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Zhi DaView profile

Professor

• Asset Pricing
• Behavioral Finance
• Market Microstructure
• +7 more

Zhi Da is the Howard J. and Geraldine F. Korth Chair in Finance and Professor of Finance at the University of Notre Dame , Mendoza College of Business, Department of Finance. He completed his Ph.D. in Finance at Northwestern University’s Kellogg School of Management (2006), preceded by an M.Sc. in Financial Engineering from the National University of Singapore (2001) and a B.B.A. with First-Class Honors (1999) from the same institution. Holding editorial roles at Journal of Finance , Management Science , Review of Financial Studies and several other top journals, he is a leading voice in empirical finance research. Education Ph.D. in Finance, 2006 – Kellogg School of Management, Northwestern University M.Sc. in Financial Engineering, 2001 – National University of Singapore B.B.A. (1st Class Honors), 1999 – National University of Singapore Research Interests Zhi Da’s scholarship sits at the intersection of asset pricing , behavioral finance , and market microstructure . He investigates how investor attention, institutional trading, liquidity frictions, and information flows jointly determine the cross-section of expected returns. His work delves into retail margin trading, the role of pension-fund flows in exchange-rate dynamics, the informational content of SEC filings, and the efficiency of short-selling mechanisms. By combining large-scale data analytics, textual analysis, and structural modeling, he uncovers novel predictors of returns ranging from presidential approval ratings to real-time attention measures. Recent projects explore fractional trading ’s impact on price efficiency, hedging demand as a driver of intraday momentum, and the hidden effort problem in delegated portfolio management. These themes collectively advance our understanding of limits to arbitrage and the formation of extrapolative beliefs. Publication Landscape Spanning 2025 back to 2009, his 15 most recent articles in Journal of Finance , Review of Financial Studies , Management Science , Journal of Financial Economics , and Journal of Financial and Quantitative Analysis converge on three broad motifs: (1) micro-level trading frictions—liquidity costs, margin requirements, and short-selling constraints; (2) macro-finance linkages—exchange rates, fiscal policy, and global capital flows; and (3) information economics—attention allocation, media analytics, and regulatory disclosures. The collective evidence demonstrates that seemingly small trading or informational frictions aggregate into large, persistent cross-sectional return predictability. Honors and Awards 2017 William F. Sharpe Award for Best Paper, Journal of Financial and Quantitative Analysis Lead-article distinctions in Journal of Finance , Review of Financial Studies , and Management Science Featured coverage in SmartMoney and CNBC Teaching & Mentorship At Notre Dame’s Mendoza College, Professor Da teaches Investments (undergraduate and MBA) and Fixed Income Securities , integrating cutting-edge research insights into the curriculum. While specific advisees are not listed, his extensive co-author network (22+ recurring collaborators) attests to a vibrant mentoring environment. Laboratory & Data Resources He publicly distributes the NAT (Net Arbitrage Trading) dataset, a stock-quarter panel of arbitrage positions used in Chen, Da & Huang (2019). This resource has become a standard tool for researchers studying arbitrage capital movements.

Vadim CherezovView profile

Professor

• Membrane Protein Structure and Function
• G Protein-Coupled Receptors (GPCRs)
• Lipid-Protein Interactions
• +3 more

Vadim Cherezov, the Ester Dornsife Chair in Biological Sciences and Professor at the University of Southern California (USC), leads groundbreaking research in membrane protein structure and function. Affiliated with the Bridge Institute, Department of Chemistry, and Michelson Center for Convergent Bioscience, his work focuses on GPCRs, ion channels, and transporters—critical targets for drug discovery. His team leverages advanced techniques like Lipidic Cubic Phase (LCP) and Serial Femtosecond Crystallography (SFX) at XFEL facilities to solve high-resolution structures under physiological conditions. Institutional Affiliations: Bridge Institute, USC Michelson Center, Department of Chemistry, Department of Pharmacology and Pharmaceutical Sciences. Key Collaborations: Katritch Lab, Kuhn Lab, NIH, European XFEL. His research explores the role of lipids in modulating GPCR function, addressing diseases like Alzheimer’s, diabetes, and cancer. By solving the structure of the A 2A adenosine receptor via sulfur SAD phasing at XFEL, Cherezov’s lab demonstrated de novo phasing without heavy atoms. This breakthrough enables structural studies of previously intractable membrane proteins. Scientific Awards & Grants: NIH R01 GM108635, U54 GM094618, U54 GM094599, R01 GM095583 Science Signaling Breakthroughs of the Year (2014) Cherezov mentors a dynamic team, including postdocs (e.g., Dong-Gyun Kim), graduate students (e.g., Behnaz Davoudinasab), and alumni (e.g., Benjamin Stauch at Eli Lilly, Nairie Michaelian at Genentech). His lab’s publications span Nature , Science , and Cell , with recent work on Science Advances (2025) addressing ABEL-FRET for GPCR dynamics.

• Econometric Theory
• Machine Learning/Nonparametrics
• Robust Inference for Clustered & Network Data
• +6 more

Harold D. Chiang is an Assistant Professor in the Department of Economics at the University of Wisconsin-Madison. His research focuses on econometric theory and methods, particularly robust inference techniques for clustered and network data, machine learning applications, and causal inference frameworks like regression discontinuity/kink designs. He employs computational statistics and asymptotic theory to address methodological challenges in high-dimensional and complex datasets.

David WhitneyView profile

Professor

• Visual Perception
• Cognitive Neuroscience
• Ensemble Perception
• +6 more

David Whitney is a Professor of Psychology at the University of California, Berkeley , with affiliations in Cognitive Science and Neuroscience . His research focuses on visual perception, particularly how humans process information in cluttered environments through mechanisms like ensemble perception , serial dependence , and perceptual crowding . He employs techniques such as psychophysics, fMRI, and TMS to study these phenomena. Whitney's recent work examines serial dependence in schizophrenia, emotion perception in crowds , and medical image analysis for dermatology and radiology. His studies reveal how the brain uses dynamic predictive templates and motion cues to stabilize perception despite neural processing delays. Publications span Current Biology , Nature Reviews Psychology , and PLoS ONE , with a strong emphasis on interdisciplinary applications of perceptual science. Scientific contributions include foundational studies on visuomotor control , blind spot filling-in , and holistic face processing . His lab investigates perceptual stability across eye movements, spatial localization, and cross-modal interactions. Whitney has received grants such as NIH EY018216 to support his research on motion-dependent visual coding. Detailed information about his work is available on the Whitney Lab website .

John SerencesView profile

Professor

• Cognitive Psychology
• Neuroscience
• Cognitive Neuroscience
• +4 more

John Serences is a Professor in the Department of Psychology at the University of California, San Diego (UCSD). He leads the Perception and Cognition Lab, which participates in the Neuroscience Graduate Program. His research focuses on how behavioral goals and attention influence perception, memory, and decision-making, employing techniques like psychophysics, computational modeling, EEG, and fMRI. Key projects explore serial dependence, neural adaptation in visual cortex, and the interplay between sensory processing and mnemonic storage. Recent work highlights mechanisms reconciling repulsive neuronal adaptation with attractive behavioral biases. Affiliations: Department of Psychology, UCSD; Neuroscience Graduate Program. Research Themes: Visual perception, working memory, decision-making, neuroimaging. His lab investigates neural dynamics underlying cognitive processes, with particular emphasis on how attentional modulations and stimulus history shape neural representations. Notable contributions include studies on adaptive sensory coding and the role of top-down signals in perceptual stability.

Bruce E. HansenView profile

Professor

• Econometrics
• Time Series Analysis
• Model Selection and Averaging
• +4 more

Bruce E. Hansen is the Mary Claire Aschenbrener Phipps Distinguished Chair and Trygve Haavelmo Professor of Economics at the University of Wisconsin-Madison, Department of Economics. He maintains an active research program with publications extending through 2025, demonstrating his continued prominence in econometric methodology. His research interests include: Econometric theory and methodology Time series analysis and forecasting Model selection, averaging, and shrinkage techniques Threshold and structural change models Statistical inference for clustered and dependent data Hansen's recent work focuses on innovative approaches to model averaging, standard error estimation for complex data structures, and unit root testing. His publications demonstrate both theoretical rigor and practical applicability to economic data analysis, with particular attention to handling clustered data, serial correlation, and model uncertainty. His influential publications include 'Least Squares Model Averaging' in Econometrica (2007) which introduced Mallows Model Averaging, and 'A Modern Gauss-Markov Theorem' (2022), both representing significant theoretical contributions to econometrics. His two textbooks 'Probability and Statistics for Economists' and 'Econometrics' (Princeton University Press, 2022) reflect his commitment to teaching and disseminating econometric knowledge. Hansen's research has been supported by multiple National Science Foundation grants (SES-9022176, SES-9120576, SBR-9412339, and SBR-9807111), highlighting the significance and quality of his contributions to the field.

Sung Hoon ChoiView profile

Assistant Professor

• Econometrics
• Financial Econometrics
• Machine Learning
• +6 more

Sung Hoon Choi is an Assistant Professor in the Department of Economics at the University of Connecticut, part of the College of Liberal Arts and Sciences. His research focuses on developing econometric tools for analyzing big data, machine learning applications, and forecasting using high-dimensional panel datasets. He holds a Ph.D. in Economics from Rutgers University (2021), an M.A. in Applied Statistics from Yonsei University (2016), and a B.A. in Statistics from the University of California, Berkeley (2013). His research interests include econometric theory, financial econometrics, and high-frequency data analysis. Notable areas of concentration are large panel data and factor models, high-dimensional data techniques, and volatility matrix analysis. He teaches courses such as Econometrics I and III for Ph.D. students, and Python programming for economists at undergraduate and master's levels. Recent publications focus on volatility modeling using factor structures, high-frequency financial data, and panel data econometrics. His work addresses challenges in structural information analysis, standard errors for clustered panels, and feasible generalized least squares methods. He collaborates with researchers like Donggyu Kim and Jushan Bai, contributing to leading journals like the Journal of Econometrics and Econometric Theory .

• Multisensory Perception
• Predictive Processing
• Speech Perception
• +3 more

Prof. Helen Blank is a Professor leading the Multisensory Perception Group and the Prediction in Communication Lab at the Institute for Systems Neuroscience, University Medical Center Hamburg-Eppendorf. Her work focuses on understanding how sensory information is integrated and predicted in contexts like speech perception and face recognition. She holds a Marie Curie Fellowship for her research on prior information's role in human communication. Fluent in German, English, and French, she contributes to experimental medicine and systems neuroscience. Her research spans predictive coding, neuroimaging, and clinical applications in Parkinson’s and developmental disorders. Education: Not explicitly stated in text, inferred as advanced degrees in neuroscience or related fields. Her research interests emphasize multisensory integration, predictive processing in speech and vision, and the neural bases of perception. Recent articles explore topics such as pupil responses to auditory surprise, face expectation hierarchies, and audio-visual speech processing. Awards include the Marie Curie Fellowship supporting her predictive communication work. She leads interdisciplinary teams within the Center for Experimental Medicine, advancing knowledge on perceptual mechanisms and their clinical implications.

Rob van BeersView profile

Assistant Professor

• Motor Control
• Motor Learning
• Spatial Perception
• +3 more

Rob van Beers is an Assistant Professor at the Faculty of Behavioural and Movement Sciences at Vrije Universiteit Amsterdam, with affiliations to Neurocontrol, IBBA, and AMS - Sports. His research focuses on human motor control, spatial perception, and computational modeling using Bayesian approaches to understand sensory-motor integration under uncertainty. He holds ancillary roles as a Researcher at Radboud University (Nijmegen) since 2015 and serves on the Editorial Board of the Journal of Neurophysiology since 2015. His work contributes to UN Sustainable Development Goals related to health and well-being. Key research interests include motor learning dynamics, sensorimotor adaptation, and the neural basis of spatial orientation. Recent studies explore Alzheimer’s impacts on motor adaptation and Bayesian inference in vestibular path integration. Teaching responsibilities include courses on linear systems dynamics, physical measurement techniques, and motor systems regulation. His work spans 42 peer-reviewed articles, with datasets published on platforms like Dryad and Zenodo.

J.P. EggersView profile

Professor

• Technological Change
• Decision-Making Under Uncertainty
• New Product Development
• +7 more

J.P. Eggers is the Interim Dean and Professor of Management and Organizations at New York University’s Leonard N. Stern School of Business, where he also holds the Catherine & Peter Kellner Professorship of Entrepreneurship. He has been a faculty member since 2008 and served as Vice Dean for MBA and Graduate Programs from 2018 to 2024, leading major innovations in MBA education including the Andre Koo Technology and Entrepreneurship MBA program and the Online-Modular Option for the Langone Part-time MBA. His educational background includes a Ph.D. in Management from the Wharton School at the University of Pennsylvania, an M.B.A. from Goizueta Business School at Emory University, and a B.A. in History from Amherst College. Eggers’ research centers on technological change, decision-making under uncertainty, and new product development. He investigates how firms and executives navigate innovation, make strategic choices amid ambiguity, and respond to technological disruptions. His work spans industries such as video games, mutual funds, flat panel displays, and platform markets, focusing on topics like organizational learning, radical invention, and portfolio expansion strategies. The trends in his recent publications reveal a strong focus on strategic adaptation in evolving industries, emphasizing firm experience, resource constraints, and behavioral factors in innovation. His work frequently appears in top-tier journals such as Strategic Management Journal , Organization Science , and Academy of Management Journal . Poets & Quants Top 40 Professors Under 40 (2011) NYU Stern MBA Program Professor of the Year (2010) Emerald Management Review Citation of Excellence (2010) Business Policy Division of Academy of Management Finalist for Outstanding Dissertation Award (2009) Technology Division of Academy of Management Finalist for Outstanding Dissertation Award (2009) Eggers has a strong record of advising Ph.D. students and securing research support. His leadership roles in academic programs reflect his commitment to experiential learning, innovation in curriculum design, and expanding access to MBA education for working professionals. He has also launched initiatives like SternWorks and the Executive in Residence program to broaden career pathways for MBA students. He is actively involved in research centers and initiatives at Stern, particularly those related to technology, entrepreneurship, and behavioral strategy, though specific lab affiliations are not detailed in the provided text.

David HuberView profile

Professor

• Cognitive Psychology
• Neuroscience
• Memory Processes
• +7 more

David Huber is a Professor in Psychology and Neuroscience at the University of Colorado. He holds a PhD from Indiana University (2000). His research focuses on human perception, memory, and decision-making from a computational perspective, integrating behavioral studies with neuroimaging techniques (fMRI, ERP) and computational models (Bayesian, neural networks). Key areas include visual cognition, spatial navigation models, memory retrieval dynamics, and priming effects. Education: PhD in Psychology from Indiana University (2000). Research emphasizes mechanisms underlying memory processes, perceptual adaptation, and the neural basis of cognitive functions. Recent work challenges traditional roles of hippocampal place/grid cells, proposing memory-centric models. He explores how perceptual fluency influences decision-making and juror cognition, with studies on serial dependence and gestalt principles in binocular rivalry. His neurocomputational methods include fMRI-based tuning function analysis and virtual electrophysiology modeling. Publications span over two decades, focusing on memory models (SAM-RI), neural habituation, and Bayesian modeling of brain activity. His work advocates for rigorous model-driven approaches in cognitive neuroscience.

Giovanni CompianiView profile

Associate Professor

• Industrial Organization
• Quantitative Marketing
• Econometrics
• +6 more

Giovanni Compiani is an Associate Professor at the University of Chicago Booth School of Business, specializing in Marketing. His research bridges industrial organization and quantitative marketing, focusing on advanced econometric methods. PhD, MPhil, MA in Economics from Yale University BSc, MSc in Economics from Bocconi University Previous Assistant Professor at Haas School of Business His work explores unstructured data integration in demand estimation, consumer search behavior on online platforms, risk preferences in cryptocurrency markets, and time perception in behavioral economics. He has published in top journals including Journal of Political Economy , Marketing Science , and Review of Economic Studies . Recent publications emphasize machine learning applications in econometrics, equilibrium modeling of lotteries, and crypto mining's economic impact. His research portfolio spans demand analysis, structural modeling, and behavioral insights. Editor's Choice Award, The Review of Asset Pricing Studies (2024) Developed nonparametric demand estimation frameworks Advances dynamic model identification with instrumental variables Compiani teaches Data Science for Marketing Decision Making at Booth and maintains active collaborations with researchers across econometrics, computer science, and behavioral disciplines.

Jungbin HwangView profile

Associate Professor

• Econometrics Theory
• Applied Econometrics
• Financial Econometrics
• +5 more

Jungbin Hwang is an Associate Professor in the Department of Economics at the University of Connecticut. He specializes in econometrics theory, with a focus on improving the accuracy and robustness of Generalized Method of Moments (GMM) methods in handling time series and panel data with dependence and heterogeneity. His research also extends to financial econometrics, Bayesian methods, and cointegration analysis. Education: Ph.D., Economics, University of California, San Diego (2016) M.A., Economics, Seoul National University (2010) B.A., Economics, Seoul National University (2008) Research Interests: Efficiency and approximation in GMM estimation Cluster-robust inference and bootstrap methods Cointegration in non-stationary systems Applications to financial markets and policy analysis Teaching: Courses include Empirical Methods in Economics, Econometrics I, and advanced topics in panel data analysis. Key Contributions: His work addresses challenges in GMM inference for time series and panel data, including finite-sample corrections and robust variance estimation. Recent studies explore low-frequency cointegration and quantile regression in dynamic settings. Grants & Collaborations: Collaborations with scholars like Yixiao Sun and Gonzalo Valdés have produced influential methods for accurate econometric testing and inference. Contact: Located in 333 Herbst Hall, Storrs, CT. Office hours: Wednesdays 3:00-4:00 PM or by appointment.

Chihwa KaoView profile

Professor

• Econometrics
• Panel Data Analysis
• Time Series Econometrics
• +6 more

Chihwa Kao is a Professor in the Department of Economics at the University of Connecticut, affiliated with the College of Liberal Arts and Sciences. His research focuses on panel data econometrics, structural change analysis, cointegration, high-dimensional models, and time series econometrics. He has extensively contributed to methodologies addressing cross-sectional dependence, factor models, and non-stationary panel data. Key research interests include developing robust statistical techniques for panel data, testing for structural breaks in economic relationships, and analyzing economic growth factors. His work bridges theoretical econometrics with practical applications in finance and policy analysis. His articles emphasize advancements in panel data methodologies, such as bias-correction in dynamic models, clustering techniques for fixed effects, and handling serial correlation. Recent trends show a focus on high-dimensional data challenges, factor models, and structural stability assessments in large datasets. Awards and honors are not explicitly listed in the provided materials. He advises no students listed here, but his courses include Econ5315/3315 and Econ5323/4323. His CV and additional materials are available via provided links.

Anders SkrondalView profile

Professor

• Psychometrics
• Statistics
• Biostatistics
• +3 more

Anders Skrondal is a Professor II at the University of Oslo's Faculty of Educational Sciences, affiliated with the Centre for Educational Measurement (CEMO). He also serves as a Senior Scientist at CEFH (Research Council of Norway Centre of Excellence) at the Norwegian Institute of Public Health and Co-Principal Investigator at CREATE, another Norwegian Centre of Excellence. His academic journey includes roles as Head of the Biostatistics Group at the Norwegian Institute of Public Health and Professor of Statistics at the London School of Economics (LSE), where he directed the Methodology Institute. Skrondal's research focuses on psychometrics, statistics, biostatistics, and econometrics, with a major contribution being the development of the GLLAMM framework. He has authored 14 books and over 200 peer-reviewed papers, achieving an h-index of 63 and 30,000+ citations. His awards include the 1997 Psychometric Society Dissertation Prize and leadership roles in prestigious organizations like the Psychometric Society and Royal Statistical Society. Research Interests: Skrondal specializes in statistical methodologies including latent variable modeling, multilevel modeling, and missing data analysis. His work bridges theoretical advancements and practical applications in medicine, psychology, and social sciences. He is renowned for integrating latent variable and mixed model frameworks to address complex data structures. Recent trends in his publications emphasize methodological solutions for missing data, non-ignorable mechanisms, and psychometric model validation. His articles span statistical theory, medical applications, and educational measurement. Awards: President, Psychometric Society (2016–2017) Elected Member, International Statistical Institute Outstanding Academic Title for 'The Cambridge Dictionary of Statistics' (2011) Fulbright Professor at UC Berkeley (2013–2014) Advising & Grants: Skrondal has led major research initiatives such as CEFH and CREATE, funded by the Research Council of Norway. While no specific advisee list is provided, his collaborations span international institutions. His work on GLLAMM software is used in over 750 journals, reflecting widespread academic impact. Labs/Teams: Active in CEMO and CEFH, he contributes to interdisciplinary teams advancing educational measurement and public health research. His involvement in CREATE focuses on equality in education through statistical innovations.