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.

Previously:
I completed my Master’s in Robotics at CMU working on 3D computer vision methods for plant phenotyping. I hold a Bachelor’s in Electrical Engineering from IIT Guwahati, India.


Recent News

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.