جستجوی استاد راهنمای دانشگاهی

Claire Célérier is an Associate Professor of Finance at the Rotman School of Management, University of Toronto, where she holds the Canada Research Chair in Household Finance. Her academic career spans prestigious institutions including the University of Toronto (where she advanced from Assistant to Associate Professor between 2016-2022) and the University of Zurich (2014-2016). She maintains an affiliation with the People's History Lab and actively contributes to academic discourse through conference organization including 'Race, Equity and Public Policy' (2023) and 'Banks, States and the People' (2024). Dr. Célérier's research explores how finance can benefit households through multiple lenses including behavioral economics, asset pricing, and financial history. Her work investigates innovation in finance, diversity and inclusion impacts, and historical perspectives on financial systems. She has developed significant expertise in household finance, banking, and the intersection of finance with social issues, particularly racial disparities in financial markets as demonstrated in her award-winning research on the Freedman's Savings Bank. Her scholarly output shows a clear trajectory from foundational work on security design and investor behavior toward increasingly socially relevant research on financial inclusion, racial equity, and household financial decision-making. Recent publications demonstrate her growing focus on historical contexts of financial systems and their contemporary implications, particularly regarding marginalized communities. Canada Research Chair in Household Finance (2024) Bank of Canada Governor's Award (2022) University of Toronto Top 5% Merit Award (2021) Petro Canada Young Innovators Award Program (2021) Multiple Rotman School of Management Teaching Awards (2019-2021) Ieke van den Burg Prize for Research on Systemic Risk (2015) Dr. Célérier has successfully secured substantial research funding including Insight Development Grants (CAD 54,500), Connaught Research Grant (CAD 10,000), and the Marianne and Marcus Wallenberg Foundation Grant (EUR 400,000). She mentors several PhD students including Andrada Bilan (2020), Alexey Vasilenko (2023), and Purnoor Tak (Expected 2026), and has supervised numerous master's theses at both University of Toronto and University of Zurich. Her service contributions include Rotman School of Management MBA program committee, executive committee, seminar coordination, and faculty recruiting committee.

• Household Finance
• Banking
• Behavioral Economics
• +۴ مورد دیگر

Paul Goldberg is a Professor of Computer Science and Director of the MSc in Mathematics and Foundations of Computer Science (MFoCS) at the University of Oxford. He holds a BA in Mathematics from Oxford University and a PhD in Computer Science from the University of Edinburgh. His research focuses on algorithmic game theory, computational complexity, and machine learning, with notable contributions to equilibrium computation, complexity classes of total search problems, and decentralized systems. Affiliations: Department of Computer Science, Oxford; Editorial Board of ACM Transactions on Economics and Computation. Education: PhD in Computer Science (1993), University of Edinburgh MSc in Computer Systems Engineering (1989), University of Edinburgh and Université Paris-Sud BA in Mathematics (1988), Oxford University Research Interests: Algorithmic game theory, computational complexity (especially total search problems like CLS and PPAD), decentralized computation of equilibria, and applications in machine learning and AI. His work bridges theoretical computer science and economics, with a focus on algorithm design and complexity analysis. Publications and Awards: Over 120 papers, including influential work on Nash equilibrium complexity (2009), gradient descent (2023), and fair division algorithms. Notable awards include the ACM SIGecom Test of Time Award (2022) and a SIAM Outstanding Paper Prize (2011). Grants and Students: Leads EPSRC-funded projects on game theory and machine learning. Supervised 11 PhD graduates and currently advises Giannis Tyrovolas and others. Active in mentoring MSc and undergraduate projects. Labs/Teams: Part of the Algorithms and Complexity Theory group at Oxford, contributing to research on optimization, equilibrium dynamics, and fair division.

• Algorithmic Game Theory
• Computational Complexity Theory
• Machine Learning
• +۳ مورد دیگر

Prof. Gabriela Hug is a Full Professor at ETH Zurich's Department of Information Technology and Electrical Engineering, serving as Deputy Head of the Department and Deputy Head of the Power Systems and High Voltage Lab. She leads the Energy Science Center (ESC) and holds adjunct roles at Carnegie Mellon University. Her research focuses on modeling, control, and optimization of electric power systems for sustainable energy transitions. Education: PhD in Information Technology and Electrical Engineering, ETH Zurich (2004–2008) MSc in Information Technology and Electrical Engineering, ETH Zurich (1999–2004) Research Interests: Her work addresses challenges in smart grid integration, renewable energy systems, and advanced control strategies. Key areas include vehicle-to-grid technologies, distribution network optimization, and energy storage system planning. She emphasizes data-driven approaches and collaborative frameworks for grid resilience and flexibility. Key Achievements: Recipient of the 2019 ALEA Award (ETH Zurich) NSF Career Award (2013) IEEE Outstanding Young Engineer Award (2013) Leadership & Roles: Co-Director, NCCR Automation (Swiss National Centre of Competence in Research) Board Chair, Energy Science Center (ESC) Adjunct Faculty, Carnegie Mellon University Labs & Teams: Power Systems Laboratory (ETH Zurich) Energy Science Center (multi-disciplinary research hub)

• Electric Power Systems
• Sustainable Energy
• Control Systems
• +۳ مورد دیگر

David A. Hsieh is the Bank of America Professor of Finance at the Fuqua School of Business, Duke University, where he has been a faculty member since 1993. Previously, he served as Associate Professor and Assistant Professor at the University of Chicago's Graduate School of Business from 1981-1989. His extensive research has significantly contributed to the understanding of hedge funds, financial risk management, and nonlinear dynamics in financial markets. Massachusetts Institute of Technology, Ph.D. in Economics, 1981 Yale University, B.S. in Economics and Mathematics, 1976 (Summa Cum Laude, Phi Beta Kappa) Phillips Academy, Andover, 1972 (Cum Laude) Dr. Hsieh's research primarily focuses on the dynamics of asset prices and their implications for financial risk management. He has made significant contributions to understanding risk and return characteristics in hedge funds and commodity funds, pioneering work on nonlinear dynamics applications to financial markets. His research has evolved from early work on exchange rates and volatility modeling to more recent comprehensive analyses of hedge fund strategies, performance measurement, and industry structure. Hsieh's publication history reveals a clear progression from foundational work on nonlinear dynamics in financial markets to increasingly sophisticated analyses of hedge fund strategies and risk characteristics. His recent work, often in collaboration with William Fung and other prominent finance researchers, has focused on mega hedge fund firms, franchise value in the industry, and the evolution of hedge fund strategies toward more index-like products. The research consistently combines rigorous theoretical frameworks with robust empirical analyses across diverse market conditions. CAIA Award for Excellence in Alternative Investment Research (2015) CFA Institute Graham and Dodd Award of Excellence (2004) Bank of America Faculty Award (2002) Duke Cross-Continent Executive MBA Teaching Excellence Award (2002) Fischer Black Memorial Foundation Robert J. Schwartz Memorial Prize (1999) Smith Breeden First Prize (1990) Yale Science and Engineering Association High Scholarship Award (1976) Russell Henry Chittenden Prize (1976) Dr. Hsieh has served as a consultant for the International Monetary Fund (2007-2016) and the Bank for International Settlements (1998), and as a Visiting Scholar at both the International Monetary Fund and the Board of Governors of the Federal Reserve System. His editorial service includes Finance Editor for Management Science (2003-2009) and Associate Editor roles for several leading finance journals. He has developed extensive research resources including a Hedge Fund Data Library that has become widely used in academic and industry research.

• Financial Risk Management
• Hedge Funds
• Asset Pricing
• +۳ مورد دیگر

Liming Feng is an Associate Professor at the Department of Industrial and Enterprise Systems Engineering, University of Illinois at Urbana-Champaign, and has served as Director of the Master of Science in Financial Engineering (MSFE) program since 2022. His academic career at the university spans from Assistant Professor (2006-2012) to his current role. He earned his Ph.D. in Industrial Engineering and Management Sciences from Northwestern University (2006), an M.S. in Mathematics from Northwestern University (2000), and a B.S. in Mathematics from Beijing Normal University (1997). Ph.D., Industrial Engineering and Management Sciences, Northwestern University, 2006 M.S., Mathematics, Northwestern University, 2000 B.S., Mathematics, Beijing Normal University, 1997 Feng’s research focuses on Financial Engineering, Stochastic Modeling, and Computational Methods. He has contributed extensively to quantitative finance, particularly in options pricing, portfolio optimization, and market impact models. His work leverages advanced numerical methods, Fourier transforms, and stochastic calculus to solve complex financial problems. The trends in his publications highlight expertise in Levy processes, jump diffusion models, and numerical algorithms for financial derivatives. He has developed innovative techniques for Bermudan options pricing, discretely monitored barrier options, and portfolio deleveraging strategies. His articles often intersect Operations Research with Financial Engineering, emphasizing computational efficiency and mathematical rigor. ISE Faculty Fellow (2025) INFORMS Financial Services Section Best Student Research Paper (2013) First runner-up of the 2012 Morgan Stanley Prize for Excellence in Financial Markets Feng has served on editorial boards for Operations Research Letters and Mathematical Finance . He has been recognized repeatedly for teaching excellence, including the Sharp Outstanding Teaching Award (2011, 2022) and multiple entries in the List of Teachers Ranked as Excellent by Their Students (2007-2024). He currently leads the MSFE program and contributes to curriculum development through courses like IE 522 (Statistical Methods in Finance) and IE 527 (MSFE Professional Development).

• Financial Engineering
• Operations Research
• Stochastic Modeling
• +۳ مورد دیگر

Suzanne S. Lee is an Associate Professor of Finance at the Scheller College of Business, Georgia Institute of Technology, where she has been a faculty member since 2005. She also serves as the Ph.D. Coordinator, playing a key role in graduate education and research training. Her educational background is highly quantitative and interdisciplinary: Ph.D., University of Chicago MBA, University of Chicago M.S. in Statistics, University of Chicago Dr. Lee's research lies at the intersection of asset pricing and financial econometrics, with a strong focus on jump processes, market microstructure, and derivative markets. She investigates how sudden price movements (jumps) impact asset returns, risk, and information flow in financial markets. Her work extends to cryptocurrency, currency markets, and carry trade strategies, combining theoretical rigor with empirical validation using high-frequency data. The analysis of her recent publications (2008–2024) reveals a consistent and influential research program centered on detecting and modeling jumps in financial time series. Her work spans equity, currency, and cryptocurrency markets, often employing advanced nonparametric and econometric techniques. A recurring theme is the role of jumps in pricing anomalies, risk measurement, and market efficiency, with increasing attention to digital assets in recent years. Dr. Lee is actively engaged in the academic community through editorial service: Associate Editor, Journal of Banking and Finance Associate Editor, Asia-Pacific Journal of Financial Studies She has presented her research at premier conferences such as the American Finance Association, European Finance Association, Econometric Society, and Society for Financial Econometrics. Her publications appear in the most prestigious journals in finance and econometrics, including the Journal of Finance , Review of Financial Studies , Journal of Financial Economics , and Journal of Econometrics . While specific grant details are not listed, her sustained publication record in top journals indicates significant research funding and academic impact. She advises Ph.D. students through her role as Ph.D. Coordinator, though individual advisees are not named in the text. Dr. Lee's work contributes to both theoretical and applied finance, improving our understanding of market dynamics, risk modeling, and asset pricing under extreme events. Her research has practical implications for risk management, trading strategies, and financial regulation.

• Financial Econometrics
• Empirical Finance
• Asset Pricing
• +۶ مورد دیگر

Na Du is an Assistant Professor in the Department of Informatics and Networked Systems at the University of Pittsburgh's School of Computing and Information. She holds a PhD in Industrial & Operations Engineering from the University of Michigan (2021) and a Graduate Certificate in Data Science. Her research focuses on human factors in smart cities, human-centered computing, and user experience design. She is affiliated with the Intelligent Systems Program, Pitt Cyber, and the Center for Governance and Markets. Education: PhD in Industrial & Operations Engineering (University of Michigan, 2021); Undergraduate in Psychology (Zhejiang University). Research emphasizes explainable AI, human-AI teaming, and smart technologies. Recent grants include funding from Honda Research Institute and Pitt Cyber Accelerator for projects on emotions in Human-AI interaction and Metaverse privacy awareness. Her work has been recognized with awards like the HFES Best Paper Award and the IOE Outstanding Student Award. Advising includes PhD students and researchers in human factors and UX design. The HAT Lab under her leadership explores interdisciplinary challenges in human-computer interaction and smart systems.

• Human-Computer Interaction
• Human Factors in Smart City Technologies
• Human-Centered Computing
• +۴ مورد دیگر

Eduardo Azevedo is the John M. Bendheim and Thomas L. Bendheim Professor of Business Economics and Public Policy at the Wharton School , University of Pennsylvania. He holds a courtesy appointment as Professor of Economics and was awarded the 2016 Sloan Foundation Fellowship. His research integrates economic theory with practical applications across science and business domains. His research interests include: Market design Selection markets Social science genetics Experimental economics Game theory Recent publication trends focus on: Economic theory applications to healthcare and digital markets Empirical Bayes methods in A/B testing Adverse selection in insurance markets Strategic behavior in two-sided matching Evolutionary behavioral economics He serves as an instructor for BEPP2500 - Managerial Economics , emphasizing real-world application of microeconomic theory to business problems. His work also involves software development for economic research, including MATLAB-based empirical Bayes tools for analyzing treatment effects in large-scale experiments. Scientific awards : Sloan Foundation Fellow (2016)

• Microeconomic theory
• Market design
• Selection markets
• +۴ مورد دیگر

Phuong H. Nguyen is an Associate Professor at the Department of Electrical Engineering at Eindhoven University of Technology (TU/e). He leads the digital Power & Energy Systems (digi-PES) lab, focusing on smart energy systems, distributed control, and IoT integration for future energy grids. His research addresses energy transition challenges, including microgrid optimization, flexibility markets, and data-driven grid management. Nguyen holds a PhD from TU/e (2010) and has held visiting researcher positions at Clemson University. His work contributes to UN SDG goals related to affordable and clean energy. Education: Bachelor's in Electrical Engineering, Hanoi University of Science and Technology (2002) Master's in Electrical Engineering, Asian Institute of Technology (2004) PhD in Electrical Engineering, TU/e (2010) Key Research Areas: Smart Grids and Cyber-Physical Systems Distributed Energy Resource Management Local Energy Markets and Flexibility Provision IoT and Big Data in Energy Systems Awards & Recognition: Best Paper Award SEST 2020 Projects & Grants: REACT-D: Reactive Power Management (Project Manager) Al-driven Applications to Smarten Power System Operation (Project Lead) UNIversity Campus as a Self-Regulated Network (Co-Manager) Labs & Teams: The digi-PES lab develops cyber-physical tools for energy transition, including microgrid simulations, flexibility markets, and grid resilience strategies.

• Smart Energy Systems
• Power System Control
• Distributed Energy Resources
• +۳ مورد دیگر

Raphael Hauser is an Associate Professor in Numerical Mathematics at the University of Oxford's Mathematical Institute, Director of Graduate Studies - Teaching, and Tanaka Fellow in Applied Mathematics at Pembroke College. His affiliations include membership in the Data Science, Numerical Analysis, and Mathematical and Computational Finance research groups, as well as a fellowship at the Alan Turing Institute. Education: PhD in Operations Research, Cornell University, Ithaca, USA Dipl. Math. ETH, Swiss Federal Institute of Technology (ETH Zurich), Switzerland Research interests span data science, numerical optimisation, medical imaging, distributed computing, and applied probability/statistics. His work integrates mathematical rigor with practical applications, particularly in optimization algorithms, machine learning theory, and medical imaging technology. Publications focus on optimization theory, stochastic processes, medical imaging systems, and computational finance, with recurring themes in non-convex optimization guarantees, PCA variants, and X-ray tomography innovations. Awards: Oxford University Teaching Award (2007) SIAM Optimization Prize (2005) SIAM Student Paper Prize (2000) Advising includes 15+ DPhil students and 40+ MSc students, with projects in optimization, finance, imaging, and machine learning. Current postdocs and students are affiliated with the Alan Turing Institute and industrial partners like Siemens and Macquarie Group. He leads teams in the Mathematical Institute's research groups and collaborates with the Alan Turing Institute on large-scale data science initiatives.

• Data Science
• Numerical Optimisation
• Medical Imaging
• +۳ مورد دیگر

Cong Shi, also known as Alex Shi, is a Professor of Management at the Miami Herbert Business School, University of Miami, since 2025. Previously, he served as Associate Professor at the University of Michigan (2019-2023) and Assistant Professor there (2012-2019). His academic journey began with a B.Sc. in Mathematics (First Class Honors) from the National University of Singapore (2007) and a Ph.D. in Operations Research from MIT (2012) under Professor Retsef Levi. Education : MIT (Ph.D.), NUS (B.Sc.) Current Role : Professor, Management, Miami Herbert Business School Prior Roles : Associate Professor (Tenured), University of Michigan; Assistant Professor, University of Michigan His research spans Revenue Management, Supply Chain Management, Healthcare Operations, Human-Robot Interaction, and Data-Driven Optimization. Recent publications focus on fairness-constrained inventory, sequential pricing, and trust-aware robotics. He has received prestigious awards including the Senior Research Award (2025) and Amazon Research Award (2021), alongside multiple INFORMS recognitions. The 15 most recent articles highlight advancements in inventory control with fairness constraints, sequential pricing algorithms, and trust propagation models in robotics. His work bridges theoretical rigor with practical applications in supply chains and human-robot collaboration. Scientific Awards : Senior Research Award, Miami Herbert Business School, 2025 Amazon Research Award, 2021 INFORMS Meritorious Service Awards (2018, 2019, 2021, 2023) IOE Graduate Course Professor of the Year, University of Michigan, 2019 He has advised 10 PhD students, many now in academia (e.g., UC Berkeley, Penn State) or tech roles (Meta, Amazon). Grants include NSF funding as PI and Co-PI.

• Revenue Management
• Supply Chain Management
• Healthcare Operations
• +۶ مورد دیگر

Assoc Prof Ying Chen is an Associate Professor at the National University of Singapore , affiliated with the Department of Mathematics, Asian Institute of Digital Finance (as Academic Director of PhD Program in Digital FinTech 2022–2024), Risk Management Institute (2019–2023), Department of Statistics and Data Science (2019–2023), and Department of Economics (2018–2023). She also contributes to NUS Graduate School for Integrative Sciences and Engineering since 2016. Research Interests include: AI forecasting and quantum computing for finance Nonstationary time series and functional data analysis Energy data analytics and precision medicine Network autoregression and spatial-temporal modeling Explainable AI and citation metrics Portfolio liquidation and market-making algorithms Article Trends demonstrate expertise in: Adaptive forecasting for gas flows and electricity prices Blockchain network influence detection Quantum computing applications in finance Functional autoregression with mixed predictors Credit rating fairness and explainability High-resolution implied volatility modeling Scientific Awards include: ISI Elected Member (2016–) International Statistical Institute Council (2023–2027) IASC Scientific Secretary (2017–2019, 2023–2025) Advisory roles for EU FIN-TECH and xAIM projects

• Financial Statistics
• Machine Learning
• Quantitative Finance
• +۷ مورد دیگر

Professor Ben Goldys is a distinguished academic at The University of Sydney's School of Mathematics and Statistics, where he conducts research at the intersection of pure mathematics and applied sciences. His work spans multiple disciplines including stochastic analysis, partial differential equations, and financial mathematics, with significant contributions to both theoretical frameworks and practical applications in science and finance. Goldys' research interests center on stochastic (ordinary and partial) differential equations and their applications. His specific focus areas include stochastic partial differential equations, stochastic geometric PDEs, stochastic boundary value problems, stochastic fluid dynamics, ergodic theory of infinite-dimensional diffusions, and applications in financial mathematics such as interest rate derivatives, credit risk, and stochastic volatility. His work bridges pure mathematical theory (Functional Analysis, PDEs, Ergodic Theory) with complex real-world problems across multiple domains. His research aligns with the University of Sydney Faculty of Science Research Strengths including Understanding the Universe, Fundamental Laws of Nature, Complex Systems, and Next Generation Materials. Professor Goldys has secured multiple significant research grants from the Australian Research Council, including recent projects such as 'Mathematics for future magnetic devices' (2024), 'Mathematics for breaking limits of speed and density in magnetic memories' (2019), and 'Novel Approaches for Problems with Uncertainties' (2015). His current research projects focus on geometric stochastic partial differential equations and applications in micromagnetism, mean field games in finance, stochastic boundary value problems, and stochastic Navier-Stokes equations on the rotating sphere. He maintains extensive international collaborations with institutions in Germany (University of Tuebingen), Italy (LUISS University), Poland (Institute of Mathematics Polish Academy of Sciences), and the United Kingdom (University of York), working on projects involving optimal control, stochastic systems with memory, and geometric stochastic PDEs. Goldys is an active member of the Applied Mathematics Research Group and The University of Sydney Nano Institute, contributing to interdisciplinary research initiatives that connect mathematical theory with cutting-edge technological applications.

• Stochastic Differential Equations
• Financial Mathematics
• Partial Differential Equations
• +۵ مورد دیگر

Ronnie Sircar is the Eugene Higgins Professor of Operations Research and Financial Engineering at Princeton University , where he contributes to the Department of Operations Research and Financial Engineering (ORFE). His work spans financial mathematics, stochastic modeling, and applied probability, with a focus on market volatility, optimal investment strategies, and dynamic game theory. Email: sircar@princeton.edu Office: Sherrerd Hall, Room 208, Princeton, NJ 08544 His research interests include: Stochastic Volatility: Asymptotic analysis, calibration, and impact on option pricing and portfolio optimization. Mean Field Games: Applications to cryptocurrency mining, energy markets, and interbank network formation. Portfolio Theory: Forward performance processes, drawdown constraints, and risk-averse strategies. Credit Risk: Multi-name credit derivatives, CDO valuation, and risk measures. Energy Systems: Renewable reliability, unit commitment, and electricity market design. Recent publications emphasize mean field games in energy and blockchain, stochastic volatility in portfolio optimization, and machine learning applications for financial engineering. He has advised graduate students such as Giulia Crippa, Nicolas Garcia, and Burak Aydin, often collaborating with researchers including M. Soner, P. Chan, and A.M. Reppen.

• Financial mathematics
• Stochastic models
• Market volatility
• +۵ مورد دیگر

Professor Jiti Gao is a Donald Cochrane Chair in Econometrics & Business Statistics at Monash University's Faculty of Business and Economics. He leads the Department of Econometrics and Business Statistics, specializing in non- and semi-parametric econometrics, time-series analysis, and panel data methodologies. His research focuses on developing statistical models for climate change, energy demand, and financial forecasting. Affiliations: Monash University, Impact Labs Grants: Multiple ARC Discovery Projects (e.g., 2020–2025 on climate-energy time series, 2017–2020 on econometric model building) Collaborations: CSIRO, Yale University, and international partners from China, Norway, and Singapore Research interests include climate econometrics, financial time series, and policy evaluation. Over 136 publications span econometric theory and applications, with recent work on nonlinear trending models and quantile regression. His grants emphasize methodological advancements in time series and panel data analysis. Awards: Not explicitly mentioned, but recognition includes Australian Professorial Fellow status and international research leadership roles. Advising/Grants: Primary Investigator on multiple ARC-funded projects, focusing on climate modeling and financial econometrics Labs/Teams: Part of Monash's Impact Labs and collaborates with global institutions on climate and econometric initiatives

• Econometrics
• Non- and Semi-Parametric Econometrics
• Time-Series Analysis
• +۳ مورد دیگر