Jonathan Scarlett

Jonathan Scarlett is an Associate Professor at the National University of Singapore (NUS), holding joint appointments in the Department of Computer Science, Department of Mathematics, and the Institute of Data Science. His research focuses on information theory, machine learning, and high-dimensional statistics, with contributions to topics such as group testing, Bayesian optimization, and coding theory. He has received notable awards including the NUS Presidential Young Professorship and MIT TR35 Innovators (Asia Pacific) recognition. Scarlett advises numerous PhD students, including Yang Sun, Zihan Li, and Arpan Losalka, whose theses address challenges in inverse problems, kernel-based optimization, and safety in Bayesian optimization.

His work spans theoretical foundations and practical applications, with recent publications exploring optimal group testing algorithms, error exponents in DNA storage, and robust bandit optimization. He leads collaborative projects on distributed statistical estimation and adversarial machine learning, leveraging interdisciplinary methods from signal processing and information theory. His academic journey includes a postdoc at the LIONS laboratory (2014–2017) before his promotion to Associate Professor in 2024.

Scarlett actively contributes to the academic community through editorial roles, conference organization, and over 100 peer-reviewed publications. His lab develops novel frameworks for handling uncertainty in data-driven systems, with applications to healthcare, communication networks, and autonomous systems.