Ali Tosyali Headshot

Ali Tosyali

Assistant Professor

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

585-475-7051
Office Location

Ali Tosyali

Assistant Professor

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

Bio

Ali Tosyali is an assistant professor of management information systems at the Rochester Institute of Technology. His research interests lie broadly at the intersection of network science, machine learning, and complex systems, driven by applications in financial services analytics, social network analysis, opinion mining and analysis, digital platforms, and business analytics.

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Invited Article/Publication
Tutun, S., Tosyali, A., Sangrody, H., Khasawneh, M., & Johnson, M. (2022). Artificial intelligence in energy industry: forecasting electricity consumption through cohort intelligence and adaptive neural fuzzy inference system. Journal of Business Analytics. . .
Yu, Y., Tosyali, A., Baek, J., & Jeong, M. (2022). A novel similarity-based link prediction approach for transaction networks. IEEE Transactions on Engineering Management. . .
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. . .
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. . .
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. . .
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. . .
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. . .
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. . .
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. . .
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. . .
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. . .
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. . .
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. . .
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. . .
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. . .
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. . .
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. . .
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. . .
Tosyali, A., & Tavakkol, B. (2021). A node-based index for clustering validation of graph data. Annals of Operations Research. . .
Tosyali, A., Choi, J., Kim, B., Lee, H., & Jeong, M. (2021). A dynamic graph-based approach to ranking firms for identifying key players using inter-firm transactions. Annals of Operations Research. . .
Tosyali, A., Song, R., Guo, W., Abolhassani, A., & Kalamdani, R. (2021). Data-Driven Gantry Health Monitoring and Process Status Identification Based on Texture Extraction. Journal of Computing and Information Science in Engineering. . .
Tosyali, A., Kim, J., Choi, J., Kang, Y., & Jeong, M. (2020). New node anomaly detection algorithm based on nonnegative matrix factorization for directed citation networks. Annals of Operations Research. . .
Choi, J., Tosyali, A., Kim, B., Lee, H., & Jeong, M. (2019). A Novel Method for Identifying Competitors Using a Financial Transaction Network. IEEE Transactions on Engineering Management. . .
Tosyali, A., Kim, J., Choi, J., & Jeong, M. (2019). Regularized asymmetric nonnegative matrix factorization for clustering in directed networks. Pattern Recognition Letters. . .
Rodriguez, A., Tosyali, A., Kim, B., Choi, J., & Lee, J. (2016). Patent clustering and outlier ranking methodologies for attributed patent citation networks for technology opportunity discovery. IEEE Transactions on Engineering Management. . .
Sevim, C., Ekiyor, A., & Tosyali, A. (2016). As a supply chain financing source, trade credit and bank credit relationship during financial crises from clustering point of view. International Business Research. . .
Published Conference Proceedings
Tavakkol, B., & Tosyali, A. (2020). Density-based clustering validation of uncertain data objects.. Northeast Decision Sciences Institute Annual Meeting.
Usta, M., & Tosyali, A. (2018). Characterization of Model-Based Uncertainties in Incompressible Turbulent Flows by Machine Learning. ASME International Mechanical Engineering Congress and Exposition.

Currently Teaching

BANA-255
3 Credits
This course serves as an introduction to the uses (and potential misuses) of data in a wide variety of social settings, including the exploration of contemporary techniques to analyze such data. Data acquisition, cleansing, management, analysis, and visualization will be addressed through hands-on projects. Project work will include contemporary social problems addressed using a dynamic set of resources and technologies. An emphasis will be placed on how insights gleaned from data analysis can be used to guide individual and group decision-making scenarios.
MGIS-355
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-650
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.

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