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)
- Algorithms & Theory
- Artificial Intelligence
- Machine Learning
- Information Theory
- High-Dimensional Statistics
- Information-Theoretic Limits of Data Science Problems
- Rigorous Inference, Learning, and Optimization Algorithms
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 Cambridge Australia Poynton International Scholarship, and the 'EPFL Fellows' postdoctoral fellowship co-funded by Marie Curie.
- Information-theoretic limits of statistical inference and learning problems
- Off-the-shelf Bayesian optimization
- Active learning algorithms for graphical model selection
- Adaptive algorithms for group testing and pooled data
- Variations of the information bottleneck method in machine learning
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.
Jonathan Scarlett and Volkan Cevher, “On the difficulty of selecting Ising models with approximate recovery,” IEEE Transactions on Signal and Information Processing over Networks, Volume 2, Issue 4, pp. 625–638, July 2016.
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.
Ilija Bogunovic, Jonathan Scarlett, Andreas Krause, and Volkan Cevher, “Truncated variance reduction: A unified approach to Bayesian optimization and level-set estimation”, Conference on Neural Information Processing Systems (NIPS), Barcelona, 2016.
Awards & Honours
- EPFL Fellows post-doctoral award co-funded by Marie Curie
- Cambridge Poynton Australia Scholarship
- CS3236: Introduction to Information Theory