I am a final year Ph.D. student at The Robotics Institute at Carnegie Mellon University advised by Michael Kaess. Currently, my research is supported by Facebook AI Research, where I am a Visiting Researcher. I am also a recipient of the CMU Presidential Fellowship (2018).
I’m interested in efficient inference from complex, partially observable sensor data sequences. I approach this problem by leveraging machine learning to extract salient information and sparse optimization to efficiently fuse such information. Currently, I work on learning deep energy-based models in sparse factor graphs for tactile image sequences. My long-term goal is to develop increasingly better computational models for perception that enable rich, human-like explanations of the world.
I have applied my work to diverse real-world robot applications with varying sensor modalities such as tactile images, underwater sonar data, and agricultural images.
|Aug '21||Check out the arXiV preprint of our recent work on learning energy-based models in factor graph optimization! Links: Paper+Code+Video.|
|Jul '21||Selected to the 2021 cohort of RSS Pioneers! Links: poster, statement.|
|May '21||Excited to present our paper on learning tactile models for factor graph-based estimation at ICRA'21. Links: Paper+Code+Video.|
|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||Spent a lovely summer at Facebook AI research 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.|