I am a computer science Ph.D. student at the University of California, Berkeley. I am broadly interested in machine learning and deep learning, particularly with applications to robotics.
You can reach me at seita at cs dot berkeley dot edu.
Sorry, this website has not been updated that frequently, though I'm hoping to change that soon (and to make it look prettier in the process!). I spend more time on my my six-year-old technical/personal blog and also on my GitHub account. I've been told that people like the stuff I write/code, so let me know what you think.
News and Updates
- 09/21/2017: A preprint on surgical debridement is now available.
- 08/02/2017: I wrote a blog post about our work on minibatch Metropolis-Hastings.
- 05/30/2017: I created this website with two old papers and two works-in-progress.
Fast and Reliable Autonomous Surgical Debridement with Cable-Driven Robots Using a Two-Phase Calibration Procedure.
Daniel Seita, Sanjay Krishnan, Roy Fox, Stephen McKinley, John Canny, Ken Goldberg.
An Efficient Minibatch Acceptance Test for Metropolis-Hastings.
Daniel Seita, Xinlei Pan, Haoyu Chen, John Canny.
Conference on Uncertainty in Artificial Intelligence (UAI), August 2017.
(Oral Presentation, Honorable Mention for Best Student Paper)
Large-Scale Supervised Learning of the Grasp Robustness of Surface Patch Pairs.
Daniel Seita, Florian T. Pokorny, Jeffrey Mahler, Danica Kragic, Michael Franklin, John Canny, Ken Goldberg.
IEEE International Conference on Simulation, Modeling, and Programming for Autonomous Robots (SIMPAR), December 2016.
Computing Abelian Complexity of Binary Uniform Morphic Words.
Francine Blanchet-Sadri, Daniel Seita, David Wise.
Theoretical Computer Science, Volume 640, June 2016.