Sebastian Souyris

Visiting Assistant Professor of Business Administration

Contact

432 Wohlers Hall

1206 S. Sixth

Champaign, IL 61820

ssouyris@illinois.edu

Vita

Google Scholar

Website

Listings

Educational Background

Ph.D., Information Risk and Operations Management, McCombs School of Business, The University of Texas at Austin, 2019
M. Phil., Operations Management, Stern School of Business, New York University, 2009
M.S., Operations Management, Faculty of Physical and Mathematical Sciences, University of Chile, 2005
B.S., Industrial Engineering, Faculty of Physical and Mathematical Sciences, University of Chile, 2003

Positions Held

Postdoc Research Associate, Business Administration, University of Illinois at Urbana-Champaign, 2020 to present
Vice President of Data Science, RSG Media, 2016-2019
Instructor, McCombs School of Business, The University of Texas at Austin, 2015-2016
Instructor, Department of Industrial Engineering, University of Chile, 2007-2007
Assistant Professor, Engineering, University of Los Andes, 2005-2007

Recent Publications

Alarcon, F., Duran, G., Guajardo, M., Miranda, J., Souyris, S., et al. (2017). Operations Research Transforms the Scheduling of Chilean Soccer Leagues and South American World Cup Qualifiers. Interfaces, 47 (1), 52-69. link >
Cortes, C., Gendreau, M., Rousseau, L., Souyris, S., & Weintraub, A. (2014). Branch-and-price and constraint programming for solving a real-life technician dispatching problem. European Journal of Operational Research, 238 (1), 300-312. link >
Souyris, S., Cortes, C., Ordonez, F., & Weintraub, A. (2013). A robust optimization approach to dispatching technicians under stochastic service times. OPTIMIZATION LETTERS, 7 (7), 1549-1568. link >
Duran, G., Guajardo, M., Miranda, J., Saure, D., Souyris, S., Weintraub, A., & Wolf, R. (2007). Scheduling the Chilean soccer league by integer programming. Interfaces, 37 (6), 539-552. link >

Other Publications

Conference Proceedings

Bose, S., Souyris, S., Ivanov, A., Mukherjee, U., Seshadri, S., & , Y. (2021). Control of Epidemic Spreads via Testing and Lock-Down. ( pp. 4272-4279). 60th IEEE Conference on Decision and Control.
Noronha, T., Ribeiro, C., Duran, G., Souyris, S., & Weintraub, A. (2007). A branch-and-cut algorithm for scheduling the highly-constrained Chilean soccer tournament. ( vol. 3867, pp. 174-186). Lecture Notes in Computer Science: Springer.

Working Papers

Souyris, S., Balakrishnan, A., Duan, J., & Rai, V. Network Effects on the Diffusion of Residential Solar Power Systems: A Dynamic Discrete Choice Approach.
Ivanov, A., Tacheva, Z., Souyris, S., Alzaidan, A., Seshadri, S., & England, A. Informational Value of Visual Nudges During Crises: Improving Public Health Outcomes Through Social Media Engagement Amid Covid-19.
Souyris, S., Seshadri, S., & Subramanian, S. Scheduling Advertisements on Cable Television.
Hao, S., Xu, Y., Mukherjee, U., Seshadri, S., Ahsen, M., Bose, S., Ivanov, A., Souyris, S., & Sridhar, P. Hotspots for Emerging Epidemics: Multi-Task and Transfer Learning over Mobility Networks.

Grants

Dynamic Resource Management in Response to Pandemics, c3.ai Digital Transformation Institute, 2020-2021

Teaching Interests

Operations and supply chain management, analytics, optimization, stochastic models.

Research Interests

Operational issues associated with environmental and human sustainability, supported by practice-driven optimization, machine learning, and economic analysis.

Current Courses

Business Analytics II (BADM 211) Builds on the foundation from the Business Analytics I (BADM 210), synthesizes concepts through hands-on application and project-based learning. Focuses on data acquisition, organization, analysis and visualization in a business setting. Expanding on the use of statistics in generating basic inferences to predictive modeling Identify opportunities for improving business decisions using data, conduct relevant analysis of the gathered and cleaned data, and finally, interpret and present analysis outcomes to decision makers. Using statistical tools and software applications to identify business problems, acquire relevant data, and generate analytic solutions using advanced analytics techniques and tools for generating insights. Introduces the students to analyzing, learning, and prediction using advanced analytics techniques and tools for generating business insights. This course will provide a practical introduction to various techniques regarding clustering, text mining, classification and decision trees, and time series analysis. Finally, the course will introduce advanced and emerging topics in predictive analytics.