Vikhyat Agarwal is an undergraduate researcher double-majoring in Mathematics and Computer Science at the University of Richmond, specializing in NLP, graph-based ML, and explainable AI. He applies rigorous mathematical foundations—differential geometry, information geometry, and statistical learning theory—to practical problems like argument mining and motion analysis, often designing custom GNN architectures and finetuning LLMs such as Llama 3 and Mistral 7B with LoRA. His projects span domains from computational astrophysics (SVM-based photometric redshift tools and a 100x code-port speedup) to robotics and few-shot object detection, reflecting a knack for bridging theory and engineering. Comfortable with PyTorch Geometric, ROS2, Hugging Face, and production tooling (Docker, WandB), he also leads small teams and supports campus tech services including a 3D printing lab. Colleagues note his ability to translate advanced math into implementable models and to spot system-level issues that improve reproducibility and performance.
8 years of coding experience
Bachelor of Science - BS, Mathematics and Computer Science, 4.0, Bachelor of Science - BS, Mathematics and Computer Science, 4.0 at University of Richmond
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