Contact
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Email
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Primary Office
- S282 Pappajohn Business Building (PBB)
- Iowa City, IA 52242
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Department
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Fax
Websites
Positions
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Assistant Professor of Business Analytics
Tippie College of Business
Education
- PhD in Electrical Engineering and Computer Science, Massachusetts Institute of Technology, 2016
- MS in Electrical Engineering and Computer Science, Massachusetts Institute of Technology, 2012
- BS in Electrical Engineering, Beijing University of Posts and Telecommunications, 2010
Areas of Interest
- Applied machine learning
- Business analytics
- Data-driven decision making
- Healthcare
- Interpretable machine learning
- Pattern detection
Selected Awards & Honors
- Best Paper Award, INFORMS Workshop on Data Science, 2020
- Runner-up of Best Paper Competition at INFORMS Data Science Workshop, INFORMS Data Science Workshop, 2019
- FICO Recognition Award for FICO Inaugural xML Challenge, FICO, 2019
- Finalist for Best Paper Competition at 13th INFORMS Workshop on Data Mining & Decision Analytics Workshop, 2018., INFORMS, 2018
- Second place winner of "Doing Good with Good OR", INFORMS, 2015
- Women in Machine Learning travel scholarship, WiML, 2015
- SAHD student travel grant, 2015
- LinkedIn Intern Women in Tech Scholarship, LinkedIn, 2014
- Joan and Irwin Jacobs Presidential Fellowship, MIT, 2010
- Research Excellence Funding, Tippie College of Business, 2019 - 2019
Selected Publications
- , , & (2021). Evaluating the Effectiveness of Marketing Campaigns for Malls Using A Novel Interpretable Machine Learning Model. Information Systems Research.
- & (In Press). Causal Rule Sets - Subgroup Identication for Enhanced Treatment Effect with Decision Rules. INFORMS Journal on Computing.
- & (2021). Hybrid Predictive Model: When an Interpretable Model Collaborates with a Black-box Model. Journal of Machine Learning Research.
- , & (2020). Transparency Promotion with Model-Agnostic Linear Competitors. International Conference on Machine Learning.
- , & (2020). Interpretable Companions for Black-box Classifiers. AISTATS.
- (2019). Gaining Free or Low-Cost Interpretability with Interpretable Partial Substitute. International Conference on Machine Learning (ICML).
- (2018). Multi-value Rule Sets for Interpretable Classification with Feature-Efficient Representations. Montreal: The Thirty-second Annual Conference on Neural Information Processing Systems (NeurIPS).
- , , , , & (2017). Bayesian Rule Sets for Interpretable Classification, with Application to Context-Aware Mobile Recommender Systems. Journal of Machine Learning Research.
- , , , , & (2016). Bayesian Rule Sets for Interpretable Classification. The IEEE International Conference on Data Mining series (ICDM). DOI: 10.1109/ICDM.2016.0171.
- , , & (2015). Finding Patterns with a Rotten Core: Data Mining for Crime Series with Cores. Big Data 3 (1) 3-21.
Editorial & Review Activities
- AAAI, Reviewer.
- Computing Surveys, Referee.
- Decision Sciences, Referee.
- ICML, Reviewer.
- Information System Research, Referee.
- International Journal of Electronic Security and Digital Forensics, Referee.
- Journal on Computing, Referee.
- Machine Learning for Health Workshop at NIPS, Review Panels.
- Management Information Systems Quarterly, Referee.
- Management Sciences, Referee.