I have received the ultimate honor!!!

In 2010, I came to the U.S. straight from undergrad for a PhD. Fifteen years later the map looks messy, but the line of best fit is clear. 🤍 2010 — PhD Year 1: my advisor said, “Take the ML course.” I had never heard of ML. With the most supportive, inspiring advisor, I pivoted from electronic communications and cognitive radio to MRFs, graphical models, and generative models—grounded by a solid foundation in signals and probability theory. Year 3 – we moved into tensor methods. I went all in on unsupervised learning and spectral methods. Before graduation – a year of internships across MSR Boston and Redmond on AI for healthcare. I was so lucky to work with the best researchers as mentors. But biology humbled me. Long nights, protein and cell-slice data, multithreaded pipelines. Progress crawled and the spark dimmed. I chose to keep a CS spine: applications are welcome when they sharpen the core. Fresh out of grad school – I joined UMD faculty,then deferred a year to do a postdoc at MSR NYC. Online learning and RL paradise, ego check included. While others shipped papers, I went back to RL textbooks and rebuilt foundations in learning theory. Back at UMD – I aimed for pure theory. Reality steered me to trustworthy AI, especially RL robustness. I also faced a truth: I missed the 2014 deep learning wave. That stung. It changed how I work. 2023 – LLMs arrived like a tide and I didn’t want to miss the wave again. I read restlessly, rallied my students, we pivoted and shipped. 2023 → now – we’re building toward foundation models for robotics: SMART, TACO, Premier-TACO, PRISE, Make-an-Agent, TraceVLA, FLARE, IVE, and more. The timing feels right. We’re going deep on robotics and physical intelligence. 🤖 What I’ve learned: careers have seasons. Adjust, ride the tide, and do not let the next wave leave you on the beach. If you’re mid-pivot too, I’m rooting for you — happy to swap notes. ✨ #Robotics #LLM #EmbodiedAI #PhysicalIntelligence #UMD #AcademicLife #ResearchJourney