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.

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Highlights

I am teaching CMSC 422 (the undergraduate level Machine Learning Course) in Spring 2019. Read More

Four new papers from my lab have been recently posted on arXiv. Read More

I'll participate in Simons-Berkeley Program in Deep Learning Foundations in Summer 2019. Read More

Note to Prospective Students:

I am looking for students and post-docs with a strong mathematical background who are interested in working in theoritical and practical aspects of machine learning.

Information for prospective students can be found here.

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NEWS:

Awarded Simons-Berkeley Fellowship


I have received the Simons-Berkeley Research Fellowship on Deep Learning Foundations.

Department Colloquium

8
FEB, 2019

I am giving a talk on deep learning foundations. Read More

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.

New Paper on arXiv

4
FEB, 2019

Our work on Compressing GANs [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.

New Paper on arXiv

5
SEPT, 2018

Our work on Adversarial Examples [paper] is available on arXiv.

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.