Maksim Anisimov

Maksim is a PhD candidate in Computer Science at Imperial College London, working on safe reinforcement learning: robustness and generalisation in deep reinforcement learning. He teaches there as a Graduate Teaching Assistant, covering reinforcement learning, operations research, and data structures and algorithms.

He holds an MSc in Econometrics from Erasmus University Rotterdam and a BSc in Mathematical Economics, with Data Science and Machine Learning, from the Higher School of Economics.

Maksim Anisimov is a PhD candidate in Computer Science at Imperial College London, in the Centre for Doctoral Training in Safe and Trusted AI. His research is on reliability and robustness of deep reinforcement learning, including safe policy updates. At Imperial he is also a Graduate Teaching Assistant for reinforcement learning, operations research, and data structures and algorithms.

During the PhD he spent five months as a Quantitative Research intern at Cubist Systematic Strategies, the systematic arm of Point72, doing machine learning research for systematic trading. Before the doctorate he was a Quantitative Developer at Aviva Investors, working on portfolio optimisation, systematic equity strategies and factor modelling in Python, and a Data Scientist at causaLens, working on machine learning and causal AI.

Earlier he completed a quantitative research internship at Robeco in Rotterdam, in the Quant Fixed Income team, and an MSc in Econometrics at Erasmus University Rotterdam. His undergraduate degree, from the Higher School of Economics, was in mathematical economics with data science and machine learning. He was a teaching assistant there in calculus, linear algebra and macroeconomics.

Lessons can cover machine learning, statistics, Python, optimisation, and the quantitative methods used in systematic investing.