About Me

I’m a Principal Machine Learning Engineer at SambaNova Systems, where I work at the intersection of machine learning research and systems engineering. My work focuses on advancing large language models through post-training, long-context inference, and agentic AI, with an emphasis on translating novel research into production systems that operate at scale.

My research has directly influenced both industry and academia. Techniques I’ve developed power inference capabilities on SambaNova Cloud, while my publications have appeared at venues including ICLR, ISC, and ICDMAI. My work on Agentic Context Engineering (ACE) received widespread media coverage, earned multiple awards at ICLR 2026 workshops, and has been presented to enterprise audiences. I also serve as a reviewer for leading machine learning conferences.

I believe the most impactful AI research is the kind that survives contact with production. That’s why I’m drawn to problems where new algorithms must prove themselves under real workloads and not just on benchmarks. Whether developing more efficient inference techniques, advancing post-training methods, or building reliable agentic systems, I’m motivated by pushing the capabilities of frontier AI while delivering technology that people actually use.