: Formal OSU Application
If, after our research conversations, we both feel that the group could be a good fit and there is an appropriate opportunity, the next step will be the formal PhD application to Oklahoma State University through the Graduate College and the School of Mechanical and Aerospace Engineering.
The OSU application is separate from the expression-of-interest form above. The university’s requirements, deadlines, and admission policies may change, so the official pages below should be treated as the current source of information when you are ready to apply.