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I am a Schmidt AI in Science Postdoctoral Fellow at Cornell University, working in Prof. Andrew M Hein's lab. I am trying to merge insights and methods from nonlinear dynamics and machine learning to better understand biological complex systems.
I received my PhD in 2023 from Prof. Ying-Cheng Lai's research group on chaos at Arizona State University, where I received the Dean's Dissertation Award. During my PhD, I focused on applying machine learning techniques (such as reservoir computing) to problems in the fields of nonlinear dynamics and complex systems (such as anticipating dynamical transitions), and vice versa, studying problems in machine learning from a dynamic point of view (such as multistability and basin structure in RNN).
I'm on the faculty job market, and I'm always eager to discuss research — please don't hesitate to reach out!
News
2026.8 Our new paper, "Messaging strategies and the emergence of echo chambers in collective decision-making" by L.-W. Kong, N.E. Leonard, and A.M. Hein has been accepted by PNAS.
2026.2 Our new paper, "Unsupervised learning for anticipating critical transitions" by S. Panahi, L.-W. Kong, B. Glaz, M. Haile, and Y.-C. Lai has been published in Physical Review Letters.
2025.9 Our new paper, "A brief natural history of misinformation" by L.-W. Kong, L. Gallart, A.G. Grassick, J.W. Love, A. Nayak, and A.M. Hein is covered by The New York Times.
2025.6 Our new paper, "Effects of growth feedback on gene circuits: A dynamical understanding" by L.-W. Kong, W. Shi, X.-J. Tian, and Y.-C. Lai has been published in eLife.
2024.6 Our new paper, "Reservoir-computing based associative memory and itinerancy for complex dynamical attractors" by L.-W. Kong, G. Brewer, and Y.-C. Lai has been published in Nature Communications. It is featured as the editor’s highlight in AI and Machine Learning and Applied Physics and Mathematics, and listed in the special collection of Neuromorphic Hardware and Computing.
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