Dhruv Meduri
Scientific Computing and Imaging Institute, University of Utah
I am a computer science researcher developing mathematical foundations and novel algorithms for scientific and computational problems. My work spans mathematical formulation, algorithm design, scalable implementation, and experimental validation, drawing on computational geometry, topological data analysis, machine learning, and visualization.
I collaborate with domain scientists to develop algorithms that address research problems in chemistry, neuroscience, and scientific simulations. I also bring experience in database systems, distributed computing, and performance optimization.
I am interested in research roles that combine mathematical depth with practical impact across domains.
Publications
Mapping Chemical Space: Topological Data Analysis of Chemical Latent Space with Mapper
Dhruv Meduri, Chuan-Shen Hu, Cong Shen, Kelin Xia, and Bei Wang. In 42nd International Symposium on Computational Geometry (SoCG 2026), LIPIcs, vol. 367, pp. 78:1–78:20. Schloss Dagstuhl – Leibniz-Zentrum für Informatik, 2026.
Visual exploration of chemical latent spaces through topological data analysis and deep learning.
Chemical space contains an immense number of possible molecules and materials, making exhaustive exploration infeasible. This framework combines topological data analysis with deep learning, using Mapper to reveal the organization of molecular representations learned by geometric deep learning models. It exposes clusters, linkages, and branching structures associated with molecular scaffolds, functional groups, chemical properties, and structural and functional evolution.
Jacobi Set Simplification for Tracking Topological Features in Time-Varying Scalar Fields
Dhruv Meduri, Mohit Sharma, and Vijay Natarajan. The Visual Computer, vol. 40, no. 7, pp. 4843–4855. Springer-Verlag, 2024.
Robust simplification of Jacobi sets for clearer and more reliable feature tracking in time-varying scalar fields.
Jacobi sets track where the gradients of two scalar fields align and, in time-varying data, trace the evolution of critical points. Because these sets are often complex and visually cluttered, this work introduces a robustness-based method for computing reduced Jacobi sets that retain structurally stable features. Mathematical analysis and experiments on synthetic and real-world datasets demonstrate clearer, more effective feature tracking.
Sheaves for Visual Encodings and Visual Fusion
A sheaf-theoretic foundation for constructing and analyzing coherent visual encodings and visual fusion.
Visual encodings map data attributes to perceptual channels, while visual fusion combines multiple channels within a shared frame of reference. This work uses sheaf theory to formalize both as coherent local-to-global constructions. Four case studies—including composite scientific visualizations, glyph-based scatter plots, and edge blending for graphs and hypergraphs—motivate a set of axioms for a rigorous, unified theory of visual fusion.