Extended Bio
Duy Duong-Tran is currently a Ph.D. Candidate in the School of Industrial Engineering (IE) and a graduate certificate candidate in Engineering Teaching and Learning in the School of Engineering Education, Purdue University. He joined Purdue IE in 2017 because of the school’s unmatched vision in approaching and solving emerging complex challenges in the big data era through the RethinkIE campaign.
In terms of research, the majority of his focus lies in the intersection between network science (an emerging research area greatly inspired by graph-theory) and theoretical/computational neuroscience. It was only in the last decade that innovations in functional Magnetic Resonance Imaging (fMRI) have achieved high-quality data for individual human brain structure and functions. Interestingly during such time, network science had also grown with unprecedented pace, in part, due to the birth of social networks and other networked data in biology, finance, to name a few. Such exciting intertwined dynamics have opened many doors for applied mathematicians, scientists and engineers to take part in solving complex neuroscientific challenges, which gave rise to the emerging interdisciplinary field of network neuroscience. With formal training in operation research (optimization) during his undergraduate and Master programs in IE and topological data analysis (TDA) during his PhD, he has developed a broad interest in creating well-defined frameworks with theoretical guarantees to solve different questions in neuroscience and other networked data. His Ph.D. training in network (neuro)science has also been supported, in part, by a Ross Doctorate Research Fellowship in 2017. Most recently, he was the recipient of a highly prestigious Bilsland Dissertation Fellowship, awarded to best dissertations from Purdue’s College of Engineering in the academic year of 2021-2022.
During summers of 2019 and 2020, he also had fantastic opportunities to work with the Data Science Summer Institute at Lawrence Livermore National Laboratory (one of the U.S. Department of Energy National Lab located in Livermore, California, U.S.), jointly with the Machine Learning Group (2019) and the Computational Engineering Division group (2020). The positive experience at the lab has deeply motivated him to continue working and contributing to improve the status-quo of important matters relating to the United States national interest. In addition to Purdue University and Indiana University School of Medicine, his research collaborations are also featured Perelman School of Medicine at the University of Pennsylvania, École Polytechnique fédérale de Lausanne (Swiss), and Université catholique de Louvain (Belgium), and Karl-Franzens University Graz (Austria), just to name a few.
Throughout the last decade, he has been participating in the pedagogical development and improvement of eight IE/Operation Research (OR) courses, including but not limited to Simulation, Statistical Quality Controls and Stochastic models. Depending on the coursework, his course involvements varied from co-/guest- lecturer to teaching assistant. In May of 2018, he participated in Purdue’s global engineering engagement program (Maymester headed by Prof. Goñi) to teaching-assist the “System Dynamics” course offered in Pamplona, Spain. In 2019, he received the Magoon teaching excellence award by Purdue College of Engineering Leadership for his excellent contributions in the OR course “Stochastic Models.” During Summer of 2020, he has also mentored two groups of IE/OR independent research projects that are sponsored by different industry partners with Purdue IE. Both projects received very positive feedbacks from Purdue IE’s partners based on their end-of-term deliverables. As a lifelong learner in education, his teaching passion has driven him to pursue a graduate certificate, specializing in Engineering Teaching and Learning, offered by Purdue School of Engineering Education - the first department (now school) of Engineering Education established in the U.S. With formal training in both Engineering Education and neuroscience, he is also genuinely interested in developing innovative pedagogy, inspired from cognitive science and neuroscience, in the inter-/multi-disciplinary teaching/learning domain.
Besides teaching and research, he has been appointed to a variety of leadership positions, spanning from both academic to industry sectors. In early 2021, he served in the search committee, at consultant capacity, for the next James Solberg Head for the School of Industrial Engineering. In addition, he also served as a chair of health and wellness for Purdue IE Graduate Student Organization in 2018. From 2014 to 2017, he earned two Bachelor's degrees in Mathematics and Industrial/Entrepreneurial Engineering, and a Master's degree in Industrial Engineering, both at Western Michigan University. Prior to joining Purdue, he was appointed to lead multi-disciplinary teams at different international corporations in automobile industry as internal Sales and Program Manager.