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
Each project has its own page with the full paper rendered for the web, plus PDF, code, demo, and BibTeX.
Complete list, also on Google Scholar. Click a title to open the paper.
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.
Esri (Environmental Systems Research Institute) May 2023 - Dec 2023
Research Scientist Intern
University of Minnesota, Twin Cities Aug 2018 - Aug 2025
Graduate Research Assistant
Teaching assistant at the University of Minnesota (600+ students).
Papers and books I am reading, with short notes and a relevance rating (out of 5). Click a title for my notes; "source" links to the original.
The canonical Brunton-Kutz textbook tying SVD, sparsity, ML, dynamical systems, control, ROMs, and physics-informed ML into one toolkit.
A first-principles, roofline-driven playbook for scaling Transformer training and inference across thousands of TPUs (and GPUs).
Kevin Murphy's 250-page modern RL monograph: value/policy/model-based, multi-agent, and a deep LLMs-and-RL chapter (RLHF, RLVR, PPO/GRPO/DPO).
A remote-sensing super-resolution benchmark that scores SR models by downstream task utility, not just PSNR/SSIM.
notes · notes (v1) · source
How CockroachDB delivers serializable, geo-distributed SQL transactions on commodity clouds without atomic clocks.
Theory + algorithm for scheduling LLM queries under prefix-cache reuse and TTFT limits; k-LPM cuts P99 latency vs FCFS/LPM.
LinkedIn's playbook for shrinking a 100B+ RecSys LLM 20x via distillation, structured pruning, and FP8 quant, then serving it fast.
Survey reframing code as the operational "harness" for LLM agents: reasoning, acting, state, verification, and multi-agent coordination.
Finds and fixes a GPU race condition in ZeRO++ hpZ that silently breaks 40B-70B LLM training on low-bandwidth clusters.
Google's native multimodal embedding model: one Gemini-initialized encoder maps text, image, video, and audio into a shared vector space.
1M-sample VQA benchmark probing whether vision-language models recognize dishes and origins across 30 languages and 189 countries.
Email: arunshar at umn dot edu