Denis Fedoseev

Denis Fedoseev is a Senior Quantitative Researcher at WorldQuant with a PhD in Mathematics.3 He is passionate about mathematics and its applications, particularly in machine learning and data science.3 Fedoseev has a background in knot theory, Hamiltonian systems, and group theory.2 He has been applying geometric ideas and instructions to machine learning and data analysis for the past two years, working on projects to improve face recognition and anomaly detection in images.1

Research and Expertise

Fedoseev's work focuses on:

• Applying geometric methods to enhance machine learning algorithms1
• Exploring the use of differential geometry in data science1
• Investigating concepts such as manifolds, tangent spaces, Riemannian metrics, and Minkowski dimensions in the context of machine learning1

He has given talks on how geometry can help in data analytics and machine learning problems, explaining complex concepts like the Manifold hypothesis in data science.1