March 12 – 16, 2018 » Workshop in Geometric Analysis and Nirenberg Lectures by Eugenia Malinnikova

Workshop organizers: Pengfei Guan (McGill), Alina Stancu (Concordia), Jérôme Vétois (McGill)

Geometric analysis has seen several major developments in recent years. Some of the most spectacular breakthroughs were made in the last decade and include Perelman’s work on Hamilton’s Ricci flow and his resolution of the Poincaré conjecture and Thurston’s geometrization conjecture; Brendle’s resolution of the Lawson conjecture; the Differentiable Sphere theorem by Schoen and Brendle; and Marques and Neves’ resolution of the Willmore conjecture. It is an ideal time to bring together mathematicians in this area to learn more about the achievements of others, foster collaboration, and enable new breakthroughs.

The workshop will focus on prominent current areas of geometric analysis including, but not limited to, geometric evolution equations, minimal surfaces, conformal geometry, complex structures and Kähler geometry, and applications to relativity. An important theme in this area has been the development and use of sophisticated techniques from the theory of partial differential equations to study natural equations that arise in geometry.

CRM Nirenberg Lectures organizers: Pengfei Guan (McGill), Dima Jakobson (McGill), Iosif Polterovich (Montréal), Alina Stancu (Concordia)

The CRM Nirenberg Lectures in Geometric Analysis have taken place every year since 2014. The series is named in honour of Louis Nirenberg, one of the most prominent geometric analysts of our time. The 2018 lectures will be delivered by Professor Eugenia Malinnikova from the Norwegian University of Science and Technology in Trondheim. Malinnikova’s contributions include a groundbreaking joint work with A. Logunov on the nodal geometry of Laplace eigenfunctions, that has led to a proof of two major conjectures in the field due to Shing-Tung Yau and Nikolai Nadirashvili. The research achievements of Eugenia Malinnikova have been recognized by the 2017 Clay Research Award and an invitation to speak at the 2018 ICM in Rio de Janeiro.

April 14 – May 11, 2018 » Mathematics of Machine Learning

Organizers: Sebastian Bubeck (Microsoft Research), Luc Devroye (McGill), Gábor Lugosi (Pompeu Fabra)

The thematic activity focuses on mathematical challenges of machine learning. The spectacular success of machine learning in a wide range of applications opens many exciting theoretical challenges in a number of mathematical fields, including probability, statistics, combinatorics, optimization, and geometry. The CRM will bring together researchers of machine learning and mathematics to discuss these problems. The principal topics include combinatorial statistics, online learning, and deep neural networks.

The main activities include a workshop on “Combinatorial Statistics” and another one on “Modern Challenges in Learning Theory,” as well as regular seminars given by the invited researchers and scholars-in-residence.

The program will go as follow.

Week 1 (Monday April 16-Friday April 20)
Opening week.
Opening keynote lecture on Monday April 16.
Arrival of the Simons Foundations researchers in residence.

Week 2 (Monday April 23-Friday April 27)
“Workshop on Learning Theory”.
24 invited speakers.
Open to all scholars.
Small registration will apply to all attendees.

Week 3 (Monday April 30-Friday May 4)
“Workshop on Combinatorial Statistics” (by invitation only).
One minicourse (TBA)

Week 4 (Monday May 7-Friday May 11)
Closing keynote lecture on Friday May 11.

June 11 – July 6, 2018 » Causal Inference in the Presence of Dependence and Network Structure

Organizers: Erica E.M. Moodie (McGill), David A. Stephens (McGill), Alexandra M. Schmidt (McGill)

The goal of most, if not all, statistical inference is to uncover causal relationships, however it is not generally possible to infer causality from standard statistical procedures. In the last three decades, the field of causal inference research has grown at a rapid pace, and yet much of the literature is devoted to relatively simple settings. In this month-long program, we aim to push the frontiers of causal inference beyond simple settings to accommodate complex data with features such as network or spatial structure. We will hold a series of lectures and workshops that address current and novel aspects of causal inference, which involves the uncovering of relationships between variables in an observationally-derived data collection setting. Throughout this program, we will investigate new and challenging settings that have been studied in the conventional statistical literature, but not viewed through the lens of causal inference. The unifying theme of the program is that of complex dependence, with a particular focus on spatial, network, and graphical structures.