Christopher Polk is Professor of Finance at LSE and former Head of the Finance Department. His research on asset pricing and investment strategies has received numerous awards including the Fama-DFA Prize for best paper in capital markets. Polk developed influential models integrating stochastic volatility into intertemporal asset pricing theory and has made significant contributions to understanding value investing cycles. He currently teaches Sustainable Finance and Impact Investing while leading research on factor premia variation across business cycles. Fama-DFA Prize (2018) AQR Insight Award (2014) Jensen Prize (2002) Q Group Research Award Inquire Europe Research Award
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 .
Academic Profile: Damir Filipovic is a Full Professor and the Swissquote Chair in Quantitative Finance at the College of Management of Technology (CDM) of École Polytechnique Fédérale de Lausanne (EPFL), Switzerland. He previously held academic positions at the University of Vienna, University of Munich, and Princeton University, and served as Head of the Vienna Institute of Finance. Research Focus: Quantitative finance, risk management, stochastic processes, term structure modeling, volatility risk, and machine learning applications in financial markets. Industry Collaboration: Co-developed the Swiss Solvency Test for insurance capital requirements while consulting for the Swiss Federal Office of Private Insurance. Publications: Contributed extensively to journals like Journal of Financial Economics, Mathematical Finance, and Annals of Applied Probability, with a textbook on Term-Structure Models. Academic Service: Editorial board member of multiple journals and organizer of advanced workshops on systemic risk and financial technology. Recent Research: His work emphasizes machine learning for portfolio risk management, kernel-based yield curve estimation, and robust stochastic modeling. Keynote speaker at international conferences on finance and insurance mathematics, with over 15 recent publications in 2023-2025 addressing high-dimensional financial problems, neural control systems, and causal inference in market data. Education: Ph.D. in Mathematics from ETH Zurich (2000). Graduate of ETH Zurich and University of Vienna. Teaching & Mentorship: Supervises current and former EPFL Ph.D. students in quantitative finance, including Nicolas Camenzind, Joshua Hayes, Andrea Ruglioni, and ten others. Former students like Damien Ackerer and Lotfi Boudabsa now lead research in risk management. Labs & Programs: Directs EPFL's Finance and Technology Programme, leads the Computational Finance Group (CSF) at EPFL, and contributes to Swiss Finance Institute initiatives. Scientific Leadership: Served on EPFL Committee of Academic Evaluation and Doctoral Program Finance committee.
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 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.
Andrea Vedolin is a Professor of Finance at the Questrom School of Business, Boston University. He is also a Research Associate at the National Bureau of Economic Research (NBER) and a Research Affiliate at the Centre for Economic Policy Research (CEPR). His research focuses on international finance, asset pricing, and macroeconomic uncertainty, with particular emphasis on exchange rate dynamics, risk premia, and monetary policy effects. Vedolin holds a Ph.D. in Economics from the University of Lugano (2010). His work spans topics such as bond risk premia, variance risk across assets, and the impact of central bank communication on financial markets. Key contributions include analyses of global factor structures in exchange rates, the role of uncertainty in shaping asset prices, and the modeling of systemic risk in international contexts. His research often integrates theoretical frameworks with empirical evidence to address questions in macro-finance and financial economics. Vedolin’s articles explore themes like interest rate risk management, liquidity in international markets, and the interplay between economic uncertainty and credit markets. His studies frequently employ advanced econometric techniques and model-free approaches to derive insights about market behavior and policy implications. Despite his prolific output, no specific scientific awards or grants are mentioned in the provided texts. His advising record and lab affiliations remain unspecified, though his work suggests involvement in collaborative projects with institutions like NBER and CEPR. The summary highlights his role as a leading scholar in understanding how uncertainty and policy regimes influence financial markets globally.
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.
Dr. Alfred Chong is an Associate Professor in the Department of Actuarial Mathematics and Statistics at Heriot-Watt University (HWU). Previously, he served as an Assistant Professor at the University of Illinois at Urbana-Champaign (UIUC) and co-founded the Illinois Risk Lab. His research focuses on Actuarial Science, Financial Mathematics, and Quantitative Risk Management, addressing emerging risks like cyber, pandemic, and climate risks, leveraging machine learning, optimization, and stochastic control. He holds a PhD from The University of Hong Kong and King's College London, and is an Associate of the Society of Actuaries. Chong actively contributes to academic governance, including roles in the EPSRC Mathematical Sciences Early Career Forum and the Maxwell Institute's Data and Decisions research theme. Education: PhD in Actuarial Science, University of Hong Kong & King's College London Research Interests: Chong explores risk sharing mechanisms, forward preferences in insurance, and mitigation strategies for large-scale risks. His work integrates data analytics and machine learning to solve decision-making challenges, such as cybersecurity risk assessment, pandemic resource allocation, and climate risk modeling. Recent projects include incident-specific cyber insurance design and delegated investment strategies for retirement savings. Awards: Michael V. Colla Prize for Mathematics Related to Medicine (2022) Best of 2020 in the Annual Meeting of the Casualty Actuarial Society (2021) Advising & Grants: Chong supervises PhD students in holistic risk management, forward preferences, and reinforcement learning applications. He has secured grants supporting interdisciplinary research in risk modeling and insurance innovation. Labs & Teams: Co-founder of the Illinois Risk Lab (UIUC), now leading research at HWU's Actuarial Mathematics & Statistics department. Engaged with the International Centre for Mathematical Sciences for knowledge exchange initiatives.
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.
Yukun Li is an Associate Professor in the Department of Mathematics at the University of Central Florida (UCF), part of the College of Sciences. His research focuses on numerical analysis, stochastic partial differential equations, and computational finance. He holds a Ph.D. in Mathematics from the University of Tennessee, Knoxville (2010-2015), followed by postdoctoral roles at Penn State (2015-2016) and The Ohio State University (2016-2019). He has secured grants including NSF REU funding (2023-2026) and led an NSF-funded project on stochastic phase field models (2021-2025). Research interests include: Continuous/Discontinuous Finite Element Methods Numerical Solutions of Stochastic ODEs/PDEs Adaptive Algorithms and Fast Solvers Computational Finance Models Recent publications emphasize stochastic wave equations, phase field models, and financial mathematics. His work spans theoretical analysis and numerical methods for complex systems. Notable recognition includes the 2015 Achievement Award from the University of Tennessee's Mathematics Department. Teaching highlights include advanced graduate courses like Computational Methods for Financial Mathematics and Numerical Linear Algebra, alongside contributions to undergraduate mathematics education. He is proficient in computational tools including MATLAB, Python, FEniCS, and MPI.
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.
Di Bu is an Associate Professor in the Department of Applied Finance at Macquarie University, leading the Macquarie University FinTech and Banking Research Centre. He holds a PhD in Finance from the University of Queensland (2015). His research focuses on FinTech innovations, climate finance, household finance, and behavioral finance, with an emphasis on embedding sustainability into financial systems. He has secured over AUD 4 million in research funding through ARC Linkage and Discovery projects, focusing on AI-driven credit assessments, Open Banking, ESG analytics, and climate resilience. Education PhD in Finance, University of Queensland (2015) Research Interests Di's work explores belief formation in financial decisions, sustainable lending practices, and climate adaptation tools. He pioneers projects such as AI credit scoring systems, behavioral interventions for sustainable investing, and digital platforms for climate resilience. His interdisciplinary approach bridges industry, government, and academia to address financial and environmental challenges. Projects & Funding AUD 4M+ in grants including two ARC Linkage and one ARC Discovery projects Current initiatives: Greenwashing detection, ESG rating divergence analysis, and climate-resilient finance platforms Labs/Teams Director of the FinTech & Banking Research Centre and affiliated with Data Horizons Research Centre and Frontier AI Research Centre at Macquarie University.
Thorsten Chmura is a Professor in the Department of Economics at Nottingham Business School, Nottingham Trent University. His work focuses on experimental and behavioral economics, utilizing laboratory and field experiments to address real-world challenges. He maintains collaborations within NTU’s Applied Economics and Policy Research Group, Public Service Management Research Group, and international partnerships across Europe, China, and the US. Chair of Industrial Economics at University of Nottingham (previous) Director, Centre for Research in the Behavioural Sciences (previous) PhD in Economics and Physics from University of Bonn Research interests span behavioral economics, experimental economics, game theory, and traffic modeling. His work examines decision-making under risk, wage discrimination, and behavioral responses in complex systems. Recent publications explore AVOD streaming economics (2024), social trading herding (2022), and toll road choice dynamics (2014). Key article trends include: Behavioral responses in financial markets Risk attitudes across 30 countries Cultural value impacts on loyalty programs Traffic flow simulations Game theory applications in coordination problems Experimental validation of economic theories
Dr. Yuki Miura serves as Assistant Professor at New York University's Tandon School of Engineering in the Department of Mechanical and Aerospace Engineering and Center for Urban Science and Progress (CUSP), with additional affiliations at NYU Stern's Volatility and Risk Institute and the New York City Panel on Climate Change (NPCC5). Her academic credentials include a Ph.D. (2022), M.Phil. (2021), and M.S. (2017) in Civil Engineering and Engineering Mechanics from Columbia University, complemented by a B.Eng. in System Design Engineering from Keio University (2015). Dr. Miura's research integrates engineering, climate science, finance, and social sciences to develop actionable resilience solutions. Her work focuses on climate risk quantification , urban adaptation strategies , and socioeconomic impact modeling through advanced data analytics. She pioneers methodologies that couple physical climate modeling with socioeconomic vulnerability assessments to deliver implementable risk mitigation frameworks for public and private institutions. Her publication portfolio demonstrates consistent advancement in climate risk analytics, with recent work addressing urban flooding dynamics, precipitation extremes, and infrastructure protection. These studies reveal a distinct trajectory toward integrated risk modeling that bridges climate physics with financial and social dimensions, increasingly leveraging AI-driven approaches for compound hazard assessment. Recognition for her contributions includes the Mindlin Scholar award from Columbia University (2022), with research featured in The New York Times , The New Yorker , and The Nikkei . As director of the Climate, Energy, and Risk Analytics Lab (CERA), Dr. Miura mentors graduate students in developing data-driven solutions for climate resilience. Her industry experience at Morgan Stanley (2021-2024) in climate risk management directly informs her applied research approach, fostering strong connections between academic innovation and real-world implementation. CERA operates as an interdisciplinary hub developing AI-driven methodologies for urban flood modeling, compound climate risk assessment, and socioeconomic impact analysis. The lab maintains active collaborations with the National Center for Atmospheric Research and New York City/State governments to translate research into actionable climate adaptation policies.
Thomas Berger is a Professor at the University of Hohenheim , affiliated with the Faculty of Agricultural Sciences and leading the Department of Economics of Land Use . He also contributes to the Computational Science Hub and Hohenheim Tropics initiatives. Focus Areas: Climate change adaptation, land-use modeling, biodiversity-productivity trade-offs, agent-based simulation, and machine learning in agricultural systems. Key Projects: Simulation frameworks for smallholder resilience in Ethiopia, bioeconomic modeling in the Amazon, and hybrid intelligence applications in European agricultural policy. Recent Publications: 2025 study on climate change effects on insecticide reduction in Germany, 2024 work on reconciling biodiversity with productivity via hybrid models, and 2023 methodological contributions to surrogate modeling and seasonal forecast integration. Research Trends: Interdisciplinary integration of climate science, agricultural economics, and computational modeling, with increasing emphasis on AI-assisted decision support systems and sustainability policy validation. Teaching & Outreach: Offers Agricultural Economics seminars and Hohenheim Tropics discussions, requiring advance email registration for office hours.
