Presentations

Line defects and tilting bundles, at Fields Institute, University of Toronto, Tuesday, November 19, 2019:

toronto-2019.pdf

Abstract:

Work on Bezrukavnikov and Kaledin provides a bridge between representation theory and algebraic geometry, giving an equivalence of derived categories between certain categories of coherent sheaves and non-commutative algebras. Their original construction involved a strange detour into the land of characteristic p, but with some insight from 3-d gauge theory, we can avoid this in the case of BFN Coulomb branches,...

PAPER: Claiming neurodiversity: Autism, expertise, and resistance, at National Women's Studies Association Conference 2019, San Francisco, Saturday, November 16, 2019:

claiming_neurodiversity_nwsa_2019.pdf

Authors:

Margaret F. Gibson, Renison University College at the University of Waterloo
Patty Douglas, Brandon University

What are we to make of neurodiversity discourse, both in its activist origins and its more recent expansions into the mainstream? Drawing upon blogs, research articles, and popular media, and engaging with critiques from feminist science and technology/disability studies, this paper considers how neurodiversity arguments for a re-valuation of autism and other forms of human variation...

Machine Learning for Modelling and Decision Making in Complex Physical Domains, at Centre for Artificial Intelligence Decision-making and Action (CAIDA), University of British Columbia, Vancouver, BC, Monday, November 4, 2019

Abstract:

My lab at the University of Waterloo carries out work on a variety of topics within Artificial Intelligence and Machine Learning with a focus on using real world problems to discover computational challenges for modelling of uncertainty, dealing with large or streaming datasets, learning predictive models and enabling decision making. In this talk I will provide a brief overview of a few ongoing projects where these challenges arise from different sources. In combustion modelling for energy production and engine design, standard practices...

Artificial Intelligence in Support of Physics and Other Complex Domains., at National Research Council, Ottawa, Canada, Thursday, October 17, 2019:

ai4design2019talk.pdf

I was invited up to NRC in Ottawa to give a talk on the work I am doing with Dr. Isaac Tamblyn there on using Reinforcement Learning for challenging problem of Material Design and Discovery. The research question from an AI perspective is essentially "Can we train a chemical lab manager using RL if we have simulations of the individual processes and begin with some foundational expert knowledge?". The talk was part of an local conference launching the new AI... Read more about Artificial Intelligence in Support of Physics and Other Complex Domains.

What is it like to be a librarian at EBCP and PXP? , at Waterloo Wellington Dufferin Health Library Network (WWDHLN) Fall Meeting, Waterloo, ON, Tuesday, October 8, 2019

Presenter: Caitlin Carter

An overview of participating in McMaster University's Evidence-Based Clinical Practice workshop as a librarian tutor, as well as the Pharmacy Experience Conference.

“Forgotten Origins: Newspaper Comics in Austria, 1923-1941”, at German Studies Association Conference, Portland, OR, Friday, October 4, 2019

Machine Learning for Modelling and Decision Making in Complex Physical Domains, at University College Dublin, Dublin, Ireland, Wednesday, September 25, 2019

My lab at the University of Waterloo carries out work on a variety of topics within Artificial Intelligence and Machine Learning with a focus on using real world problems to discover computationally hard challenges for modelling of uncertainty, dealing with large or streaming data, learning predictive models and enabling decision making. The methods we focus on include Deep Reinforcement Learning, Convolutional and Recurrent Neural Networks, Ensemble Tree methods and manifold based data/dimensionality reduction analysis. Motivation for our work comes from domains such as automotive,... Read more about Machine Learning for Modelling and Decision Making in Complex Physical Domains

Compact Representation of a Multi-dimensional Combustion Manifold Using Deep Neural Networks, at European Conference on Machine Learning (ECML 2019), Wurzburg, Germany, Thursday, September 19, 2019:

ecml2019_talk.pdf

The computational challenges in turbulent combustion simulations stem from the physical complexities and multi-scale nature of the problem which make it intractable to compute scale-resolving simulations. For most engineering applications, the large scale separation between the flame (typically sub-millimeter scale) and the characteristic turbulent flow (typically centimeter or meter scale) allows us to evoke simplifying assumptions–such as done for the flamelet model–to pre-compute all the chemical reactions and map them to a low-order manifold. The resulting manifold is then tabulated...

Graph Processing: A Panaromic View and Some open Problems, at Los Angeles, USA, Tuesday, August 27, 2019:

vldb19-keynote.pdf

Keynote at 45th International Conference on Very Large Data Bases Read more about Graph Processing: A Panaromic View and Some open Problems

What I Learned about Pedagogy from Magic School, at Indianapolis, IN, Thursday, August 15, 2019

At the Actuarial Research Conference, I spoke about an experience I had at a magic school, where non-teachers pretended to teach classes. It turns out they had many similar ideas about what makes an effective lesson!

You can find a video of the talk here: https://www.youtube.com/watch?v=okiXN10fDqE

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