2023 France-Berkeley Fund: 2 CREST recipients


The France-Berkeley Fund

Established in 1993 as a partnership with the French Ministry of Foreign Affairs, the France-Berkeley Fund (FBF) promotes and supports scholarly exchange in all disciplines between faculty and research scientists at the University of California and their counterparts in France.

Through its annual grant competition, the FBF provides seed money for innovative, bi-national collaborations. The Fund’s core mission is to advance research of the highest caliber, to foster interdisciplinary inquiry, to encourage new partnerships, and to promote lasting institutional and intellectual cooperation between France and the United States.

2023-2024 Call: 2 CREST recipients

For the 2023-2024 call, 2 projects have been submitted and are getting funded:

• Decentralizing divorces
A project developed by Matias Nunez (CREST, CNRS Research fellow) and his counterpart Federico Echenique, Professor of Economics and Social Sciences at UC Berkeley.

Abstract:
This project focuses on the development of practical applications of mechanism design, a branch of economics concerned with developing well-functioning institutions that ensure efficient and fair outcomes. In particular, we will focus on legal settings where two persons need to reach an agreement while their preferences are misaligned. Examples are dissolution of partnerships, allocation of rights and duties among conflicting agents, and divorces. While a judge, legal experts and lengthy bargaining procedures are often needed in practice, we plan to develop economic tools to appraise reasonable compromises, reducing both cost and time.

• Towards Local, Distribution-Free and Efficient Guarantees in Aggregation and Statistical Learning
A project developed by Jaouad Mourtada (CREST, ENSAE Paris) and his counterpart Nikita Zhivotovskiy, Assistant Professor in Statistics at UC Berkeley.

Description:
Statistical learning theory is dedicated to the analysis of procedures for learning based on data. The general aim is to understand what guarantees on the prediction accuracy can be obtained, under which conditions and by which procedures. It can inform the design of sound and robust methods, that can withstand corruption in the data or departure from an idealized posited model, without sacrificing accuracy or efficiency in more favorable situations. In particular, the problem of aggregation can be formulated as follows: given a class of predictors and a sample, form a new predictor that is guaranteed to have an accuracy approaching that of the best predictor within the class, up to an error that should be as small as possible.
This problem can be cast in several settings and has been investigated through various angles in Statistics and Computer Science. While the topic is classical, it has seen a renewed interest through (for instance) the recent direction of robust statistical learning, which raises the question of the most general conditions under which a good accuracy can be achieved. Despite important progress, several important and basic questions have remained unanswered in the literature, which we aim to study.

CREST, a multidisciplinary laboratory


On June 19, 2023, CREST organized a day dedicated to doctoral students was held.

At this event, doctoral students from the 4 research divisions (economics, sociology, finance-insurance and statistics) were able to exchange ideas with their colleagues and present their areas of research.

Multidisciplinarity…

CREST favors an interdisciplinary approach to tackling complex issues. This synergy between different areas of expertise enriches research and provides innovative perspectives in a variety of fields such as the sociology of work, public economics, green finance, political economy, statistical analysis of networks and many others.

Thanks to this multidisciplinary approach, the CREST laboratory fosters fruitful collaborations between researchers from different backgrounds, encouraging the emergence of innovative solutions to contemporary societal challenges

Fields of research by division

… At all levels

CREST maintains a wide range of academic and industrial partnerships beyond its core themes. These enriching interdisciplinary collaborations help to provide innovative solutions and tackle complex challenges in a wide range of sectors. CREST works with financial institutions (Caisse des dépôts et consignation, La Banque Postale Asset Management, HSBC AM) and public institutions (Ile de France region) to examine the determinants and impacts of integrating environmental, social and governance issues into investment decisions or to assess their climate and sustainable finance action plans (City of Paris, Ile de France region).

These interdisciplinary partnerships demonstrate CREST’s commitment to tackling contemporary challenges by mobilizing a wide range of knowledge and expertise.

Doctoral studies at CREST 

Working in the CREST laboratory, doctoral students benefit from a stimulating environment, conducive to the exchange ideas and collaboration with researchers from a variety of backgrounds. This diversity of approaches fosters the acquisition of cross-disciplinary skills and enables doctoral students to develop a holistic vision of their field of study, strengthening their ability to conduct innovative research and meet the challenges of tomorrow.

Bayes Comp 2023 15-17 March


Nicolas Chopin will give a presentation on March 15 at the third edition of the conference of the Bayesian Computation Section of the International Society for Bayesian Analysis

ENSAI_ PhD Day 2023 : Focus sur les doctorants


Le 2 février, les doctorants de l’ENSAI ont présenté leurs travaux à leurs pairs ainsi qu’aux enseignants-chercheurs et au personnel. Leurs thèses, dans le domaine des mathématiques et de leurs interactions avec la data science, le plus souvent motivées par une problématique concrète, portent sur une grande variété de sujets

Hi! PARIS present the project of Arnak Dalalyan, Hi! PARIS Fellow 2021


“Toward a better understanding of AI algorithms”. Arnak Dalalyan, professor at ENSAE Paris and director of the CREST, revolves around statistical methods for machine learning. He is going to further develop those methods in his project with Hi! PARIS entitled “Statistical Analysis of Generative Models: Sampling Guarantees and Robustness (SAGMOS)”.