معرفی
Pierre Nyquist is an Associate Professor in the Department of Mathematical Sciences at Chalmers University of Technology and Gothenburg University. Previously, he held a position at KTH Royal Institute of Technology. His research focuses on probability theory, mathematical statistics, and applied mathematics, with an emphasis on large deviations, stochastic numerical methods, and statistical learning theory. He is supported by grants from the Swedish Research Council, Wallenberg AI, Autonomous Systems and Software Program (WASP), and the Swedish e-Science Research Center (SeRC).
Education: Ph.D. in Applied and Computational Mathematics (2014, advisor Henrik Hult), postdoc at Brown University (2014–2016), and assistant professor at TU Eindhoven (2016–2020).
Research interests include large deviations theory, stochastic processes, gradient flows, and applications to machine learning and computational statistics. His work spans theoretical developments and practical methodologies, such as Monte Carlo algorithms and uncertainty quantification tools.
Recent activities include organizing seminars, supervising PhD students, and collaborating with industry partners. He has been awarded membership in the Young Academy of Sweden (2024–2029) and serves as a scientific ambassador for EURANDOM.
Key publications address topics like large deviations in stochastic approximations, Metropolis-Hastings algorithms, and neural entropy estimation. His teaching includes courses on statistical learning, probability, and Monte Carlo methods.
