Research

Interests

ML-Powered Computer Vision3D Reconstruction & Human ModelingMulti-Person TrackingMaterials InformaticsBayesian OptimizationKnowledge Graphs

Publications

DataScribe: An AI-Native, Policy-Aligned Web Platform for Multi-Objective Materials Design and Discovery

Digital Discovery, 2026

Department of Materials Science and Engineering, Texas A&M University

AI-native cloud platform unifying experimental data through knowledge graphs, FAIR-compliant metadata, and multi-objective Bayesian optimization for closed-loop materials discovery workflows.

— Unifies experimental and computational data into a machine-actionable knowledge graph
— FAIR metadata capture and schema/units harmonization
— Uncertainty-aware surrogate modeling with human-in-the-loop governance
— Closed-loop multi-objective Bayesian optimization for materials discovery
Materials informaticsFAIR dataOntologyKnowledge graphActive learningBayesian optimizationHigh-entropy alloys
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TRAM-MP: Multi-Person Trajectory and Motion Reconstruction from Videos in the Wild

ArXiv Preprint, 2025

Divyanshu Singh (Thesis Research)

Feed-forward method for reconstructing global trajectories and body motions of multiple people from monocular videos. Achieves comparable error reduction in trajectory estimation compared to leading models with speedup via transformer-based depth estimation.

— Speedup in camera estimation via feed-forward transformer
— Error reduction in root trajectory estimation vs. leading models
— State-of-the-art accuracy on multi-person benchmarks
— Generalizes to diverse human activities beyond MoCap-trained scenarios
Computer Vision3D ReconstructionHuman Motion CaptureDeep LearningMulti-Person TrackingSLAM
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Efficient Parallelization of Point Cloud Registration: Spatial Indexing and Multi-Resolution

ArXiv Preprint, 2025

Divyanshu Singh

Demonstrates significant speedup in classical ICP algorithms through CPU parallelization, multi-resolution refinement, and spatial indexing without specialized hardware.

— MPI-based parallelization with KD-tree spatial indexing
— Dynamic multi-resolution adjustment for registration accuracy
— Significant speedup over base ICP implementation
— No specialized hardware required
Point CloudICPParallel ComputingMPISpatial Indexing3D Processing
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Current Work

As an MS student at Texas A&M, my research focuses on ML-powered computer vision for human modeling and 3D reconstruction in dynamic multi-person scenes. I also contribute to materials informatics research through the DataScribe platform, bridging AI/ML with materials science.