Jonathan SCARLETTAssistant Professor
Ph.D. (Information Engineering, University of Cambridge, 2014)
B.Eng. (Electrical Engineering, University of Melbourne, 2010)
B.Sci. (Computer Science, University of Melbourne, 2010)
- Artificial Intelligence
- Algorithms & Theory
- Machine Learning
- Information Theory
- High-Dimensional Statistics
Jonathan is an assistant professor jointly in the Department of Computer Science and Department of Mathematics, National University of Singapore. His research interests are in the areas of information theory, machine learning, and high-dimensional statistics. In 2010, Jonathan received the B.Eng. degree in electrical engineering and the B.Sci. degree in computer science from the University of Melbourne, Australia. From October 2011 to August 2014, he was a Ph.D. student in the Signal Processing and Communications Group at the University of Cambridge, United Kingdom. From September 2014 to September 2017, he was a post-doctoral researcher with the Laboratory for Information and Inference Systems at the École Polytechnique Fédérale de Lausanne (EPFL), Switzerland. He received the Singapore National Research Foundation (NRF) fellowship, and the 'EPFL Fellows' postdoctoral fellowship co-funded by Marie Curie.
- Information-theoretic limits of statistical inference and learning problems
- Robustness considerations in machine learning
- High-dimensional Bayesian optimization
- Algorithms and theory for large-scale graph learning
- Adaptive algorithms for group testing and pooled data
- Deep learning methods in signal processing and communications
Matthew Aldridge, Oliver Johnson, and Jonathan Scarlett, "Group testing: An information theory perspective," Foundations and Trends in Communications and Information Theory, Volume 15, Issue 3-4, pp. 196-392, Dec. 2019.
Jonathan Scarlett, "Tight regret bounds for Bayesian optimization in one dimension," International Conference on Machine Learning (ICML), 2018.
Ilija Bogunovic, Jonathan Scarlett, Stefanie Jegelka, and Volkan Cevher, "Adversarially robust optimization with Gaussian processes," Conference on Neural Information Processing Systems (NeurIPS), 2018.
Jonathan Scarlett, Ilija Bogunovic, and Volkan Cevher, “Lower bounds on regret for noisy Gaussian process bandit optimization,” Conference on Learning Theory (COLT), Amsterdam, 2017.
Volkan Cevher, Michael Kapralov, Jonathan Scarlett, and Amir Zandieh, "An adaptive sublinear-time block sparse Fourier transform," ACM Symposium on Theory of Computing (STOC), 2017.
Jonathan Scarlett and Volkan Cevher, “Limits on support recovery with probabilistic models: An information-theoretic framework,” IEEE Transactions on Information Theory, Volume 63, Issue 1, pp. 593–620, January 2017.
Awards & Honours
- Singapore National Research Foundation (NRF) Fellowship
- NUS Early Career Research Award
- CS5339: Theory and Algorithms for Machine Learning