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Dr. Syed Alam

NPRE 596 Graduate Seminar Series - Syed Alam

Event Type
NPRE 596 Graduate Seminar Series
1310 Digital Computer Laboratory, 1304 W. Springfield Avenue, Urbana, IL
Apr 11, 2023   4:00 - 4:50 pm  
Syed Alam, Assistant Professor, Nuclear Engineering and Radiation Science, Missouri University of Science and Technology and Director, Machine Learning and Artificial Intelligence for Advancing Nuclear Systems Lab
Free and Open to the Public
Originating Calendar
NPRE seminars

Uncertainty-Aware, Intelligent & Explainable Digital Twin-Enabling Technologies for Complex Nuclear Systems: An International Collaborative Effort

Abstract: Dr. Syed Alam will be presenting his research group's ongoing efforts on IDEA (Intelligent Digital Twins with Explainable AI) and UNITED (Uncertainty-Aware Intelligent, Trustworthy & Explainable Digital Twin) - an international collaboration with partners from the US: (Oracle Lab, INL, ORNL, Framatome, General Atomics, Ameren); UK: (University of Bristol, University of Sheffield), and India: (IIT Delhi). According to the US DOE and NRC, the nuclear industry has yet to take advantage of recent advances in artificial intelligence/machine learning (AI/ML) techniques. Digital Twin (DT) will play a significant role in risk-informed decision-making. For example, "FY2021-23 NRC Planned Research Activities state that "Methodology and Evaluation Tools for Digital Twin Applications" is one of the top priority strategic areas. However, the major challenges related to DT for complex nuclear systems are (a) Incorporating trustworthy data analytics algorithms with the treatment of noisy or erroneous datadata unavailability, and approximate physics used for nuclear reactor structural/component problems; (b) "Conventional" prediction algorithms suffer from "slower prediction" and hence, ineffective for real-time DT prediction; (c) Propagation of uncertainty with robust optimization, (d) Update module in DT by solving the "on-the-fly temporal synchronization;" and (e) Most existing AI/ML techniques for nuclear systems do not reveal causal explanations for their prediction mechanism, leading to a lack of explainability and interpretability. This seminar will encompass the recent collaborative research performed by Dr. Alam's research group on the above-mentioned technical challenges in DT-enabling technologies for nuclear systems in terms of data-physics fusion technique combining sparse data and approximate physics, uncertainty quantification, temporal synchronization, explainable AI, and operational digital twin framework.

Bio: Dr. Syed Alam is an Assistant Professor of Nuclear Engineering at Missouri S&T. He is the Director of MARTIANS (Machine Learning & ARTificial Intelligence for Advancing Nuclear Systems) laboratory and Faculty Investigator at the Center for Intelligent Infrastructure at Missouri S&T. He received Ph.D. (2018) and MPhil (2013) in Nuclear Engineering from the University of Cambridge. Prior to joining Missouri S&T, he was a Researcher at French Atomic Energy Commission. He also worked as a MeV Fellow at Argonne National Laboratory and a Nonproliferation Fellow at the KAIST. Dr. Alam's research interests and expertise broadly lie in the intersection of nuclear systems, explainable digital twins, and computational materials — focusing on the boundaries of applied mathematics and data science. 

Dr. Alam's research group has been supported by DOE, NRC, IAEA, Taylor Geospatial Intelligence, the State of Missouri, and Rhode Island. He is a technical reviewer for DOE, DOD, and NRC federal grants, conducted 100+ reviews in different journals, and is currently Guest Editor of journals: Journal of Nuclear Engineering, AI, Algorithms, Applied Sciences, and Energies for the Special Issue "Intelligent, Explainable, and Trustworthy AI for Advanced Nuclear Systems." To date, he authored/co-authored 80+ articles (including journals, conferences, and book chapters). He received several awards and honors for his research and teaching. Dr. Alam received the "University Outstanding Teaching Award" consecutively in 2021 and 2022 by Missouri S&T. He was also nominated for the "Faculty Achievement Award 2021" by Missouri S&T Nuclear Department. His research advisees received the DOE Innovations in Nuclear Technology R&D Award (2021), DOE Nuclear Leadership Scholarship (2022), and DOD Scholarship (2022). Previously, he was also the winner of the ANS Best Student Paper Award (2016), ANS Best Technical Poster Award (2015), and nominated for the AESJ Young generation Award (2017). He was awarded the Most Exemplary Graduate Fellow on "Nuclear Nonproliferation Fellowship 2017" by KAIST. Furthermore, he was awarded the Cambridge Philosophical Society's Research Award 2017 for a Promising Piece of Doctoral Research. His work has also been featured in the "ICE Business Times" Magazine, and he was invited for a TV interview on Channel S (a UK-based TV Channel).

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