Yuri Kalnishkan

Yuri Kalnishkan is a Reader in Computer Science at the Department of Computer Science, Royal Holloway, University of London. He is affiliated with the Centre for Machine Learning and the Centre for Reliable Machine Learning. His research focuses on machine learning, prediction with expert advice, and regression methods. He holds a visiting researcher position at the Moscow Institute of Physics and Technology. His work contributes to UN Sustainable Development Goals related to innovation and infrastructure.

Key research areas include algorithmic prediction, online learning, and financial applications of machine learning. He has authored 47 research outputs and led two major projects: 'On-line Self-Tuning Learning Algorithms for Handling Historical Information' (Leverhulme Trust, 2013-2016) and 'PCP: Practical Competitive Prediction' (EPSRC, 2007-2010). His collaborations span international institutions and involve topics like portfolio optimization and equilibrium models.

His recent articles explore practical investment strategies, portfolio hedging, and theoretical bounds in prediction algorithms. While no awards are explicitly listed, his extensive publication record reflects significant scholarly contribution. His research emphasizes adaptive learning systems and their real-world applications.