About
I am a multimodal generative AI researcher and Ph.D. candidate at Columbia University focused on LLMs, multimodal learning, and human–AI interaction. I build and evaluate large-scale transformer models and scalable training/evaluation pipelines to improve model reliability and reduce hallucinations, and I am now applying these methods toward biological AI. I have industry experience at OpenAI and a DeepMind research internship collaborating across product, safety, and infrastructure teams.
Looking Ahead
I apply multimodal generative AI and rigorous LLM evaluation methods to solve real-world problems. I am now focusing on translating these techniques to biological AI applications.
Skills
Languages —
Python,
C++,
Java,
SQL,
JavaScript
Frameworks —
PyTorch,
TensorFlow,
Hugging Face,
LangChain
Cloud & Tools —
AWS,
Docker,
Kubernetes,
Linux,
Git
Research Areas —
LLMs,
NLP,
Reinforcement Learning,
Computer Vision,
Multimodal Learning,
Generative AI,
Human-AI Interaction,
Knowledge Retrieval,
Document Understanding
Methods & Concepts —
Evaluation & Metrics,
Retrieval-Augmented Generation (RAG),
Long-context Reasoning
Systems & Infrastructure —
Distributed Training,
Scalable Evaluation Pipelines
Models —
Transformer Models
Safety & Alignment —
AI Alignment / Model Safety
Deployment & Production —
Production AI Systems
Professional Skills —
Mentoring,
Cross-functional Collaboration,
Research Leadership,
Experimental Research & Evaluation Design
Selected Projects
Multimodal Evaluation Framework to Reduce Hallucinations
Reduced hallucination rates by 22% and improved overall model reliability by implementing comprehensive evaluation frameworks that combined safety checks, automated metrics, and human-in-the-loop validation. Frameworks were integrated with production pipelines to enable continuous monitoring and informed model improvements.
Python · Hugging Face · LangChain · Docker · Kubernetes · AWS
Scalable Generative AI Evaluation Pipeline
Enabled processing of millions of samples daily to validate generative models by building scalable evaluation pipelines that supported experiments in retrieval-augmented generation and long-context reasoning. Delivered reproducible infrastructure used to surface findings for senior research leadership.
Python · Docker · Kubernetes · Linux · Git
Distributed Training Pipeline for Large-Scale Transformer Models
Accelerated research throughput and enabled training of large-scale transformer models by designing distributed training pipelines used in multimodal document understanding work that led to publications at NeurIPS and ACL. Pipelines supported efficient resource utilization and collaboration across graduate researchers.
PyTorch · Kubernetes · AWS
Experience
July 2028 – Present
Research Scientist
OpenAI
Improved model reliability and reduced hallucination rates by 22% through a suite of evaluation frameworks and production integrations. Achieved this by designing evaluation pipelines that embedded safety checks, partnering with product, safety, and infrastructure teams to deploy models in production, and mentoring researchers on large-scale experimentation and AI alignment practices.
Summer 2026 – Summer 2026
AI Research Intern
Google DeepMind
Enabled large-scale evaluation of generative AI by building pipelines that processed millions of samples daily to validate retrieval-augmented generation and long-context reasoning. Delivered reproducible experimental infrastructure and presented results to senior research and engineering stakeholders to inform model direction.
Aug 2023 – May 2028
Graduate Research Assistant
Columbia AI Research Lab (Columbia University)
Advanced multimodal document understanding and knowledge retrieval research, resulting in publications at NeurIPS and ACL. Supported this by designing distributed training pipelines for large-scale transformer models using PyTorch and Kubernetes and collaborating on human–AI interaction studies with faculty and peers.
Education
2023 – May 2028
Ph.D. in Computer Science
Columbia University
Research Focus: Generative AI, Multimodal Learning, Human-AI Interaction
2018 – May 2022
B.S. in Electrical Engineering and Computer Science
Massachusetts Institute of Technology (MIT)
GPA: 4.8/5.0