Milan Vojnovic, Professor of Data Science

Short bio, CV

Teaching

• ST443 Machine Learning and Data Mining (Autumn Term)
• ST456 Deep Learning (Winter Term)
• ST510 Foundations of Machine Learning (Winter Term)

Past teaching

• ST445 Managing and Visualising Data
• ST446 Distributed Computing for Big Data
• ST449 Artificial Intelligence and Deep Learning

Research

My current research focus is in the area of machine learning, including optimisation for machine learning, active learning algorithms, sequential decision making, online algorithms, bandit algorithms, and statistical inference.

I am interested in supervising PhD students. If you are interested, please do not hesitate to contact me.

My research has been supported by various industrial partners.

Research publications: Google Scholar

PhD students

• Yiliu Wang (completed in 2022)
• Jialin Yi (completed in 2023)
• Kaifang Zhou (completed in 2023)

Recent papers and preprints

• On the convergence of loss and uncertainty-based active learning algorithms, D. Haimovich, D. Karamshuk, F. Linder, N. Tax, and M. V., NeurIPS 2024
• An adaptive approach for infinitely many-armed bandits under generalized rotting constraints, J. Kim, M. V., and S. Yun, NeurIPS 2024
• Combinatorial bandits for maximum value reward function under value-index feedback, Y. Wang, W. Chen, and M. V., ICLR 2024
• Doubly adversarial federated bandits, J. Yi and M. V., ICML 2023
• Accelerated MM algorihms for inference of ranking scores from comparison data, M. V., S. Yun, and K. Zhou, Operations Resarch, 2023
• Test score algorithms for budgeted stochastic utility maximization, D. Lee, M. V., S. Yun, INFORMS Journal on Optimization, 2023