Balaji Padmanabhan is the Anderson Professor of Global Management, the director of the Center for Analytics & Creativity and a professor in the School of Information Systems and Management. Previously, he served as the chair of the department. He has created courses and taught undergraduate, MBA/MS, and doctoral courses in areas related to AI and machine learning, business/data analytics and computational thinking.
He designs analytics-driven algorithms to solve business problems. Padmanabhan's specific interests and expertise include AI and machine learning, designing analytics-driven algorithms for business applications, managing analytics, building and evaluating predictive models, patterns discovery in data, business value of analytics, enabling citizen data science and applications of analytics in churn, health care, recommender systems, fraud detection and elections. He often works with industry partners on applied research and has worked with more than twenty firms on various machine learning and analytics initiatives, often with a focus on innovative applications to drive business value.
His research has been published in the premier computer science and business journals and conferences including ACM KDD Proceedings, ACM RecSys, ACM Transactions on MIS, Big Data, Decision Support Systems, IEEE Transactions on Knowledge and Data Engineering, Information Systems Research, the INFORMS Journal on Computing, JAMIA, Management Science and MIS Quarterly. He serves on the editorial board and program committees of many leading academic journals and conferences in the field.
Padmanabhan earned a PhD from the Stern School of Business at New York University and a B.Tech in computer science from Indian Institute of Technology Madras.
ISM 6136 - Data Mining (introductory course on machine learning with business applications for MBA/MS students)
ISM 6543 – Data Science Programming (graduate machine learning course using the Python ecosystem)
ISM 6930 – Computational Methods in IS Research (doctoral course)
- "Can Cost Transparency in Recommendations Reduce Healthcare Costs? The Role of Time
Pressure and Cost-Framing," (2019), L. Bouayad, Padmanabhan, B. and Chari, K., Forthcoming in MIS Quarterly.
- "Audit Policies under the Sentinel Effect: Deterrence-Driven Algorithms," L. Bouayad,
Padmanabhan, B. and Chari, K., (2019), Information Systems Research, Volume 30, Issue 2, pp. 466-485.
- "Taming Complexity in Search Matching: Two-Sided Recommender Systems on Digital
Platforms," forthcoming, O. Malgonde, H. Zhang, B. Padmanabhan and M.Limayem, Forthcoming in MIS Quarterly.
- "Machine Learning for Psychiatric Patient Triaging: An Investigation of Cascading Classifiers," (2018), V. Singh, Shrivastava, U., Bouayad, L., Padmanabhan, B., Ialynytchev, A. and Schultz, S., Journal of the American Medical Informatics Association (JAMIA), Volume 25, Issue 11, 1, Pages 1481–1487
- “Predicting Presidential Election Outcomes from What People Watch,” (2017), A. Barfar and Padmanabhan, B., Big Data, 5(1): 32-41.
Padmanabhan's professional service includes work on the program committees of several academic conferences such as WITS, ICIS, ACM KDD and IEEE ICDM. He has served on the editorial boards of the premier journals in the discipline including Information Systems Research, the INFORMS Journal on Computing, Management Science, MIS Quarterly, Big Data and ACM Transactions on MIS.