About
I am a research scientist and Ph.D. candidate with expertise in multimodal generative models, large language models, and human–AI interaction. I build evaluation frameworks, distributed training pipelines, and scalable systems to improve model reliability and deployment, and I am seeking roles that apply these skills to biological AI.
Looking Ahead
I am seeking roles in biological AI where I can apply my expertise in generative and multimodal models to biological problems and systems. I want to contribute to computational biology, drug discovery, or bioinformatics using scalable AI research and deployment practices.
Skills
Languages —
Python,
C++,
Java,
SQL,
JavaScript
Frameworks —
PyTorch,
TensorFlow,
Hugging Face,
LangChain
Cloud & Tools —
AWS,
Docker,
Kubernetes,
Linux,
Git
Research Areas —
Generative AI,
Multimodal Learning,
Human-AI Interaction,
Large Language Models (LLMs),
Natural Language Processing (NLP),
Reinforcement Learning,
Computer Vision
ML Engineering —
Distributed training,
Distributed training pipelines (PyTorch & Kubernetes),
Evaluation frameworks,
Scalable evaluation pipelines,
Retrieval-augmented generation (RAG),
Long-context reasoning,
Model deployment,
Hallucination mitigation / model reliability,
Large-scale model experimentation,
AI alignment
Professional —
Mentoring,
Cross-functional collaboration,
Academic publishing,
Presenting to stakeholders
Selected Projects
Model Evaluation Framework to Reduce Hallucinations
Developed an automated evaluation framework for multimodal LLMs that improved model reliability and reduced hallucination rates by 22%. The framework enabled standardized metrics and large-scale evaluation suites to support cross-functional deployment and continuous monitoring of production models.
Python · Hugging Face · LangChain · AWS · Docker · Kubernetes
Scalable Evaluation Pipeline for Generative AI
Built scalable evaluation pipelines that processed millions of samples daily to benchmark generative models and support retrieval-augmented generation and long-context reasoning experiments. The system improved throughput and enabled rapid iteration on model architectures and retrieval strategies.
Python · Kubernetes · AWS · SQL
Distributed Training Pipelines for Large Transformers
Designed and implemented distributed training pipelines for large-scale transformer models to accelerate research experiments and training at scale. The pipelines leveraged PyTorch and Kubernetes to manage distributed workloads and improve training efficiency.
PyTorch · Kubernetes · Docker · AWS
Experience
July 2028 – Present
Research Scientist
OpenAI
Led research on multimodal large language models and reasoning systems, developing evaluation frameworks that improved model reliability and reduced hallucination rates by 22%. Collaborated with product, safety, and infrastructure teams to deploy production AI systems and mentored junior researchers on large-scale model experimentation and alignment practices.
Aug 2023 – May 2028
Graduate Research Assistant
Columbia AI Research Lab (Columbia University)
Conducted research on multimodal AI systems for document understanding and knowledge retrieval, published at top-tier venues (NeurIPS, ACL), and collaborated on human-AI interaction studies. Designed distributed training pipelines for large-scale transformer models using PyTorch and Kubernetes to support large experiments.
Summer 2026 – Summer 2026
AI Research Intern
Google DeepMind
Built scalable evaluation pipelines for generative AI systems that processed millions of samples daily and ran experiments on retrieval-augmented generation and long-context reasoning. Presented experimental findings to senior research leadership and engineering stakeholders.
Education
Aug 2023 – May 2028
Ph.D. in Computer Science
Columbia University
Research focus: Generative AI, Multimodal Learning, Human-AI Interaction. Expected May 2028.
Sept 2018 – May 2022
B.S. in Electrical Engineering and Computer Science
Massachusetts Institute of Technology (MIT)
GPA: 4.8/5.0.