Open post-doc and Ph.D. student positions:
- Post-Doc: I have an open post-doc position in the areas of machine learning for perception, including learning with limited labeling (few-shot learning, semi-supervised learning, etc.), multi-domain/task learning, and incorporating structured information for such tasks.
- Ph.D. students: I am also looking for Ph.D. students in these areas and more, including multi-modal fusion, goal-driven perception, and applications to robotics.
For both please send me an email (see bottom of page) with CV and succinct statement of relevant research and references.
03/12/2019 Joined the Program Committee
of UAI 2019
03/02/2019 Oral CVPR paper
. Congrats Chih-Yao! (work with Salesforce Research)
02/01/2019 Dataset released
for our WACV PCB component detection paper! [download
01/26/2019 Two ICRA/RA-L accepted papers
. Congrats Jing-Dao and Angel!
01/03/2019 Journal paper
on point cloud scene understanding
12/24/2018 Three ICLR accepted papers
. Congrats Yen-Chang, Yen-Cheng, and Chih-Yao!
12/05/2018 Pytorch framework for continual/lifelong learning
11/13/2018 NIPS Continual Learning Workshop
11/06/2018 WACV paper
accepted. Congrats Albert!
10/29/2018 Teaching Deep Learning
cross-listed undergraduate and graduate course.
09/18/2018 New ONR funding
with Lu Feng (UVA), Pratap Tokekar (VT), and Ufuk Topcu (UT Austin).
paper on TS-LSTMs accepted
07/16/2018 Assistant Professor!
07/15/2018 Pytorch code
release for our clustering work on github
06/16/2018 News article
about new DARPA lifelong learning
02/18/2018 CVPR paper
on Higher-Order Object Interactions for Video Understanding
01/29/2018 ICLR paper
(in top 7%
of reviews!) for Learning to Cluster paper
I am an Assistant Professor at the School of Interactive Computing in the College of Computing. I am also affiliated with the Georgia Tech Research Institute and serve as an Associate Director of ML@GT which is the machine learning center recently created at Georgia Tech. Previously I was a Research Scientist at SRI International Sarnoff in Princeton, and before that received my Ph.D. in 2010 with Professor Ron Arkin as my advisor.
I lead the RobotIcs Perception and Learning (RIPL) lab. My areas of research specifically focus on the intersection of learning methods for sensor processing and robotics, developing novel machine learning algorithms and formulations towards solving some of the more difficult perception problems in these areas. I am especially interested recently in unsupervised/semi-supervised methods, continual/lifelong learning, multi-modal fusion, and distributed perception.