Tong Wang

Profile image of Tong Wang
Assistant Professor

Contact

Websites

Positions

  • 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

  • Rafique, H., Wang, T. & Lin, Q. (2020). Transparency Promotion with Model-Agnostic Linear Competitors. International Conference on Machine Learning.
  • Pan, D., Wang, T. & Hara, S. (2020). Interpretable Companions for Black-box Classifiers. AISTATS.
  • Wang, T. (2019). Gaining Free or Low-Cost Interpretability with Interpretable Partial Substitute. International Conference on Machine Learning (ICML).
  • Bloxham, J., Badheka, A., Schmitz, A., Freyenberger, B., Wang, T., Rampa, S., Allareddy, V., Auslender, M. & Allareddy, V. (2019). Outcomes Associated with Peripherally Inserted Central Catheters in Hospitalized Children: 7-year single center experience. BMJ Open.
  • Wang, T. & Lin, Q. (In Press). Hybrid Predictive Model: When an Interpretable Model Collaborates with a Black-box Model. Journal of Machine Learning Research.
  • Robb, K., Badheka, A., Wang, T., Rampa, S. & Allareddy, V. (2019). Use of Extracorporeal membrane oxygenation and Associated Outcomes in Children Hospitalized Due to Sepsis in the United States: A large population based study. PLoS ONE.
  • Wang, T. (2018). Multi-value Rule Sets for Interpretable Classification with Feature-Efficient Representations. Montreal: The Thirty-second Annual Conference on Neural Information Processing Systems (NeurIPS).
  • Wang, T., Rudin, C., Velez-Doshi, F., Liu, Y., Klampfl, E. & MacNeille, P. (2017). Bayesian Rule Sets for Interpretable Classification, with Application to Context-Aware Mobile Recommender Systems. Journal of Machine Learning Research.
  • Wang, T., Rudin, C., Velez-Doshi, F., Liu, Y., Klampfl, E. & MacNeille, P. (2016). Bayesian Rule Sets for Interpretable Classification. The IEEE International Conference on Data Mining series (ICDM). DOI: 10.1109/ICDM.2016.0171.
  • Wang, T., Rudin, C., Wagner, D. & Sevieri, R. (2015). Finding Patterns with a Rotten Core: Data Mining for Crime Series with Cores. Big Data 3 (1) 3-21.
    • Expand publications

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.