Arun Sharma

Arun Sharma

Ph.D., Computer Science

University of Minnesota, Twin Cities

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I build physics-informed AI systems that make spatial reasoning reliable for real-world deployment: detecting GPS spoofing for safer autonomous navigation, modeling energy-efficient vehicle routing, and downscaling climate data for coastal flood-risk assessment. My research fuses deep generative models, diffusion models, transformers, physics-informed learning, and geostatistical priors to address data incompleteness, distribution shift, and violations of physical laws in spatial machine learning.

I earned my Ph.D. in Computer Science from the University of Minnesota, Twin Cities, advised by Prof. Shashi Shekhar, with committee members Prof. Vipin Kumar, Prof. Ravi Janardan, and Prof. Ying Song. My dissertation is Distortion-Aware Spatial Data Science (Doctoral Dissertation Fellowship, 2022-2023). At Esri, I shipped a maritime anomaly-detection pipeline on AWS that improved classification accuracy from 55% to 73% and reduced API latency by 30%.

Actively seeking Postdoc, Machine Learning Engineer, and Research Scientist roles.

Research Statement Teaching Statement Diversity Statement Ph.D. Thesis

News

  1. [12/2025] One paper accepted in AAAI Workshop on AI to Accelerate Science and Engineering.
  2. [11/2025] Two oral presentations in ACM SIGSPATIAL 2025 in Minneapolis.
  3. [09/2025] Four papers accepted at ACM SIGSPATIAL 2025.
  4. [08/2025] One paper accepted in SSTD 2025.
  5. [07/2025] Successfully defended my Ph.D. dissertation.
  6. [02/2025] Two papers accepted at AAAI Bridge on Knowledge-Guided ML Workshop 2025.
  7. [12/2024] One paper accepted at SIAM Data Mining 2025.
  8. [11/2024] Oral presentation in ACM SIGSPATIAL 2024 in Atlanta, GA.
  9. [10/2024] NSF Travel Award for SIGSPATIAL 2024.
  10. [10/2024] One paper accepted at ACM Transactions on Spatial Algorithms and Systems.
  11. [09/2024] Two papers accepted at ACM SIGSPATIAL 2024.
  12. [08/2024] Invited poster presentation at Knowledge-Guided Machine Learning Workshop 2024.
  13. [06/2024] One paper accepted at COSIT 2024.
  14. [05/2024] Invited lightning talk and poster presentation in the AI-CLIMATE annual meeting.
  15. [12/2023] One paper accepted at SIAM Data Mining 2024.
  16. [08/2023] Completed my internship at Esri under Dr. Erik G. Hoel.
  17. [04/2023] Invited presentation at MIDAS Future Leader Summit at University of Michigan.
  18. [03/2023] One paper accepted in GIScience 2023.
  19. [11/2022] Oral presentation in ACM SIGSPATIAL 2022 in Seattle, WA.
  20. [10/2022] NSF Travel Award for SIGSPATIAL 2022.
  21. [09/2022] Oral presentation in COSIT 2022 in Kobe, Japan.
  22. [08/2022] One paper accepted in ACM SIGSPATIAL 2022.
  23. [05/2022] Received Doctoral Dissertation Fellowship 2022-2023.
  24. [04/2022] One paper accepted in COSIT 2022.
  25. [03/2022] One paper accepted in AGILE 2022.
  26. [09/2021] Oral presentation in GIScience 2021 online.
  27. [05/2021] One paper accepted at ACM Transactions in Intelligent System and Technology.
  28. [10/2020] Invited presentation at University of Maryland, College Park online.
  29. [06/2020] One paper accepted in GIScience 2021.

Selected Projects

Each project has its own page with the full paper rendered for the web, plus PDF, code, demo, and BibTeX.

Publications

Complete list, also on Google Scholar. Click a title to open the paper.

Education & Experience

Education

University of Minnesota, Twin Cities 2018 - 2025

Ph.D. in Computer Science

Advisor: Prof. Shashi Shekhar. Committee: Prof. Vipin Kumar, Prof. Ravi Janardan, and Prof. Ying Song. Dissertation: Distortion-Aware Spatial Data Science. Doctoral Dissertation Fellowship, 2022-2023.

State University of New York at Buffalo 2016 - 2018

M.S. in Computer Science

Graduate training in computer science before joining the University of Minnesota spatial computing and spatial data science research group.

Experience

Esri (Environmental Systems Research Institute) May 2023 - Dec 2023

Research Scientist Intern

  • Improved detection of route deviations and dark shipping from 55% to 73% accuracy with an end-to-end anomaly-detection pipeline using Transformer-based models, Evidential Deep Learning, AWS SageMaker, Lambda, ECS, and Step Functions on roughly 500M AIS records.
  • Reduced maritime route-query latency by 40% for real-time vessel tracking with a scalable Graph-based Traffic Representation and Association framework built on PySpark and GeoAnalytics APIs.
  • Cut model retraining time by 35% and API latency by 30% using model quantization, SageMaker Multi-Model Endpoints, Step Functions, SQS, CloudWatch, and CI/CD.

University of Minnesota, Twin Cities Aug 2018 - Aug 2025

Graduate Research Assistant

  • Led Pi-DPM, a physics-informed diffusion model for detecting GPS-spoofed and AI-generated deep-fake trajectories across maritime and urban domains.
  • Co-led Kriging-informed conditional diffusion for regional sea-level downscaling, turning coarse climate projections into fine-grained coastal risk maps.
  • Built GeoTrace-Agent, a multi-agent framework for auditable spatiotemporal reasoning over AIS feeds, OSM road networks, Copernicus weather, Sentinel imagery, and space-time-prism tools.
  • Designed Pi-GRPO, a physics-informed RL stack for trajectory generation and trajectory-reasoning policies with PPO, DPO, GRPO, vLLM-backed rollouts, and human-in-the-loop preference curation.

Teaching

Teaching assistant at the University of Minnesota (600+ students).

Contact

Email: arunshar at umn dot edu