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 a core faculty of University of Maryland Center for Machine Learning. I am also 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 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.
I am also a research fellow at Simons-UC Berkeley in foundations of deep learning.

Research

My research focuses on understanding various theoretical and practical aspects of machine learning including Generative Adversarial Networks (GANs), Variational AutoEncoders (VAEs), domain adaptation, Adversarial examples, optimization landscape of deep learning, its interpretability, fairness and generalization.

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Highlights

Two papers have been accepted in ICML 2019 . Read More

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

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

For more info, see my profiles in Google Scholar, DBLP, LinkedIn and Twitter.

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NEWS:

IBM Faculty Award

May, 2019

I am honored to receive the 2019 IBM Faculty Award.

Qualcomm Faculty Award

May, 2019

I am honored to receive the 2019 Qualcomm Faculty Award. Read more here.

New Papers on arXiv

May, 2019

See our new arXiv papers on deep learning interpretation and adversarial examples here .

ICML Paper

APR, 2019

Our work on deep learning interpretation [paper] has been accepted to ICML 2019.

ICML Paper

APR, 2019

Our work on Entropic GANs meet VAEs [paper] has been accepted to ICML 2019.

Best Paper Award

APR, 2019

Our work on Multivariate Maximal Correlation [paper] has received TNSE's best paper award.

Awarded Simons-Berkeley Fellowship


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

Department Colloquium

8
FEB, 2019

I gave 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.

ICLR Paper

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.