Soheil Feizi

Assistant Professor, CS @ UMD

I am a faculty in Computer Science department at University of Maryland, College Park (UMD). 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. My research spans various theoritical and practical aspects of Machine Learning. I received NSF CAREER Award in 2020.

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.


Research

My research focuses on understanding various theoretical and practical aspects of deep learning to develop accurate, robust, interpretable and fair machine learning methods. Topics of interest include Adversarial Robustness, Generative Models (GANs, VAEs, etc), Deep Learning Interpretation, Domain Adaptation, and Fairness.

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Highlights

Three papers in ICML 2020 .

AWS Machine Learning Research Award. Read More.

NSF CAREER AWARD. Read More.

Two papers in AISTATS 2020 .

Three papers in AAAI 2020 .

Three papers in NeurIPS 2019 .

Simons-Berkeley fellowship in DL Foundations. Read More

UMD-CS Teaching Award Fall 2018 and 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:

NSF CAREER AWARD

2020

Received CAREER award on foundations of deep generative models. [Read more]

Three ICML Papers

May, 2020

On curvature-based robustness certificates, smoothing-based robustness certificates, and influence functions. [Read more]

AWS ML Research Award

APL, 2020

For “Explainable Deep Learning: Accuracy, Robustness and Fairness”. [Read more]

Area Chair at NeurIPS 2020

2020

I am serving as an area chair in NeurIPS 2020. [Read more]

UMD Research Excellence

Feb, 2020

I was an honeree at 2020 Maryland Research Excellence Celebration. [Read more]

Deep Generative Model at ITA

Jan, 2020

I organized a session on deep generative models at ITA 2020. [Read more]

Talk at NIST

Jan, 2020

I gave a talk on certifiably robust method against adversarial examples at NIST.

Talk at NeurIPS

Dec, 2019

Gave a talk in the ML with Guarantees workshop at NeurIPS [Watch the Video]

Two AISTATS Papers

Dec, 2019

Two AISTATS papers on non-LP adv. robustness and flow-based generative models. [Read more]

Three NeurIPS papers


Three NeurIPS papers on GANs, interpretability and adversarial examples. [Read more]

Robustness Talk

Oct, 2019

I gave a talk on certifiably robust method against adversarial examples [Read more]

Paper on arXiv!

Oct, 2019

Our work on Wasserstein Smoothing [paper] is available on arXiv.

Teaching Award


I received the teaching award at UMD for my Fall 2018 and Spring 2019 courses. [Read more]

ICCV Paper

Jul, 2019

Our work on Normalized Wasserstein [paper] has been accepted to ICCV 2019.

Deep Learning Workshop

Sept, 2019

I am attending a theory of deep learning workshop at IST, Austria.

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.