About
I am a research scientist specializing in multimodal generative AI, large language models, and human-AI interaction, with experience building distributed training pipelines, scalable evaluation systems, and deploying production AI. I am seeking roles at physical AI companies where I can apply my expertise in multimodal perception, model reliability, and systems integration to real-world embodied AI products.
I am looking for roles at physical AI companies where I can apply my multimodal generative AI, human-AI interaction, and systems deployment expertise. I want to work on embodied or robotics-aligned products that require reliable, real-world AI systems.
Projects
01
Evaluation Framework for Multimodal LLMs
Developed an evaluation framework to measure reliability and hallucination in multimodal large language models, enabling systematic assessment across diverse inputs and reducing hallucination rates by 22%. The framework supported cross-functional deployment and informed model-safety improvements in production systems.
Python · PyTorch · Hugging Face · LangChain · AWS
02
Distributed Training Pipelines for Large Transformers
Designed and implemented distributed training pipelines for large-scale transformer models to accelerate experimentation and model scaling. The pipelines leveraged containerized infrastructure and orchestration to enable reproducible large-batch training and efficient resource utilization.
PyTorch · Kubernetes · Docker · AWS
03
Scalable Evaluation Pipelines for Generative AI
Built scalable evaluation pipelines used in internship work to process millions of generative AI samples daily, enabling large-scale benchmarking of retrieval-augmented generation and long-context reasoning approaches. Results were presented to senior research and engineering stakeholders to guide model development.
Python · Kubernetes · Docker · SQL
Experience
July 2028 – Present
Research Scientist
OpenAI
Led research on multimodal large language models and reasoning systems and developed 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 and interns on large-scale model experimentation and AI alignment practices.
Summer 2026 – Summer 2026
AI Research Intern
Google DeepMind
Built scalable evaluation pipelines for generative AI systems processing millions of samples daily and conducted experiments on retrieval-augmented generation and long-context reasoning. Presented experimental findings to senior research leadership and engineering stakeholders.
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 conferences including NeurIPS and ACL. Designed distributed training pipelines for large-scale transformer models using PyTorch and Kubernetes and collaborated on human-AI interaction studies.
Education
Aug 2023 – May 2028
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 (MIT)
GPA: 4.8/5.0