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NEXT SEMINAR 2024/2025
In this section, you will find the information (Speaker/Guest, abstract, date & time slot , location, online registration link..) about the upcoming seminar during the year. Everyone is welcome, from within ESSEC as well as from outside.
Mario GUAJARDO - NHH Norwegian School of Economics
Wednesday, June 18 from 3pm to 4pm
Room : N406
Zoom link : https://essec.zoom.us/j/96013477038
Title : Sports scheduling : Optimization perspectives and applications in professional football leagues
Abstract : A main problem in the organization of sports leagues is to define a schedule of games. This involves decisions on when and where the teams should meet to play against each other. The problem is challenging because many conditions must be taken into account on behalf of different stakeholders, such as league officials, players, television broadcasters, and fans. Some examples of these conditions include stadium availability, travel distances, home and away sequences on consecutive games, fairness, and tournament attractiveness. The progress of optimization methods and computational resources have made possible to address this problem in a more efficient way than simple manual or random approaches. This seminar will give an overview on sports scheduling, referring to the different criteria considered in the literature and illustrating with real-world cases how the application of optimization models has helped decision makers to schedule football leagues.
If you need more information, please contact matta@essec.edu
Best regards,
******
Alp SUNGU - Wharton
Thursday, June 12 from 12pm to 1pm
Room : N406
Zoom link : https://essec.zoom.us/j/94048013830
Title : Generative AI Can Harm Learning
Abstract : Generative artificial intelligence (AI) is poised to revolutionize how humans work, and has already demonstrated promise in significantly improving human productivity. However, a key remaining question is how generative AI affects learning, namely, how humans acquire new skills as they perform tasks. This kind of skill learning is critical to long-term productivity gains, especially in domains where generative AI is fallible and human experts must check its outputs. We study the impact of generative AI, specifically OpenAI's GPT-4, on human learning in the context of math classes at a high school. In a field experiment involving nearly a thousand students, we have deployed and evaluated two GPT based tutors, one that mimics a standard ChatGPT interface (called GPT Base) and one with prompts designed to safeguard learning (called GPT Tutor). These tutors comprise about 15% of the curriculum in each of three grades. Consistent with prior work, our results show that access to GPT-4 significantly improves performance (48% improvement for GPT Base and 127% for GPT Tutor). However, we additionally find that when access is subsequently taken away, students actually perform worse than those who never had access (17% reduction for GPT Base). That is, access to GPT-4 can harm educational outcomes. These negative learning effects are largely mitigated by the safeguards included in GPT Tutor. Our results suggest that students attempt to use GPT-4 as a "crutch" during practice problem sessions, and when successful, perform worse on their own. Thus, to maintain long-term productivity, we must be cautious when deploying generative AI to ensure humans continue to learn critical skills.
If you need more information, please contact matta@essec.edu
Best regards,
******
Justo PUERTO - University of Seville
Monday, May 26th from 12:15 pm to 1:15 pm
Room : N406
Zoom link : https://essec.zoom.us/j/94005579044
Title : On optimization with ordering: A unified mathematical programming framework
Abstract : In this talk we address a unified mathematical optimization framework to compute a wide range of measures used in operations research and data science contexts. The goal is to embed such metrics within general optimization models allowing their efficient computation.
We assess the usefulness of this approach applying it to three different families of measures, namely linear, nested, and quadratic ordered measures. Computational results are reported showing the efficiency of our methods as compared with other approaches. We illustrate this methodology with some applications to well-known problems such as regression analisis, location theory and portfolio optimization.
If you need more information, please contact matta@essec.edu
Best regards,
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