Seminars

Complex/Dynamical Systems Seminar - Christopher Kulp

April 1, 2021

Christopher Kulp, Departments of Physics and Astronomy, Lycoming College Taxation, Redistribution, and Wealth in Simple Economic Models The distribution of wealth has been a topic of much interest over the last several years. In this presentation, I will discuss the effects of various tax and redistribution strategies on the distribution...

APPM Department Colloquium - Sanja Fidler

March 31, 2021

Sanja Fidler, Department of Computer Science, University of Toronto; and Director of AI, NVIDIA corporation Towards AI for 3D Content Creation 3D content is key in several domains such as architecture, film, gaming, and robotics. However, creating 3D content can be very time consuming -- the artists need to sculpt...

APPM Department Colloquium - Ruslan Salakhutdinov

March 26, 2021

Ruslan Salakhutdinov, UPMC Professor of Computer Science, Department of Machine Learning, Carnegie Mellon University Integrating Domain-Knowledge into Deep Learning. In this talk I will first discuss deep learning models that can find semantically meaningful representations of words, learn to read documents and answer questions about their content. I will introduce...

Complex/Dynamical Systems Seminar - Edgar Knobloch

March 25, 2021

Edgar Knobloch; Department of Physics; University of California, Berkeley Buckling, wrinkling and folding Buckling, wrinkling and folding of elastic structures will be discussed and illustrated. Two cases will be examined in detail: the case of a floating elastic sheet subjected to horizontal compression, and the case of a laterally compressed...

APPM Department Colloquium - Colin Raffel

March 19, 2021

Colin Raffel; Assistant Professor of Computer Science; University of North Carolina, Chapel Hill; and Staff Research Scientist, Google Brain T5 and large language models: The good, the bad, and the ugly T5 and other large pre-trained language models have proven to be a crucial component of the modern natural language...

Complex/Dynamical Systems Seminar - Laurent Hebert-Dufresne

March 18, 2021

Laurent Hebert-Dufresne, Department of Computer Science, University of Vermont Approximate master equations for contagions on higher-order networks Simple models of contagions tend to assume random mixing of elements (e.g. people), but real interactions are not random pairwise encounters: they occur within clearly defined higher-order structures (e.g. communities) which can be...

APPM Department Colloquium - Stephen Kissler

March 17, 2021

Stephen Kissler, Postdoctoral Fellow at Harvard T.H. Chan School of Public Health SARS-CoV-2 across scales Mathematical models have provided key insights into SARS-CoV-2 dynamics at the global, local, and physiological scales. I will summarize some of our research efforts within each of these contexts. Early in the pandemic, a deterministic...

APPM Recruitment Colloquium - Adrianna Gillman and Francois Meyer

March 12, 2021

Adrianna Gillman, Department of Applied Mathematics, University of Colorado Boulder Fast algorithms group: Accurate, efficient and robust techniques for solving partial differential equations Partial differential equations are often used to model physical phenomena. Unfortunately, it is impossible to simply write down the solution to most of the equations. Instead we...

APPM and AWM Joint Colloquium - Marsha Berger

March 12, 2021

Marsha Berger; Silver Professor of Computer Science and Mathematics; Courant Institute of Mathematical Sciences, New York University Computing Fluid Flows in Complex Geometry We give an overview of the difficulties in simulating fluid flow in complex geometry. The principal approaches use either overlapping or patched body-fitted grdis, unstructured grids, or...

APPM Department Colloquium - Joan Bruna

March 5, 2021

APPM Department Colloquium - Joan Bruna Joan Bruna; Assistant Professor; Courant Institute of Mathematical Sciences, New York University Mathematical aspects of neural network approximation and learning High-dimensional learning remains an outstanding phenomena where experimental evidence outpaces our current mathematical understanding, mostly due to the recent empirical successes of Deep Learning...

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