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The Challenge of Incorporating AI/Machine Learning Explainability into Engineering Simulation

The Challenge of Incorporating AI/Machine Learning Explainability into Engineering Simulation

 

 

Date: Friday, September 17th, 2021
11:00am EDT (New York) / 4:00pm BST

 

 

 

Hosted By:

NAFEMS Americas

 

The state of Explainable AI/ML, a topic of much interest during the NAFEMS Americas April 2021 virtual event on, "AI, Data Driven Models & Machine Learning: How Will Advanced Technologies Shape Future Simulation Processes?" had attendees asking for more discussion.

This one-hour panel will continue the conversation by setting the context of Explainable AI/ML as part of the larger topic on Artificial Intelligence trustworthiness. The panel will address some unanswered audience questions from the last discussion and then allow for live Q&A with participants.

 

Agenda

  • Welcome & Introduction
    Andrew Wood, NAFEMS

  • Do We Need Explainable Artificial Intelligence/Machine Learning?

    Panelists will include:
  • Mahmood Tabaddor, UL LLC (Panel Discussion Leader)
  • Peter Chow, Fujitsu UK
  • Olivia Pinon Fischer, Aerospace Systems Design Laboratory
  • Ankit Patel, Rice University & Baylor College of Medicine

  • Discussion and Q&A

 

Free Registration

 

About our speakers

Dr. Mahmood Tabaddor, Manager, Predictive Modeling and Analytics, UL LLC

Dr. Mahmood Tabaddor, Manager, Predictive Modeling and Analytics, UL LLC

Dr. Mahmood Tabaddor has been involved in modeling and simulation for over 25 years, and is currently the Manager for the Predictive Modeling and Analytics Team at UL. He is a member of ASME V&V 50 and a member of the NAFEMS Americas Steering Committee. He has a graduate degrees in Mechanical Engineering from University of Michigan, Ann Arbor and Engineering Mechanics from Virginia Tech. His doctoral dissertation focused on the dynamics of nonlinear systems.

 

Dr Peter Chow, Research Fellow, Fujitsu UK

Dr Peter Chow, Research Fellow, Fujitsu UK

Dr. Peter Chow is a Research Fellow at Fujitsu UK. He received his BSc Hons and PhD from University of Greenwich, London, UK, 1988 and 1991 respectively (Ph.D. in Computational Science and Engineering). His current focus is Societal Digital Twin to a more sustainable and belonging world. AI for simulation is a key driver for verification and validation of the virtual and physical worlds, with real-time demands and responses are some of the challenges. Specialties include AI for simulation (AI4SIM) and AI for non-destructive testing (AI4NDT). His previous role at Fujitsu Laboratories of Europe was head of Industry 4.0 and innovation covering Engineering Cloud and AI for Design & Manufacturing.

 

Dr. Olivia J. Pinon Fischer, Chief, Digital Engineering Division, Aerospace Systems Design Laboratory (ASDL)

Dr. Olivia J. Pinon Fischer, Chief, Digital Engineering Division, Aerospace Systems Design Laboratory (ASDL)

Dr. Olivia Pinon Fischer is a Senior Research Engineer within the School of Aerospace Engineering at the Georgia Institute of Technology, where she leads the Aerospace Systems Design Laboratory’s Digital Engineering Division. In her current position, Dr. Pinon Fischer leads and manages multi-disciplinary research teams in the fields of digital engineering, digital twins & ecosystems, model-based systems engineering, digital factories, production analytics, and machine learning and deep learning applications to engineering and design problems.

 

Ankit Patel - Rice University

Dr. Ankit Patel, Assistant Professor in ECE at Rice University and Neuroscience at Baylor College of Medicine

Dr. Ankit B. Patel is currently an Assistant Professor at the Baylor College of Medicine in the Dept. of Neuroscience, and at Rice University in the Dept. of Electrical and Computer Engineering. Ankit is broadly interested in the intersection between machine learning and computational neuroscience, two research areas that are essential for understanding and building truly intelligent systems, with a focus on learning abstractions. In his current role, he is continuing to pursue the unification of traditional hierarchical machine learning with deep neural networks, with applications to a variety of fields, including neuroscience, robotics, and particle physics.