Prof. Neil Chada received his master degree from University of Warwick, in 2014. He obtained his PhD in 2018 from the University of Warwick. From 2018 to 2020, he was a postdoctoral research fellow at the National University of Singapore. He then moved to the KAUST as a research fellow from 2020-2022. Before joining City University of Hong Kong in 2024, he worked as an assistant professor at Heriot Watt University.
Prof. Neil Chada's research is focused on the design of Monte Carlo methods within the interface of applied mathematics and computational statistics. Further details are available on his personal webpage https://sites.google.com/view/neilc/home.
Service in CityUHK
Administrative Assignment
- 2024 - 2025, MA Representative for CS Programme Committees, Department representative.
Recent Publications
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Paulin D.; Whalley P. A.; Chada N K.; Leimkuhler B. Sampling from Bayesian Neural Network Posteriors with Symmetric Minibatch Splitting Langevin Dynamics. (2025), International Conference on Artificial Intelligence and Statistics (AISTATS), (accepted).
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Nong Minh H.; Houssineau J.; Chada N K.; Delande E. Decoupling Epistemic and Aleatoric Uncertainties with Possibility Theory. (2025), International Conference on Artificial Intelligence and Statistics (AISTATS), (accepted).
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Bergou E.; Chada N. K.; Diouane Y. A Stochastic Iteratively Regularized Gauss-Newton Method. (2025), Inverse Problems, 41 (015005).
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Chada N. K.; Lang Q.; Lu F.; Wang X. A Data-Adaptive RKHS Prior for Bayesian Learning of Kernels in Operators. (2024), Journal of Machine Learning Research, 25, 1-37.
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Weissmann S.; Chada N. K.; Tong X T. The Ensemble Kalman Filter for Dynamic Inverse Problems. (2025), Information and Inference: A Journal of the IMA, 18(4).
Last update date :
24 Jan 2025
