Olivia J Fischer
Olivia Pinon Fischer is an Assistant Professor in the Daniel Guggenheim School of Aerospace Engineering at the Georgia Institute of Technology, where she leads the Digital Engineering Laboratory for Transformative Aerospace (DELTA). The lab’s mission is to develop rigorous methods for integrating models, data, engineering knowledge, and AI-enabled capabilities across the aerospace system lifecycle, with applications in aeronautics, space, and defense. Her work spans engineering knowledge discovery and representation, digital threads and twins, AI for systems and digital engineering, and digital engineering ecosystems. Before joining the faculty, Dr. Pinon Fischer served as a Principal Research Engineer and as Chief of the Digital Engineering Division in Georgia Tech’s Aerospace Systems Design Laboratory. Her expertise has been recognized through numerous honors, appointments, and leadership roles. She is an alumna of the National Academy of Engineering’s U.S. Frontiers of Engineering Symposium and received the 2024 Georgia Tech EVPR Institute Research Award for Outstanding Achievement in Research Program Impact. In the same year, she was selected as a Boeing Visiting Professor, spending part of the summer engaging with Boeing experts on artificial intelligence and digital engineering. She also served on the National Academies consensus study on digital transformation for the Department of the Air Force, whose report was released in December 2025. An Associate Fellow of the American Institute of Aeronautics and Astronautics, Dr. Pinon Fischer currently chairs AIAA’s Digital Engineering Integration Committee and serves as Director of the Digital Systems & Integration Group within the AIAA Aerospace Integrated Engagement Division. Internationally, she recently served as Co-Chair of the NATO STO AVT-407 System Qualification and Certification by Analysis Technical Activity and led the Strategy Group under NATO AVT-ET-259. She is also a member of the International Council of the Aeronautical Sciences Programme Committee and an invited member of NAFEMS’ Engineering Data Science Working Group. Dr. Pinon Fischer holds degrees across multiple engineering disciplines and earned her Ph.D. in Aerospace Engineering from Georgia Tech.
Professor Fischer’s goal as an educator is to help students build the technical and reflective capacities needed to contribute thoughtfully to a world that is increasingly complex and demanding. She believes engineering education must continually evolve alongside the technologies and capabilities shaping our field. Professor Fischer aims to develop engineers who can understand systems holistically, communicate across disciplines, and make decisions rooted in data and evidence. Professor Fischer is committed to fostering collaborative and interdisciplinary learning environments for both undergraduate and graduate students.
Professor Fischer’s research centers on advancing digital engineering through methods, architectures, and evaluation frameworks that make engineering knowledge, data, and models accessible, interoperable, trustworthy, and actionable throughout the system lifecycle. Her current research focuses on distributed AI and multi-agent approaches for adaptive, composable engineering workflows; evaluation methods for AI in model-centric aerospace engineering; and architectures for open, collaborative digital engineering ecosystems. Her broader work spans engineering knowledge discovery and representation, digital twins, digital threads, and design-to-mission analysis, with applications across aeronautics, space, and defense.
Lab/Collaborations:
- Aerospace Systems Design Laboratory (ASDL)
- Digital Engineering Laboratory for Transformative Aerospace (DELTA)
Disciplines:
- Systems Design & Optimization
- Ph.D., Aerospace Engineering, Georgia Institute of Technology, 2012
- M.S., Space Studies, International Space University, 2006
- M.S. (Dual Major), Mechanical Engineering & Wood Science and Engineering, Oregon State University, 2005
- M.S., Mechanical Engineering, Institut des Sciences et Techniques de l'Ingénieur de Lyon, 2003
- B.S., Mathematics and Physics, University Claude Bernard Lyon 1, Lyon – France, 2000
• Outstanding Achievement in Research Program Impact, EVPR Institute Research Award, 2024
• The Grainger Foundation Frontiers of Engineering (USFOE) Symposium, National Academy of Engineering, Speaker, 2024
• National Academies Consensus Study on Digital Transformation for the Department of the Air Force, The National Academies of Sciences, Engineering, and Medicine, 2024
• Boeing Visiting Professor Program, 2024
• Advisor to first place team, SLB’s Academia Innovation Program, SLB Digital Forum, 2024
• Advisor to first place team, Dr. David M. Aber Scholarship Competition, Dassault Systèmes (DS) Community of Experts (CoE), 2024
• Associate Fellow, American Institute of Aeronautics and Astronautics (AIAA), 2022
• Service Award, Georgia Institute of Technology, Atlanta, Georgia, 2022
• Georgia Tech Police Department’s Student Partnership Award, Georgia Tech Police Department, Georgia Institute of Technology, 2020
• Herman, Michael, Olivia J. Pinon Fischer, and Dimitri N. Mavris. "Predictive calibration for digital sun sensors using sparse submanifold convolutional neural networks." Acta Astronautica, 2026
• McGrath, Michael, John Matlik, and Olivia Pinon Fischer. "Connecting the Kill Chain to the Supply Chain: Building Industrial Surge Capacity." In Twenty-Third Annual Acquisition Research Symposium and Innovation Summit, 2026
• Wei, Xiao Olin, Olivia J. Pinon Fischer, Dimitri N. Mavris, and Emmanuel Motheau. "Online Adaptive Reinforcement Learning and Approximate Bayesian Computation (OARLABC) for real-time long-term calibration of rotordynamic models." Mechanical Systems and Signal Processing 252, 2026
•Wang, Paul, Olivia J. PinonFischer, and Dimitri Mavris. "Adaptive digital twins: Continuous subspace learning for dynamic domains." In AIAA SCITECH 2026 Forum, p. 1531, 2026.
• Karagoz, Esma, Olivia J. Pinon Fischer, and Dimitri Mavris. "Identification of Missing Knowledge in MBSE System Models Using GraphBased Machine Learning." Systems Engineering 29, no. 2, 133-149, 2026