Maxime Thomas

Biography

Maxime Thomas is a researcher whose work lies at the intersection of engineering, generative design, and artificial intelligence, with a particular focus on innovative methods applied to digital and organizational transitions. His research explores the ability of generative design algorithms (GDAs) to expand designers’ creative possibilities, particularly by analyzing their *generativity*—that is, their capacity to produce varied and novel solutions. His contributions include comparisons of algorithms (such as NSGA-II and MAP-Elites) to assess their impact on the diversity of design proposals, as well as theoretical frameworks for characterizing the generativity of generative AI (GenAI) models. At the same time, he is interested in the integration of design methods into organizations, studying their adoption through training programs and their transformative potential on managerial practices. His recent work also addresses the dynamics of digital platforms, identifying strategies of subversion or resistance among actors, as well as the conditions governing their evolution (technical genericity, functional expansion).

Publication(s)

2026
Are generative design algorithms truly generative? Comparing two genetic algorithms by the degrees of freedom they offer designers DOI : 10.1007/s00163-025-00465-x
2025
Training for transforming: design theory-based training for managing the unknown DOI : 10.1017/pds.2025.10338
2024
What is generative in generative artificial intelligence? A design-based perspective DOI : 10.1007/s00163-024-00441-x
2024
What Is Generative in Generative Artificial Intelligence? A Design-Based Perspective DOI : 10.1007/978-3-031-71922-6_8
2023
Design science for organizational change: How design theory uncovers and shapes generativity logics in organizations
2023
CAN PARETO FRONTS MEET THE SPLITTING CONDITION? COMPARING TWO GENERATIVE DESIGN ALGORITHMS BASED ON THE VARIETY OF DESIGN PARAMETERS COMBINATIONS THEY GENERATE DOI : 10.1017/pds.2023.83
2022
TEACHING ENGINEERING STUDENTS HOW TO DESIGN A PROOF-OF-CONCEPT: A WAY TO EXPERIENCE THE VALUE OF REFLECTIVE PRACTICES FOR ENGINEERS? DOI : 10.5821/conference-9788412322262.1208
2021
The future of digital platforms: Conditions of platform overthrow DOI : 10.1111/caim.12422
2021
What is generative in generative design tools? Uncovering topological generativity with a C-K model of evolutionary algorithms. DOI : 10.1017/pds.2021.603
2020
L’apport de la théorie de la conception à la gestion de crise L’exemple d’une war room créative et activatrice face à la Covid-19 DOI : 10.3166/RFG.2021.00498
2017
Rise and fall of platforms: Systematic analysis of platform dynamics thanks to axiomatic design

Teaching

POC and Industry of the Future (Entrepreneurship Quarter)

Course Director

To achieve this ambitious goal, students will carry out a proof-of-concept (POC) project in partnership with an industrial company and will draw on a body of knowledge covered in three course modules. The courses are as follows: Production Systems and Logistics (SPL): Designing for Innovation (CPI). Introduction to Statistical and Generative Learning Models (IMASG) These courses will provide the foundations for understanding industrial challenges and the reasoning required for innovation. POC projects are conducted with industrial partners from the Center for Scientific Management. The teaching team and its industry partners identify areas of innovation that are relevant from an educational perspective and for which appropriate POCs must be developed. The students’ task is to define the POC, carry it out, and analyze the resulting outcomes. Since the fields of innovation can vary, they will lead to different types of POCs (technical POCs, organizational POCs, process POCs, etc.), but they will always follow a dual approach of validation and exploration. Below are a few examples: Data analysis for inventory management (technical POC: Upstream database structuring) A calmer reception for older adults in the emergency room (organizational POC: Simulation of an alternative patient triage protocol) Low-carbon individual urban delivery (process POC: Prototype of a container enabling co-delivery) Launching a business model in the healthcare sector (economic POC: Market launch test to validate usage)