جست‌وجوی استاد و پژوهشگر

Joel Hasbrouckمشاهده پروفایل

استاد

• Market Microstructure
• Financial Econometrics
• Securities Trading
• +۷ مورد دیگر

Joel Hasbrouck is the Kenneth G. Langone Professor of Business Administration and Professor of Finance at the Leonard N. Stern School of Business, New York University, where he has been a faculty member since 1983. His research focuses on market microstructure, financial econometrics, and the design and regulation of securities markets. He is the author of the influential book Empirical Market Microstructure and has published extensively in top finance journals. Ph.D., Finance, University of Pennsylvania B.S., Chemistry, Haverford College His research interests include market microstructure, financial econometrics, high-frequency trading, liquidity, price discovery, and decentralized exchanges . His recent work analyzes trading mechanisms in decentralized exchanges, FX market networks, and high-resolution price formation. He has made significant contributions to the understanding of limit order behavior, trading costs, and the impact of low-latency trading. His publications demonstrate a strong focus on empirical analysis of trading data , with recurring themes in market design, liquidity measurement, and the impact of technology on financial markets . He frequently employs advanced econometric techniques to analyze high-frequency and daily market data. 1986 Iddo Sarnat Prize by the European Finance Association Fellow, Society of Financial Econometrics Fellow, Columbia Law School Program in the Law and Economics of Capital Markets Advisory Editor, Journal of Financial Markets Associate Editor, Journal of Financial Econometrics Hasbrouck has advised numerous public and private institutions, including the SEC, CFTC, NYSE, Nasdaq, and the Federal Reserve Bank of New York. He has served on editorial boards of major journals such as the Journal of Finance , Review of Financial Studies , and Journal of Financial Markets . He co-developed the TORQ database, a comprehensive dataset used by researchers worldwide. He teaches graduate courses including Foundations of Finance and Principles of Securities Trading, and organizes the annual Stern Microstructure Conference.

Masakazu Ishiharaمشاهده پروفایل

دانشیار

• Quantitative Marketing
• Empirical Industrial Organization
• Dynamic Effects of Marketing Strategies
• +۱۲ مورد دیگر

Masakazu Ishihara is an Associate Professor of Marketing at the Leonard N. Stern School of Business, New York University, where he has been a faculty member since 2011. His research is centered on quantitative marketing and empirical industrial organization, employing structural econometric models to study consumer and firm behavior in dynamic markets. Leonard N. Stern School of Business, New York University Department of Marketing His academic training includes a Ph.D. in Marketing from the Rotman School of Management at the University of Toronto, and both a B.S. and M.S. in Economics from the University of Wisconsin-Madison. Professor Ishihara's research interests span quantitative marketing , empirical industrial organization , the dynamic effects of marketing strategies , forward-looking consumer and firm decision-making , and marketing in the entertainment and luxury industries . He frequently investigates how consumers and firms make intertemporal choices, particularly in durable goods markets such as video games, pharmaceuticals, and alcohol. His work often integrates economic theory with large-scale consumer data to estimate structural models of demand, adoption, and competition. The recent publications highlight a consistent trend in applying dynamic structural models to analyze consumer behavior in complex markets. His research spans topics such as rational addiction (e.g., soda and alcohol), price promotions, scarcity marketing, advertising causality, software piracy, and brand extensions. The keywords across his publications indicate strong expertise in econometrics , consumer behavior , pricing , and empirical IO , with applications in entertainment, healthcare, and digital platforms. His scientific awards include: Inaugural 2010 ISMS Doctoral Dissertation Competition Award 2021 Best Paper Award, Journal of Advertising Research Honorable Mention, 2011 Dick Wittink Prize Professor Ishihara has collaborated extensively with leading scholars such as Andrew Ching, Tülin Erdem, and Eric Yanfei Zhao. He has secured research funding implicitly through publication in top journals and active working paper pipeline, though specific grants are not listed. He advises emerging scholars and contributes to the academic community through peer-reviewed publications and methodological advancements in structural modeling. There is no mention of specific labs or research teams in the provided text, though his work suggests involvement in quantitative marketing and empirical IO research groups at NYU Stern.

Jiawei Zhangمشاهده پروفایل

استاد

• Business Analytics
• Optimization
• Machine Learning
• +۷ مورد دیگر

Jiawei Zhang is the Michael Armellino Professor in Business and Professor of Information, Operations and Management Sciences at the Leonard N. Stern School of Business, New York University. He chairs the Department of Technology, Operations, and Statistics and serves as Academic Director of the Master of Science in Data Analytics & Business Computing program. He joined NYU Stern in September 2004 and held a joint position at NYU Shanghai from 2014 to 2017. PhD in Management Science and Engineering, Stanford University, 2004 MS in Operations Research, Tsinghua University, 1999 BS in Applied Mathematics, Tsinghua University, 1996 Professor Zhang's research centers on business analytics, optimization, and operations management. His work integrates machine learning, robust optimization, and stochastic modeling to solve complex problems in supply chain management, healthcare operations, pricing, and revenue management. He investigates decision-making under uncertainty, online learning, and algorithmic approaches to resource allocation. His recent publications reveal a strong focus on theoretical and applied aspects of stochastic optimization, prophet inequalities, assortment optimization, and process flexibility. These works appear in top journals such as Operations Research , Management Science , and Mathematics of Operations Research , reflecting deep contributions to both methodological innovation and practical applications in operations. His editorial service highlights scholarly leadership: Associate Editor, Management Science (2014–present) Associate Editor, Manufacturing & Service Operations Management (2021–present) Associate Editor, Mathematics of Operations Research (2009–present) Associate Editor, Operations Research (2006–present) Professor Zhang teaches a range of courses from undergraduate to PhD levels, including Decision Models and Analytics, Convex Optimization, and Dynamic Programming. He advises PhD students and contributes to executive education programs, including in Risk and Decision Analytics and FinTech. His research is supported by theoretical rigor and real-world applicability, with implications for technology, finance, and healthcare operations.

• Health Economics
• Public Policy
• Financial Science
• +۳ مورد دیگر

Gianluca Fiorentini is a Full Professor in the Department of Economic Sciences at the University of Bologna. His work focuses on health economics, public policy, and healthcare systems, with a particular emphasis on regulatory frameworks and resource allocation. He has contributed extensively to understanding healthcare utilization patterns, disease management strategies, and the impact of policy interventions on health outcomes. His research spans topics such as pandemic response strategies, clinical guideline compliance, and the economic implications of pharmaceutical patent expiries. Fiorentini has analyzed healthcare systems in multiple countries, including Italy, Germany, and Australia, highlighting the importance of integrated care models and evidence-based decision-making. Recent publications emphasize the role of behavioral economics in preventive healthcare and the interplay between healthcare providers and policy incentives. Fiorentini’s work frequently intersects with public health challenges, offering insights into achieving equitable and efficient healthcare delivery systems.

Fabian Bastinمشاهده پروفایل

استاد

• Operations Research
• Optimization
• Stochastic Programming
• +۴ مورد دیگر

Fabian Bastin is a Full Professor in the Department of Computer Science and Operational Research (IRO) at Université de Montréal. He holds a prestigious academic position within the university's research and teaching community. His work focuses on optimization, stochastic programming, simulation, and their applications in transportation, energy systems, and finance. Teaching Responsibilities: Bastin teaches advanced courses such as IFT-2505 (Linear Optimization), IFT-3515 (Nonlinear Programming), and IFT-6512 (Stochastic Programming). He also contributes to graduate-level courses on dynamic programming and simulation techniques. His courses emphasize theoretical foundations alongside practical applications, often using tools like MATLAB and the ORATIO library he helped develop. Research Interests: Bastin's research spans stochastic optimization, simulation methodologies, and decision-making under uncertainty. Key areas include air traffic management optimization, hydroelectric reservoir scheduling, and synthetic population generation using copula-based models. He has pioneered work on scenario tree generation for multistage stochastic programming and developed algorithms for efficient mixed logit model estimation. Research Contributions: His publications highlight advancements in stochastic models for transportation systems, energy planning, and financial engineering. Notable works include contributions to the progressive hedging algorithm, recursive logit models for route choice analysis, and Monte Carlo methods for option pricing. Bastin is also actively involved in software development, notably the ORATIO simulation library used in discrete-event modeling. Professional Engagements: He has co-organized conferences on optimization and simulation, and his work has been supported by grants from NSERC and other funding bodies. Despite no explicit mention of awards in the text, his extensive publication record and methodological innovations suggest significant recognition in his field.

• Operations Research
• Robust Optimization
• Queueing Theory
• +۷ مورد دیگر

Chaithanya Bandi is an Associate Professor at the National University of Singapore (NUS) in the Analytics and Operations Department of the NUS Business School, with a joint appointment in the Department of Mathematics. His research focuses on decision-making under uncertainty, robust optimization, and their applications in operations management, healthcare, e-commerce, and energy systems. He develops robust optimization models for queueing control, risk optimization, and mechanism design. Key research areas include robust queue inference, two-stage distributionally robust optimization, and multi-item auction mechanisms. He has contributed to operational challenges in healthcare (e.g., patient re-entry scheduling), energy systems (electricity generation optimization), and e-commerce (price optimization for fashion products). His work integrates theoretical advancements with practical implementations in large-scale systems. Recent publications emphasize adversarial evaluation of large language models, dynamic scheduling algorithms, and robust policies for uncertain environments. His methodologies often involve novel optimization frameworks and scalable computational approaches. Dr. Bandi holds a PhD in Operations Research and has collaborated with industry leaders like Flipkart and healthcare providers to apply his models in real-world settings. His contributions bridge theoretical rigor and practical applicability in complex operational systems.

Martin Herdegenمشاهده پروفایل

استاد

• Mathematical Finance
• Probability Theory
• Stochastic Processes
• +۵ مورد دیگر

Prof. Martin Herdegen is a Professor in Financial Mathematics at the University of Stuttgart, leading the Research Group Stochastics and Applications within the Department of Stochastics and Applications. Previously, he held positions as a Reader at the University of Warwick (UK) and a postdoc at ETH Zurich under Prof. Martin Schweizer. His research focuses on mathematical finance, including equilibrium theory, utility maximization, stochastic differential utility, and financial risk measures, alongside probability theory topics like stochastic optimal control and weak convergence of measures. Education: PhD in Mathematics from ETH Zurich (2014), supervised by Prof. Martin Schweizer. His academic journey includes postdoctoral research at ETH Zurich and Warwick. Research interests span mathematical finance and probability, with emphasis on frictions in financial markets, risk modeling, and stochastic processes. Recent work explores mean-variance equilibria, market making dynamics, and ρ-arbitrage frameworks. Publications (2020–2024) highlight contributions to equilibrium asset pricing under transaction costs, optimal portfolio strategies with frictions, and algorithmic trading models using reinforcement learning. His work bridges theoretical stochastic analysis with practical financial applications. Advising: Supervised multiple PhD students including Osian Shelley (JP Morgan), Nazem Khan (Dublin City University), and Joseph Jerome (Liverpool University), with current advisees focusing on stochastic finance and machine learning integration. Research Group: Includes postdocs and PhD students working on topics like risk measures, market microstructure, and computational finance. Collaborations span institutions across Europe and North America.

Joep Lustenhouwerمشاهده پروفایل

مدرس ارشد

• Behavioral Economics
• Discrete Choice Models
• Macroeconomic Policy
• +۳ مورد دیگر

Joep Lustenhouwer is a Senior Lecturer in the Department of Economics at the University of Bath. His research focuses on behavioral economics, discrete choice models, macroeconomic policy, and bounded rationality. He explores topics including fiscal policy effectiveness, heterogeneous expectations, liquidity traps, and the role of cognitive biases in economic decision-making. Key research interests include the dynamics of economic expectations, policy design under bounded rationality, and the implications of stickiness and extrapolation in macroeconomic forecasts. His experimental work examines how group identity influences income inequality perceptions and how news impacts rational learning processes. Lustenhouwer's publications analyze stabilization mechanisms for liquidity traps, the effects of central bank communication, and fiscal stimulus strategies. He has contributed to understanding self-reinforcing mechanisms in unanchored expectations and the interplay between monetary and fiscal policies in crisis contexts. His work often combines theoretical models with experimental evidence to address real-world policy challenges. He has no listed scientific awards but maintains active collaborations in behavioral economics and macroeconomic policy analysis.

Max Pachaliمشاهده پروفایل

دانشیار

• Consumer Heterogeneity
• Sustainable Food Policies
• Digital Platform Economics
• +۴ مورد دیگر

Max Pachali is an Associate Professor of Marketing at the Tilburg School of Economics and Management , Tilburg University. His research integrates Bayesian econometric models with empirical industrial organization to analyze consumer responses to regulatory policies and digital platform dynamics. Education: PhD (2015-2019), Master of Science in Quantitative Economics (2015), Bachelor of Science in Business Administration and Economics (2011) at Goethe University Frankfurt. On the substantive side , Pachali focuses on sustainable food policies (e.g., nutritional warning labels, animal welfare certifications) and digital platform economics (e.g., Spotify playlist dynamics, fashion e-commerce). Methodologically, he specializes in: Bayesian models of consumer heterogeneity Conjoint analysis with budget constraints Causal inference techniques Two-way fixed effects and difference-in-differences models His article trends analyze: Environmental impact of food choices through policy interventions Platform power over content suppliers via algorithmic promotion Budget constraint biases in competitive pricing models Scientific awards include: 2023 Paul E. Green Award ISMS Early Career Fellow (2022) University of Washington Foster School Early Career Fellow (2024) He contributes to the Marketing Research Group , collaborating on datasets like the Spotify demand replication package , and engages in interdisciplinary work connecting econometrics with sustainable development goals.

• Theoretical Computer Science
• Algorithms
• Computational Complexity
• +۶ مورد دیگر

Maria Jose Serna Iglesias is a Full Professor at the Faculty of Informatics of Barcelona (FIB), Universitat Politècnica de Catalunya (UPC)-BarcelonaTech, where she is affiliated with the Department of Computer Sciences. She is a leading member of the ALBCOM research group, focusing on Algorithmics, Bioinformatics, Complexity, and Formal Methods, and serves as the coordinator of the PhD program in Computing. Her academic leadership and research excellence are central to UPC's contributions in theoretical computer science. Licenciado en Ciencias Matemáticas Licenciado en Informática Doctorat en Informàtica (Doctorate in Computing) Her research interests lie at the intersection of theoretical computer science and game theory, with a strong focus on algorithmic game theory , social network analysis , and computational complexity . She investigates influence propagation models, cooperative games, weighted voting systems, and graph algorithms. Her work often involves the design and analysis of algorithms for discrete structures, with applications in network science and decision-making systems. The analysis of her recent publications reveals a consistent trend in studying strategic interactions in networks, particularly through influence models and cooperative game theory . Her work spans both theoretical foundations—such as complexity and dimensionality of games—and practical applications in social networks, education, and synthetic data validation. The keywords across her articles highlight a deep engagement with discrete mathematics, optimization, and algorithmic decision theory. She has received scientific recognition, including competitive awards and active participation in scientific committees of major international conferences such as the International Conference on Algorithmic Decision Theory and the Discrete Mathematics Days. Premiada (Awarded) Scientific committee member for multiple international conferences Maria Jose Serna has led and participated in numerous competitive R+D+i projects, often in collaboration with other prominent researchers in the ALBCOM group. She has advised multiple students and researchers, contributing to the training of the next generation of computer scientists. Her collaborations span institutions in Spain and internationally, particularly in the domains of algorithms and game theory. She is a core member of the ALBCOM research group (Algorísmia, Bioinformàtica, Complexitat i Mètodes Formals) at UPC, which is a leading force in theoretical computer science in Spain. The group fosters interdisciplinary research in algorithms, complexity, and formal methods, with strong ties to mathematical and computational sciences.

Jim Wisemanمشاهده پروفایل

استاد

• Dynamical Systems
• Topological Dynamics
• Conley Index
• +۹ مورد دیگر

Jim Wiseman is a Professor of Mathematics at Agnes Scott College, where he is affiliated with the Department of Mathematics within the College of Arts and Sciences. He teaches a wide range of courses including Calculus, Linear Algebra, Probability, and Chaotic Dynamical Systems, and remains actively involved in both undergraduate instruction and research mentorship. His research focuses on dynamical systems from a topological perspective, with significant contributions to recurrence, the Conley index, rigorous computation, and applications in social choice and voting theory. His work bridges pure mathematics and interdisciplinary applications, particularly in political science and public policy. The recent publications highlight a strong trend in topological dynamics and symbolic systems, with increasing interest in recurrence structures and interdisciplinary modeling. His collaborative work, such as on NOMINATE and political history, reflects a growing engagement with data-driven social science. While no formal scientific awards are listed in the provided texts, his sustained scholarly output in high-quality journals demonstrates recognition within the mathematical community. Jim Wiseman advises students in senior research projects and senior seminars, and integrates research into his teaching, especially in advanced modeling and dynamical systems courses. Although specific grants are not mentioned, his publication record suggests ongoing research activity supported by institutional or external funding. He collaborates with scholars across disciplines and institutions, including statisticians, political scientists, and computer scientists. He maintains active research interests in topological data analysis and rigorous computational methods in dynamics, often working on theoretical frameworks that can be applied to real-world systems. His professional network includes colleagues from Northwestern University, Swarthmore College, and Dickinson College, reflecting long-standing academic collaborations.

Eric Pauwelsمشاهده پروفایل

پژوهشگر

• Operations Research
• Transportation Systems
• Machine Learning
• +۳ مورد دیگر

Eric Pauwels is a Scientific Staff Member at Centrum Wiskunde & Informatica (CWI) in Amsterdam, The Netherlands, affiliated with the Intelligent and Autonomous Systems department. His research spans interdisciplinary domains including transportation systems, energy markets, and behavioral economics. Key Research Areas: Transportation demand modeling, machine learning applications in mobility, reinforcement learning for cooperative systems, and energy systems optimization. Active Projects: ALIGN4energy (aligning citizen behavior with systems), Energy Intranets (NEAT project), and industrial collaborations like Impactstudie Noord/Zuidlijn. Scientific Contributions: 2005 ERCIM Best Working Group Prize recipient, with expertise in discrete choice modeling, wavelet analysis, and production scheduling optimization. Publications: Recent work focuses on bicycle-train integration, electric vehicle bidding frameworks, and CKLS financial process analysis. Contact: Email Eric.Pauwels@cwi.nl , Phone +31 20 592 4225 (Room M358).

Min Xuمشاهده پروفایل

استادیار

• Machine Learning
• High-dimensional Statistics
• Network Data Analysis
• +۲ مورد دیگر

Min Xu is an Assistant Professor in the Department of Statistics at Rutgers University – New Brunswick. He is affiliated with the School of Arts and Sciences and focuses his research on theoretical and methodological aspects of machine learning and high-dimensional statistics, with applications in network analysis and nonparametric estimation. Education: Ph.D. in Machine Learning, Carnegie Mellon University (2015) B.S. in Electrical Engineering and Computer Science (with minor in Mathematics), UC Berkeley Research Interests: Min Xu’s research lies at the intersection of machine learning , high-dimensional statistics , and network science . He develops computationally scalable methods with strong theoretical guarantees for complex data structures, particularly in nonparametric estimation , network analysis , and large-scale inference . His work addresses fundamental challenges in estimating high-dimensional distributions and understanding the structure of evolving networks, with applications in economics and social sciences. Grants & Funding: NSF Grant DMS-2113671 NSF Grant DMS-2311299 Research Trends: Across his publications, a consistent theme is the development of statistically rigorous methods for high-dimensional and network data. His work spans optimal estimation in stochastic block models, convex M-estimation, and inference on dynamic network structures, with a strong emphasis on theoretical guarantees and practical scalability. Affiliations: Previously, Min Xu served as a departmental postdoctoral researcher in the Statistics Department at the Wharton School, University of Pennsylvania. He is currently based at Hill Center, Rutgers University.

Matthew Shumمشاهده پروفایل

دانشیار مهمان

• Econometrics
• Industrial Organization
• Empirical Microeconomics
• +۳ مورد دیگر

Matthew Shum is the William D. Hacker Professor of Economics in the Division of Humanities and Social Sciences at the California Institute of Technology (Caltech). He holds a B.A. from Columbia University (1992) and a Ph.D. from Stanford University (1998). His academic appointments at Caltech include Professor (2008-2016), Johnson Professor (2016-2022), Hacker Professor (2023-2024), and currently Visiting Associate (2024-2027). His research focuses on econometrics, industrial organization, and empirical microeconomics, with specific interests in discrete choice modeling, dynamic decision processes, market design, and behavioral economics. He teaches courses on Industrial Organization, Econometrics, and Empirical Methods. Shum's extensive publication record demonstrates a consistent focus on developing and applying innovative econometric methods to real-world market behaviors. His recent work explores dynamic discrete choice models, rational inattention, empirical industrial organization, and behavioral phenomena in markets ranging from pharmaceuticals to online gaming. While no specific awards are mentioned in the provided materials, Shum maintains an active research program with ongoing work on bounded rationality, two-sided platforms, and empirical auction theory. He leads econometrics courses and supervises graduate research at Caltech.

• Econometrics
• Financial Time Series Analysis
• Bayesian Statistics
• +۳ مورد دیگر

Lennart Oelschläger is a researcher at Universität Bielefeld , affiliated with the Faculty of Business Administration and Economics and specifically the Department of Empirical Methods . He serves as a research assistant to Prof. Dr. Bauer and contributes to the Bielefeld Graduate School in Theoretical Sciences as a member. His work focuses on econometric modeling and financial data analysis, with a particular emphasis on discrete choice modeling and decision-making heterogeneity. His research includes the development of computational tools like the fHMM package for hidden Markov model applications in financial time series analysis and the RprobitB package for Bayesian probit choice modeling. He also serves as an E-Learning Officer in the Faculty of Business Administration, demonstrating his commitment to digital teaching initiatives. His publications highlight innovative applications of hidden Markov models for financial market regime detection, Bayesian estimation techniques, and numerical optimization methods. His work bridges theoretical statistics with practical applications in financial econometrics and behavioral economics.