Building reliable AI systems.
I am an AI researcher and quantitative modeler specializing in machine learning, model validation, large language models, AI agents, verification, and neural routing architectures.
Selected Research & Key Projects
Core research initiatives, open-source toolkits, and publications in AI agents, verification, and neural routing.
Conference Presentations & Invited Talks
Research presentations, papers, and keynotes delivered at major artificial intelligence and data science conferences.
ACM Conference on AI and Agentic Systems
Paper Presentation: "The Verifier Tax: Safety-Success Tradeoffs in Tool Using LLM Agents"
Presented empirical research examining safety-success tradeoffs in tool-using LLM agent execution, introducing formal verifier tax metrics to mitigate agentic execution risks.
AI Engineer World's Fair
Workshop: "LLM Inference at Scale"
Conducted technical workshop on scaling LLM inference, memory optimization (KV Caching, PagedAttention), quantization, and high-throughput model serving infrastructure.
SciPy Conference
Conference Attendee & Mentor
Attended the SciPy Scientific Computing in Python conference and served as a mentor for a conference attendee in scientific computing and Python data science workflows.
ODSC AI West (Open Data Science)
Training Session: "Serving LLMs Efficiently: A Hands-On Journey from Single-GPU Basics to Distributed Inference"
Will be conducting hands-on training session covering efficient LLM serving, single-GPU optimization strategies, memory management (KV Caching, PagedAttention), quantization, and multi-GPU distributed inference pipelines.
North Carolina State University
Invited Speaker: "TanML Model Validation & Development"
Presented TanML automated model validation and development framework to graduate researchers and practitioners in the Master in Financial Mathematics Program Quant Workshop.