Timezone: »

Machine Learning for Engineering Modeling, Simulation and Design
Alex Beatson · Priya Donti · Amira Abdel-Rahman · Stephan Hoyer · Rose Yu · J. Zico Kolter · Ryan Adams

Sat Dec 12 04:50 AM -- 03:00 PM (PST) @
Event URL: https://ml4eng.github.io/ »

For full details see: [ protected link dropped ]

Modern engineering workflows are built on computational tools for specifying models and designs, for numerical analysis of system behavior, and for optimization, model-fitting and rational design. How can machine learning be used to empower the engineer and accelerate this workflow? We wish to bring together machine learning researchers and engineering academics to address the problem of developing ML tools which benefit engineering modeling, simulation and design, through reduction of required computational or human effort, through permitting new rich design spaces, through enabling production of superior designs, or through enabling new modes of interaction and new workflows.

Author Information

Alex Beatson (Princeton University)
Priya Donti (Carnegie Mellon University)
Amira Abdel-Rahman (MIT)
Stephan Hoyer (Google)
Rose Yu (University of California, San Diego)
J. Zico Kolter (Carnegie Mellon University / Bosch Center for AI)

Zico Kolter is an Assistant Professor in the School of Computer Science at Carnegie Mellon University, and also serves as Chief Scientist of AI Research for the Bosch Center for Artificial Intelligence. His work focuses on the intersection of machine learning and optimization, with a large focus on developing more robust, explainable, and rigorous methods in deep learning. In addition, he has worked on a number of application areas, highlighted by work on sustainability and smart energy systems. He is the recipient of the DARPA Young Faculty Award, and best paper awards at KDD, IJCAI, and PESGM.

Ryan Adams (Princeton University)

More from the Same Authors