جست‌وجوی استاد و پژوهشگر

Xin Guoمشاهده پروفایل

استاد

• Mathematical Finance
• Financial Engineering
• FinTech
• +۷ مورد دیگر

Xin Guo is Professor and Department Chair of Industrial Engineering and Operations Research (IEOR) at UC Berkeley's College of Engineering, holding the Coleman Fung Chair in Financial Modeling. Her research bridges mathematical finance, stochastic control, and machine learning with applications in risk analytics and quantitative trading. Education: Ph.D. in Mathematics, Rutgers University (1999) Research Interests: Professor Guo's work centers on mathematical finance , stochastic games , and reinforcement learning . She develops theoretical frameworks for α-potential games and mean-field systems while applying signature methods and GANs to financial data. Her research addresses critical problems in portfolio optimization, fraud detection (e.g., Medicare analytics), and market forecasting, emphasizing the intersection of stochastic control with machine learning for real-world decision-making under uncertainty. Publication Trends: Recent work (2023-2025) shows increasing focus on multi-agent reinforcement learning through mean-field game theory, with applications spanning finance (corporate bonds, trading), healthcare (fraud detection), and transportation (rate forecasting). Key innovations include BSDE approaches for stochastic games, signature-based time series analysis, and theoretical guarantees for GAN training dynamics. Scientific Awards: Holds the prestigious Coleman Fung Chair in Financial Modeling, reflecting significant contributions to quantitative finance research. Advising and Grants: As IEOR Department Chair, Professor Guo mentors graduate students in stochastic modeling and financial engineering. Her research is supported by the Coleman Fung Endowment Fund, with collaborations spanning finance, healthcare, and transportation sectors through industry partnerships. Labs and Teams: Leads the Risk Analytics & Data Analysis Research (RADAResearch) Lab ( https://risklab.ieor.berkeley.edu/ ), which develops cutting-edge methodologies for risk assessment, data-driven decision-making, and game-theoretic solutions to complex systems. The lab fosters interdisciplinary work connecting mathematical theory with practical applications in FinTech and beyond.

Christos Faloutsosمشاهده پروفایل

استاد

• Data Mining
• Graph Mining
• Fractals and Power Laws
• +۳ مورد دیگر

Christos Faloutsos is the Fredkin Professor of Computer Science at Carnegie Mellon University, with a courtesy appointment in Electrical and Computer Engineering. He holds a B.Sc. from the National Technical University of Athens and M.Sc./Ph.D. from the University of Toronto. His research focuses on data mining, graph analysis, fractals, and database systems. Notable contributions include foundational work on R-trees, graph mining laws (e.g., Kronecker graphs), and applications in medical imaging, network security, and fraud detection. Key projects include PEGASUS (petascale graph mining), fraud detection in online auctions (NetProbe), and tools for human trafficking analysis (TrafficVis). He has led NSF-funded projects on tensor mining, network anomaly detection, and bioinformatics. Over 300 refereed publications highlight his contributions across databases, data mining, and networks. Awards include the KDD Best Paper (2005, 2016), SIGMOD Test-of-Time Award, and recognition as a top nurturer in IT. His lab collaborations span the Parallel Data Lab (PDL), Machine Learning Department, and Computational Biology.

Alexandra Livadaمشاهده پروفایل

استاد

• Quantitative economics
• Applied econometrics
• Time series analysis
• +۶ مورد دیگر

Alexandra Livada is a Professor at the Department of Statistics within the School of Information Sciences and Technology at Athens University of Economics and Business (AUEB). She holds office at two locations: 12 Codringtonos Street, 2nd Floor and 76 Patision Street, Antoniadou Wing, 3rd Floor in Athens, Greece. Her contact information includes email livada@aueb.gr and phone number +30 210-8203521. Dr. Livada earned her BA and MA in Economics from Athens School of Economics and Business followed by a PhD in Economics from Essex University, UK in 1988. Her academic career has spanned several decades with extensive teaching experience at both undergraduate and postgraduate levels. Her research interests encompass a diverse range of quantitative fields: Quantitative economics and applied econometrics Time series analysis and forecasting techniques Income distribution and inequality measurement Applied financial econometrics Business cycles analysis Medical statistics Index numbers and official statistics Professor Livada's publication record demonstrates consistent scholarly productivity across multiple disciplines, with a noticeable trend toward interdisciplinary work connecting economics with healthcare and social policy. Her recent research shows increasing focus on income inequality across different geographic regions, economic sentiment during crises, and the intersection of medical conditions with statistical analysis. Her scholarly contributions have been recognized through numerous citations in leading journals and books. She has served as a referee for prestigious journals including the European Journal of Political Economy, Journal of Public Economics, and Journal of Insurance, Mathematics and Economics. Professional service highlights include: Member of multiple project teams Marie-Curie project supervisor External evaluator for the Greek State Scholarship Foundation (IKY) External evaluator for the Social Sciences and Humanities Research Council of Canada Co-author of the book "Index Numbers and Official Statistics" Professor Livada maintains an active research agenda with collaborations spanning economics, statistics, and medical fields, demonstrating the interdisciplinary nature of contemporary quantitative research.

Martin T. Wellsمشاهده پروفایل

استاد

• Applied Statistics
• Theoretical Statistics
• Bayesian Statistics
• +۷ مورد دیگر

Martin T. Wells is the Charles A. Alexander Professor of Statistical Sciences at Cornell University, with joint appointments in the Department of Statistical Science, Department of Biological Statistics and Computational Biology, Department of Social Statistics, and as Professor of Clinical Epidemiology and Health Services Research at Weill Medical School. He serves as Editor-in-Chief of the ASA-SIAM Book Series and Co-Editor of the Journal of Empirical Legal Studies. Cornell University, Ithaca, NY Weill Cornell Medical College Research Interests span applied and theoretical statistics, Bayesian methods, biostatistics, clinical epidemiology, and computational biology. His work bridges disciplines like finance, legal studies, and health services research. Article Trends highlight advancements in Bayesian modeling, quantum cognition machine learning, tensor analysis, and misclassification correction, with applications in genomics, finance, and public health. Fellow of the American Statistical Association Fellow of the Royal Statistical Society Contributions include developing statistical software (e.g., rTensor), methodological innovations in clinical trials, and empirical legal studies on civil rights and the death penalty.

Athanasios TRIANTAFYLLOUمشاهده پروفایل

دانشیار

• Commodity Markets
• Risk Management
• Volatility Forecasting
• +۴ مورد دیگر

Athanasios TRIANTAFYLLOU is an Associate Professor of Finance at IÉSEG School of Management in France. He holds a Ph.D. in Economics from the University of Athens (2017) and has held academic positions including Visiting Professor at Cornell University (2019), Lecturer at the University of Essex (2017–2022), and General Equilibrium Modeler at the National Technical University of Athens (2015). His research focuses on commodity markets, volatility forecasting, risk management, and monetary economics. Key contributions include analyzing commodity price uncertainty's impact on economic activity and financial markets. He has published extensively in journals like Journal of Futures Markets , Economics Letters , and International Journal of Finance and Economics . Education includes a BSc in Mathematics (2008), MSc in Accounting and Finance (2010), and M.Phil. in Economics (2013), all from Greek institutions. Professional experience includes risk management consulting at Ernst & Young (2016–2017). His teaching covers financial derivatives, multinational finance, and commodity markets. Research highlights include exploring the role of commodity price uncertainty in global economic dynamics and the predictive power of market information in corporate defaults. His work often bridges theoretical econometrics with practical policy implications, addressing issues like inflation dynamics driven by oil market risks and the tourism-led growth hypothesis in the Eurozone. TRIANTAFYLLOU’s publications emphasize volatility modeling, commodity market linkages, and the interplay between economic uncertainty and financial stability.

Scott Sissonمشاهده پروفایل

استاد

• Computational Statistics
• Bayesian Statistics
• Big Data
• +۴ مورد دیگر

Professor Scott Sisson is Director of the UNSW Data Science Hub (uDASH) and Professor of Statistics and Data Science at the University of New South Wales, School of Mathematics and Statistics. His research focuses on computational statistics, particularly solving 'intractable' statistical problems through Bayesian methods, big data techniques, simulation algorithms, and extreme value theory with environmental applications. Education includes a PhD in Statistics from Bristol University (2002), MSc in Environmental Statistics from Lancaster University (1997), and BSc in Mathematics and Statistics from Lancaster University (1996). Research interests span: Bayesian statistics and uncertainty quantification Big data analytics and scalable algorithms Machine learning integration with statistical methods Extreme value modeling for climate/environment Computational techniques for intractable problems Recent publications (2022-2025) demonstrate strong emphasis on Bayesian computation, spatiotemporal modeling, and interdisciplinary applications in materials science, oncology, quantum computing, transportation policy, and ecology. Methodological innovations include likelihood-free inference, modular Bayesian analyses, and symbolic data modeling. Awards and honors: 2020 Service Award (Statistical Society of Australia) 2017 ARC Future Fellowship 2015 G. N. Alexander Medal (Engineers Australia) 2011 Moran Medal (Australian Academy of Science) 2010 J.G. Russell Award (Australian Academy of Science) 2010 Queen Elizabeth II Research Fellowship As Director of uDASH, he leads data science initiatives across UNSW. He maintains sustained ARC funding and supervises students in computational statistics, Bayesian methods, extreme value theory, and machine learning. Professional service includes editorial roles for Statistics and Computing and past presidency of Statistical Society of Australia.

Shan Luمشاهده پروفایل

مدرس

• Financial Derivatives
• Option Pricing
• Quantitative Finance
• +۳ مورد دیگر

Dr. Shan Lu is a Lecturer in Finance at the Department of Accounting and Finance, Kent Business School, University of Kent, since August 2021. He previously held positions at the University of Aberdeen and the University of Bradford and earned his PhD from the University of Aberdeen. Research interests: Financial derivatives, option pricing, and quantitative finance. His work focuses on volatility modeling, risk-neutral density estimation, and computational finance, with publications in journals such as the European Journal of Finance, Journal of Futures Markets, and Economics Letters. Teaching: Covers financial markets, derivatives, econometrics, and quantitative methods at undergraduate and postgraduate levels. Scientific awards: Fellow (FHEA) of Higher Education Academy Advising: Offers PhD supervision in topics aligned with his research interests, including financial derivatives and quantitative finance. He emphasizes collaboration on research ideas directly related to his expertise. Publications: Recent work explores volatility dynamics in VIX/VXX options, risk-neutral density extraction, and implied volatility forecasting, leveraging computational methods and empirical finance techniques.

Suzanne S. Leeمشاهده پروفایل

دانشیار

• Financial Econometrics
• Empirical Finance
• Asset Pricing
• +۶ 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.

Ly Fie Sugiantoمشاهده پروفایل

دانشیار

• Accounting Information Systems
• Data Analytics
• Artificial Intelligence
• +۸ مورد دیگر

Dr. Ly Fie Sugianto is an Associate Professor in the Department of Accounting at Monash Business School, Monash University. Her research focuses on the integration of data analytics, artificial intelligence, and machine learning in accounting and business systems, with applications in the energy sector and organizational behavior. Monash Business School, Monash University Department of Accounting Specialization: Accounting Information Systems, Data Analytics, AI Her research interests span Accounting Information Systems , Agent-Based Simulation , Decision Support Systems , and Technology Adoption . She applies computational methods to study competitive dynamics in deregulated electricity markets and the impact of digital tools on employee well-being and organizational resilience. The recent publications reflect a strong trend in using AI and simulation to analyze complex socio-technical systems, particularly in energy markets and leadership dynamics. Keywords across her work include agent-based modeling , data analytics , servant leadership , and enterprise social media , indicating interdisciplinary research at the intersection of information systems, management, and public policy. Her scientific awards include competitive grants from the ARC (SPIRT/Linkage) , the Australia Indonesia Governance Research Partnership (AIGRP) , and the Sumitomo Foundation . ARC Grant: Dispatch Optimisation in the Australian National Electricity Market Sumitomo Foundation: Technology Use and Employee Well-Being AIGRP: Governance and MSME Resilience during Pandemic Dr. Sugianto has advised research projects and collaborated with industry partners such as Western Power , Ecogen Energy , and AEMO . She is currently accepting PhD students and leads externally funded research initiatives. Her work contributes to UN Sustainable Development Goals related to industry innovation and responsible consumption. She is affiliated with research teams focusing on intelligent decision support systems and digital transformation in business , with active collaborations in Australia and Indonesia.

Ricardo Caballeroمشاهده پروفایل

استاد

• Macroeconomics
• Financial Economics
• International Economics
• +۳ مورد دیگر

Ricardo Caballero is the Ford International Professor of Economics at the Massachusetts Institute of Technology's School of Humanities, Arts, and Social Sciences, where he previously served as Chairman of the Economics Department from 2008 to 2011. A leading scholar in macroeconomics and financial economics, his research focuses on safe assets, monetary policy, financial crises, and international economics. His research interests center on the macroeconomic implications of financial frictions, with particular emphasis on safe asset shortages, risk premium dynamics, and monetary policy transmission mechanisms. Caballero's work has pioneered the risk-centric approach to macroeconomics, explaining phenomena such as the Wall Street/Main Street disconnect, global imbalances, and the collapse of interest rates through the lens of safe asset scarcity and risk intolerance. His research bridges theoretical modeling with empirical analysis of financial crises and policy interventions. Caballero's recent publications demonstrate a consistent focus on financial conditions indexing, monetary policy frameworks, and the interaction between financial markets and the real economy. His work increasingly examines how central banks can target financial conditions directly and how risk premia evolve during crises, with applications to pandemic-era economic policy and zero lower bound environments. 2002 Frisch Medal of the Econometric Society Smith Breeden Prize by the American Finance Association Journal of Finance 2014 Brattle Group Prize 2022 Banque de France-TSE Senior Prize in Monetary Economics and Finance Elected Fellow of the Econometric Society (1998) Elected Fellow of the American Academy of Arts and Sciences (2010) As an NBER Research Associate and frequent policy advisor, Caballero has influenced central bank thinking globally through his work on financial stability, monetary policy frameworks, and global imbalances. His research has informed policy discussions at the Federal Reserve, IMF, and multiple central banks regarding crisis management, safe asset creation, and the appropriate monetary response to financial shocks. Caballero maintains active collaborations with major financial institutions and central banks worldwide, translating theoretical insights into practical policy frameworks.

Alexandre RUBESAMمشاهده پروفایل

دانشیار

• Finance
• Asset Pricing
• Financial Econometrics
• +۵ مورد دیگر

Alexandre RUBESAM is an Associate Professor at IÉSEG School of Management (France), specializing in Finance with a focus on asset pricing, financial econometrics, and quantitative trading. He holds a Ph.D. in Finance from Cass Business School (UK), an MSc in Statistics from the State University of Campinas (Brazil), and a Bachelor in Statistics from the same university. Education: Ph.D., Finance, Cass Business School, UK (2008) MSc., Statistics, State University of Campinas, Brazil (2004) Bachelor, Statistics, State University of Campinas, Brazil (2001) His research interests span behavioral finance, risk management, machine learning applications in finance, and portfolio optimization. Notably, he explores topics like market herding during crises, volatility forecasting, and the low-beta anomaly through behavioral lenses. Prof. Rubesam has authored influential papers on information transmission in financial markets, risk parity strategies, and the efficacy of linear models in volatility prediction. His work bridges theoretical finance with practical applications, such as developing machine learning-based portfolio construction methods for emerging markets. Awards: 2007 Dimitris N. Chorafas Foundation Prize 2006 Best Paper Award, Cass Business School His professional roles include Chief Risk Officer at Itaú-Unibanco (2013–2017) and Quantitative Researcher/Trader at Principia Capital Management (2009–2011). He is a member of LEM (Laboratory of Economics and Management) and teaches courses on financial programming, risk management, and portfolio analysis.

Ying Chenمشاهده پروفایل

دانشیار

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

• Applied Statistics
• Big Data
• Economic Models and Forecasting
• +۲ مورد دیگر

Prof Terry O'Neill serves as Executive Dean at the Bond Business School , Bond University, while also holding the title of Professor and Director at the Centre for Data Analytics. His research spans applied statistics , big data analytics , and financial modeling , with over 80 publications (20% in A* journals). Grants: Lead investigator on five ARC Discovery/Linkage grants totaling $2.99M, including studies on retirement savings, climate change modeling, and financial crisis resilience. Research Trends: Focus on financial literacy , retirement economics , and myelopoiesis , bridging finance with computational biology through collaborations with his spouse Helen O'Neill. Academic Leadership: Established Bond University's University Centre in Actuarial and Financial Big Data Analytics to elevate institutional research prominence in data science.

John Cotterمشاهده پروفایل

استاد

• Finance
• Quantitative Finance
• Volatility Modelling
• +۵ مورد دیگر

John Cotter is a Full Professor of Finance and Chair in Quantitative Finance at University College Dublin's Smurfit School of Business. He holds a PhD from Queen's University Belfast and prior academic roles include Associate Professor (2006-2012) and Senior Lecturer (2004-2006). His research focuses on volatility modeling, risk management, and asset pricing with applications in equity, real estate, and derivative markets. Cotter directs the Centre for Financial Markets and the Financial Mathematics Computation Cluster (FMC2), a multi-university research initiative funded by Science Foundation Ireland. Education: BComm and MEconSc from University College Cork, PhD in Finance from Queen's University Belfast. Research interests span asset pricing, volatility modeling, risk management, and financial market integration. His work has been published in top journals like Journal of Banking and Finance and Journal of International Money and Finance . He has secured grants including the ADAPT Phase 2 project (2021-2026). Cotter advises the European Securities Markets Authority (ESMA) and has consulted for numerous organizations globally. Notable awards include the UCD Outstanding Educator Teaching Award and UCD School of Business Research Contribution Award. He serves as Associate Editor for three journals and has supervised numerous PhD students through FMC2.

Ehsan Abbasnejadمشاهده پروفایل

دانشیار

• Machine Learning
• Computer Vision
• Natural Language Processing
• +۳ مورد دیگر

Dr. Ehsan Abbasnejad is an Associate Professor at Monash University's Department of Data Science and Artificial Intelligence, and holds adjunct positions at the Australian Institute for Machine Learning (AIML, University of Adelaide) and the Centre for Augmented Reasoning (CAR). He specializes in foundational AI, focusing on vision-language tasks, adversarial machine learning, and reinforcement learning. His work bridges theory with real-world applications in agriculture, energy, healthcare, and sports. Education: PhD in Computer Science from Australian National University (ANU). Research Interests: Machine Learning Theory and Adversarial Defenses Neural Network Robustness and Generalization Multimodal Learning (Vision-Language) Continual and Transfer Learning Applications in Energy, Healthcare, and Robotics Awards: Finalist for Australian AI Academic/Researcher of the Year (2024) Multidisciplinary competition wins (e.g., OzMineral Explorer Challenge) Advising & Grants: Australian Research Council (ARC) Discovery Project on Reinforcement Learning CSIRO's Next Generation Graduate Fund Accepting PhD students in foundational AI and applications Labs & Teams: Director of Foundational Machine Learning & Reasoning at Monash, leading global teams in AI competitions and industry collaborations (Microsoft Research, NEC Labs America).