PhD & Before

NMR-Based Quantum Computing Simulation

Undergraduate thesis project: developed computational simulations of NMR-based quantum computing systems, modeling qubit dynamics and gate operations under realistic physical conditions.

Electronic Properties Modeling of 2D Materials

PhD thesis: modeled the electronic properties of two-dimensional materials including graphene and MoS2, investigating transport behavior, band structure, and interface effects using ab initio and tight-binding methods.

Kaggle Competitions

Competed in 14 Kaggle competitions across a range of domains — computer vision, NLP, time series, and tabular data — earning Competition Expert status with 2 silver and 2 bronze medals. Peak global rank: 666 out of 200,000+ competitors.

SambaNova Systems

PyPEF Parser Development

Developed a C/C++ parser for an internal binary format (PEF) during the first week at SambaNova, bridging it to Python via pybind. Delivered a merged PR by end of week one.

HIPNN Model Optimization

Led integration and optimization of the HIPNN machine learning model to run 3x faster on SambaNova hardware than on NVIDIA V100, coordinating across compiler, kernels, and AI framework teams.

PyPEF-Based Runtime Validation

Designed and built a Python-based dynamic PEF validator that proactively catches symbol mismatches before they reach hardware, eliminating system crashes that previously required reboots.

Gradient Sync Overlapping (GSO)

Optimized distributed training by overlapping gradient synchronization with compute via pipeline parallelism, achieving a 7% performance boost on BERT and GPT-3, leading to a US patent filing.

Modelbox Pipeline Bringup

Redesigned an unstable ML platform into a production-grade Kubernetes and gRPC-based deployment pipeline, enabling 10+ model deployments for Accenture and onboarding the first generative AI model.

Composition of Experts (CoE) Model Scaling

Transformed an experimental 150-expert LLaMA 7B Mixture-of-Experts proof-of-concept into a production-ready endpoint on SambaStudio, standardizing interfaces, artifact management, and deployment practices.

Dynamic Batching for Enhanced Hardware Utilization

Integrated dynamic batching into the app server for LLaMA 2 (7B and 70B), achieving notable improvements in Time-to-First-Token, throughput, and hardware utilization for a major customer release.

Artifact Dashboard for Streamlined Model Deployment

Built a self-service artifact dashboard in Next.js and Material UI that enabled product, PM, and SRE teams to independently track and manage model checkpoints, accelerating deployment workflows.

Modelbox Testing Infrastructure Improvement

Overhauled the modelbox testing framework with mock integration tests and Bazel-based caching, cutting app server + model integration test time from over an hour to seconds.

Transformers Version Compatibility Consulting

Unblocked a critical release stalled for several days by a Transformers 4.45.1 tokenizer regression — led root cause analysis, proposed a rollback to 4.43.2, and enabled on-time QA and release.

Argument Management with Hydra + Pydantic

Solved a growing argument management pain point across ML models by designing a Hydra + Pydantic hybrid configuration system, adopted as a best practice across the ML stack.

Amazon Web Services

Security Data Management Platform

Architected and implemented key components of a security data management platform to analyze data from AI products across AWS, enabling secure data handling, access control, and insights at scale.

AWS DevOps Agent Evaluation & Improvement

Drove evaluation infrastructure and performance and accuracy improvements for AWS DevOps AI agents, establishing robust benchmarks and identifying key areas for model and system-level gains.