Aritra Guha
Papers
3
Total Citations
11
H-Index
2
About
Aritra Guha is a researcher whose work bridges the frontiers of unsupervised learning and functional data analysis, with a particular emphasis on modeling complex, high-dimensional interactions. His key research areas include robust machine learning, optimal transport theory, and the analysis of spatiotemporal systems. Guha’s major contributions are twofold. First, in his 2022 work on "Robust unsupervised learning of temporal dynamic vehicle-to-vehicle interactions" (7 citations), he developed novel methods to infer the latent, evolving relationships between moving agents, a critical step for autonomous driving and traffic flow optimization. Second, his pioneering 2021 papers on "Functional Optimal Transport" (each with 2 citations) introduce a groundbreaking formulation of the optimal transport problem for distributions on function spaces. By representing the stochastic map between functional domains as an infinite-dimensional Hilbert-Schmidt operator, Guha provides a powerful new framework for domain adaptation and mapping estimation in functional data, opening avenues for analyzing curves, surfaces, and other complex objects. Though early in its impact, this work establishes a rigorous mathematical foundation for transferring knowledge across functional datasets, marking Guha as a rising innovator at the intersection of statistical theory and practical, dynamic systems.
Research Focus
Key Achievements
Top Papers
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