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 aims at building empirically-relevant theoretical foundations of deep learning with a focus on understanding its robustness, generalizability, interpretability and fairness. Topics of interest include Adversarial Robustness, Deep Generative Models, Deep Learning Interpretation, Domain Adaptation, Out-of-Distribution Generalization and Fairness.
Highlights
Five papers in NeurIPS 2020 .
Best paper award from MIT-IBM Watson AI Lab. Read More .
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 .
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.
NEWS:
NIST AWARD
2020
Received an award from National Institute of Standards and Technology supporting our research on robustness.
Best Paper Award
2020
from MIT-IBM Watson AI Lab at KDD's Adv ML workshop for our provable poisoning defense. [Read more]
Talk at Princeton's IAS
2020
On Generalizable Adversarial Robustness to Unforeseen Attacks. [Talk Video]
Talk at Capital One
2020
I gave a talk on Unsupervised Anomaly Detection at Capital One Modeling and Analytics Conference.
Three ICML Papers
2020
On curvature-based robustness certificates, smoothing-based robustness certificates, and influence functions. [Read more]
AWS ML Research Award
2020
For “Explainable Deep Learning: Accuracy, Robustness and Fairness”. [Read more]
UMD Research Excellence
2020
I was an honeree at 2020 Maryland Research Excellence Celebration. [Read more]
Deep Generative Model at ITA
2020
I organized a session on deep generative models at ITA 2020. [Read more]
Talk at NIST
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]
Teaching Award
I received the teaching award at UMD for my Fall 2018 and Spring 2019 courses. [Read more]
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.
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.
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.
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.
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.