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More New Python Notebook Examples that Combine Data Science and Mathematical Optimization

How can you use different prediction models for price optimization and to keep avocado toast on your breakfast menu? How can you identify plagiarism with text similarity? How can you effectively plan for airline disruption in a time of continual flight delays and cancelations?

By combining data science tools and mathematical optimization.
In this session, Gurobi will introduce several of our newest (and free) educational examples that students and instructors can use to learn and teach real-world applications of combined data science and optimization problem solving. We will review our new data science library of Python Notebook Examples that combine data science tools and prescriptive analytics and offer new data science learners an entry point into problem-solving with optimization.

Meet the Experts

Rahul Swamy

Data Science Intern, Gurobi Optimization

Rahul Swamy is a researcher passionate about creating innovative tools that combine machine learning and mathematical optimization. During his current Ph.D. in Operations Research at UIUC, his work in mathematical optimization and game theory has led to publications in journals such as Operations Research and the Journal of Combinatorial Optimization. His work on fairness-optimized political redistricting has received honors such as First Place in the 2019 INFORMS Service Science Best Paper Award.

Jerry Yurchisin

Data Science Strategist

Mr. Yurchisin has over ten years’ experience applying operations research, machine learning, statistics, and data visualization to improve decision making. Before joining Gurobi, Jerry (who also goes by Jerome) was a Senior Consultant at OnLocation, Inc. where he customized several linear programming models within the National Energy Modeling System (NEMS) to analyze implementing specific energy policies and utilizing new technologies.

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