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, urban electric vehicle transportation routing, waste management, environmental informatics, climate 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.
Project Title Role Award Amount Year Awarded Granting Agency Project TitleCombining Simulation-Decomposition, Simulation-Optimization, and Modelling-to-Generate-Alternatives for Planning Under Uncertainty RolePrincipal Investigator Award Amount$130,000.00 Year Awarded2022-27 Granting AgencyNSERC
‘Monte Carlo Enhancement Using Simulation Decomposition: Visual Analytics in Environmental Decision-Making’, International Conference on Environmental and Energy Engineering (IC3E), Yangzhou, China, March, 2021
‘Decomposition Approach for Simulated and Empirical Data’, July 2022 (with M. Kozlova, C. Lohrmann, S. Zakrytnoy, M. Beeler, M. Bulleri)
‘Simulation Decomposition for Analysis of Non-Simulated Data’, January 2022 (with M. Kozlova, S. Zakrytnoy)
‘Transforming Simulation-Decomposition for Multi-Objective Analysis’, September 2021 (with M. Kozlova)
‘Monte Carlo Simulation Enhancement for Risk Analysis’, June 2021 (with M. Kozlova, M. Collan, P. Luukka)
‘Forecasting of Waste Amounts in the Face of Uncertainty’, April 2001 (with J. Linton)