Soheil Feizi
Assistant Professor, CS @ UMD
I am a faculty in Computer Science department at University of Maryland, College Park
(UMD). I am also a member of University of Maryland Institute for Advanced Computer Studies (UMIACS) and
affiliated with the Electrical and Computer Engineering (ECE) Department at UMD. My research spans various theoritical and practical aspects of
Machine Learning.
Before joining UMD, I was a a post-doctoral researcher at
Stanford University .
I received my Ph.D. and M.Sc. in Electrical Engineering and Computer Science, with a minor degree in Mathematics, at
MIT.
Research
My research focuses on understanding various theoretical and practical aspects of machine learning and statistical inference problems. In my group, we are working on problems related to Generative Adversarial Networks (GANs), Variational AutoEncoders (VAEs), domain adaptation, Adversarial examples, the optimization landscape of deep learning, its interpretability and generalization, etc.
Highlights
Note to Prospective Students:
NEWS:
Awarded Simons-Berkeley Fellowship
I have received the Simons-Berkeley Research Fellowship on Deep Learning Foundations.
New Paper on arXiv
4
FEB, 2019
Our work on Normalized Wasserstein Distance [paper] is available on arXiv.
New Paper on arXiv
4
FEB, 2019
Our work on Deep Learning Interpretation [paper] is available on arXiv.
New Paper on arXiv
4
FEB, 2019
Our work on Robustness Certificates against Adversarial Examples [paper] is available on arXiv.
Talk at American University
4
FEB, 2019
I gave a talk at American University on generative models.
Paper Accepted!
27
Dec, 2018
Our work on Inevitability of Adversarial Examples [paper] has been accepted in ICLR.
Talk at Capital One
17
Dec, 2018
I gave a talk at Capital One's Machine Learning center on Unsupervised Anomaly Detection.
Talk at NIH
30
Nov, 2018
I gave a talk at NIH on ML in biological applications.
Talk at IBM Research
26
Oct, 2018
I gave a talk at IBM research on a statistical approach to generative models.
Lecture at CS Honors
17
Oct, 2018
I gave a lecture at CS honors class on GANs.
New Paper on arXiv
3
OCT, 2018
Our work titled Entropic GANs meet VAEs [paper] is available on arXiv.
Paper Accepted!
29
SEPT, 2018
Our work on Spectral Alignment of Graphs [paper] has been accepted to IEEE Transactions on Network Science and Engineering.
Talk at Quantum Machine Learning Workshop
27
SEPT, 2018
I gave an invited talk titled Generative Adversarial Networks: Formulation, Design and Computation in the QML workshop.
Paper Accepted!
5
SEPT, 2018
Our work on understanding the Landscape of Neural Networks [paper] has been accepted to NeurIPS.
Paper Accepted!
20
JUL, 2018
Our work on Source Inference in Graphs [paper] has been accepted to IEEE Transactions on Network Science and Engineering.
Machine Learning Course
01
JUL, 2018
I am teaching CMSC 726 in Fall 2018. See the course webpage here.
First Day @ UMD
01
JUL, 2018
I am officially starting my faculty career at CS@UMD. I am also a member of UMIACS.
Talk @ Google Research
28
JUN, 2018
I am giving a talk titled "GANs: model-based or model-free?" in google research.