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Interdisciplinary Machine Learning in Science and Engineering ()

Description

Deep learning is becoming a cornerstone for many applications. Finding appropriate models in the presence of data requires merging physical, computational, and statistical models. This workshop brings together experts working on mathematical, statistical, computational, and engineering aspects of deep learning to share their research experience in model learning.

The workshop will be hosted by Texas A&M University in College Station, Texas, and is supported by the Institute for Scientific Computation, Energi Simulation, Artie McFerrin Department of Chemical Engineering, and Department of Computer Science & Engineering.

We will provide and update information for this workshop online at http://isc.tamu.edu/events/Fall2019/.

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Organizing Committee

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Speakers

Ulisses Braga-Neto, Department of Electrical & Computer Engineering
Neural Network Prediction of Dengue Fever Severity Based on Genetic Polymorphisms
Suman Chakravorty, Department of Aerospace Engineering
A Decoupling Principle in Stochastic Optimal Control and Its Implications
Yalchin Efendiev, Department of Mathematics
Reduced-Order Deep Learning for Flow Dynamics. The Interplay Between Deep Learning and Model Reduction
Eduardo Gildin, Harold Vance Department of Petroleum Engineering
Applications of Machine Learning to Life-Cycle Reservoir Engineering: From Drilling to Reservoir Simulation
Boris Hanin, Department of Mathematics
The Neural Tangent Kernel in Deep Networks
Shuiwang Ji, Department of Computer Science & Engineering
Deep Learning on Images and Graphs
Peter Kuchment, Department of Mathematics
Deep Learning in Detecting Illicit Nuclear Materials
Siddharth Misra, Harold Vance Department of Petroleum Engineering
Applications of Machine Learning to Predict the Physical Properties of the Subsurface
Abhinav Narasingam, Artie McFerrin Department of Chemical Engineering
Koopman Operator Based Model Predictive Control for Hydraulic Fracturing
Guni Sharon, Department of Computer Science & Engineering
Learning an Interpretable Control Policy Through Deep Neural Networks
Rui Tuo, Department of Industrial & Systems Engineering
Projection Pursuit Gaussian Process Regression
Zhangyang (Atlas) Wang, Department of Computer Science & Engineering
Re-Comparing ImageNet Classifiers: Accuracy Should Not Be the Only Goal

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Schedule

All workshop presentations will take place in Zachry Engineering Education Complex (ZACH) 297. Please note that the schedule is tentative and subject to change. All times listed are local time.

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Welcome
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Shuiwang Ji, Department of Computer Science & Engineering
Deep Learning on Images and Graphs
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Zhangyang (Atlas) Wang, Department of Computer Science & Engineering
Re-Comparing ImageNet Classifiers: Accuracy Should Not Be the Only Goal
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Break
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Peter Kuchment, Department of Mathematics
Deep Learning in Detecting Illicit Nuclear Materials
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Abhinav Narasingam, Artie McFerrin Department of Chemical Engineering
Koopman Operator Based Model Predictive Control for Hydraulic Fracturing
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Lunch Break
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Suman Chakravorty, Department of Aerospace Engineering
A Decoupling Principle in Stochastic Optimal Control and Its Implications
–
Rui Tuo, Department of Industrial & Systems Engineering
Projection Pursuit Gaussian Process Regression
–
Guni Sharon, Department of Computer Science & Engineering
Learning an Interpretable Control Policy Through Deep Neural Networks
–
Boris Hanin, Department of Mathematics
The Neural Tangent Kernel in Deep Networks
–
Ulisses Braga-Neto, Department of Electrical & Computer Engineering
Neural Network Prediction of Dengue Fever Severity Based on Genetic Polymorphisms
–
Eduardo Gildin, Harold Vance Department of Petroleum Engineering
Applications of Machine Learning to Life-Cycle Reservoir Engineering: From Drilling to Reservoir Simulation
–
Siddharth Misra, Harold Vance Department of Petroleum Engineering
Applications of Machine Learning to Predict the Physical Properties of the Subsurface
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Yalchin Efendiev, Department of Mathematics
Reduced-Order Deep Learning for Flow Dynamics. The Interplay Between Deep Learning and Model Reduction
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Closing Remarks

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Contact Information

If you have any questions concerning this workshop, email Brad Shumbera at shumbera@tamu.edu.

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