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 graphs and networks, with focus on:
I am motivated by compelling applications, and some of the areas that I study are personalization, social media, social networks, journalism, and e-commerce.
I am looking for highly motivated Ph.D. students to work with me. Send me an email with your interests and CV if you would like to explore topics together. You may also want to read this article on how to be a successful Ph.D. student. If you are a prospective graduate student, you need to apply to the CS Ph.D. program first.
October 2017: Giving an 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 KDD 2017 Workshop on Data Science + Journalism (DS+J) in Halifax, Canada
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 2017 - CS 594 Data Science for Networks
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. Under submission to a journal. PDF.
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. PDF
T. Sun, S. Viswanathan, E. Zheleva. Creating Social Contagion through Firm-mediated Message Design: Evidence from A Randomized Field Experiment. International Conference on Information Systems (ICIS) 2014. INFORMS 2014 Service Science Best Student Paper, Third Place. Under submission to Management Science Journal. PDF
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. Prediction, Evolution and Privacy in Social and Affiliation Networks. PhD Dissertation. July, 2011. Abstract, PDF.
E. Zheleva, L. Getoor, S. Sarawagi. Higher-order Graphical Models for Classification in Social and Affiliation Networks. NIPS Workshop on Networks Across Disciplines: Theory and Applications 2010. Abstract, 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). Abstract, 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). Abstract, 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). Abstract, PDF. Slides.
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. Abstract, 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. Abstract, PDF.
E. Zheleva, L. Getoor. Preserving the Privacy of Sensitive Relationships in Graph Data. KDD Workshop on Privacy, Security, and Trust in KDD (PinKDD) 2007. LNCS vol. 4890, p. 153-171, 2008. Abstract, 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.
Co-organizer of KDD 2017 Data Science + Journalism workshop
ICDE 2017 Publicity Chair, NIPS 2017 Area Chair
Conference Program Committee Member:
World Wide Web Conference (WWW) 2017, 2018
ACM Conference on Web Search and Data Mining (WSDM) 2013, 2014, 2015, 2017
Conference on Neural Information Processing Systems (NIPS) 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 Knowledge Discovery and Data Mining (KDD) 2010
AAAI Conference on Artificial Intelligence (AAAI) 2010
ACM Conference on Information and Knowledge Management (CIKM) 2008, 2009
ACM Symposium on Applied Computing (SAC) 2010
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 assistant professor in Computer Science at the University of Illinois at Chicago. 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 Dr. Lise Getoor.