Director & Founder
Joseph C. Paradi
Founder & Director Emeritus
PhD, P.Eng, FCAE
Expertise in data analytics (especially data envelopment analysis (DEA)) and world leader in DEA applied to bank branch productivity
He is a serial Entrepreneur; after graduation he founded Dataline Inc. in 1968. The Company was very successful, engaged in the time-sharing of computer services business and grew to a $25 million (~$45 M today), large Canadian company that he sold in late 1987 and left in January 1989 after 20 years at the helm. Dataline served the financial industry for almost all of its time in business and numerous applications were built for banks, securities brokerage, trust and other institutions. He founded Parcorp Inc. an investment company; co-founded Translucent Technologies Inc. in 1990; Director and investor in Softek Computer Services and other involvements in business.
He returned to the UofT in 1989 and today, he is the Founder and Executive Director Emeritus of the Centre for Management of Technology and Entrepreneurship, the Founder of the Engineering Hatchery and a Professor Emeritus. He was the Chair holder in Information Engineering in the Faculty. His research is mostly focused on the Financial Services industry with which he had a more than 25-year engagement. His research on bank branches and technology applications to the Financial Services Industry had gained him wide recognition in academia for innovation and the development of numerous methods to improve efficiency, productivity and effectiveness in bank branch operations. He co-authored a practitioner oriented book on the subject in 2018. Presently, his interests are focussed on the Fintech, Big Data and Machine learning.
He is the co-author of 60 peer reviewed papers, 6 books and 7 Chapters in books. He has participated in conference sessions and acted as an organizer (hosted NAPW 2004), Chairman, Moderator and Key-note speaker in 50+ events.
PhD, P.Eng, MBA
Expertise in numerical/financial modelling as well as finance and strategy
Professor Yuri Lawryshyn received his BASc, MASc and Phd degrees in engineering at the University of Toronto, an MBA from the Richard Ivey School of Business (University of Western Ontario) and a Financial Engineering Diploma from the Schulich School of Business (York University). After spending over 10 years in industry, Professor Lawryshyn joined the Centre for Management of Technology and Entrepreneurship (CMTE) at the Faculty of Engineering at the University of Toronto as a faculty member. Since joining the CMTE, Professor Lawryshyn has supervised over 150 projects including topics related to financial modelling, trading, econometrics, customer analytics, operational risk, cyber security and FinTech. Professor Lawryshyn specializes in numerical modelling, financial modelling and real options analysis, and is now expanding his research scope in areas related to data science and machine learning.
He has been listed as an inventor on nine patents, has co-authored over 40 peer reviewed publications and over 100 conference presentations, and currently holds the Joseph C. Paradi Chair in Information Engineering at the University.
Expertise in dynamic optimization, reinforcement learning and deep reinforcement learning
Chi-Guhn Lee is a Professor of Industrial Engineering and the Director of the Centre for Maintenance Optimization and Reliability Engineering (C-MORE) at the University of Toronto. His research interest includes reinforcement learning, transfer learning, supply chain optimization and physical asset management. Recent and on-going projects cover topics such as transfer learning, domain adaptation and Bayesian learning.
He has worked closely with private firms including Nestle, DND of Canada, IBM, General Motors, Magna International, LG, Fujitsu, State Grid Corp of China to name a few.
Expertise in cybersecurity, signal processing and complex dynamical networks (machine learning)
Deepa Kundur is Professor & Chair of The Edward S. Rogers Sr. Department of Electrical & Computer Engineering at the University of Toronto. A native of Toronto, Canada, she received the B.A.Sc., M.A.Sc., and Ph.D. degrees all in Electrical and Computer Engineering in 1993, 1995, and 1999, respectively, from the University of Toronto. Professor Kundur’s research interests lie at the interface of cybersecurity, signal processing and complex dynamical networks. She is an author of over 200 journal and conference papers and is also a recognized authority on cyber security issues. She has served in numerous conference executive organization roles and has participated on several editorial boards and funding panels. Professor Kundur’s research has received best paper recognitions at numerous venues including the 2015 IEEE Smart Grid Communications Conference, the 2015 IEEE Electrical Power and Energy Conference, the 2012 IEEE Canadian Conference on Electrical & Computer Engineering, the 2011 Cyber Security and Information Intelligence Research Workshop and the 2008 IEEE INFOCOM Workshop on Mission Critical Networks. She has also been the recipient of teaching awards at both the University of Toronto and Texas A&M University. She is a Fellow of the IEEE, a Fellow of the Canadian Academy of Engineering, and a Senior Fellow of Massey College.
Konstantinos N. Plataniotis
Expertise in machine learning as it relates to cognitive dynamic systems and general intelligence
Konstantinos (Kostas) N. Plataniotis received his B. Eng. degree in Computer Engineering from University of Patras, Greece and his M.S. and Ph.D. degrees in Electrical Engineering from Florida Institute of Technology Melbourne, Florida. Dr. Plataniotis is currently a Professor with The Edward S. Rogers Sr. Department of Electrical and Computer Engineering at the University of Toronto in Toronto, Ontario, Canada, where he directs the Multimedia Laboratory. He holds the Bell Canada Endowed Chair in Multimedia since 2014. His research interests are primarily in the areas of image/signal processing, machine learning and adaptive learning systems, visual data analysis, multimedia and knowledge media, and affective computing. Dr. Plataniotis is a Fellow of IEEE, Fellow of the Engineering Institute of Canada, and registered professional engineer in Ontario.
Dr. Plataniotis has served as the Editor-in-Chief of the IEEE Signal Processing Letters. He was the Technical Co-Chair of the IEEE 2013 International Conference in Acoustics, Speech and Signal Processing, and he served as the inaugural IEEE Signal Processing Society Vice President for Membership (2014 -2016). He was the General Co-Chair for the 2017 IEEE GLOBALSIP, and the 2018 IEEE International Conference on Image Processing (ICIP 2018). Dr. Plataniotis is the 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2021) General Co-Chair.
Expertise in nonlinear optimization and portfolio analysis
Roy H. Kwon is a professor in the Department of Mechanical & Industrial Engineering at the University of Toronto, St. George Campus. Also, he is a member of the faculty in the Masters of Mathematical Finance (MMF) Program at U of T.
He received his PhD from the University of Pennsylvania in operations research from the Department of Electrical and Systems Engineering in 2002. His research focuses on financial engineering (portfolio optimization, asset allocation, risk management, and option pricing) and supply chain management (logistics and production control).
Dr. Kwon has published articles in such journals as Management Science, Quantitative Finance, Journal of Computational Finance, Naval Research Logistics, the European Journal of Operational Research, and Operations Research Letters, among others. In addition, he has worked and consulted in the use of operations research (optimization) for the military, financial, and service sectors.
Karush is a graduate student at U of T completing his M.A.Sc in Electrical and Computer Engineering under the supervision of Prof. Yuri Lawryshyn and Prof. Konstantinos N. Plataniotis. Karush is supported by the Edward S. Rogers Graduate Scholarship and RBC Capital Markets where he is affiliated as a Thesis Researcher in collaboration with CMTE. His research revolves around the intersection of reinforcement learning, deep learning and robotic control. Karush earned his Bachelor of Technology from Amity University where he was affiliated as an undergraduate research assistant at the Signal Processing and Deep Learning Lab under the supervision of Prof. Rinki Gupta.
Thesis: Deep Hierarchical Reinforcement Learning
Danmei is a MASc student at CMTE whose research involves advanced CFD modelling for water/wastewater treatment process. She investigated the uneven hydraulic distribution among cassettes in an effort to provide optimization for full-scale FSMBR application. She also carried out research in flocculation process using CFD as a tool to estimate particle size distribution (PSD) and evaluate the flocculation efficiency.
Thesis: Optimization of full-scale multi-cassette flat-sheet membrane bioreactor (FSMBR) using CFD modelling
Shi is currently a MASc student in computer engineering at University of Toronto, supervised by Prof. Yuri Lawryshyn. Shi received my BASc degree (with honours) in 2019 also from UofT. Shi was a software developer intern at Kijiji from 2017 to 2018. Currently, most of Shi’s work is related to applying machine learning methods to solving real-world problems. Shi started to build interests when he was in third year. Shi’s capstone project was about using machine learning to detect autism patients based on brain graphs (a kind of network indicating connectivity among neurons). Now, Shi working closely with Prof. Lawryshyn to develop an automated system to identify invalid journal entries.
Thesis: Anomaly Detection for Journal Entries.
Pazinski Hong is an M.A.Sc candidate in the Department of Mechanical & Industrial Engineering at the University of Toronto. He has finished his bachelor's degree in Applied Mathematics at the University of Toronto in 2018. His research focuses on financial engineering. His recent work includes solving the index-tracking problem using data mining and graph clustering techniques on a market graph utilizing purpose-built hardware Digital Annealer.
Thesis: Cluster-Based Index Tracking Via QUBO and Digital Annealing
David did his undergrad in engineering science - physics (1T8 + PEY) and am now in the second year of my MASc with the CMTE & RBC. Aside from machine learning and cybersecurity, his interests and hobbies include skateboarding, cycling, cooking, baking, weightlifting, and hiking.
Thesis: Adversarial Learning in Opcode Based Malware Detection
Eric graduated from Engineering Science at UofT in 2020 specializing in Finance. He began his MASc in Fall of 2020, with an emphasis in machine learning, reinforcement learning and its applications to financial industries. He has prior industry and academic research experience with respect to using ML to model financial trading algorithms, disease diagnosis and recommendation systems.
Thesis: Reinforcement Learning in Portfolio Rebalancing
Dave is a MASc student at the University of Toronto and holds a BASc in Engineering Science. He is an experienced machine learning engineer and has previously co-founded a fashion AI start-up. His research interests lie in deep learning and natural language processing. In his free time, Dave volunteers as a product manager at Helpful Engineering, a non-profit innovation accelerator developing solutions to combat Covid-19. He also lends his support to pro-bono strategy advisory services aimed at Canadian non-profits and charities as a board executive of University Consulting Group.
Thesis: Multi-objective reinforcement learning in supply chain optimization
Tinglin Francis Duan
He is a Machine Learning Analyst at Fidelity Canada. He is currently working in collaboration with UofT's Center for Management of Technology and Entrepreneurship. His on-going research focus on high dimensional time series modelling and forecasting. He received Master of Applied Science degree at ECE, University of Toronto. He received my Bachelor of Applied Science degree at Robotics, Engineering Science, University of Toronto. Advised by Prof. K. N. Plataniotis and Prof. Y. A. Lawryshyn for the degrees of M.A.Sc. Honor B. A. Sc. from Robotics, Engineering Science, University of Toronto
Thesis: Unsupervised Multivariate Time Series Anomaly Detection via Transformer-based models and Time Series Encoding