Julian Scott Yeomans
Julian has been a Professor of Operations Management and Information Systems at the Schulich School of Business, York University since 1993. He is the Director for both the Master of Business Analytics (MBAN) program and the Master of Management in Artificial Intelligence (MMAI) program at Schulich. He holds degrees in management science/information systems, environmental engineering, business, and statistics. He generally teaches courses on VBA programming and spreadsheet-based decision support systems. He has published 6 books and over 125 peer-reviewed, academic journal articles (with 60+ being single-authored) on a wide range of topics. His research has focused on the development of simulation-optimization algorithms, simulation-decomposition, population-based metaheuristics, machine learning, and modelling-to-generate-alternatives with applications to such diverse areas as aviation electrification, electric vehicle routing, waste management, environmental informatics, sustainability, empirical finance, healthcare, agriculture, urban planning, and cascading domino-effect risks. His research program is currently funded by an NSERC Civil, Industrial, and Systems Engineering Discovery Grant (2022-2027) and supported by a Schulich Research Excellence Fellowship (2021-24).
In collaboration with Mariia Kozlova from LUT university in Finland, Julian’s recent research efforts have been channeled into the development and software commercialization of their newly created computational “trick” referred to as simulation-decomposition (SimDec). SimDec is a technique that can be appended to any simulation model (or to raw data sets) to provide a visualizable analytical evaluation of its impacts and interactions that can be readily understood by both technical specialists and non-technical users. At its core, SimDec enhances the explanatory capabilities of Monte Carlo simulation methods by visually “teasing out” and uncovering inherent cause-and-effect relationships between groups of input and output parameters. While straightforward and elegant, this novel approach significantly enhances the analytical capabilities of users by readily exposing seemingly, a priori, counter-intuitive behaviors. This productive research stream has generated a book, numerous journal articles, and 4 patent filings.