Milos Tomic

Machine Learning Expert

Biography

Milos Tomic studied Mathematics & Statistics at University of Warwick where he focused on mathematical analysis (PDE’s), probability theory, stochastic analysis, and Bayesian statistics. He currently works at a Quantitative Hedge Fund as a Quantitative Researcher (Quant). At his work he researches and builds sophisticated trading strategies by implementing stochastic and machine learning models (ML) for statistical arbitrage. Previously he was a Data Scientist building ML models from massive datasets that were used to increase the volume of mortgage originations.

His main research areas are:

Machine Learning - especially kernel methods and generalisations to infinite-dimensional spaces, and neural networks.
Stochastic Analysis
Partial Differential Equations (PDE’s)
Dimensionality Reduction
Hawkes Processes

He has a strong experience in:

Python
R Language
C++

Projects Worked On

Statistical arbitrage of financial instruments using stochastic methods and mean-reversion models.
Modelling volatility via Computational Stochastic Differential Equations.
Clustering financial time-series based on risk.
Built a custom unsupervised ML model that was trained by massive datasets, such as UK’s mortgage search engine results. This resulted in optimised mortgage rates that increased company’s volume of originations.
Research includes generalising the Tikhonov regularisation to infinite-dimensional spaces and thus enabling its use in kernel-based methods in Machine Learning.
Worked on cutting-edge pattern recognition in data.