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M.Z. Naser, PhD, PE

Seminars, Outreach & Media mentions:

  • 2024 Flocode Podcast with James O'Reilly [Machine Learning in Structural Engineering] [link]

  • 2024 Invited presentation at the 3rd International Conference on ACE (Architectural, Civil and Environmental) Forensic Engineering, Forensic Research Center for Infrastructure, Korea University. Title: Integrating Machine Learning into Building Codes: Establishing Equivalence through Causality and Intuition. [flyer]

  • 2023 Invited presentation at the ACI Fall Convention in Boston, MA. Title: Big Data to Discovery: Advancing Concrete Material through Explainable and Causal AI.

  • 2023 Seminar at the 14th International Symposium on Fire Safety Science (Tsukuba, Japan). Title: Knowledge Discovery through Machine Learning. 

  • Appeared on the Fire Science Show (October 2023) - Revolutionizing Civil Engineering Through AI (Ep. 121)

  • 2023 Seminar at Michigan State University (October 2023). Title: Machine Learning and Fire Design.

  • 2023 SFPE Student Chapter at the University of Queensland. Title: Machine Learning: Verifying Domain Theories and Uncovering Hidden Knowledge. [flyer] [link]

  • 2023 The 14th North American Masonry Conference, Omaha, NE. Title: Buildings, Robotics and AI: Future of AEC (with Prof. Javier Irizarry). [flyer] [link]

  • 2023 Clemson University's Artificial Intelligence Symposium. Title: Causality, Engineering, Machine Learning, and Systems.

  • 2023 May 2023 Middle Tennessee SFPE Chapter Meeting. Machine Learning and Causality for Fire Engineers. [link]

  • 2023 at the ASCE Central PA chapter. Title:Machine Learning 101 for CEE: Navigating Data, Explainability & Causality. [flyer] [link]

  • 2023 The 2nd International Conference on ACE (Architectural, Civil, and Environmental) Forensic Engineering, Korea University. Title, Beyond Machine Learning: Causality & Knowledge Discovery in structural engineering. [flyer]

  • 2022 ASCE Convention. Title, Unboxing Machine Learning for Civil Engineers: From Data-driven to Explainability & Causality. [flyer]

  • Paper: “Mechanistically Informed Machine Learning and Artificial Intelligence in Fire Engineering and Sciences.” Fire Technology. https://doi.org/10.1007/s10694-020-01069-8. [Pre-print draft] Selected for Editor’s Choice [link] as well as Fire Science Reviews [link]

  • Sustainability, Intelligence and Resilience Seminar series hosted by the School of Civil Engineering and Transportation, South China University of Technology. March 31st, 2022. Title: Machine Learning in Structural Engineering: From Navigating the Realms of Data-driven Analysis to Explainability and Causal Knowledge Discovery.

  • Paper: Demystifying Ten Big Ideas and Rules Every Fire Scientist Engineer Should Know About Blackbox, Whitebox Causal Artificial Intelligence (featured at DeepAI)

  • Appeared on the Fire Science Show - Easy entry into the world of AI in fire (Ep. 28)

 

  • Appeared on the Civil Engineering Vibes Podcast (Ep. 38)

 

  • Featured at the Journal of Fire Technology

  • Featured at BLDG Blog by Geoff Manaugh

  • Interviewed by “Big 10” TV network to highlight my research contributions at Michigan State University. 

  • Appeared on Michigan State University College of Engineering “President’s Book” as well as “Big 10” TV network and "Spartan Engineer".

Civil Engineering Vibes with M.Z. Naser
00:00 / 40:22
Easy entry into the world of AI in fire with MZ Naser
00:00 / 55:40
Revolutionizing Civil Engineering Through AIThe Fire Science Show
00:00 / 47:59
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