About
I am a research scientist focused on multimodal generative AI, model evaluation, and human–AI interaction, with experience deploying production AI systems and reducing hallucination rates. A Columbia Ph.D. candidate (expected 2028) with publications at NeurIPS and ACL, I bring expertise in large-scale model training, distributed systems, and cross-functional collaboration and am pursuing roles in biological AI to apply these strengths to biological research and applications.
Goals
I am seeking roles in biological AI where I can apply my expertise in generative and multimodal AI, model evaluation, and human–AI interaction to advance biological research and applications.
Stack
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·
Large Language Models (LLMs)·
Natural Language Processing (NLP)·
Reinforcement Learning·
Computer Vision·
Human–AI Interaction·
Knowledge Retrieval·
Retrieval-Augmented Generation (RAG)·
Long-Context Reasoning·
Distributed Training·
Model Evaluation & Reducing Hallucinations·
AI Alignment
Professional Skills
Evaluation Pipeline Engineering·
Production Deployment of AI Systems·
Cross-functional Collaboration·
Mentoring & Team Leadership·
Experimental Design & Large-scale Experimentation·
Scientific Communication & Presentations·
Research Publication
Selected Work
Experience
July 2028 – Present
Research Scientist
OpenAI
Led research on multimodal large language models and reasoning systems, developed evaluation frameworks that improved model reliability and reduced hallucination rates by 22%, and collaborated with product, safety, and infrastructure teams to deploy production AI systems while mentoring junior researchers.
Aug 2023 – May 2028
Graduate Research Assistant
Columbia AI Research Lab
Conducted research on multimodal AI systems for document understanding and knowledge retrieval, published at top-tier conferences (NeurIPS, ACL), and designed distributed training pipelines for large-scale transformer models using PyTorch and Kubernetes while collaborating with faculty and peers on human-AI interaction studies.
Summer 2026 – Summer 2026
AI Research Intern
Google DeepMind
Built scalable evaluation pipelines for generative AI systems processing millions of samples daily, ran experiments on retrieval-augmented generation and long-context reasoning, and presented findings to senior research leadership and engineering stakeholders.
Education
Aug 2023 – May 2028 (expected)
Ph.D. in Computer Science
Columbia University
Research focus: Generative AI, Multimodal Learning, Human-AI Interaction; Dean's Fellowship.
2018 – May 2022
B.S. in Electrical Engineering and Computer Science
Massachusetts Institute of Technology
GPA: 4.8/5.0