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Esteban Real, Software Engineer, Google Brain Evolving Machine Learning Algorithms The effort devoted to hand-crafting machine learning (ML) models has motivated the use of automated methods. These methods, collectively known as AutoML, can today optimize the models' architectures to surpass the performance of manual designs. I will discuss how evolutionary...

Christian Szegedy, Staff Research Scientist, Google Machine Learning for Mathematical Reasoning In this talk I will discuss the application of transformer based language models and graph neural networks on automated reasoning tasks in first-order and higher-order logic. After a short introduction of the type of problems addressed and the general...

Rico Sennrich, Professor of Computational Linguistics, University of Zurich Lessons from Multilingual Machine Translation Neural models have brought rapid advances to the field of machine translation, and have also opened up new opportunities. One of these is the training of machine translation models in two or more translation directions to...

Rob Fergus, Professor of Computer Science, New York University and Research Scientist, DeepMind Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences In the field of artificial intelligence, a combination of scale in data and model capacity enabled by unsupervised learning has led to major...

Susan Murphy, Radcliffe Alumnae Professor at the Radcliffe Institute and Professor of Statistics and Computer Science, Harvard University Challenges in Developing Learning Algorithms to Personalize Treatment in Real Time There are a variety of formidable challenges to reinforcement learning and control for use in designing digital health interventions for individuals...

APPM Colloquium: Speaker : Susan Murphy Affiliations: Department of Statistics , Harvard University Department of Computer Science , Harvard University Radcliffe Institute for Advanced Study , Harvard University Day/Time: Friday, December 4th 2020, 4:10pm-5:10pm MST Location: Virtual talk on Zoom: https://cuboulder.zoom.us/j/95938791886 Talk Title: Challenges in Developing Learning Algorithms to Personalize...

Alex Hening, Department of Mathematics, Tufts Universy The competitive exclusion principle in stochastic environments The competitive exclusion principle states in its simplest form that a number of species competing for a smaller number of resources cannot coexist. Nevertheless, in nature there are many instances where this is not true. One...

Vrushali Bokil, Department of Mathematics, Oregon State University Compatible Discretizations for Maxwell’s Equations in Complex Materials In this talk, we discuss the construction of a specific compatible discretization, the Mimetic Finite Difference (MFD) method, for time domain electromagnetic wave propagation in linear dispersive media. The discretization utilizes an optimization procedure...

William J. Layton, Department of Mathematics, University of Pittsburgh 5 ideas, good and bad, in computational fluid dynamics The goal of numerical analysis of the Navier-Stokes equations is to extend the accuracy, reliability and predictive ability of numerical simulations of fluid motion. This extension means improving the complexity (space, computational...

James Sethian; Department of Mathematics; University of California, Berkeley Advances in Advancing Interfaces: The Mathematics of Manufacturing of Industrial Foams, Fluidic Devices, and Automobile Painting How do inkjet printers work? What are the dynamics of a dripping faucet? How are foams mixed, bicycle helmets manufactured, and cars painted? Complex dynamics...