Office: SEO 1140
My research spans different aspects of data science, including machine learning, causal inference, graph mining, network science, and privacy. My research goal is to unify these aspects in a single framework that allows us to reason better with data and solve important societal problems. I am especially interested in algorithms for heterogeneous graphs and networks, with focus on the following goals:
I am motivated by compelling applications, and some of the areas that I study are personalization, social media, social networks, psychology, journalism, and e-commerce.
If you are a prospective student interested to work with me, you need to apply to the CS Ph.D. program. If you are already admitted to the program, send me an email with your interests and CV. You may also want to read this article on how to be a successful Ph.D. student.
August 2021: Presenting a tutorial on "Causal Inference from Network Data" @ KDD 2021
July 2021: Zohreh Ovaisi is presenting our paper on "Propensity-independent Bias Recovery in Offline Learning-to-rank Systems" at SIGIR 2021
June 2021: Excited and humbled to receive NSF CAREER Award to study Relational Causal Inference (news blurb)
June 2021: Shishir Adhikari is presenting our paper on "Understanding the Dynamics between Vaping and Cannabis Legalization Using Twitter Opinions" at ICWSM 2021
May 2021: Receiving COE Research Award
April 2021: Giving an invited talk at Facebook, Computational Social Science group
March 2021: Our research lab receives an Adobe Research grant to study heterogeneous treatment effect estimation
March 2021: Panelist on "Developing Bias Free AI Solutions" at IIT
February 2021: Co-organizing the Women's Mentoring Session @ AAAI 2021
August 2020: Our lab has three papers accepted at the KDD Workshop on Mining and Learning with Graphs (MLG)
June 2020: Our paper on "Minimizing Interference and Selection Bias in Network Experiment Design" receives an Honorable Mention for Best Paper at ICWSM 2020
May 2020: Our lab receives an NSF grant to study stay-at-home attitudes and their impact on the COVID-19 pandemic
April 2020: Zohreh Ovaisi presents our paper on "Correcting for Selection Bias in Learning-to-rank Systems" at WWW 2020
January 2020: Our research lab receives a grant from Anthem
October 2019: Our lab has two papers accepted at DSAA 2019
October 2019: UIC receives a TRIPODS grant from NSF to establish a Foundations of Data Science Institute
July 2019: Our research lab receives a grant from DARPA to study learning individualized interventions
May 2019: Giving an invited talk at the NetSci Workshop on Machine Learning in Network Science
March 2019: Usman Shahid is presenting our paper on "Counterfactual learning in networks: an empirical study of model dependence"
at AAAI Symposium on Causality
January 2019: Looking forward to serving as a mentor at Women's Mentoring Breakfast at AAAI 2019
January 2019: Serving as publicity co-chair for DSAA 2019
January 2019: Chris Tran is presenting our paper on "Learning Triggers for Heterogeneous Treatment Effects" at AAAI 2019
November 2018: Giving an invited talk on "Privacy in personal-data networks" at the TRIPODS Workshop on Privacy in Graphs
October 2018: Co-organizing the 3rd workshop on Translational Data Science hosted by NYU
September 2018: Our letter to Nature on "How to deliver translational data-science benefits to science and society" has been published
August 2018: Excited to receive a four-year NSF grant to study retrospective data management for enabling long-term security and privacy, with Chris Kanich and Blase Ur
August 2018: Co-organizing the 2nd Workshop on Data Science, Journalism & Media at KDD 2018
February 2018: Giving an invited talk at the Northwestern University Institute on Complex Systems Seminar Series
February 2018: Giving an invited talk at the UIC Social Psychology Seminar Series
February 2018: Invited speaker at UIUC's weSTEM Conference
October 2017: Invited talk at DePaul University's CDM Research Colloquium
August 2017: Giving a keynote at the Mining and Learning with Graphs (MLG) Workshop at KDD 2017
August 2017: Co-organizing the Workshop on Data Science + Journalism at KDD 2017
August 2017: Our paper on "Optimizing the Effectiveness of Incentivized Social Sharing" was accepted at ASONAM 2017
June 2017: Co-organizing the workshop on Translational Data Science in Chicago
May 2017: Receiving a DCFemTech Award which recognizes "powerful women programmers, designers, and data scientists" in Washington, DC
April 2017: Invited talk on Data Science in Social Spaces: Personalization vs. Privacy at DIMACS Workshop on Privacy and Security in Big Data
April 2017: Serving as publicity co-chair for ICDE 2017
February 2017: Our paper on "Directed Edge Recommender System" is accepted at WSDM 2017
November 2016: Presenting in the NIH BD2K Webinar Series on "Databases & Data Warehouses, Data: Structures, Types, Integrations"
Fall 2023 - CS 520 Causal Inference and Learning
Fall 2022 - CS 520 Causal Inference and Learning
Spring 2022 - CS 418 Introduction to Data Science
Fall 2021 - CS 520 Causal Inference and Learning
Spring 2021 - CS 412 Introduction to Machine Learning
Fall 2020 - CS 520 Causal Inference and Learning
Spring 2020 - CS 418 Introduction to Data Science
Fall 2019 - CS 594 Causal Inference and Learning
Spring 2019 - CS 418 Introduction to Data Science
Fall 2018 - CS 412 Introduction to Machine Learning
Spring 2018 - CS 412 Introduction to Machine Learning
Fall 2017 - CS 594 Data Science for Networks
Z. Fatemi, E. Zheleva. Network experiment designs for inferring causal effects under interference. Frontiers in Big Data, section Data Mining and Management, Vol.6, 2023. PDF.
Z. Fatemi, A. Bhattacharya, A. Wentzel, V. Dhariwal, L. Levine, A. Rojecki, G.E. Marai, B. Di Eugenio, E. Zheleva. Understanding Stay-at-home Attitudes through Framing Analysis of Tweets. IEEE/ACM Conference on Data Science and Advanced Analytics (DSAA) 2022. PDF. Data.
C. Tran, E. Zheleva. Improving Data-driven Heterogeneous Treatment Effect Estimation under Structure Uncertainty. 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) 2022. PDF. Code.
R. Ahsan, D. Arbour, E. Zheleva. Relational Causal Models with Cycles: Representation and Reasoning. Conference on Causal Learning and Reasoning (CLeaR) 2022. PDF.
Z. Ovaisi, S. Heinecke, J. Li, Y. Zhang, E. Zheleva, C. Xiong. RGRecSys: A Toolkit for Robustness Evaluation of Recommender Systems. Demo paper. 15th ACM International Conference on Web Search and Data Mining (WSDM) 2022. PDF.
A. Faruk, J. Sulskis, E. Zheleva. Estimating Causal Effects in Networks with Cluster-based Bandits. AAAI Workshop on Artificial Intelligence for Behavioral Change (AI4BC) 2022.
E. Zheleva, D. Arbour. Causal Inference from Network Data. Tutorial. KDD 2021. PDF.
T. Ginossar, I. Cruikshank, J. Sulskis, E. Zheleva, T. Berger-Wolf. Cross-Platform Spread: Vaccine-Related Content, Sources, and Conspiracy Theories in YouTube Videos Shared in Early Twitter COVID-19 Conversations. Human Vaccines and Immunotherapeutics, vol. 17, issue 12, 2021.
I. Cruikshank, T. Ginossar, J. Sulskis, E. Zheleva, T. Berger-Wolf. Content and Dynamics of Websites Shared over Vaccine-Related Tweets in COVID-19 Conversations: A Computational Analysis. Journal of Medical Internet Research (JMIR) vol. 23, issue 12, 2021.
A. Rojecki, E. Zheleva, L. Levine. The Moral Imperatives of Self-Quarantining. 117th American Political Science Association (APSA) Annual Meeting 2021.
T. Sun, S. Viswanathan, N. Huang, E. Zheleva. Designing Promotional Incentive to Embrace Social Sharing: Evidence from Field and Online Experiments. Management Information Systems Quarterly (MISQ) 45(2) June 2021. PDF.
Z. Ovaisi, K. Vasilaky, E. Zheleva. Propensity-Independent Bias Recovery in Offline Learning-to-Rank Systems. 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) 2021. PDF.
S. Adhikari, A. Uppal, R. Mermelstein, T. Berger-Wolf, E. Zheleva. Understanding the Dynamics between Vaping and Cannabis Legalization Using Twitter Opinions. AAAI Conference on Web and Social Media (ICWSM) 2021. PDF. Code & Data.
C. Amornbunchornvej, E. Zheleva, T. Berger-Wolf. Variable-lag Granger Causality and Transfer Entropy for Time Series Analysis. ACM Transactions on Knowledge Discovery from Data (TKDD) 15(4), 2021. PDF. Code & Data
Y. He, C. Tran, J. Jiang, K. Burghardt, E. Ferrara, E. Zheleva, K. Lerman. Heterogeneous Effects of Software Patches in a Multiplayer Online Battle Arena Game. 16th International Conference on the Foundations of Digital Games (FDG) 2021.
M. Khan, C. Tran, S. Singh, D. Vasilkov, C. Kanich, B. Ur, E. Zheleva. Helping Users Automatically Find and Manage Sensitive, Expendable Files in Cloud Storage. 30th USENIX Security Symposium (USENIX Security) 2021. PDF.
T. Sun, S. Viswanathan, E. Zheleva. Creating Social Contagion through Firm-mediated Message Design: Evidence from A Randomized Field Experiment. Management Science 67(2), p. 808-827, 2021. PDF.
S. Biradar, E. Zheleva. Personalized Privacy Protection in Social Networks through Adversarial Modeling. AAAI Workshop on Privacy-Preserving Artificial Intelligence (PPAI) 2021. PDF.
Z. Fatemi, E. Zheleva. Network Experiment Design for Estimating Direct Treatment Effects. KDD Workshop on Mining and Learning with Graphs (MLG) 2020. PDF.
C. Tran, E. Zheleva. Heterogeneous Threshold Estimation for Linear Threshold Modeling. KDD Workshop on Mining and Learning with Graphs (MLG) 2020.
Z. Fatemi, E. Zheleva. Minimizing Interference and Selection Bias in Network Experiment Design. 14th International AAAI Conference on Web and Social Media (ICWSM) 2020. Best Paper Honorable Mention. PDF.
C. Amornbunchornvej, E. Zheleva. T. Berger-Wolf. Variable-lag Granger Causality for Time Series Analysis. IEEE Conference on Data Science and Advanced Analytics (DSAA) 2019. PDF.
M. Roshanaei, C. Tran, S. Morelli, C. Caragea, E. Zheleva. Paths to Empathy: Heterogeneous Effects of Reading Personal Stories Online. IEEE Conference on Data Science and Advanced Analytics (DSAA) 2019. PDF.
M. Mondal, G. Yilmaz, N. Hirsch, M. Khan, M. Tang, C. Tran, C. Kanich, B. Ur, E. Zheleva. Moving Beyond Set-It-And-Forget-It Privacy Settings on Social Media. 26th ACM Conference on Computer and Communications Security (CCS) 2019. PDF.
U. Shahid, E. Zheleva. Counterfactual Learning in Networks: An Empirical Study of Model Dependence. AAAI Spring Symposium on Beyond Curve Fitting: Causation, Counterfactuals, and Imagination-based AI (AAAI-WHY) 2019. PDF.
T. Sun, L. Shi, S. Viswanathan, E. Zheleva. Motivating Effective Mobile App Adoptions: Evidence From A Large-Scale Randomized Field Experiment. Information Systems Research Journal (ISR). Vol. 30, No. 2, June 2019. PDF.
C. Baru, S. Bird, A. Blatecky, D. Culler, R. Grossman, B. Howe, V. Janeja, M. Lee, R. Machiraju, E. Zheleva. Translational Data Science: From Foundations to Impact. Harvard Data Science Review. Position paper. 2019 (forthcoming).
A. Rojecki, U. Shahid, E. Zheleva. Segregation and Racial Threat: Human and Computational Frame Analyses of Racial Unrest. 114th American Political Science Association (APSA) Annual Meeting 2018.
J. Pfeiffer, E. Zheleva. Incentivized Social Sharing: Characteristics and Optimization. Lecture Notes in Social Networks book series (LNSN). Springer. December 2018.
N. Hirsch, C. Kanich, M. Khan, X. Liu, M. Mondal, M. Tang, C. Tran, B. Ur, W. Wang, G. Yilmaz, E. Zheleva. Making Retrospective Data Management Usable. Poster. 14th Symposium On Usable Privacy and Security (SOUPS). Baltimore, MD, August 2018.
J. Pfeiffer, E. Zheleva. Optimizing the Effectiveness of Incentivized Social Sharing. 9th IEEE/ACM International Conference on Advances in Social Network Analysis and Mining (ASONAM) 2017. PDF.
I. Kotsogiannis, E. Zheleva, A. Machanavajjhala. Directed Edge Recommender System. 10th ACM International Conference on Web Search and Data Mining (WSDM) 2017. PDF.
T. Sun, L. Shi, S. Viswanathan, E. Zheleva. Motivating Mobile App Adoption: Evidence From A Large-Scale Field Experiment. Conference on Information Systems and Technology (CIST) 2016.
T. Sun, S. Viswanathan, E. Zheleva. Monetize Sharing Traffic Through Incentive Design: A Randomized Field Experiment. Conference on Information Systems and Technology (CIST) 2015. INFORMS 2015.
T. Sun, S. Viswanathan, E. Zheleva. Creating Social Contagion through Message Design: A Randomized Field Experiment. International Conference on Information Systems (ICIS) 2014. INFORMS 2014 Service Science Best Student Paper, Third Place.
T. Sun, S. Viswanathan, E. Zheleva. Antecedences and Consequences of Multichannel Sharing Behaviors. INFORMS 2014.
T. Sun, S. Viswanathan, E. Zheleva. Impact of Message Design on Online Interactions: An Empirical Investigation. International Conference on Electronic Commerce (ICEC) 2014.
J. Pfeiffer III, E. Zheleva. Incentivized Sharing in Social Networks. VLDB workshop on Online Social Systems (WOSS) 2012. PDF.
E. Zheleva, E. Terzi, L. Getoor. Privacy in Social Networks. Synthesis Lectures on Data Mining Series. Book published by Morgan and Claypool Publishers. 2012. PDF.
E. Zheleva. Prediction, Evolution and Privacy in Social and Affiliation Networks. PhD Dissertation. July, 2011. PDF.
E. Zheleva, L. Getoor. Privacy in Social Networks: a Survey. Invited book chapter in Social Network Data Analytics. Ed. by Charu Aggarwal. Springer. March 2011. PDF.
E. Zheleva, L. Getoor, S. Sarawagi. Higher-order Graphical Models for Classification in Social and Affiliation Networks. NeurIPS Workshop on Networks Across Disciplines: Theory and Applications 2010. PDF.
E. Zheleva, J. Guiver, E. Mendes Rodrigues, N. Milic-Frayling. Statistical Models of Music-listening Sessions in Social Media. 19th International World Wide Web Conference (WWW) 2010 (12% acceptance rate). PDF. Conference talk on VideoLectures.
E. Zheleva, H. Sharara, L. Getoor. Co-evolution of Social and Affiliation Networks. 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) 2009 (10% acceptance rate). PDF. Also presented at INFORMS 2009.
E. Zheleva, L. Getoor. To Join or not to Join: The Illusion of Privacy in Social Networks with Mixed Public and Private User Profiles. 18th International World Wide Web Conference (WWW) 2009 (12% acceptance rate). PDF.
E. Zheleva, A. Kolcz, L. Getoor. Trusting Spam Reporters: A Reporter-based Reputation System for Email Filtering. ACM Transactions on Information Systems (TOIS), vol. 27, no. 1, December 2008. PDF. Patent.
E. Zheleva, L. Getoor, J. Golbeck, U. Kuter. Using Friendship Ties and Family Circles for Link Prediction. KDD Workshop on Social Network Mining and Analysis 2008. PDF.
A. Don, E. Zheleva, M. Gregory, S. Tarkan, L. Auvil, T. Clement, B. Shneiderman, C. Plaisant. Discovering Interesting Usage Patterns in Text Collections: Integrating Text Mining with Visualization. 16th ACM International Conference on Information and Knowledge Management (CIKM) 2007. Abstract, PDF. Application.
E. Zheleva, A. Arslan. Fast Motif Search in Protein Sequence Databases. Computer Science Symposium 2006.
V. Kantabutra, B. Tsendjav, E. Zheleva. Glide Algorithm with Tunneling: A Fast, Reliably Convergent Algorithm for Neural Network Training. Conference on Artificial Neural Networks in Engineering (ANNIE) 2003.
V. Kantabutra, E. Zheleva. Gradient Descent with Fast Gliding over Flat Regions: A First Report. IEEE Industrial Electronics Conference (IECON) 2002.
NSF CAREER Award: Relational Causal Inference
NSF HDR TRIPODS Grant: Institute for Data, Econometrics, Algorithms and Learning (IDEAL)
Adobe Data Science Research Award
DARPA Grant "EDIFICE: Early Detection of Influence Indicators with Machine Intelligence"
NSF III RAPID Grant "Stay-at-home Attitudes and Their Impact on the COVID-19 Pandemic"
Anthem Grant "Knowledge Discovery through Process Mining and Machine Learning of Patients Health and Claim Records"
DARPA Grant "Bespoke: Learning Individualized Interventions for Human Performance"
NSF HDR TRIPODS Grant UIC Foundations of Data Science Institute
NSF SaTC Grant "Enabling Long-term Security and Privacy through Retrospective Data Management"
AWS Cloud Credits for Research
Computation + Journalism Symposium 2019
Third Workshop on Translational Data Science 2018
2nd Workshop on Data Science, Journalism & Media at KDD 2018
1st Workshop on Data Science + Journalism at KDD 2017
First Workshop on Translational Data Science 2017
Associate Editor: ACM Transactions on Intelligent Systems and Technology
Poster Chair: KDD 2023
Workshop Chair: KDD 2022
Publicity Chair: ICDE 2017, DSAA 2019
Area Chair: AAAI 2020, NeurIPS 2017, 2018, 2019
Senior PC: AAAI 2022
Conference Program Committee Member:
World Wide Web Conference (WWW) 2017, 2018, 2019, 2021
ACM Conference on Knowledge Discovery and Data Mining (KDD) 2010, 2019, 2020
AAAI Conference on Artificial Intelligence (AAAI) 2010, 2019
International Conference on Machine Learning (ICML) 2018
ACM Conference on Web Search and Data Mining (WSDM) 2013, 2014, 2015, 2017, 2021
Conference on Neural Information Processing Systems (NeurIPS) 2016
Joint Conference on Artificial Intelligence (IJCAI) 2016
European Conference on Machine Learning (ECML) 2013, 2014, 2015
AAAI Conference on Weblogs and Social Media (ICWSM) 2013
ACM Conference on Information and Knowledge Management (CIKM) 2008, 2009, 2023
ACM Symposium on Applied Computing (SAC) 2010
October 2022 - Improving Data-driven Heterogeneous Treatment Effect Estimation Under Structure Uncertainty. Invited talk at Adobe Research.
August 2022 - Causal Discovery from Social Networks. Keynote at the KDD 2022 Causal Discovery Workshop
May 2022 - Causal inference from social media. Invited speaker at North Carolina State University’s Analytics Initiative Roundtable
May 2022 - Relational causal inference from social media data. Invited speaker at Graph Exploitation Symposium (GraphEx)
April 2021 - Towards Relational Causal Inference. Facebook, Computational Social Science group.
November 2019 - Causal Inference and Counterfactual Learning from Relational Data. Marquette University and Northern Illinois University
May 2019 - Causal Inference and Counterfactual Learning in Networks. NetSci Symposium on Machine Learning in Network Science
November 2018 - Privacy in Personal-data Networks. UCSC TRIPODS Workshop on Privacy in Graphs
February 2018 - Panel on How to Survive & Thrive on the Tenure-Track. UIUC's weSTEM Conference
October 2017 - Data Science in Social Spaces: Machine learning, causal inference, privacy. DePaul University's CDM Research Colloquium
August 2017 - Sharing and Gifting: Lessons from E-commerce. Keynote at the Mining and Learning with Graphs Workshop at KDD 2017.
April 2017 - Data Science in Social Spaces: Personalization vs. Privacy. DIMACS Workshop on Privacy and Security in Big Data.
November 2016 - Databases & Data Warehouses, Data: Structures, Types, Integrations. BD2K Seminar Series.
April 2013, 2014 - Deal Personalization. Class on Data Mining for Business Intelligence, Wharton School of Business, UPenn
March 2013 - Incentivized Sharing in Social Networks. Class on Networks, Crowds and Markets, Stern School of Business, NYU
February 2013 - Analytics in a Personalized Online World: Complex Networks and Incentives. Smith School of Business, UMd
June 2012 - A/B Testing, Personalization and Privacy. Digital Marketing Analytics Roundtable, DMAR
November 2011 - Data Science Classroom: Naive Bayes and Logistic Regression. DC Data Science Meetup
February 2011 - Prediction and Privacy in Social and Affiliation Networks. Tepper School of Business, CMU
November 2009 - Privacy in Social Networks. Workshop on Security and Artificial Intelligence, AISec 2009
I am an associate professor of Computer Science at the University of Illinois at Chicago. My research focuses on the intersection of causal inference and machine learning for network data, with applications in graph mining, recommender systems and privacy. I received an NSF CAREER Award in 2021. Prior to joining UIC, I spent a few years in industry as a data scientist, working on large-scale recommender systems, personalization, incentivized sharing, and data science tools for journalists. I built and led the data science team at LivingSocial, and later was a principal data scientist at Vox Media. I also spent a year in government as an AAAS Science & Technology Policy Fellow at the National Science Foundation, contributing to national initiatives at the intersection of data science and public policy. Many of the research problems I am interested to solve are informed by my industry and public policy experience. I received my Ph.D. in Computer Science in 2011 from the University of Maryland, College Park where my advisor was Lise Getoor.