Maksym Tretiakov works as a Quantitative Developer at Scalable Capital in a market-making team, where he develops optimization and machine learning methods for trading and risk management.

He is also a Ph.D. Candidate in Statistics at LMU Munich under the supervision of Professor David Rugamer and is part of the Munich Uncertainty Quantification AI Lab (muniq.ai). His current research focuses on structured stochastic variational inference for Gaussian Process Latent Variable Models (GP-LVMs), with an emphasis on improving uncertainty estimation beyond mean-field approximations.

He is also a visitor at the ELPIS Lab, led by Professor Vincent Fortuin (ELPIS Lab).

Before this, he worked on research projects at Harvard Medical School (Kirschner Lab), where he applied machine learning methods to single-cell transcriptomics and contributed to a peer-reviewed publication in Developmental Biology.

You can find full details in the CV.