prospective students

I am actively looking for highly motivated individuals at all levels—including Ph.D. students, master’s students, undergraduates, and postdoctoral researchers—to join my research group at the University of California, Los Angeles (UCLA), Department of Mechanical and Aerospace Engineering (MAE).

If you are interested in joining the lab, please email me directly at with the subject line: "Prospective Student/Postdoc Inquiry: [Your Name]".

potential research overlap

I am looking for motivated PhD students interested in working at the intersection of control and estimation theory, machine learning, and applied mathematics. My research focuses on developing data-driven methods for decision-making in complex and uncertain systems, with a particular emphasis on geometric approaches to learning and optimization. With the rapid advancement of AI, this is an exciting time to study online data assimilation and decision-making under increasingly demanding performance requirements. Current research directions include policy optimization on nonlinear spaces, reinforcement learning, multi-agent coordination, and learning-based control without requiring full system models. The overarching goal is to develop principled, scalable algorithms for real-world systems where uncertainty and structure are fundamental—what I broadly refer to as trustworthy AI.

Students working with me will gain experience in both rigorous theoretical analysis and practical algorithm design, with opportunities to contribute to cutting-edge research and publish in leading venues. Ideal candidates have a strong background in mathematics, control, or machine learning, and are interested in bridging theory and applications to improve the safety and reliability of critical systems, such as commercial aviation, urban air mobility, etc. . If you are interested in how geometry and data can be combined to enhance decision-making in dynamic systems, I encourage you to reach out and explore potential research opportunities.

what to include in your email

To ensure I can properly assess the potential fit, your initial email should include the following attachments and information:

  • A concise cover letter (in the email body or as a PDF attachment) that clearly states:
    • The position you are applying for (Ph.D., M.S., Undergraduate Researcher, Postdoc).
    • Your specific research interests and how they align with my group's focus on Geometric Learning and Control, Data-Driven Decision-Making, and Mathematical Foundations of Machine Learning.
    • Your availability (e.g., intending to apply for Fall 2026 admission, seeking a summer 2026 internship, etc.).
  • Your Curriculum Vitae (CV) or Resume (PDF format).
  • Your Academic Transcripts (unofficial copies are fine for initial inquiry).
  • If applicable, a sample of your published or unpublished research work (e.g., a paper or thesis).

Due to the volume of inquiries, I may not be able to respond to every email individually. I will follow up if there is an opportunity that aligns closely with your background and interests.

prospective Ph.D. students

I am currently recruiting motivated Ph.D. students interested in Geometric Learning and Control, Data-Driven Decision-Making, and Mathematical Foundations of Machine Learning. I anticipate recruiting students primarily during the regular application cycle in December 2025 for Fall 2026 admission.

Applicants should possess a strong mathematical background and hold a B.S. or M.S. degree in a related field, including (but not limited to) Mathematics, Applied Mathematics, Computer Science, Electrical Engineering, Mechanical Engineering, Aerospace Engineering, or Systems Engineering.

Competitive applicants often have:

  • Robust mathematical foundations in analysis, control and estimation theory, differential geometry, stochastic processes, optimization, or machine learning.
  • Strong programming skills and experience with machine learning frameworks (e.g., PyTorch, JAX).
  • Prior research experience in topics like data-driven control and estimation, reinforcement learning, policy optimization, Riemannian optimization, and related areas.

master’s and undergraduate students

If you are currently affiliated with UCLA (or will be starting soon) and are interested in research, please email me directly as detailed above. We can discuss opportunities for independent study, capstone projects, or research assistant positions. Non-UCLA students interested in a summer research internship should also reach out via email by April 1st of the year they wish to intern.

postdoctoral scholars

If you are seeking a Postdoctoral appointment, please email me directly including all the required attachments listed above, and specifically include a 1-2 page research statement summarizing your past research, current expertise, and future research directions you propose to pursue that align with the group's focus on geometry-informed learning and control.