Aashutosh A V
MS Computer Science, Georgia Tech
Graduate Researcher · ex-Microsoft
Research
I'm an MS Computer Science student at Georgia Tech, specializing in machine learning. My work sits between generative modeling, multimodal learning and ML systems, and it tends to share one theme: I care as much about how you measure a model as how you train it.
At C21U I work with Dr. Jeonghyun (Jonna) Lee and Dr. Rod Valente on autonomous LLM teaching agents for Socratic Minds, Georgia Tech's AI tutoring platform. Alongside the agents, I built the pipeline that codes student dialogue against the ICAP engagement framework, and the evaluation harness that checks it against human coders.
With Dr. James Hays I work on sparse-view novel view synthesis — building an evaluation framework for diffusion and feedforward models on ScanNet++, and probing temporal self-attention inside a view-synthesis diffusion model to find where its geometry breaks down.
Before Georgia Tech I was a Research Intern at Microsoft Research India, working on workload forecasting and placement optimization for Azure Cosmos DB, which led to a VLDB paper and a U.S. patent filing. I graduated from BITS Pilani with a CS degree and a minor in Data Science, where my thesis was on representation learning for vision-language models.
I'm currently looking for ML research and engineering roles. If you think there's a fit, or you just want to talk about any of this, please reach out.
Selected work
Autonomous LLM teaching agents running in Georgia Tech classrooms, with a retrieval-assisted coder that classifies student engagement from dialogue. Live ↗
A sparse-view novel view synthesis benchmark on ScanNet++, with a next-best-view selector that combines a Fisher-information proxy with the diffusion model's own attention entropy.
Quantile forecasting of peak load, an online bin-packing placement algorithm, and a simulator to benchmark placement policies before they reach production. Paper ↗
Publications
Patent
Workload Distribution Based on Projected Error Counts
An error-projection scheme that preemptively redistributes NoSQL workloads using forecasted error counts, to prevent QoS violations across distributed database clusters. Application 505804-US01.