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Maytal Saar-Tsechansky


Department:     Information, Risk & Operations Management

Maytal Saar Tsechansky Headshot



Chevron Centennial Fellowship in Business



MBA Honor Roll Award for Outstanding MBA Class Instruction, McCombs School of Business, Spring


National Science Foundation award: “Active Learning System for Audit Selection,”


Research Grant, The University of Texas at Austin


Research Excellence Grant, McCombs School of Business


Research Excellence Grant, McCombs School of Business


Yunyi Li, Maria De-Arteaga, and Maytal Saar-Tsechansky. When More Data Lead Us Astray: Active Data Acquisition in the Presence of Label Bias. Proceedings of the Tenth AAAI Conference on Human Computation and Crowdsourcing 10(1):133-146.

Thomas Zueger, Simon Schallmoser, Mathias Kraus, Maytal Saar-Tsechansky, Stefan Feuerriegel, and Christoph Stettler. Machine Learning for Predicting the Risk of Transition from Prediabetes to Diabetes. Diabetes Technology & Therapeutics. Forthcoming.

Maria De‐Arteaga, Stefan Feuerriegel, and Maytal Saar‐Tsechansky. Oct 2022. Algorithmic Fairness in Business Analytics: Directions for Research and Practice. Production & Operations Management 31(10): 3749-3770.

Maytal Saar-Tsechansky, Tomer Geva, and Harel Lustiger. More for Less: Adaptive Labeling Payments in Online Labor Markets. Data Mining and Knowledge Discovery, forthcoming.

Tomer Giva and Maytal Saar-Tsechansky. 2021. Who Is a Better Decision Maker? Data-Driven Expert Ranking Under Unobserved Quality. Production and Operations Management 30(1), 127-144.

Elad Liebman, Maytal Saar-Tsechansky, and Peter Stone. 2019. The Right Music at the Right Time: Adaptive Personalized Playlists Based on Sequence Modeling. MIS Quarterly 43(3), 765-A6.

Hilah Geva, Gal Oestreicher-Singer, and Maytal Saar-Tsechansky. 2019. Using Retweets When Shaping Our Online Persona: Topic Modeling Approach. MIS Quarterly 43(2), 501-524.

Markus Peters, Maytal Saar-Tsechansky, Wolfgang Ketter, Sinead Williamson, Perry Groot, and Tom Heskes. 2018. A Scalable Preference Model for Autonomous Decision-Making. Machine Learning 107(6), 1039-1068.

Wolfgang Ketter, John E. Collins, and Maytal Saar-Tsechansky. 2018. Information Systems for a Smart Electricity Grid: Emerging Challenges and Opportunities. ACM Transactions on Management Information Systems 9(3), 1-22.

Meghana Deodhar, Joydeep Ghosh, Maytal Saar-Tsechansky, and Vineet Keshari. 2017. Active Learning with Multiple Localized Regression Models. INFORMS Journal on Computing 29(3), 503-522.

Maytal Saar-Tsechansky. 2015. The Business of Business Data Science in IS Journals. MIS Quarterly 39(4), iii-vi.

David Pardo, Peter Stone, Maytal Saar-Tsechansky, Tayfun Keskin, and Kerem Tomak. 2010. Adaptive Auction Mechanism Design and the Incorporation of Prior Knowledge. INFORMS Journal on Computing 22(3), 353-370.

Maytal Saar-Tsechansky, Prem Melville, and Foster Provost. 2009. Active Feature-Value Acquisition. Management Science 55, 664-684.

Gary Weiss, Bianca Zadrozny, and Maytal Saar-Tsechansky. 2008. Editorial: Special Issue on Utility Based Data Mining. Data Mining and Knowledge Discovery 17(2).

Maytal Saar-Tsechansky and Foster Provost. 2008. Handling Missing Values when Applying Classification Models. Journal of Machine Learning Research 9, 1625-1657.

Paul C Tetlock, Maytal Saar-Tsechansky, and Macskassy, Sofus. 2008. More Than Words: Quantifying Language to Measure Firms' Fundamentals. Journal of Finance 63, 1437-1467.

Provost, Foster, Melville, Prem, and Maytal Saar-Tsechansky. 2007. Data acquisition and cost-effective predictive modeling: targeting offers for electronic commerce, in The Proceedings of The Ninth International Conference on Electronic Commerce, Minneapolis, Minnesota.

Maytal Saar-Tsechansky and Provost, Foster. 2007. Decision-Centric Active Learning of Binary-Outcome Models. Information Systems Research 18, 4-22.

Pardoe, David, Sone, Peter, Maytal Saar-Tsechansky, and Tomak, Kerem. 2006. Adaptive Mechanism Design: A Metalearning Approach, in The Proceedings of The Eighth International Conference on Electronic Commerce,

Melville, Prem, Yang, Stewart M., Maytal Saar-Tsechansky, and Mooney, Raymond J. 2005. Active Learning for Probability Estimation using Jensen-Shannon Divergence, in The Proceedings of The 16th European Conference on Machine Learning (ECML), Porto, Portugal: ECML 2005.

Pardoe, David, Stone, Peter, Maytal Saar-Tsechansky, and Tomak, Kerem. 2005. Adaptive Auctions: Learning to Adjust to Bidders, in Workshop on Information Technologies and Systems (WITS), 2005.

Melville, P., Maytal Saar-Tsechansky, Provost, F., and Mooney, R.J.. 2005. An Expected Utility Approach to Active Feature-value Acquisition, in The Proceedings of the Fifth International Conference on Data Mining (ICDM-2005), Houston, TX.

Melville, P., Maytal Saar-Tsechansky, Provost, F., and Mooney, R.J.. 2005. Economical Active Feature-value Acquisition through Expected Utility Estimation, in Proceedings of the KDD-05 Workshop on Utility-Based Data Mining, Chicago, IL.

Maytal Saar-Tsechansky and Chen, Hsuan-Wei Michelle. 2005. Variance-Based Active Learning for Classifier Induction, in Workshop on Information Technologies and Systems (WITS), WITS 2005.

Melville, Prem, Maytal Saar-Tsechansky, Provost, Foster, and Mooney, Raymond J.. 2004. Active Feature Acquisition for Classifier Induction, in The Proceedings of The Fourth International Conference on Data Mining (ICDM-2004), Bighton, UK.

Maytal Saar-Tsechansky and Foster Provost. 2004. Active Sampling for Class Probability Estimation and Ranking. Machine Learning 54 (2), 153-178.

Maytal Saar-Tsechansky and Provost, Foster. 2001. Active Learning for Class Probability Estimation and Ranking, in Seventeenth International Joint Conference on Artificial Intelligence (IJCAI-01), Seattle, WA.

Maytal Saar-Tsechansky, Nava Pliskin, Gad Rabinowitz, and Mark Tsechansky. 2001. Monitoring Quality of Care with Relational Patterns. Topics in Health Information Management 22, 24-35.

Maytal Saar-Tsechansky, Nava Pliskin, Gad Rabinowitz, and Mark Tsechansky. 2001. Pattern Extraction for Monitoring Medical Practices, in Proceedings of the 34th Hawaii International Conference on Systems Sciences (HICSS), Maui, HI: IEEE Computer Society Press.

Maytal Saar-Tsechansky, Nava, Pliskin, Gadi, Rabinowitz, and Avi, Porath. 1999. Mining Relational Patterns from Multiple Relational Tables. Decision Support Systems 27, 177-195.