I am an M.S. student in Computer Science at Stanford University, being funded by the NSF Graduate Research Fellowship Program. Within computer science, I am most interested in machine learning and applications to computational biology. I am especially interested in the probabilistic graphical model framework. I think the `deep' generative neural network models is conceptually fascinating. In biology, I am interested in the problem of fusing data from various assays to decipher regulatory networks.
I am currently working in Professor Serafim Batzoglou's lab at Stanford, on a project using machine learning techniques to model the regulation of gene expression in humans cells. I am also currently collaborating with a physician on a project involving biomedical informatics in the hospital setting.
As an undergraduate student at UC Berkeley, I did research with Assistant Professor Pieter Abbeel on probabilistic robotics and computer vision for robotics. My most recent research effort at Berkeley involved autonomous handling, sensing, and planning with deformable cloth objects and a general-purpose two-armed robot. The most recent paper introduced a novel method for simulating cloth objects that is suitable for robotic applications, a novel method of planning manipulations with cloth objects, and a method for probabilistically tracking the state of a cloth object over a sequence of robotic manipulations. The robotic cloth work at Berkeley was demonstrated using the PR2 personal robotics platform manufactured by Willow Garage.
Bringing Clothing into Desired Configurations with Limited
Marco Cusumano-Towner, Arjun Singh, Stephen Miller, Pieter Abbeel.
To appear in the proceedings of the International Conference on Robotics and Automation (ICRA), 2011. (pdf)
We formulate a cloth simulator suitable for robotics applications as a convex optimization problem, as well as a HMM-based framework for tracking the state of cloth objects through a sequence of manipulations. We apply these methods to autonomously and reliably perform the challenging task of bringing an unidentified crumpled article of clothing into a desired configuration.
An Optical Flow-Based Approach for Cloth Grasp Point Detection
with Application to Robotic Towel Folding
Jeremy Maitin-Shepard, Marco Cusumano-Towner, Jinna Lei, Pieter Abbeel.
In the proceedings of the International Conference on Robotics and Automation (ICRA), 2010. (pdf, video)
We present a novel vision-based cloth grasp point detection algorithm and demonstrate it on the end-to-end task of autonomously folding towels. The system successfully folds 50 out of 50 previously-unseen towels.