جست‌وجوی استاد و پژوهشگر

Ali Lazrakمشاهده پروفایل

دانشیار

• Asset Pricing
• Corporate Finance
• Behavioral Finance
• +۵ مورد دیگر

Ali Lazrak is an Associate Professor at the Sauder School of Business , University of British Columbia , specializing in Finance . He holds the Peter Lusztig Professorship in Finance and teaches courses such as International Financial Markets and Institutions and Theory of Finance (2024-2025). His research bridges Political Economy , Asset Pricing , and Behavioral Finance , with a focus on ESG concerns , Voting Theory , and Time Inconsistency . Education: ENSAE (B.Sc.), Sorbonne (M.Sc.), Toulouse (Ph.D.) Contact: Henry Angus Building (HA 872), +1 604.822.9481, ali.lazrak@sauder.ubc.ca His work explores group decision-making in corporate investment, green finance (e.g., the green premium in ESG markets), and time-inconsistent preferences in continuous games. Recent publications highlight how responsible consumption and demand elasticity shape asset prices, and how institutional divestiture impacts harmful asset stranding through informational and economic channels. Scientific accolades include the Jacob Gold & Associates Best Paper Prize (2019), Best Paper Awards at HEC-McGill, UBC, Luxembourg, and Stanford conferences, and the Peter Lusztig Professorship . His methodological expertise spans stochastic control , recursive utility , and dynamic equilibrium analysis .

• Computer Architecture
• Interconnection Networks
• Approximate Computing
• +۵ مورد دیگر

Natalie Enright Jerger is a Professor in the Department of Electrical and Computer Engineering at the University of Toronto's Faculty of Applied Science and Engineering. She holds the Canada Research Chair in Computer Architecture and serves as Director of the Division of Engineering Science (2023-2028). Previously, she was the Percy Edward Hart Professor (2016-2019). She received her B.S. in Computer Engineering from Purdue University (2002), and M.S./Ph.D. in Electrical Engineering from University of Wisconsin-Madison (2004/2008). Her research focuses on: Multi/many-core architectures and on-chip networks Cache coherence protocols and memory hierarchy optimization Approximate computing and sustainable systems Intermittent computing for energy-harvesting devices Hardware acceleration for machine learning Her publications demonstrate strong emphasis on networks-on-chip (NoC) innovations, including routing algorithms, deadlock handling, power-efficient designs, and topology optimizations. Recent work expands into approximate computing, mobile architectures, and ML-driven hardware design. Major Awards: Fellow of Engineering Institute of Canada (2023) McLean Senior Fellow (2019) IEEE Micro Top Picks (2016) ACM/IEEE Microarchitecture Hall of Fame (2015) Sloan Research Fellowship (2015) Canada Research Chair (current) Distinguished Scientist, ACM Fellow, IEEE She leads the NEJ research group and collaborates with industry partners including Intel, AMD, Qualcomm, and IBM. Her work is funded by NSERC, CFI, and industrial grants. She co-chaired ASPLOS 2023 and HPCA 2014, and actively promotes diversity through WICARCH and ACM initiatives.

Justin Wanمشاهده پروفایل

استاد

• Scientific Computing
• Computer Graphics Simulation
• Medical Image Processing
• +۴ مورد دیگر

Justin Wan is a Professor in the Department of Computer Science at the University of Waterloo. His research focuses on scientific computing, medical image processing, computational finance, and machine learning. He holds a Ph.D. from UCLA (1998), an M.A. from UCLA (1995), and a B.Sc. from the Chinese University of Hong Kong (1992). Wan’s work bridges numerical methods, optimization, and deep learning, with applications in financial modeling, medical imaging, and fluid dynamics. His research interests include advanced techniques in scientific computing (e.g., multigrid methods), computer graphics simulation, and medical image enhancement (e.g., CT scan artifact reduction). He has pioneered applications of machine learning to computational finance, including option pricing and hedging using deep neural networks and GANs. His recent work explores denoising diffusion models and multi-agent systems for optimal execution in finance. Publications span topics like volatility surface computation, optimal mass transport for image registration, and parallel solvers for fluid dynamics. His methods address challenges in high-dimensional problems, robust numerical valuation, and scalable algorithms for large datasets. Wan collaborates across disciplines, integrating mathematical rigor with practical engineering solutions.

Philipp Afecheمشاهده پروفایل

استاد

• Operations Management
• Revenue Management
• Supply Chain Management
• +۵ مورد دیگر

Philipp Afeche is a Professor of Operations Management and Statistics at the Rotman School of Management, University of Toronto. His research bridges operations and marketing/economics, focusing on revenue management, pricing strategies, and service design in congestion-prone systems like healthcare and transportation. He holds a BA from the University of St. Gallen and MS/PhD degrees from Stanford University. Afeche has been recognized with the 2014 Best Paper Award (MSOM) and the 2018 Roger Martin Teaching Award. Education: BA, University of St. Gallen, Switzerland MS, Stanford University, USA PhD, Stanford University, USA Research Interests: Afeche explores optimization challenges in dynamic service systems, including pricing under uncertainty, strategic customer behavior in queues, and platform design for shared mobility systems. His work integrates queueing theory, game theory, and empirical analysis to address real-world operational inefficiencies in healthcare delivery and transportation networks. Recent studies focus on ride-hailing market mechanisms and bipartite matching systems. Awards: 2014 Best Paper Award, Manufacturing & Service Operations Management 2018 Roger Martin Award for Excellence in Teaching Grants & Editorial Roles: Editor for Management Science and Operations Research, with funding reviews for agencies in Canada, Hong Kong, Israel, and the US. Past chair of the Service Management SIG for MSOM Society. Labs/Teams: Active in Rotman's Operations Management group and collaborates with industry partners on supply chain optimization and revenue management projects.

Samuel W.K. Wongمشاهده پروفایل

دانشیار

• Data Science Methodology
• Protein Structure Analysis
• Reliability Engineering
• +۴ مورد دیگر

Samuel W.K. Wong is an Associate Professor in the Department of Statistics and Actuarial Science at the University of Waterloo. He holds a Ph.D. in Statistics from Harvard University (2013) under Prof. Samuel Kou. His research focuses on statistical methodology for complex data science challenges in protein structure modeling, dynamic systems inference, and reliability engineering of wood-based products. He has held academic positions at the University of Florida (2013–2018) and has been at Waterloo since 2018. His research interests include Bayesian computation, statistical inference for dynamic systems, and spatial-temporal data analysis. Notable contributions include the development of manifold-constrained Gaussian processes (MAGI package) and sequential Monte Carlo methods for protein folding studies. He has advised over 15 graduate students and researchers, many of whom are now in academic or industry roles worldwide. Wong has received teaching distinctions at Harvard and holds awards including the Nash Medal (2008) for academic excellence. His work bridges computational statistics with applications in bioinformatics, structural engineering, and environmental science. He has published extensively in top-tier journals like Journal of Computational and Graphical Statistics and Biometrics , and collaborates with wood scientists to improve real-time lumber quality assessment using laser imaging data. His teaching portfolio includes courses on probability theory, statistical inference, and spatial data analysis at both undergraduate and graduate levels. Beyond academia, he maintains an active passion for classical piano performance, having performed recitals combining music with his statistical research interests.

Rajesh Karkiمشاهده پروفایل

استاد

• Power Systems
• Renewable Energy Integration
• Grid Reliability
• +۳ مورد دیگر

Rajesh Karki is a Professor in the Department of Electrical and Computer Engineering at the University of Saskatchewan’s College of Engineering. He holds a B.E., M.Sc., and Ph.D. in related fields. His research focuses on power system reliability, renewable energy integration, and microgrid resilience, with particular emphasis on addressing challenges posed by extreme weather, cyber threats, and decarbonization targets. Dr. Karki’s work spans theoretical modeling, probabilistic analysis, and practical implementation strategies for smart grids, energy storage systems, and distributed generation. His educational background includes advanced degrees in electrical engineering, complemented by professional engineering licensure (P.Eng.). His research has explored diverse topics such as wind energy curtailment mitigation, energy storage optimization, and demand response mechanisms in developing economies like Nepal. He has authored numerous peer-reviewed publications on grid resilience, reliability economics, and cyber-physical system security. Key themes in his work include: (1) quantifying the reliability value of energy storage in active distribution systems, (2) modeling cyber-physical threats to microgrids, and (3) developing frameworks for extreme weather-resilient infrastructure. Despite the volume of his publications (over 50 articles), no specific awards or grants are explicitly listed in the provided materials. His research often intersects technical, economic, and policy dimensions of sustainable energy systems.

Christoph Freiمشاهده پروفایل

استاد

• Quantitative Finance
• Risk Management
• Mathematical Economics
• +۴ مورد دیگر

Christoph Frei is a Professor and Chair of the Department of Mathematical and Statistical Sciences at the University of Alberta. He holds a PhD in mathematical finance from ETH Zurich and previously worked as a researcher at École Polytechnique in Paris. His research focuses on quantitative finance, risk management, and mathematical economics, with applications to algorithmic trading, credit risk, and digital currencies. Education: PhD in Mathematical Finance, ETH Zurich Postdoctoral Researcher, École Polytechnique (Paris) Bachelor/Master in Mathematics, ETH Zurich His work bridges academia and industry through collaborations with institutions like ATB Financial, Canadian Western Bank, and the Federal Reserve System. Key research interests include over-the-counter markets, financial regulation, and machine learning applications in risk prediction. Frei has received notable recognition, including the Best Paper in Asset Pricing Award (2019). Current industry partnerships include AI-driven customer risk prediction projects with ATB Financial and credit risk analysis with Canadian Western Bank. He actively contributes to professional organizations like PRMIA Edmonton and advises on financial technology innovation. Research grants come from NSERC, SSHRC, and Mitacs. His consulting roles have spanned risk modeling at UBS and Credit Suisse, emphasizing practical applications of theoretical frameworks.

Ankush Agarwalمشاهده پروفایل

دانشیار

• Mathematical finance
• Data science in finance
• Financial statistics
• +۳ مورد دیگر

Ankush Agarwal is an Associate Professor in the Department of Statistical and Actuarial Sciences at the University of Western Ontario. His research focuses on mathematical finance, financial statistics, and Monte Carlo methods, with applications to risk management and derivatives pricing. He supervises PhD students in quantitative finance and has taught courses on Monte Carlo methods and advanced financial modeling at Western University. Education: PhD in Mathematics from Tata Institute of Fundamental Research (2015) Research interests span regime-switching models, longevity risk hedging, stochastic differential equations, and rare event simulation. His work combines theoretical probability with computational techniques for financial applications. Recent publications include studies on McKean-Vlasov SDEs, implied Sharpe ratio estimation, and optimal portfolio strategies under stochastic volatility. These works demonstrate his expertise in stochastic processes and financial engineering. Supervision: Current PhD advisees include Ying Liao, Buchun Wang, and Shuya Zhang at the University of Glasgow. Former advisees include Yongjie Wang and Yihan Zou.

David Saundersمشاهده پروفایل

دانشیار

• Quantitative Risk Management
• Mathematical Finance
• Stochastic Optimization
• +۳ مورد دیگر

David Saunders is an Associate Professor in the Department of Statistics and Actuarial Science at the University of Waterloo, affiliated with the David R. Cheriton School of Computer Science. His research focuses on quantitative risk management, mathematical finance, and stochastic optimization with applications to credit risk, portfolio optimization, and derivatives pricing. Professor Saunders has held academic positions at the University of Pittsburgh and Cyprus International Institute of Management, and has consulted for institutions like RiskMetrics, TD Bank, and Central Bank of Cyprus. Education: PhD studies at the University of Toronto with industry collaboration at Algorithmics Inc., followed by roles at Cyprus International Institute of Management and University of Cyprus's HERMES Center. His work bridges theoretical stochastic analysis and practical financial risk management challenges. Research interests prominently feature credit risk management, optimal stopping problems, and analytical techniques in finance. Recent work addresses inverse default boundary problems, wrong-way risk in derivatives, and efficient algorithms for portfolio optimization under complex return distributions. Collaborations with financial institutions drive applied research in capital allocation, operational risk modeling, and regulatory compliance. Advising and grants include directing the Professional Science Master's in Mathematical Finance at Pitt, supervising student-industry projects on credit risk and CDO pricing, and securing funding for computational finance initiatives. Active in RiskLab Cyprus and HERMES European Center, his research extends to equity risk management and market risk frameworks.

Hyejin Kuمشاهده پروفایل

استاد

• Mathematical Finance
• Machine Learning
• Risk Management
• +۴ مورد دیگر

Hyejin Ku is a Full Professor in the Department of Mathematics and Statistics at York University's Faculty of Science. Her research focuses on the intersection of Mathematical Finance and Machine Learning, addressing challenges in risk measurement, portfolio optimization, and quantitative finance. She develops advanced mathematical models to enhance decision-making through reinforcement learning and data analytics. Notable projects include novel algorithms for credit rating prediction using neural networks and sequence-based clustering for credit risk assessment. Her work integrates applied mathematics with real-world financial applications, such as systemic risk reduction in multi-layer networks and option pricing under liquidity constraints. She holds a prominent position in mathematical finance, contributing to both theoretical advancements and practical solutions for financial markets. Her research trends emphasize interdisciplinary approaches, combining machine learning techniques with financial modeling to solve complex problems in risk management and asset valuation. Her publications span over two decades, showcasing contributions to portfolio optimization, derivatives pricing, and computational finance. Dr. Ku is affiliated with York University’s Department of Mathematics and Statistics, where she contributes to academic leadership and research mentorship. Her office is located in DB 2025, and she can be reached at hku@yorku.ca.

Samuel Wongمشاهده پروفایل

دانشیار

• Protein Structure Prediction
• Bayesian Inference
• Stochastic Systems
• +۲ مورد دیگر

Dr. Samuel Wong is Associate Professor in the Department of Statistics and Actuarial Science at the University of Waterloo. His research develops statistical methods for complex data science problems in protein structure analysis, dynamic systems inference, and materials reliability. Education includes PhD from Harvard Statistics Department (2013). Research addresses challenges in conformational sampling for protein folding, inference for differential equation models, and uncertainty quantification in materials science. Leads development of MAGI software for manifold-constrained Gaussian processes. Publications showcase innovations in Sequential Monte Carlo methods, spatial data fusion, and Bayesian approaches to industrial problems. Recent work focuses on protein structure variability and COVID-19 transmission modeling. Supervises graduate students in Bayesian analysis and computational statistics. Teaches courses including Analysis of Spatial Data and Applied Linear Models.

Manuel Moralesمشاهده پروفایل

دانشیار

• Financial Mathematics
• Actuarial Science
• Machine Learning Applications in Finance
• +۵ مورد دیگر

Manuel Morales is an Associate Professor in the Department of Mathematics and Statistics at the University of Montreal since 2005. He holds a Ph.D. in Mathematics (2003) from Concordia University, an M.Sc. in Statistics (2000) from Concordia, and a B.Sc. in Mathematics (1996) from the National Autonomous University of Mexico. His research focuses on Financial and Actuarial Mathematics, particularly in Ruin Theory, Lévy processes, and High-Frequency Finance. He leads applied projects integrating Machine Learning in Banking and ESG Investment, and has pioneered AI governance frameworks at the National Bank of Canada as their Chief AI Scientist. Education: Ph.D. Mathematics, Concordia University, 2003 M.Sc. Statistics, Concordia University, 2000 B.Sc. Mathematics, National Autonomous University of Mexico, 1996 Research Interests: His work spans theoretical and applied directions, including non-Gaussian option pricing, regime-switching models, Limit Order Book dynamics simulation, and AI applications in finance. He emphasizes responsible investment and ESG factors through alternative data analysis. Advising & Partnerships: Supervises Master’s/Ph.D. students in Insurance and Financial Mathematics. Leads the FinML Network (since 2018) and collaborates with industry partners like the National Bank of Canada on AI-driven financial projects. His grants and contracts enable applied research in high-frequency market surveillance and model governance. Labs & Teams: Directs the FinML Network and oversees the National Bank’s AI initiatives, focusing on AI governance and algorithmic trading strategies.

Milton Boydمشاهده پروفایل

استاد

• Quantitative Finance
• Risk Management
• Derivatives
• +۷ مورد دیگر

Milton Boyd is a Professor in the Department of Agribusiness and Agricultural Economics at the University of Manitoba, Faculty of Agricultural and Food Sciences. He also serves as an Adjunct Professor in Risk Management and Actuarial Science at the Asper School of Business. His research and teaching focus on quantitative finance, risk management, derivatives, and international business. PhD, Purdue University, United States MA, Washington State University, United States BA, Seattle Pacific University, United States His research interests center on quantitative finance and agricultural risk management , with a strong emphasis on derivatives, portfolio strategies, and market behavior. He applies statistical and financial modeling to real-world agricultural and commodity markets, often integrating international and policy perspectives. His work bridges academic rigor with practical application in agribusiness and government sectors. His recent publications reflect a consistent focus on financial risk modeling, commodity pricing, and market efficiency. Themes include hedging strategies, volatility analysis, and the role of futures markets in price discovery and income stability. His work spans both developed and emerging markets, with increasing attention to Asia and global trade dynamics. Two University of Manitoba Awards for Research and Outreach Fellow of Seattle Pacific University Best Conference Presentation Award Outstanding Journal Paper Award Dr. Boyd has advised numerous graduate students and contributed to academic service through editorial roles, conference committees, and keynote presentations. He has secured research funding through university and external grants, though specific grants are not listed. His outreach includes media commentary, industry training, and policy advising, particularly in commodity market development. He has been actively involved in professional development and public service, including teaching short courses at the Winnipeg Commodity Exchange (ICE), Canadian International Grains Institute, and Canadian Securities Course. He has served as a Policy Advisor for the Frontier Center for Public Policy and as a columnist for Grainews , contributing to public understanding of financial and commodity markets.

Giovanni Gallipoliمشاهده پروفایل

استاد

• Macroeconomics
• Applied Microeconomics
• Economic Inequality
• +۵ مورد دیگر

Giovanni Gallipoli is a Professor at the Vancouver School of Economics, part of the Faculty of Arts at the University of British Columbia. His research focuses on macroeconomics and applied microeconomics, with particular emphasis on economic inequality, labor market dynamics, firm heterogeneity, and computational methods. He holds a Ph.D. from University College London. Key professional roles include Associate Editor of the Journal of Political Economy , Faculty Associate at the Stone Centre on Wealth and Income Inequality, and membership in the CEPR and HCEO. His work bridges theoretical rigor and empirical analysis, exploring topics such as general equilibrium effects of policies, household responses to shocks, and skill dispersion across workers and firms. Notable awards include the Killam Research Prize and the Young Economist Award from the European Economic Association. His research outputs span influential journals in economics, with recent studies addressing structural transformation, digital intensity impacts, and intergenerational transfers. He actively contributes to policy discussions through affiliations with institutions like the Panel Study of Income Dynamics (PSID).

• Stochastic Processes
• Financial Volatility Modeling
• Machine Learning Applications
• +۵ مورد دیگر

Aerambamoorthy Thavaneswaran is a Professor in the Department of Statistics at the University of Manitoba , specializing in inference for stochastic processes and dynamic data science applications. His research bridges financial economics, machine learning, and fuzzy logic to develop innovative volatility models and trading strategies. University: University of Manitoba Department: Statistics Email: Aerambamoorthy.Thavaneswaran@umanitoba.ca Research Interests: His work focuses on neuro volatility models, financial network analysis, and fuzzy logic applications in portfolio optimization. Recent projects include hybrid deep learning architectures for cryptocurrency prediction and dynamic covariance modeling. Publications (2023-2025): His articles highlight advancements in volatility forecasting, algorithmic trading strategies, and neuro-fuzzy systems. Key topics include transformer networks for stock markets, adaptive fuzzy adjacency matrices, and Kalman filter integration for cryptocurrency trading.