Ali Tosyali Headshot

Ali Tosyali

Associate Professor, MIS

Department of MIS, Marketing, and Analytics
Saunders College of Business
atosyali@saunders.rit.edu
585-475-7051
Office Location
LOW-3344

Ali Tosyali

Associate Professor, MIS

Department of MIS, Marketing, and Analytics
Saunders College of Business

Bio

Ali Tosyali is an Associate Professor of Management Information Systems at the Saunders College of Business, Rochester Institute of Technology, where he has taught since 2021. Before joining RIT, he was an Assistant Professor of MIS at the University of Delaware. He holds a Ph.D. and an M.S. in Industrial and Systems Engineering from Rutgers University and a B.S. in Systems Engineering from the Turkish Military Academy in Ankara, Turkey. His research uses network science, artificial intelligence, and machine learning to address problems on digital platforms and in healthcare and cybersecurity, including detecting fake reviews, explainable AI for mental health screening, and cybersecurity risk and disclosure. His work has appeared in the Proceedings of the National Academy of Sciences, the Journal of Management Information Systems, IEEE Transactions on Engineering Management, and the Annals of Operations Research, among others. His study of fake-review buyers on Amazon was covered by The Wall Street Journal, Forbes, and WIRED, and was a finalist for the MSI Robert D. Buzzell Best Paper Award. At RIT he teaches data analytics, business intelligence, and generative AI for business.

atosyali@saunders.rit.edu
585-475-7051

Select Scholarship

See my Google Scholar for all publications.

Tutun, S., Topuz, K., Tosyali, A., Bhattacherjee, A., & Li, G. (2024). Explainable artificial intelligence for mental disorder screening: A computational design science approach. Journal of Management Information Systems, 41(4), 958-981.

Xie, Y., Wang, T., Jeong, Y. S., Tosyali, A., & Jeong, M. K. (2024). True sparse PCA for reducing the number of essential sensors in virtual metrology. International Journal of Production Research, 62(6), 2142-2157.

He, S., Hollenbeck, B., Overgoor, G., Proserpio, D., & Tosyali, A. (2022). Detecting fake-review buyers using network structure: Direct evidence from Amazon. Proceedings of the National Academy of Sciences, 119(47), e2211932119.

Tosyali, A., Kim, J., Choi, J., Kang, Y., & Jeong, M. K. (2020). New node anomaly detection algorithm based on nonnegative matrix factorization for directed citation networks. Annals of Operations Research, 288(1), 457-474.

Choi, J., Tosyali, A., Kim, B., Lee, H. S., & Jeong, M. K. (2019). A novel method for identifying competitors using a financial transaction network. IEEE Transactions on Engineering Management, 69(4), 845-860.

Tosyali, A., Kim, J., Choi, J., & Jeong, M. K. (2019). Regularized asymmetric nonnegative matrix factorization for clustering in directed networks. Pattern Recognition Letters, 125, 750-757.

Rodriguez, A., Tosyali, A., Kim, B., Choi, J., Lee, J. M., Coh, B. Y., & Jeong, M. K. (2016). Patent clustering and outlier ranking methodologies for attributed patent citation networks for technology opportunity discovery. IEEE Transactions on Engineering Management, 63(4), 426-437.

 

Currently Teaching

MGIS-355
Business Intelligence
3 Credits
The course is intended to provide an integrative foundation in the field of business intelligence at both the operational and strategic levels. Students will experience a variety of contemporary tools to analyze complex business data and arrive at a rational solution. Topic such as data warehousing, visualization and data mining will be covered, along with other topics relevant to the field of business intelligence. The computer will be used extensively throughout the course.
MGIS-489
Seminar in MIS
3 Credits
Advanced study of MIS topics reflecting contemporary issues and/or current technological advancements impacting the development, implementation and management of information systems in organizations. Seminar topics have ranged from new technological developments to management security issues in MIS systems. Topics for a specific semester will be announced prior to the course offering.
MGIS-505
Generative Artificial Intelligence for Business
3 Credits
This course introduces students to Generative AI (GenAI) technologies and their applications across business functions. Students will explore the foundational concepts of GenAI — including how large language models work, how to engineer effective prompts, and how to integrate AI capabilities into business workflows using Python and APIs. Through hands-on labs and business case exercises, students will build practical GenAI applications, including retrieval-augmented generation systems and AI agents, while gaining awareness of the ethical, legal, and governance considerations surrounding AI deployment. Emphasis is placed on developing applied technical skills, understanding organizational use cases, and communicating AI-driven recommendations to business stakeholders.
MGIS-599
Independent Study Management Information Systems
3 Credits
The student will work independently under the supervision of a faculty advisor. (Instructor approval)
MGIS-650
Introduction to Data Analytics and Business Intelligence
3 Credits
This course serves as an introduction to data analysis including both descriptive and inferential statistical techniques. Contemporary data analytics and business intelligence tools will be explored through realistic problem assignments.

In the News

  • November 13, 2025

    a headshot of a man with his name and role next to it.

    RIT researcher develops mental health equivalent of MRI

    Across the country, millions struggle to find timely or affordable mental health support. This imbalance was an inspiration for Ali Tosyali, who researched a potential assistive technology that could help practitioners identify potential disorders in a faster and efficient manner.

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