Author: UTORG

Who: Giorgio Costa, Ph.D. candidate, University of Toronto

When: Thursday, November 08th @ 12:00pm – 1:00pm

Where: BA8256

Bio-sketch:  Giorgio Costa is a Ph.D. candidate in Industrial Engineering at the University of Toronto under the supervision of Professor Roy H. Kwon. His research interests are financial optimization and risk management, specifically the field of portfolio optimization under uncertainty. As part of his research, Giorgio also works at the Toronto-Dominion Bank as a Senior Risk Analyst performing quantitative research and financial modelling.  Before starting his degree at the University of Toronto, Giorgio worked as an engineering consultant for the mining industry at Amec Foster Wheeler for three years. He holds a B.Eng. (Hon.) in Mechanical Engineering from McGill University.

Abstract:  Formulating a novel Markov regime-switching factor model to describe the cyclical nature of asset returns in modern financial markets. Maintaining a factor model structure allows us to easily derive the asset expected returns and their corresponding covariance matrix. By design, these two parameters are calibrated to describe the properties of the different market regimes. In turn, these regime-dependent parameters serve as the inputs during portfolio optimization, thereby constructing portfolios adapted to the current market environment. This model can be improved by periodically rebalancing the portfolios, ensuring proper alignment between the estimated parameters and the transient market regimes. An out-of-sample computational experiment over a long investment horizon shows that the proposed regime-dependent portfolios are better aligned with the market environment, and can consistently outperform competing portfolios.

Who: Kyle E. C. Booth, Ph.D. candidate, University of Toronto

When: Thursday, October 25th @ 12:00pm – 1:00pm

Where: BA8256

Bio-sketch:  Kyle E. C. Booth is a Ph.D. candidate at the University of Toronto conducting research on constraint programming, focusing on its use in decomposition techniques and its application to various optimization problems, including multi-robot coordination and quantum computing. Kyle became interested in quantum computing during a research placement with the NASA Ames Research Center, and his work on constraint programming for quantum compilation was published at the International Conference on Automated Planning and Scheduling (ICAPS).

Abstract:  Recently, the makespan-minimization problem of compiling a general class of quantum algorithms into near-term quantum processors has been introduced to the AI community. The research demonstrated that temporal planning is a strong approach for a class of quantum circuit compilation (QCC) problems. In this paper, we explore the use of constraint programming (CP) as an alternative and complementary approach to temporal planning. We extend previous work by introducing two new problem variations that incorporate important characteristics identified by the quantum computing community. We apply temporal planning and CP to the baseline and extended QCC problems as both stand-alone and hybrid approaches. Our hybrid methods use solutions found by temporal planning to warm start CP, leveraging the ability of the former to find satisfying solutions to problems with a high degree of task optionality, an area that CP typically struggles with. The CP model, benefiting from inferred bounds on planning horizon length and task counts provided by the warm start, is then used to find higher quality solutions. Our empirical evaluation indicates that while stand-alone CP is only competitive for the smallest problems, CP in our hybridization with temporal planning out-performs stand-alone temporal planning in the majority of problem classes.