Ambrosio Valencia-Romero is a postdoctoral research fellow in engineering at The Roux Institute. He has experience in decision-making in engineering design, multi-agent systems modeling and simulation, discrete choice analysis, and behavioral game theory. Valencia-Romero's current research interests include strategic engineering and design, digital engineering, and the design of incentive mechanisms for collaborative product and systems development.
Valencia-Romero earned his Ph.D. in systems engineering in 2021 at the Stevens Institute of Technology. As part of his doctoral research, he studied collective decision-making processes in engineering systems design, their interrelationship with the technical and social factors of the design problem, and how designers make tradeoffs between their individual and shared objectives. He also holds master’s and bachelor’s degrees in mechanical engineering from the Recinto Universitario de Mayagüez in Puerto Rico and the Universidad del Atlántico in Colombia, respectively.
At Stevens, Valencia-Romero served as the student representative of the Graduate Student Academic Integrity Board. He also volunteered for the Doctoral Student Peer Mentoring program and served as program chair of the 2019 edition of the Stevens Graduate Research Conference. Outside Stevens, Valencia-Romero has been a member of the ASME DED Committee for Broadening Participation of Underrepresented Groups since 2018.
Valencia-Romero worked at the Colombian Navy-run Science and Technology Corporation for the Development of Naval, Maritime, and Riverine Industries (COTECMAR) where he helped lay out and design machinery, propulsion, and piping systems in ships. He also gained industry experience at mining and construction equipment repair shops in his home country, first as an intern and later as field service engineer. Before joining The Roux Institute, Valencia-Romero completed a postdoctoral stint at Carnegie Mellon University, where he applied qualitative research and design thinking to identify opportunities for artificial intelligence and machine learning in the management of capital projects.