Bio: Dr. Clint Nicely serves on the AESCAPE 2026 Advisory Board and co-chairs the conference's Industry Panel. He is a Principal Investigator and Structural Analysis Engineer at Raytheon, where he leads AI efforts across the company's mechanical engineering, structural and thermal analysis, design, and digital twin manufacturing efforts. His technical work centers on computational mechanics, large-scale finite element automation, and isogeometric and immersed-meshing method application. He is a vocal supporter of next-generation computational technologies that will advance the aerospace and defense industries and brings an industry practitioner's perspective to questions and research topics the academic community is actively exploring. Dr. Nicely also chairs the internal RTX Mechanical, Aero-Thermal, Materials, and Structures Technology Network Symposium and contributes to the NDIA Digital Manufacturing Working Group.

Bio: Dr. Chanwook Park is the Co-Founder and Chief Technology Officer of HIDENN-AI, an AI-native engineering software company developing agentic AI systems and next-generation computational technologies for computer-aided engineering (CAE). Dr. Park's research focuses on mechanistic computational intelligence, scientific machine learning, physics-informed AI, and AI-empowered simulation technologies. He is a key developer of the Convolutional Hierarchical Deep-Learning Neural Network (C-HiDeNN) and Interpolating Neural Network (INN) frameworks, as well as related technologies for scalable uncertainty quantification, surrogate modeling, digital engineering, and computational mechanics. His recent work explores how agentic AI systems can automate engineering workflows spanning CAD, meshing, simulation, verification, validation, and design optimization. As CTO of HIDENN-AI, Dr. Park leads the development of AI-powered engineering software products for applications in aerospace, defense, semiconductors, and advanced manufacturing. His work bridges academia and industry, advancing the transition toward Engineering Software 3.0 and autonomous engineering ecosystems.

Bio: Dr. Shengyen Li is a research engineer at the National Institute of Standards and Technology (NIST) specializing in computational materials for manufacturing. His expertise spans science-based modeling, data-driven methods across various length and time scales, and data informatics. Dr. Li’s research focuses on material development and process optimization for transformation-induced plasticity (TRIP) steels and Ni-base superalloys in both conventional and additive manufacturing. He has also led several investigations into the root causes of data variability to improve product quality. Currently, Dr. Li leads a Data Informatics and Management project at NIST designed to help the additive manufacturing community streamline technology transfer and accelerate the adoption of industry standards.

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Bio: Dr. Shane Zabel is the corporate Technology & Global Engineering artificial intelligence (AI) lead for RTX. RTX is an $88B aerospace and defense company with 185,000 employees across its three businesses – Collins Aerospace, Pratt & Whitney and Raytheon. In his role Dr. Zabel leads the RTX technology and global engineering AI strategy and the company’s AI technology roadmap. His organization leads companywide research & development (R&D) efforts including the development of the RTX AI Factory, the AI forward deployed engineering team, engineering AI strategic workforce

planning, and product AI governance.

With 22 years of experience in the aerospace and defense industry, Shane has a proven track record in electro-optical, infrared and synthetic aperture radar sensor processing; computer vision; artificial intelligence; machine learning and autonomy. Dr. Zabel’s expertise extends to architecture development for large-scale data processing and autonomous systems for both ground and on-board applications. He has a deep understanding of the underlying mathematics in the fields of machine learning and artificial intelligence. Over his career he has applied this expertise across the full life cycle model for the aerospace and defense industry; from pursuit-to-startup to

development, production and sustainment.

Dr. Zabel holds a doctorate in physics from Carnegie Mellon and a master’s degree in applied cognition and neuroscience from the University of Texas at Dallas. Dr. Zabel is the RTX executive sponsor for university relations with Carnegie Mellon University. He is a member of AI organizations at Aerospace Industries Association (AIA), National Defense Industrial Association (NDIA), and National Institute of Standards and Technology (NIST) and has received RTX program leadership, technical honors and engineering fellow recognitions.

Bio: Victor earned his PhD from Duke University in 1996 in the area of computational mechanics. He then joined what was the Abaqus R&D development in Rhode Island, which today is part DASSAULT SYSTEMES SIMULIA Corp. Victor has worked in a variety of R&D positions through the years and is today the Senior Technology Director and the Chief Scientific Officer for Structural applications. In the last few years at SIMULIA Victor has led from a simulation technology perspective a variety of multiphysics/multiscale simulation initiatives including machine learning for physics-based surrogates, battery cell engineering, additive manufacturing, micro-mechanics based multiscale materials, particle methods for extreme deformation, realistic human simulation capabilities, co-simulation-based multi-physics modeling.

Bio: Wing Kam Liu is the Walter P. Murphy Professor of Mechanical Engineering at Northwestern University—with courtesy appointments in Materials Science and Civil and Environmental Engineering—and the Co-Founder and Chief Strategy Officer of HIDENN-AI, Inc. He holds both an MS and PhD from the California Institute of Technology (Caltech). A global leader in computational mechanics, Professor Liu has served as President of the International Association for Computational Mechanics (IACM) and Chair of the US National Committee on Theoretical and Applied Mechanics (USNCTAM) within the National Academies.

His pioneering research focuses on AI-empowered Computer-Aided Engineering (CAE), advanced design, and manufacturing systems. A highly prolific scholar with over 77,000 citations on Google Scholar, he has authored seminal textbooks, including Mechanistic Computational Intelligence for Engineering, Mechanistic Data Science for STEM Education and Applications, and Nonlinear Finite Elements for Continua and Structures. His current work advances the frontiers of Software 3.0, driving pioneering research in Agentic AI and Large Language Model (LLM) ecosystems for Design for Manufacturing (DfM).

Professor Liu is a Thomson Reuters Highly Cited Researcher in Computer Science and holds seven US and international IP/patents. He has been recognized with the field's highest global honors, including the IACM Gauss-Newton Medal, the USACM John von Neumann Medal, the ASME Melville Medal, and the Japan Society for Computational Engineering and Science (JSCES) Grand Prize, among many others.

Bio: Miguel Bessa is an Associate Professor of Engineering at Brown University, where he develops methods in artificial intelligence and computational mechanics to solve frontier engineering problems. His contributions include being one of the earliest to introduce data-driven methods in computational mechanics, the co-development of self-consistent clustering analysis (SCA), uncertainty-aware design with new Bayesian methods, and the foundations of neural topology optimization — with applications spanning supercompressible metamaterials, nanomechanical resonators with quality factors above one billion at room temperature, lightsails for interstellar propulsion, and sustainable polymer composite design. Bessa received a PhD from Northwestern University in 2016 as a Fulbright scholar, held a postdoctoral position at Caltech, and was Assistant and then Associate Professor at TU Delft before joining Brown in 2022. His group is a strong proponent of open-source software (github.com/bessagroup).

Bio: Prith Banerjee is Senior Vice President of Innovation at Synopsys, and a member of the Executive Leadership team. Prior to that, he was CTO of Ansys, CTO of Schneider Electric, CTO of ABB, Managing Director at Accenture, and Director of HP Labs. Previously, he spent 20 years in academia as Professor, Chairman and Dean at the University of Illinois and Northwestern University. In addition, Prith has founded two EDA software companies, Accelchip and Binachip. He has served on the Board of Directors of Cray, CUBIC, and Turntide. He is a Fellow of the AAAS, ACM, IEEE, and National Academy of Inventors. He received a B.Tech. in electronics engineering from the Indian Institute of Technology, Kharagpur, and an M.S. and Ph.D. in electrical engineering from the University of Illinois, Urbana.

Bio: Dr. Hector Gomez is the Morris Goldman Chair in Engineering, a Professor in the School of Mechanical Engineering, the Weldon School of Biomedical Engineering (by courtesy), and the Purdue Institute for Cancer Research. Gomez specializes in computational mechanics with particular emphasis in simulation at the interface of engineering and medicine, and simulation of multiphysics systems. Prof. Gomez’s research has been recognized with multiple awards including the Juan C. Simo Award from the Spanish Society of Computational Mechanics, the MIT Innovators Under 35, the Young Investigator Award from the Royal Academy of Engineering of Spain, the Gallagher Young Investigator Award from the US Association for Computational Mechanics and the Princess of Girona Scientific Research Award (presented by the King of Spain; all fields of science, engineering and humanities). He has also received the Fellow Award from US Association for Computational Mechanics. Prof. Gomez has published over 120 journal papers and made over 200 contributions to conferences.

Bio: Dr. Jiachen Guo is the Chief Scientific Officer at HIDENN-AI, Inc., where he leads the scientific development of agentic AI frameworks for next-generation computer-aided engineering (CAE). He recently received his Ph.D. in Theoretical and Applied Mechanics from Northwestern University.

Dr. Guo’s research lies at the intersection of artificial intelligence, computational mechanics, and physical simulations. He is the principal developer of the Interpolating Neural Network-Tensor Decomposition (INN-TD) methodology, a highly scalable surrogate modeling approach that dramatically accelerates ultra-large-scale PDE solving and physical world modeling.

Prior to his executive role at HIDENN-AI, he served as a Student Researcher in Google, focusing on data-driven physics discovery and inverse design optimization for manufacturing. As an award-winning researcher and inventor, Dr. Guo’s work has been recognized at the NIST AMBench Challenges and published in premier venues including ICML and CMAME.

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