About
I'm a research scientist focused on multimodal generative AI, large language models, and human-AI interaction. I design evaluation frameworks and distributed training pipelines to improve model reliability and support production deployments. I collaborate cross-functionally and mentor junior researchers to translate research into robust, scalable AI systems.
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
Infrastructure —
Distributed training,
Scalable evaluation pipelines
Professional Skills —
Evaluation frameworks,
Model evaluation,
Model deployment,
Hallucination mitigation,
AI alignment practices,
Large-scale experimentation,
Cross-functional collaboration,
Mentoring,
Research publishing,
Presentation to stakeholders
Selected Projects
Evaluation Framework to Reduce Hallucinations
Built evaluation frameworks that improved model reliability and reduced hallucination rates by 22%, enabling safer multimodal LLM deployments. Designed evaluation metrics, test suites, and integration points with production pipelines to monitor and mitigate hallucinations across model families. Informed cross-functional safety and product decisions with quantitative results.
Python · PyTorch · Hugging Face · LangChain · Kubernetes
Scalable Evaluation Pipelines for Generative AI
Delivered scalable evaluation pipelines capable of processing millions of samples daily to accelerate research and validation of generative models. Engineered data ingestion, distributed processing, and result aggregation components to support large-batch experiments and long-context reasoning evaluations. Enabled faster feedback loops for research teams and supported presentation of findings to senior stakeholders.
Python · Docker · Kubernetes · AWS
Experience
July 2028 – Present
Research Scientist
OpenAI
Improved model reliability and reduced hallucination rates by 22% by designing and deploying evaluation frameworks. Drove multimodal large language model and reasoning system research that was integrated into production AI systems through close collaboration with product, safety, and infrastructure teams. Mentored junior researchers and interns on large-scale model experimentation and AI alignment practices while supporting cross-functional deployment.
Summer 2026 – Summer 2026
AI Research Intern
Google DeepMind
Enabled scalable evaluation of generative AI systems processing millions of samples daily by building robust evaluation pipelines. Advanced retrieval-augmented generation and long-context reasoning experiments that informed product and research directions, and communicated results to senior research leadership and engineering stakeholders. Supported production-grade experimentation practices to accelerate model evaluation at scale.
Aug 2023 – May 2028
Graduate Research Assistant
Columbia AI Research Lab (Columbia University)
Produced research on multimodal AI systems for document understanding and knowledge retrieval that led to publications at NeurIPS and ACL. Implemented distributed training pipelines for large-scale transformer models using PyTorch and Kubernetes to scale experiments and reduce iteration time. Collaborated with faculty and graduate researchers on human-AI interaction studies and designed reproducible, large-scale experiments.
Education
Aug 2023 – May 2028
Ph.D. in Computer Science
Columbia University
Research focus: Generative AI, Multimodal Learning, Human-AI Interaction. Expected May 2028.
2018 – May 2022
B.S. in Electrical Engineering and Computer Science
Massachusetts Institute of Technology (MIT)
GPA: 4.8/5.0.