On May 6th NeEDS will close the Online Seminar Series Machine Learning Mathematical Optimization

The Machine Learning NeEDs Mathematical Optimization online seminar series is drawing to a close, and what an exciting journey it has been! Hosted by the NeEDS Network of European Data Scientists, this seminar series has brought together experts, researchers, and enthusiasts from around the world to explore the intersection of machine learning and mathematical optimization. We extend our heartfelt gratitude to all the speakers, participants, and contributors who have made this initiative a resounding success. With over 100 talks and more than 20,000 views on our YouTube channel (https://www.youtube.com/c/NeEDSNetworkofEuropeanDataScientists), this seminar series has truly enriched our understanding of the fascinating world of machine learning and optimization.

The online seminar series has been a platform for discussing cutting-edge research, practical applications, and emerging trends in both machine learning and mathematical optimization. One of the key highlights has been the exploration of how Operations Research (OR) plays a crucial role in shaping the field of artificial intelligence (AI). By bridging the gap between theory and practice, Machine Learning NeEDs Mathematical Optimization online seminar series has emphasized the importance of optimization techniques in solving real-world problems.

Remaining Session:

On May 6, 16.30 (CET), Prof Pierre Pinson will give a talk at the Online Seminar Series Machine Learning NeEDS Mathematical Optimization, https://congreso.us.es/mlneedsmo/, branding the role of OR in AI with the support of EURO.

Title: Online learning and decision-making for renewables participating in electricity markets

Abstract: There is extensive literature on the analytics involved in the participation of renewable energy producers in electricity markets, covering both forecasting and decision-making. In their simplest form, participation strategies are to be seen as newsvendor problems (taking a decision-making perspective), or quantile regression problems (if taking a forecasting perspective instead). We will therefore explore recent advances at the interface between learning, forecasting and stochastic optimisation of relevance to renewable energy producers participating in electricity markets. This will cover online learning and decision-making, as well as distributionally robust optimisation.

Link to the talk:


Thank you for being part of this incredible journey, and we look forward to seeing you at the closing session on May 6th.