Research topics
Quantum Simulation & Algorithms, Biophysics & Complex Systems
Contact: thiparat.c [AT] chula.ac.th
Office: MHMK Building Room 1905
About me: I’m a theorist working at the intersection of physics and computer science. Our current research aims to develop a fundamental understanding of learning algorithms and information processing systems that operate across diverse physical substrates, drawing insights from computational neuroscience, statistical physics, and quantum information science. I also work closely with industry partners, solving real-world problems using tools from network science and optimization theory.
Education & past employments
Faculty member, Department of Physics, Faculty of Science, Chulalongkorn University (2019-present)
Visiting scientist at EPFL, Lausanne, Switzerland (2025, 2023), NTU, Singapore (2025, 2024) & KITP, Santa Barbara, USA (2019)
Postdoctoral scholar, SUTD, Singapore (2016-2019)
Ph.D. in Theoretical Physics, Harvard University, USA (2016)
M.A. in Physics, Harvard University, USA (2012) & in Mathematics, The University of Virginia, USA (2010)
B.S. in Physics and Mathematics (with Highest Distinction, Phi Beta Kappa), The University of Virginia, USA (2009)
Outreach and academic services
Program committee: Quantum Techniques in Machine Learning (QTML2023-2024), Workshop organizer: Bangkok Workshops on Discrete Geometry, Dynamics & Statistics (link), Quantum industry conference organizer (link)
Reviewer: Physical Review Journals, New Journal of Physics, Quantum Science and Technology, Neurocomputing, AAAI, NeurIPS.
Media Partners: Suthichai Live (link), the Standard (link), Tam-Eig (link)
Quantum Ecosystem and Education by Techsauce Ep. [1][2][3][4][5]
BrainCodeCamp: AI and computational neuroscience for enthusiastic Thais (link)
Selected publications
Rungratsameetaweemana, N., Kim, R., Chotibut, T., Sejnowski, R, Random noise promotes slow heterogeneous synaptic dynamics important for robust working memory computation. Proc. Natl. Acad. Sci. U.S.A. 122 (3) e2316745122 (2025) (link)
Bielawski, J., Chotibut, T., Falniowski, F., Misiurewicz, M., and Piliouras, G. Heterogeneity, reinforcement learning and chaos in population games. Proc. Natl. Acad. Sci. U.S.A (In Press) [link]
Xiong, W., Facelli, G., Sahebi, M., Agnel, O., Chotibut, T., Thanasilp, S., and Holmes, Z. On fundamental aspects of quantum extreme learning machines. Quantum Mach. Intell. 7, 20 (2025) [link]
Sornsaeng, A., Dangniam, N., and Chotibut, T. Quantum Next Generation Reservoir Computing: An Efficient Quantum Algorithm for Forecasting Quantum Dynamics. Quantum Mach. Intell. 6, 57 (2024) [link]
Tangpanitanon, J., Saiphet, J.,…, Chotibut, T., Hybrid Quantum-Classical Algorithms for Loan-Collection Optimization with Loan-Loss Provisions. Phys. Rev. Applied 19, 064001 (2023) [industry collaboration with QTFT and KBTG] (link)
Pakornchote, T., Ektarawong, A., Chotibut, T. StrainTensorNet: Predicting crystal structure elastic properties using SE(3)-equivariant graph neural networks. Phys. Rev. Res. 5 (4), 043198 (2023) [link]
Tangpanitanon, J., Mangkang, C., Bhadola, P., Minato, Y., Angelakis, D., Chotibut, T., Explainable Natural Language Processing with Matrix Product States. New J. Phys. 24 053032 (2022) [featured on tensornetwork.org] (link)
Sornsaeng, A., Dangniam, N., Palittapongarnpim, P., Chotibut, T. Quantum diffusion map for nonlinear dimensionality reduction. Phys. Rev. A 104, 052410 (2021) (link)
Chotibut, T., Falniowski, F., Misiurewicz, M., Piliouras, G. The route to chaos in routing games: When is Price of Anarchy too optimistic? Advances in Neural Information Processing Systems 33 - NeurIPS 2020 (link)
Chotibut, T., Succi, S., and Nelson, D. R. Striated populations in disordered environments with advection. Physica A, 465, 500-514 (2017) (link)
Chotibut, T., Nelson, D. R. Population genetics with fluctuating population sizes. Journal of Statistical Physics: special edition dedicated to the memory of Leo Kadanoff, 167 (3-4) 777-791 (2017) (link)
Job Opportunities
I am looking for driven and motivated postdocs and students. If you're interested in joining our team, please send your CV and a two-page research statement to thiparat.c[at]chula.ac.th.