Nwe bridges between mathematics and data science

DATE: 8-11 November 2021
TIME: Monday from 10:00 to 17:00 and Tuesday-Thursday from 9:30 to 19:00
LOCATION: VALLADOLID

Description
Understanding the world through data and computation has always been at the heart of scientific discovery. In the past decade primarily data-driven approaches, such as neural networks, have been very successful. Nevertheless, the reason for this success is to some extent mysterious and raises multiple questions regarding the robustness, explainability, interpretability and fairness of the algorithms used. The answer to these questions is crucial when it comes to decision making.

In recent years, there has been increasing activity in building bridges between these new ideas and other well-established approaches based on models typically derived from first principles. However, the computational cost of the latter makes them unaffordable except in low dimensions, which is a limitation from which neural networks are exempt. Establishing solid connections between these two different points of view have already proved to be extremely fruitful.

The Spanish "Mathematical Strategic Network" ("Red Estratégica de Matemáticas", REM) organizes a one-week workshop in 8-11 November 2021 in Valladolid, Spain, with the aim of bringing together researchers in mathematics, machine learning, and data science, to exchange ideas and progress in the construction of new bridges among these fields and make visible the work already done.

The workshop will be structured around five plenary sessions by leading scientists on the international scene, plus three round-tables and a large number (20-25) of thematic minisymposia.

The REM is a network of mathematical research institutes which aims at fostering the international presence of the Spanish research in mathematics, the creation of synergies among the mathematical scientific community and the socioeconomic impact of Spanish mathematical research. It promotes the dissemination and transfer of mathematical technology, orienting research towards the needs of companies, industries and public administrations.

Plenary Speakers
- Joan Bruna (Courant Institute, New York University)
- Coralia Cartis (University of Oxford)
- Marco Cuturi (Google Brain/ENSAE, Institut Polytechnique de Paris)
- Jeff Goldsmith (Columbia University)
- Alfio Quarteroni (Politecnico di Milano)

Round Tables
- New Bridges between Mathematics and Data Science: a Scientifc Debate
- Challenges and opportunities for Mathematics in Data Science: the point of view of funding agencies and policy-makers
- Mathematics, Data Science and transfer: the point of view of industry 

Minisynmposia
MS1. High-dimensional Bayesian networks
MS2. Functional Data Analysis (I)
MS3. Spatio-temporal Data Science
MS4. Interpretability and explainability of algorithms
MS5. High-dimensional variable selection
MS6. Fair learning
MS7. Optimal transport for data science
MS8. Adversarial Machine Learning
MS9. Probabilistic Learning
MS10. New Approaches in Combinatorial Optimization
MS11. Mathematical Optimization Methods for Decision Making
MS12. Decision aid and data science models for disaster management
MS13. (Mathematical support to the) resource and process management in health
MS14. Mathematical Optimization for Data-Driven Decision-Making
MS15. Mathematical Optimization, Classification and Regression
MS16. Data Science Applications
MS17. Non-linear approximation, vision and images
MS18. Neural networks for Mathematicians
MS19. Machine learning techniques in control theory and inverse problems
MS20. Solving inverse problems using data-driven models
MS21. New perspectives in Computational Mathematics (I)
MS22. New perspectives in Computational Mathematics (II)
MS23. Statistical analysis of complex data (I)
MS24. Statistical analysis of complex data (II)
MS25. Digital Twins
MS26. New Perspectives in Data Science
MS27. Heuristics in Industry
MS28. ML and NLP models: from notebook to production deployment
MS29. Functional Data Analysis (II)
MS30. Industrial Applications 


More info at http://nbmds.uva.es/