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.
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Katherine Quinn (Georgetown University)Big Data in Matter, Materials, and Beyond / Le Big Data dans la matière, les matériaux et au-delàInvited Speaker / Conférencier(ère) invité(e)
Understanding the innovation landscape, and tracing how novel or emerging ideas become applied elsewhere in science or in a technological innovation that is then commercialized, is a complex challenge. We gather hundreds of millions of scientific articles and patents from 197 countries and in 165 languages, extract 6 billion links connecting them via citations and text similarity, and use the...
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Oleg Rubel (McMaster University)Big Data in Matter, Materials, and Beyond / Le Big Data dans la matière, les matériaux et au-delàInvited Speaker / Conférencier(ère) invité(e)
The discovery of novel materials increasingly relies on first-principles electronic-structure theory, but the value of these predictions depends on their reproducibility and connection to measurable observables. A recent community effort demonstrates that standardized, automated workflows (such as AiiDA) can establish high-precision reference datasets. By cross-validating all-electron...
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John DutcherBig Data in Matter, Materials, and Beyond / Le Big Data dans la matière, les matériaux et au-delàInvited Speaker / Conférencier(ère) invité(e)
Machine learning (ML) and artificial intelligence (AI) approaches are revolutionizing the analysis of large data sets. We are contributing to this effort by applying a deep learning approach to analyzing a very large number of infrared (IR) spectra of polymer cross-linked polyethylene (PEX) pipes that are used in household and industrial applications. This work is at the forefront of the...
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Hazhir Aliahmadi (Queens University)Big Data in Matter, Materials, and Beyond / Le Big Data dans la matière, les matériaux et au-delàInvited Speaker / Conférencier(ère) invité(e)
Optimization has long served as the primary paradigm for inference and design across science and engineering. More recently, machine learning has shifted this picture by trading explicit structure for universality in highly over-parameterized black-box models. Though powerful, over-parameterization obscures the interpretability of individual degrees of freedom, an effect further amplified by...
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Prof. Andrew Rutenberg (Dalhousie University)Big Data in Matter, Materials, and Beyond / Le Big Data dans la matière, les matériaux et au-delàInvited Speaker / Conférencier(ère) invité(e)
High-dimensional health data is measured regularly for large study populations. We do physics with this big data, with a particular focus on the complex dynamics of human aging. While we started with flexible deep-learning approaches to predict future health, we have used them to identify simpler stochastic dynamical models within interpretable latent spaces. I will tell you about our methods,...
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Rachael Mansbach (Concordia University)Big Data in Matter, Materials, and Beyond / Le Big Data dans la matière, les matériaux et au-delàInvited Speaker / Conférencier(ère) invité(e)
Peptides are short biomolecules with numerous desirable properties for biomaterials design including multifunctionality and biocompatibility. Over the past decade, there has been an explosion in the use of generative deep learning models for design of general de novo molecular design, including peptides; however, analysis of generative models and design spaces remains an open area of research....
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Dr Benjamin BaylisCondensed Matter and Materials Physics / Physique de la matière condensée et matériaux (DCMMP-DPMCM)Oral (Non-Student) / Orale (non-étudiant(e))
Atomic force microscopy force spectroscopy (AFM-FS), in which the AFM tip is pressed into and retracted from a surface to generate force-distance curves, can be used to obtain high resolution maps of the topography and mechanical properties of materials. This technique generates a large amount of data where each pixel in an image corresponds to a single force-distance curve. AFM-FS has been...
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Yuki Nagai (The University of Tokyo)Big Data in Matter, Materials, and Beyond / Le Big Data dans la matière, les matériaux et au-delàInvited 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...
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Stefanie Czischek (University of Ottawa)Big Data in Matter, Materials, and Beyond / Le Big Data dans la matière, les matériaux et au-delàInvited 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...
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Carlo Bradac (Trent University)Big Data in Matter, Materials, and Beyond / Le Big Data dans la matière, les matériaux et au-delàInvited 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...
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