I am a computer science PhD student at the University of California, Berkeley.
My current research interests are an eclectic mix of robotics, deep learning, and machine teaching.
I am particularly interested in developing robotic systems for deployment in complex, unstructured environments.
I am extremely fortunate to be advised by John Canny.
I am also very privileged to be part of the AUTOLAB.
I am generously supported by a National Physical Science Consortium fellowship (from 2015 to 2021), funded through the National Security Agency.
You can reach me at email@example.com.
I frequently blog about technical (and non-technical) topics, and push code on GitHub.
News and Updates
- 04/24/2018: I passed my qualifying exam. I wrote a transcript of the event and will post it online sometime in the future.
- 01/11/2018: Our paper on surgical debridement and calibration has been accepted to ICRA 2018.
- 08/16/2017: I had a great time at UAI 2017 in Sydney, Australia! I especially enjoyed giving a talk on the fourth day.
- 08/02/2017: I wrote a blog post on the BAIR Blog about our work on minibatch Metropolis-Hastings.
- 05/30/2017: I created my academic website. Sorry for the delay.
Preprints and Publications
If a paper is on arXiv, that's where you can find the latest version.
Robot Bed-Making: Deep Transfer Learning Using Depth Sensing of Deformable Fabric.
Daniel Seita*, Nawid Jamali*, Michael Laskey*, Ron Berenstein, Ajay Tanwani, Prakash Baskaran, Soshi Iba, John Canny, Ken Goldberg.
Risk Averse Robust Adversarial Reinforcement Learning.
Xinlei Pan, Daniel Seita, Yang Gao, John Canny.
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.
IEEE International Conference on Robotics and Automation (ICRA), May 2018.
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.
Coursework and Teaching Assistantships
I've taken a number of graduate courses as part of the PhD program at UC Berkeley.
I also write reviews, which might be helpful for those thinking about taking future iterations of these classes.
I was also the GSI (i.e., Teaching Assistant) for the Deep Learning class in Fall 2016.
At the time I took it, UC Berkeley had an oral preliminary exam requirement for PhD students. Here's the transcript of my prelims. Nowadays, it seems like there's a written exam, since the number of AI PhD students has skyrocketed.
- (Review) CS 267, Applications of Parallel Computing
- (Review) CS 280, Computer Vision
- (Review) CS 281A, Statistical Learning Theory
- (Review) CS 287, Advanced Robotics
- (Review) CS 288, Natural Language Processing
- (Review) CS 294-112, Deep Reinforcement Learning
- (Review) CS 294-112, Deep Reinforcement Learning (self-study)
- (Review) CS 294-115, Algorithmic Human-Robot Interaction
- (Review) CS 294-129, Deep Neural Networks (GSI/TA)
- (Review) CS 294-131, Special Topics in Deep Learning
- (Review) EE 227BT, Convex Optimization
- (Review) EE 227C, Convex Optimization and Approximation
- (Review) STAT 210A, Theoretical Statistics (Classical)
- (Review) STAT 210B, Theoretical Statistics (Modern)
© Daniel Seita. Last updated: September 17, 2018.