MS Computational Data Science @ CMU (GPA 4.03) | ML Intern @ Honeywell | NeurIPS 2025 Datasets Track | Building scalable LLM inference engines & agentic systems
| Role | Key Impact | Tech Stack |
|---|---|---|
| Honeywell ML Intern (Jun-Aug 2025) | 2x faster sentence-transformer inference via ONNX + Kubernetes HPA; 35% better RAG w/ multimodal agent; Hackathon winner: 41% maintenance cost reduction | ONNX, vLLM, Kubernetes, MCP |
| CMU Research Assistant (Prof. Carolyn Rose, Jan-May 2025) | 40x speedup in image-guided code editing w/ QwenVL-32B agentic workflow; 150-instance dataset + SOTA baselines (0.7686 similarity) | vLLM, QwenCoder-32B, GPT-4o |
| UBS Software Engineer (Jul 2022-Jul 2024) | 87.5% faster doc processing w/ GPT-3.5 OCR/ETL; 60% data latency reduction via Kafka pipelines | OpenAI APIs, Kafka, Java |
Python • Java • SQL • C++ • Scala • R
MySQL • PostgreSQL • MongoDB • Redis
PyTorch • Hugging Face • vLLM • TensorFlow • ONNX
Kubernetes • Docker • AWS • Ray • LangChain
Git • SLURM • Helm • Terraform • FastAPI • Kafka
MLflow • Databricks • Modal • CUDA
CMU, Dec 2025 | PyTorch, vLLM, Kubernetes
300 concurrent requests on 2x A100s with dynamic batching from first principles. Task routing: Self-Consistency (MMLU), Tool-use (Graph), Greedy (InfoBench).
CMU, Dec 2025 | C++, CUDA, Python
PyTorch-like framework with custom CPU/GPU backends for sparse ops. Optimized efficient GNN training and inference.
NeurIPS 2025 Datasets Track | Python, Hugging Face, Ray
Benchmarked 8 data attribution methods across Pythia-1B to Llama-3.1-8B. Hugging Face leaderboard with 70% evaluation burden reduction.
Jupyter Notebook | vLLM, LangChain, FastAPI
Scalable multi-agent orchestration framework for complex workflows and distributed inference.
Carnegie Mellon University — MS Computational Data Science (Aug 2024 - Dec 2025)
- GPA: 4.0/4.0
- TA: Mathematical Foundations of ML, Interactive Data Science
- Coursework: LLM Methods, Deep Learning Systems, LM Inference, Cloud Computing
NeurIPS 2025 — Data Attribution Benchmark for LLMs (Datasets & Benchmarks Track) [Paper Link]
🔍 Open to ML Engineer roles in inference, distributed systems, & LLM infrastructure | Always excited to discuss deep learning systems and scalable ML solutions!
Last updated: December 2025