Thank you for your interest in our lab!
How to apply
- Prospective PhD students: I take new PhD students each year. I do not admit PhD students directly; if you are interested in a PhD position, apply to the CEE, SES (IDSS), and/or ORC PhD programs and list me in your application. I am also open to advising students from other programs with the right fit. I ask that you do not contact me directly with regard to PhD admissions until after you are admitted, as I will not be able to reply to emails from individual applicants.
- Prospective postdocs: If you are interested in a postdoc position, please read this form.
- MIT undergraduate or Masters students: If you are interested in research positions, please read this form.
- Potential interns and visitors: If you are not a MIT student and interested in visiting research positions, please read this form.
If you cannot view the form (because you don’t have a Google account), you may request an application by email with the subject heading “[Application Request]”. Please specify which application form you need.
If your case is not covered by the above, then consider cold emailing me!
I especially encourage those who are underrepresented in STEM to apply. I greatly value diversity and inclusion in STEM through teaching, mentorship, and outreach. We are a highly collaborative and welcoming lab with diverse backgrounds that come together to make the whole stronger than the sum of its parts. I strongly believe that diversity in perspective is necessary to solve the world’s most challenging problems.
Topic areas (last updated: Apr 2026)
We are recruiting in the following topic areas. For more information, see the listed work. We are open to applicants at all levels. If any of these topics excite you, please reference them in your application.
- AI for Optimization, especially machine learning for combinatorial optimization, including foundation models for optimization, learning-guided optimization, neural combinatorial optimization, and applications
- Deep Reinforcement Learning (RL), especially training reliability and generalization, including contextual RL, multi-task learning, and applications
- Traffic Digital Twins, especially for infrastructure decision support, including scalable traffic modeling, trajectory prediction-enabled traffic modeling, Large Language Model (LLM)-enabled traffic modeling, and applications
- Generative AI for Scientific Integrity, especially in transportation research, including using LLMs for reproducing studies, auditing research transparency, and supporting the creation of benchmarks