2026 CAP Congress / Congrès de l'ACP 2026

Session

W2-2 SYMPOSIUM: Big Data in Matter, Materials, and Beyond | Le Big Data dans la matière, les matériaux et au-delà

W2-2

24 Jun 2026, 14:15

U. Ottawa - Learning Crossroads (CRX) Building

Conveners

W2-2 SYMPOSIUM: Big Data in Matter, Materials, and Beyond | Le Big Data dans la matière, les matériaux et au-delà

• Bill Atkinson

Description

Physics has been an empirical science for centuries, but contemporary data exist in quantities that dwarf Newton’s Principia. Today, marshalling data to distill the understanding to describe emergent behaviours, invent new materials, and develop new solutions for improving technologies and human health requires fundamentally different approaches. This symposium will draw together physicists working across disciplinary borders to forge new approaches to empirical science in the 21st century.

Planned speakers include expertise in machine learning and artificial intelligence, information geometry, big data, and applications in materials, condensed matter, biophysics, and the physics of medicine. In addition, speakers include Canadians currently abroad, foreign physicists near (ish) to Ottawa or with research connections to Canada, and Canadian physicists at diverse career stages. We expect contributed talks and participation from students in condensed matter and beyond with academic and industry interests in machine learning, data science, and applied physics.

La physique est une science empirique depuis des siècles, mais les données contemporaines existent en quantités qui éclipsent les Principia de Newton. Aujourd'hui, rassembler des données pour distiller la compréhension afin de décrire les comportements émergents, inventer de nouveaux matériaux et développer de nouvelles solutions pour améliorer les technologies et la santé humaine nécessite des approches fondamentalement différentes. Ce symposium réunira des physiciens travaillant au-delà des frontières disciplinaires afin de forger de nouvelles approches de la science empirique au XXIe siècle.

Parmi les intervenants prévus figurent des experts en apprentissage automatique et en intelligence artificielle, en géométrie de l'information, en mégadonnées et en applications dans les domaines des matériaux, de la matière condensée, de la biophysique et de la physique médicale. En outre, les intervenants comprennent des Canadiens actuellement à l'étranger, des physiciens étrangers proches (ou presque) d'Ottawa ou ayant des liens de recherche avec le Canada, ainsi que des physiciens canadiens à différentes étapes de leur carrière. Nous attendons des contributions et la participation d'étudiants en matière condensée et au-delà, qui s'intéressent à l'apprentissage automatique, à la science des données et à la physique appliquée dans le milieu universitaire et industriel.

507. Statistical and Machine Learning Methods for Material Science and Quantum Sensing

Carlo Bradac (Trent University)

24/06/2026, 14:15

Big Data in Matter, Materials, and Beyond / Le Big Data dans la matière, les matériaux et au-delà

Invited Speaker / Conférencier(ère) invité(e)

Machine learning (ML) is now ubiquitous. Fuelled by rapid advances in computational power and data accessibility, it has become the preferred paradigm for solving problems involving pattern recognition, classification, and complex, dynamic interactions. In this talk, I discuss the role that machine learning is playing in advancing material and device fabrication, as well as quantum...

515. Smarter Quantum Experiments: Boosting Quantum Research with Artificial Intelligence

Stefanie Czischek (University of Ottawa)

24/06/2026, 14:45

Big Data in Matter, Materials, and Beyond / Le Big Data dans la matière, les matériaux et au-delà

Invited Speaker / Conférencier(ère) invité(e)

The optimized and efficient control of experimentally realized quantum systems is becoming increasingly crucial in the current era of quantum science and technology. Progress in fields like quantum computation, simulation, cryptography, sensing, or metrology depends strongly on the precise preparation, control, and understanding of quantum systems. At the same time, artificial intelligence has...

528. Self-learning Monte Carlo with an Equivariant Transformer

Yuki Nagai (The University of Tokyo)

24/06/2026, 15:15

Big Data in Matter, Materials, and Beyond / Le Big Data dans la matière, les matériaux et au-delà

Invited Speaker / Conférencier(ère) invité(e)

Machine-learning-based approaches have become increasingly important in computational physics, particularly for simulations of complex many-body systems. In this context, equivariance provides a natural way to incorporate physical symmetries into models, acting as an inductive bias on the learned probability distributions. While such symmetry constraints are desirable, their direct...