Visit Leah Chong's website

Dr. Leah Chong is an Assistant Professor in the Walker Department of Mechanical Engineering at the University of Texas (UT) at Austin. Prior to joining UT in 2025, she was a postdoctoral associate in the Department of Mechanical Engineering at Massachusetts Institute of Technology. Dr. Chong received her Ph.D. and MS degrees at Carnegie Mellon University in 2022 and 2020, respectively, and a BS degree at Rice University in 2017, all in Mechanical Engineering. Throughout her academic career, Dr. Chong has been recognized for her contributions to research through ASME Design Theory and Methodology Best Paper Awards and Carnegie Mellon University's Milton Shaw Ph.D. Research Award and Presidential Fellowship.

Dr. Chong's research primarily focuses on human-AI collaboration in engineering design and human-centered engineering design, using a combination of quantitative and qualitative research methods such as computational modeling and human subject experiments. She is interested in understanding how the integration of computational tools/agents in the engineering design process affects the way designers think, feel, and make decisions, as well as exploring opportunities for complementary partnership between human designers and AI to enable effective and human-centered design. Some ongoing topics of research include: AI-assisted decision-making, qualitative design with AI, and artificial empathy.

Significant Publications

1. Chong, L., Raina, A., Goucher-Lambert, K., Kotovsky, K., and Cagan, J., 2023, “The Evolution and Impact of Human Confidence in Artificial Intelligence and in Themselves on AI-Assisted Decision-Making in Design,” ASME Journal of Mechanical Design, 145(3), pp. 031401. https://doi.org/10.1115/1.4055123
2. Chong, L., Zhang, G., Goucher-Lambert, K., Kotovsky, K., and Cagan, J., 2022, “Human Confidence in Artificial Intelligence and in Themselves: The Evolution and Impact of Confidence on Adoption of AI Advice,” Computers in Human Behavior, 127, pp. 107018. https://doi.org/10.1016/j.chb.2021.107018
3. Zhu, Q., Chong, L., Yang, M., and Luo, J., 2024, “Reading Users' Minds with LLMs: Mental Inference for Artificial Empathy in Design,” ASME Journal of Mechanical Design, pp. 1-38. https://doi.org/10.1115/1.4067527
4. Chong, L., Rayan, J., Lykourentzou, I., Dow, S., and Ahmed, F., 2024 “CAD-Prompted Generative Models: A Pathway to Feasible and Novel Engineering Designs,” ASME International Design Engineering Technical Conferences – Design Theory and Methodology Conference, Washington, DC, August 25-28, 2024. https://doi.org/10.1115/DETC2024-146325
5. Saadi, J., Yang, M., and Chong, L., “The Effect of Targeting Both Quantitative and Qualitative Objectives in Generative Design Tools on the Design Outcomes,” Research in Engineering Design, 35, pp. 409-425. https://doi.org/10.1007/s00163-024-00440-y