Shahin Jabbari

Office: 3675 Market St, Office 1151, Philadelphia, PA, 19104
Email: shahin@drexel.edu
Phone: 215-895-2474

I am an assistant professor in the Computer Science Department of the College of Computing & Informatics at Drexel University where I am part of the EconCS group. From 2013 to 2019, I was a PhD student in the Computer and Information Science Department at University of Pennsylvania where I was fortunate to be advised by Michael Kearns. For the following two years, I was a CRCS postdoctoral fellow in the Computer Science Department of the School of Engineering and Applied Sciences at Harvard. I was lucky to be hosted by Milind Tambe and was also affiliated with the EconCS group.


Research Interests

My main interests are in machine learning, game theory, and their intersection. These days, I mostly focus on the ethical aspects of algorithmic decision-making and think about how AI-driven technology can lead to positive societal impact.
If you are interested in working on any of these topics with me, instead of contacting me directly, please apply to our PhD program and mention me in your application.


Publications

Unless specified otherwise, authors are listed in alphabetical order.

Learning-Augmented Robust Algorithmic Recourse

K. Kayastha, V. Gkatzelis, SJ (contributional order)
Preprint

A Game-Theoretic Approach for Hierarchical Epidemic Control

F. Jia, A. Mate, Z. Li, SJ, M. Chakraborty, M. Tambe, M. Wellman, and Y. Vorobeychik (contributional order)
Preprint

The Disagreement Problem in Explainable Machine Learning: A Practitioner's Perspective

S. Krishna, T. Han, A. Gu, Z.S. Wu, SJ, and H. Lakkaraju (contributional order)
TMLR 2024

The Evolving Meaning of Trust and Risk in Reddit Discourse about ChatGPT

D. Gefen, SJ, R. Rezapour, A. Pessianzadeh, K. Kayastha, H. Van den Bulck (contributional order)
AMCIS 2024 (ERF)

Improving Fairness in Adaptive Social Exergames via Shapley Bandits

R. Gray, J. Villareale, T. Fox, D. Dallal, S. Ontañón, D. Arigo, SJ, and J. Zhu (contributional order)
IUI 2023

Human-Subject Experiments on Risk-Based Cyber Camouflage Games

P. Aggarwal, SJ, O. Thakoor, E. Cranford, P. Vayanos, C. Lebiere, M. Tambe, and C. Gonzalez (contributional order)
Book Title: Cyber Deception: Techniques, Strategies, and Human Aspects
Book Chapter 2023

Solving Structured Hierarchical Games Using Differential Backward Induction

Z. Li, F. Jia, A. Mate, SJ, M. Chakraborty, M. Tambe, and Y. Vorobeychik (contributional order)
UAI 2022 (Oral Presentation)

Combining Machine Learning and Cognitive Models for Adaptive Phishing Training

E. Cranford, SJ, H-C. Ou, M. Tambe, C. Gonzalez, and C. Lebiere (contributional order)
ICCM 2022

Designing Effective Masking Strategies for Cyberdefense through Human Experimentation and Cognitive Models

P. Aggarwal, O. Thakoor, SJ, E. Cranford, C. Lebiere, M. Tambe, and C. Gonzalez (contributional order)
Computers & Security 2022

Towards the Unification and Robustness of Perturbation and Gradient Based Explanations

S. Aggarwal, SJ, C. Agarwal, S. Upadhyay, Z.S. Wu, and H. Lakkaraju (contributional order)
ICML 2021 (Spotlight Presentation)

Active Screening for Recurrent Diseases: A Reinforcement Learning Approach

H-C. Ou, H. Chen, SJ, and M. Tambe (contributional order)
AAMAS 2021 (Best Paper Finalist)

Fair Influence Maximization: A Welfare Optimization Approach

A. Rahmattalabi*, SJ*, H. Lakkaraju, P. Vayanos, M. Izenberg, R. Brown, E. Rice, and M. Tambe (contributional order, * equal contribution)
AAAI 2021

Modeling Between-Population Variation in COVID-19 Dynamics in Hubei, Lombardy, and New York City

B. Wilder, M. Charpignon, J. Killian, H-C. Ou, A. Mate, SJ, A. Perrault, A. Desai, M. Tambe, and M. Majumder (contributional order)
PNAS 2020

Risk-based Cyber Camouflage Games and Exploiting Bounded Rationality

O. Thakoor, SJ, P. Aggarwal, C. Gonzalez, M. Tambe, and P. Vayanos (contributional order)
GameSec 2020 (Best Paper)

Network Formation under Random Attack and Probabilistic Spread

with Y. Chen, M. Kearns, S. Khanna, and J. Morgenstern
IJCAI 2019

Equilibrium Characterization for Data Acquisition Games

with J. Dong, H. Elzayn, M. Kearns, and Z. Schutzman
IJCAI 2019

Fair Algorithms for Learning in Allocation Problems

with H. Elzayn, C. Jung, M. Kearns, S. Neel, A. Roth, and Z. Schutzman
ACM FAccT (formerly ACM FAT*) 2019

Fairness in Criminal Justice Risk Assessments: The State of the Art

with R. Berk, H. Heidari, M. Kearns, and A. Roth
Sociological Methods & Research 2018

A Convex Framework for Fair Regression

with R. Berk, H. Heidari, M. Joseph, M. Kearns, J. Morgenstern, S. Neel, and A. Roth
FATML 2017

Fairness in Reinforcement Learning

with M. Joseph, M. Kearns, J. Morgenstern, and A. Roth
ICML 2017

Strategic Network Formation with Attack and Immunization

with S. Goyal, M. Kearns, S. Khanna, and J. Morgenstern
WINE 2016
ICML 2013

PAC-Learning with General Class Noise Models

SJ, R. Holte, and S. Zilles (contributional order)
KI 2012 (Best Paper)

Teaching

CS 589: Responsible Machine Learning

Winter 2022, Winter 2024

CS 465: Privacy & Trust

Spring 2022, Spring 2024

CS 590: Privacy

Fall 2022, Spring 2023

Service

Senior Program Committee/Area Chair

NeurIPS 2024

Junior Program Committee/Reviewer

AAAI 2019
AIES 2023
AISTATS 2024
FAccT 2022
EC 2020, 2021
ICLR 2023, 2025
ICML 2020, 2021, 2022, 2023, 2024
NeurIPS 2014, 2015, 2018 (Top Reviewer!), 2019, 2020, 2021, 2022 (Top Reviewer!), 2023
UAI 2023
WINE 2021

External Reviewer

EC 2018, 2019
NeurIPS 2013
SAGT 2017
SODA 2022
WINE 2019

Journal Reviewer

AIJ 2020
PNAS Nexus 2022
TEAC 2021
TMLR 2022, 2023, 2024