Research scientist working on multimodal language models, vision-language models, and model controllability. Currently a key researcher for the post-training stack for SLMs and VLMs at Adobe Research — shipping models that power grounding and retrieval in Adobe products.
I'm a Research Scientist at Adobe Research, where I am a key researcher in the post-training stack for small language models and vision-language models. My team's models serve grounding and retrieval use-cases across Adobe products. I also contribute to continuous image-editing models — see SliderEdit.
I completed my PhD at the University of Maryland with Soheil Feizi in the Center for Machine Learning. Before that, I spent two years at Microsoft AI as an Applied Scientist, with research stints at Microsoft Research (Cambridge & Redmond) and prior internships at Adobe.
My research sits at the intersection of understanding and control: how knowledge is stored and transferred inside multimodal models, and how to steer or edit those models with minimal intervention. Recent work spans mechanistic interpretability, post-training for VLMs, and large-scale reinforcement learning.
Full publication list on Google Scholar
Always happy to discuss research on multimodal models, post-training, interpretability, and the systems work that supports them. Reach out for collaborations, mentorship, or just to chat about ideas.