I’m a Generative AI researcher and design technologist working where machine learning meets spatial reasoning and human creativity. I build end-to-end systems—ML pipelines, model training, and intuitive interfaces—that turn complex planning and design problems into elegant, scalable tools. My practice spans VLM- and diffusion-driven co-pilots, reinforcement-learning agents, 3d generative models and multimodal workflows that connect geometry, simulation, and evaluation.

I care as much about usefulness as I do about novelty: human-in-the-loop feedback, custom evaluators, clear metrics, and reproducible experiments are baked into my process. I’m equally comfortable prototyping inside design toolchains and production-ready services, bridging computational design environments with modern ML stacks.

At Carnegie Mellon University and Studio.Compute, I’ve led cross-disciplinary teams, mentored emerging researchers, and delivered interactive tools for automation, decision support, and immersive XR/VR. Whether the brief involves augmenting creativity, spatial planning, or 3D generation workflows, my goal is the same: amplify designers’ judgment and accelerate high-quality outcomes. This portfolio documents that journey—and my commitment to expanding what’s possible with generative AI.