I am a final year PhD student at The Robotics Institute at Carnegie Mellon University advised by Michael Kaess. I’m interested in learning and optimization methods for robot perception — How should robots efficiently infer latent physical states from a stream of complex measurements to make better decisions?
In my Ph.D., I developed algorithms for learning deep observation models and optimizing with physics constraints in factor graphs. I have applied my work to real-world tactile manipulation and underwater navigation applications. I actively collaborate with Facebook AI Research and am a recipient of the CMU Presidential Fellowship.
|Mar '21||Our work on learning tactile models for factor graph-based estimation accepted at ICRA'21. Preprint available on arXiv.|
|Dec '20||Successfully completed my thesis proposal! Talk available here.|
|Dec '20||Excited to present our work on learning tactile observation models in factor graphs as a contributed talk at the WiML workshop, NeurIPS'20.|
|Aug '20||I spent a lovely summer at Facebook AI research working with Stuart Anderson and Mustafa Mukadam on tracking object states using tactile image sensors during robot manipulation.|
|Jun '20||Excited to present our work on incremental constrained smoothing (ICS) in factor graphs at ICRA'20. Check out our paper and talk!|
|Nov '19||Check out our work on online and consistent occupancy mapping with factor graphs at IROS'19 paper, IROS'18 paper.|