I am an AI Research Scientist focused on building intelligent systems that are not only powerful, but also reliable, transparent, and useful in real-world settings. My research sits at the intersection of LLMs, multi-agent systems, and trustworthy AI, with recognition at venues including NeurIPS, AAAI, AIED, IROS, and ICRA, including a Best Paper Nomination at AIED 2025.

Right now, I am exploring how agentic AI systems can reason over complex tasks, coordinate across tools and roles, and remain robust under uncertainty. A major part of my work investigates how next-generation models can support high-quality assessment and instruction, especially in AI-augmented learning environments where reliability and fairness are critical.

Across NSF/IES-funded projects, I design and deploy transformer-based systems (BERT, GPT, LLaMA), build multi-agent workflows for planning and tool use, and evaluate model behavior through human-centered and task-grounded benchmarks. I am particularly interested in bridging fundamental model research with deployable systems that deliver measurable educational and societal impact.

My earlier work in distributed robotics and intelligent coordination (including IROS and RA-L publications) continues to inform how I think about collective intelligence, communication efficiency, and decision-making under constraints. This cross-domain perspective helps me connect ideas from robotics, learning sciences, and language intelligence into unified AI solutions.

I actively collaborate across disciplines and welcome partnerships with researchers, labs, and industry teams working on trustworthy LLMs, AI for education, multi-agent intelligence, evaluation, and responsible deployment. If our interests align, I would be excited to connect and co-create impactful research.