Research output per year
Research output per year
Personal profile
Author IDs
ORCID iD: 0000-0002-2180-0985
Scopus Author ID: 57201986411
Impact
Qualifications (Brief)
PhD (University of Warwick)
Biography
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.
Research Interests/Areas
- Computational Statistics
- Monte Carlo Methods
- Inverse Problems
- Data Assimilation
Expertise related to UN Sustainable Development Goals
In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This person’s work contributes towards the following SDG(s):
Fingerprint
Dive into the research topics where Neil Kumar CHADA is active. These topic labels come from the works of this person. Together they form a unique fingerprint.
- 1 Similar Profiles
Collaborations from the last five years
Recent external collaboration on country/territory level. Dive into details by clicking on the dots or
Research output
-
A statistical framework and analysis for perfect radar pulse compression
CHADA, N. K., PIIROINEN, P. & ROININEN, L., Apr 2025, (Online published) In: Inverse Problems and Imaging.Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
-
A Stochastic iteratively regularized Gauss-Newton method
Bergou, E., Chada, N. K. & Diouane, Y., Jan 2025, In: Inverse Problems. 41, 1, 015005.Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
Open AccessFile9 Downloads (CityUHK Scholars) -
Bayesian Deep Learning with Multilevel Trace-class Neural Networks
Chada, N. K., Jasra, A., Law, K. J. H. & Singh, S. S., 4 May 2025, arXiv.Research output: Working Papers › Preprint
-
Decoupling epistemic and aleatoric uncertainties with possibility theory
Hieu, N. M., Houssineau, J., Chada, N. K. & Delande, E., May 2025, Proceedings of the 28th International Conference on Artificial Intelligence and Statistics (AISTATS) 2025. Li, Y., Mandt, S., Agrawal, S. & Khan, E. (eds.). ML Research Press, p. 2899-2907 (Proceedings of Machine Learning Research; vol. 258).Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review
Open Access -
Sampling from Bayesian Neural Network Posteriors with Symmetric Minibatch Splitting Langevin Dynamics
Paulin, D., Whalley, P. A., Chada, N. K. & Leimkuhler, B., May 2025, Proceedings of The 28th International Conference on Artificial Intelligence and Statistics. Li, Y., Mandt, S., Agrawal, S. & Khan, E. (eds.). PMLR, Vol. 258. p. 5014-5022 (Proceedings of Machine Learning Research).Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review
Open Access