ORSOL team mentored by Siva Seetharaman wins SAS Hackathon 2024
Author: | Sivaranjani Seetharaman |
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Team ORSOL (Operations Research SOLutions) developed an interactive dashboard that displays geo-mapped insights, highlighting top-priority counties most at risk from climate impacts.

Team (L-R) ) Jaewon Cho, Hyunsang (Ethan) Cho, Siva Seetharaman, Hsin-Wei (Ryan) Hsieh, and Jihyo Park.
Team ORSOL, a research group mentored by Professor Siva Seetharaman from the Edwardson School of Industrial Engineering and led by senior undergraduate student Hsin-Wei (Ryan) Hsieh (ECE), comprised of team members Hyunsang (Ethan) Cho, Jihyo Park, and Jaewon Cho, has achieved top honors at the prestigious 2024 SAS Hackathon. The SAS Hackathon, a globally recognized competition organized by SAS in partnership with Intel and Microsoft this year, saw over 1,731 registrants from more than 70 countries, including 1,000 student participants from across the globe. Team ORSOL won first place in the student competition.
The event challenges teams to utilize SAS Viya tools and other advanced analytics platforms to develop data-driven solutions to tackle real-world problems. With large-scale raw data provided by SAS, the ORSOL team focused on identifying the U.S. counties most vulnerable to the combined effects of climate change and economic vulnerability.
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Using SAS Viya for data visualization and Python for machine learning and data processing, the team developed an interactive dashboard showcasing a custom risk scoring system. The dashboard displays geo-mapped insights, highlighting top-priority counties most at risk from climate impacts. This visualization enables policymakers to better allocate resources in a data-driven manner.
Beyond the hackathon, Team ORSOL will continue its interdisciplinary work on predictive modeling and optimization solutions for real-world problems as part of Purdue’s Vertical Integrated Projects (VIP) program working with Prof. Seetharaman. Their ongoing efforts focus on modeling, forecasting, and optimization for electric vehicle charging infrastructures, combining machine learning and operations research methodologies and working with SAS mentors for computational tools and platforms.
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