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Victor Emilio TrosterView profile

Associate Professor

• Financial Econometrics
• Time Series
• Econometric Theory
• +4 more

Dr. Victor Emilio Troster is an Associate Professor in the Department of Applied Economics at the Universitat de les Illes Balears (UIB), where he has been teaching since 2015. His academic focus spans financial econometrics, time series analysis, and econometric theory, with particular expertise in causality tests, quantile regression models, and financial asset forecasting. He teaches undergraduate and graduate courses including Econometrics, Survey Analysis and Multivariate Techniques, and Econometrics for Big Data across various business administration and economics programs. Ph.D. in Economics, Universidad Carlos III de Madrid, Spain (2015) Dr. Troster's research interests center on advanced econometric methods applied to financial markets and economic phenomena. His work focuses on developing and applying statistical techniques to analyze financial time series data, with particular attention to causality testing in quantile frameworks, specification testing for regression models, and forecasting of financial assets. His research bridges theoretical econometrics with practical applications in finance and economics, addressing complex questions in market behavior, risk analysis, and economic policy evaluation. His methodological contributions have significant implications for both academic research and practical financial decision-making. His publication record demonstrates consistent contributions to high-impact econometrics and finance journals, with a clear trajectory of increasingly sophisticated methodological work. His research spans both theoretical developments in econometric methodology and applied studies addressing contemporary financial and economic issues. The publications show a strong focus on time series analysis, with particular emphasis on nonlinear relationships, quantile-based approaches, and cross-market spillovers. His work frequently addresses methodological challenges in financial econometrics while providing insights relevant to practitioners and policymakers. Dr. Troster serves as a thesis advisor for the PhD in Applied Economics program at UIB. He has participated in competitive research projects funded by Spain's Ministry of Science and Innovation. His teaching portfolio spans multiple degree programs including Business Administration, Economics, and specialized Master's programs in Big Data Analysis and Tourism Economics. He is an active member of the Econometrics and Data Science (ECD) Consolidated R+D+I Group at UIB, contributing to the department's research infrastructure and collaborative projects. His work integrates traditional econometric approaches with emerging data science methodologies, positioning him at the intersection of established economic theory and contemporary analytical techniques.

• Risk Management
• Insurance
• Public Procurement
• +6 more

Mirosław Urbanek is an Assistant Professor at the Department of Economic Policy and Banking, Institute of Economics and Finance, Faculty of Social Sciences, John Paul II Catholic University of Lublin. He holds a Doctor of Economics degree and is a graduate of the Faculty of Social Sciences at the same university. Additionally, he completed postgraduate studies in Risk Diagnostics and Insurance Claims Adjustment at the Academy of Finance in Warsaw and holds certifications including PRINCE 2® Foundation in Project Management. His research focuses on risk management, insurance, public procurement, management accounting, and strategic management. He employs multivariate statistical analysis and econometric models to study economic and financial phenomena, particularly in the context of EU market reforms and agricultural sector dynamics. His recent publications include analyses of dairy cooperatives' development in Poland and forecasting models for the EU and global sugar markets post-quota abolition. Urbanek has contributed extensively to peer-reviewed journals and book chapters, with works on public procurement systems, centralized management in local government, and legal-economic frameworks for agricultural cooperatives. He has also presented findings at national conferences, emphasizing policy implications for responsible development and market stability. He has supervised numerous bachelor's, master's, and postgraduate theses, covering topics such as household consumption patterns, enterprise revenue optimization, poverty analysis, and regional unemployment trends.

Mareike DresslerView profile

Senior Lecturer

• Real Algebraic Geometry
• Computational Algebraic Geometry
• Polynomial Optimization
• +6 more

Mareike Dressler is a Senior Lecturer (A/Professor, tenured) and ARC Discovery Early Career Research Fellow at the School of Mathematics and Statistics, University of New South Wales (UNSW Sydney). She joined UNSW in February 2022 as a Lecturer (Assistant Professor, tenure-track) and was promoted to Senior Lecturer with tenure in July 2024. Her educational background includes: PhD in Mathematics from Goethe-Universität Frankfurt/Main (2018), supervised by Thorsten Theobald M.Sc. in Mathematics from Goethe-Universität Frankfurt/Main (2013) B.Sc. in Mathematics from Goethe-Universität Frankfurt/Main (2010) Mareike's research focuses on real and computational algebraic geometry and polynomial and convex optimization. She also works on problems intersecting with convex geometry, matrix and tensor computation, applied algebraic geometry, algebraic and geometric combinatorics, and real analysis. Her work particularly involves nonnegativity of polynomials, optimization methods, and ranks of matrices and tensors, with a special interest in sums of nonnegative circuits (SONCs) for sparse polynomials. Her research has strong applications in data science and machine learning. Mareike has received several prestigious awards including the ARC Discovery Project 2025 grant for "Quantifying Uncertainty of Risk-Aware Optimization for Safe Decision-Making," the J G Russell Award from the Australian Academy of Science, and the Early Career Impact Award from UNSW Science. She actively supervises research students, including PhD student Hongzhi Liao, Master's student Qi Wang, and recently supervised Moritz Schick to completion of his PhD in 2025. Mareike has secured significant research funding, including an ARC Discovery Early Career Researcher Award (DECRA) for 2024-2026. Mareike is an active member of the academic community, regularly organizing workshops and conferences such as "Optimization Days" at UNSW and minisymposia at major conferences like SIAM Conference on Applied Algebraic Geometry.

Michael PfarrhoferView profile

Assistant Professor

• Bayesian Econometrics
• Time Series Analysis
• Forecasting
• +3 more

Michael Pfarrhofer is an Assistant Professor at WU Vienna University of Economics and Business, Department of Economics, specializing in econometrics and macroeconomics. He previously held positions at the Universities of Vienna and Salzburg and contributes to the European Commission’s Joint Research Centre (JRC) in Ispra. His research focuses on Bayesian econometrics, time series analysis, forecasting, and empirical macroeconomics, with applications to business cycles and policy evaluation. Current Role: Tenure-track Assistant Professor Primary Affiliation: WU Vienna University of Economics and Business Collaborations: JRC (European Commission) Research interests include macroeconomic forecasting using advanced Bayesian methods, nonlinear time series analysis, and the impact of climate shocks on financial markets. His work bridges econometric theory and empirical applications, often involving large datasets and machine learning techniques. Publications span topics like tail-risk prediction, international financial spillovers, and nowcasting methodologies. He teaches courses such as Econometrics II and Economic Modeling, emphasizing practical applications of econometric tools. While no specific scientific awards are listed, his contributions to leading journals (e.g., Journal of Econometrics , Journal of Applied Econometrics ) highlight his scholarly impact. Advising and grant activities are not detailed in the provided texts, though his research often involves collaborative projects with institutions like the JRC.

Ravi KashyapView profile

Associate Professor

• Artificial Intelligence
• Machine Learning
• Finance
• +5 more

Ravi Kashyap is an Associate Professor at the Estonian Business School (since 2021). Previously, he held academic roles including Visiting Professor at Singapore University of Social Sciences (2020–2021) and Assistant Professor at SolBridge International School of Business (2017–2020). Before academia, he worked in senior roles such as CEO and Head of Quantitative Research at institutions like Goldman Sachs, Morgan Stanley, and Citigroup. His expertise spans finance, artificial intelligence, and blockchain. Education: PhD in Finance, City University of Hong Kong (2020) MBA, Leonard N. Stern School of Business, New York University (2009) Bachelor of Technology in Computer Science and Engineering, National Institute of Technology Calicut (1999) Research Interests: Artificial Intelligence and Machine Learning applications in finance Risk management and portfolio optimization Blockchain technology and decentralized finance Economics of knowledge and uncertainty Evolutionary behavioral models and cognitive adaptation Recent Article Trends: Focus on integrating blockchain protocols with wealth management, dynamic trading cost analysis, and interdisciplinary applications of machine learning in finance and economics. Recent work addresses practical challenges in decentralized finance (DeFi) and the theoretical underpinnings of market microstructure. Grants & Industry Collaboration: Advises on AI incubation and collaborates with blockchain firms to develop methodologies for human collaboration systems. Former industry experience informs his research on financial markets and quantitative strategies.

• Financial Planning
• Risk Management
• Household Finance
• +5 more

Dr. hab. Paweł Rokita, professor at the Wrocław University of Economics, specializes in financial investments and risk management. He holds a position in the Department of Financial Investments and Risk Management. His research focuses on household financial planning, longevity risk analysis, stochastic modeling, and applications of copula functions in financial risk assessment. Notable areas include optimization of multi-goal financial plans, risk tolerance verification, and analysis of extreme market dependencies. Key contributions include development of cumulated surplus approaches for retirement planning, methodologies for determining longevity risk aversion, and studies on dependence structures between extreme losses in stock markets. His work spans both theoretical frameworks (e.g., adaptive market hypothesis applications) and practical modeling techniques for household financial stability. Publications analyze Polish financial markets extensively, with comparative studies to German and global markets. Research emphasizes dynamic financial planning methodologies under uncertainty, stochastic goal optimization, and international portfolio diversification strategies.

• Survival Analysis
• Meta-analysis of Survival Data
• Experimental Designs
• +3 more

Emmanouil Androulakis is an Assistant Professor at the Department of Statistics and Insurance Science, University of Piraeus. His research focuses on Survival Analysis, Meta-analysis of Survival Data, Experimental Designs, and Biostatistics. He holds a Ph.D. in Statistics from the National Technical University of Athens (2015) and has extensive teaching experience across multiple institutions, including the Hellenic Naval Academy and the University of West Attica. Education: Ph.D. in Statistics, National Technical University of Athens (2015) M.Sc. in Applied Mathematical Sciences, National Technical University of Athens (2009) Diploma in Applied Mathematical and Physical Sciences, National Technical University of Athens (2007) Research Interests: Emmanouil specializes in advanced statistical methodologies for healthcare and environmental applications, including meta-analysis, frailty models, and experimental design optimization. His work bridges theoretical statistics with practical challenges in biostatistics, geriatric care, and renewable energy forecasting. Publications: His 30+ articles span biostatistical methodologies, healthcare outcomes research, and environmental modeling. Recent work emphasizes meta-analysis heterogeneity and frailty modeling in longitudinal studies. Awards: While no named awards are listed, his peer-reviewed contributions and 240+ citations highlight his academic impact. He serves as a reviewer for 14 journals. Teaching & Advising: He teaches Probability and Statistics courses, including Biostatistics and Statistical Packages. No formal advisee list is provided, but his roles as a scientific associate and instructor suggest active academic mentoring.

Jan BullaView profile

Professor

• Hidden Markov Models
• Time Series Analysis
• Statistical Modeling
• +5 more

Jan Bulla is a Professor in the Department of Mathematics at the University of Bergen, Norway. His contact information includes email jan.bulla@uib.no and phone number +47 55582875, with his office located at Allégaten 41, 5007 Bergen. His research spans multiple disciplines with a focus on statistical methodology development and applications in diverse fields. Bulla's research interests center around Hidden Markov Models , Time Series Analysis , and Statistical Modeling , with significant applications in Tinnitus Research , Financial Mathematics , and Biostatistics . His work demonstrates expertise in developing advanced statistical techniques for analyzing complex data structures, particularly in longitudinal settings. He has made notable contributions to regime-switching models, hidden semi-Markov models, and computational approaches for parameter estimation. Analysis of Bulla's recent publications (2020-2024) reveals a clear research trajectory with three major strands: (1) methodological advances in hidden Markov models and related statistical techniques, (2) extensive research on tinnitus including clinical, physiological, and genetic aspects, and (3) applications of statistical models in financial mathematics. His work shows increasing interdisciplinary collaboration, particularly with medical researchers studying tinnitus, while maintaining strong methodological contributions to statistics. Bulla has been consistently productive with publications spanning from 2005 to the present, demonstrating sustained research activity. His most recent work focuses on computational improvements for statistical models and continued investigation into tinnitus mechanisms and correlates.

Robert E BristowView profile

Professor

• Ovarian Cancer
• Biomarker Development
• Early Cancer Detection
• +3 more

Dr. Robert E. Bristow is a Professor and Chair of Obstetrics & Gynecology at the University of California, Irvine (UCI) School of Medicine, holding the Philip J. DiSaia Chair in Gynecologic Oncology. His research focuses on improving early detection of ovarian cancer through multivariate index assays (MIA), comparative analysis with CA 125-II and ACOG guidelines, and developing clinical tools for adnexal mass evaluation. His work spans biomarker validation, proteomics, and clinical decision-making frameworks in gynecologic oncology. Key research contributions include validating MIA's superior sensitivity (95.3%) for early-stage ovarian malignancy compared to traditional methods, with consistent performance across menopausal status. Clinical trials involving 44 sites demonstrated MIA's enhanced diagnostic accuracy in premenopausal (89.3% sensitivity) and postmenopausal patients (98.3% sensitivity). Collaborative studies explore metabolomic and proteomic signatures for tumor differentiation, while economic evaluations highlight MIA's cost-effectiveness relative to CA 125. Dr. Bristow's publications emphasize translational research bridging biomarker discovery (e.g., serum proteins, plasma metabolites) and clinical implementation for ovarian cancer risk stratification. He has contributed to national guidelines and comparative effectiveness research, though no specific awards or student mentorship details were found in the provided texts.

• Functional Neuroimaging
• Brain Connectivity
• Biomedical Engineering
• +3 more

Joaquín Goñi is an Assistant Professor in the departments of Industrial Engineering and Biomedical Engineering at Purdue University. His research bridges biomedical engineering, neuroscience, and computational methods to study brain structure-function coupling, functional connectomes, and their applications to neuropsychiatric and neurodegenerative disorders. Ph.D. in Sciences from University of Navarra, Spain (2008) Postdoctoral fellowship at Functional Neuroimaging Laboratory, Center for Applied Medical Research, Spain Research Associate at Indiana University (2011-2014) Associate Research Scientist and Adjunct Assistant Research Professor at Indiana University School of Medicine His research interests span functional neuroimaging , brain connectivity analysis , and cognitive neuroscience , with a focus on machine learning techniques applied to neuroimaging data . Recent methodological work includes Tucker tensor decomposition , persistence homology , and penalized regression approaches to study Alzheimer's disease and alcohol use disorder . His publications highlight trends in topological data analysis , connectome fingerprinting , and phenotypic trait mapping through dynamic functional connectivity modeling. Dr. Goñi has contributed to multimodal neuroimaging techniques, including bimodal EEG-fMRI decomposition and MEG-theta alpha dynamics analysis. His work at the Center for Neuroimaging and Indiana Alzheimer Disease Center underscores his expertise in neurodegenerative disease research. Current affiliations include the Functional Neuroimaging Laboratory and Human Connectome Project collaborations.

Thuy Xuan Uyen PhanView profile

Assistant Professor

• Sensory Evaluation
• Consumer Behavior
• Food Science
• +4 more

Dr. Thuy Xuan Uyen Phan is an Assistant Professor in the Department of Food Science at Schmid College of Science and Technology, Chapman University. Her office is located in the Keck Center for Science and Engineering 154. Dr. Phan earned her educational credentials from prestigious institutions across Asia and the United States: Bachelor of Engineering in Food Technology from Ho Chi Minh City University of Technology, Vietnam Master of Science in Cognitive Science from National Cheng Kung University, Taiwan Ph.D. in Sensory Analysis and Consumer Behavior from Kansas State University, USA With nearly 19 years of professional experience in sensory evaluation and consumer research, Dr. Phan's work bridges academic theory with industry applications. Her research focuses on understanding how people interact with food products through multiple lenses. She examines sensory properties of food and non-food products, investigates consumer behavior related to food choices, and conducts cross-cultural studies exploring how different populations perceive and relate to food. Her methodological expertise includes multivariate statistical analyses to determine consumer preferences and relationships between sensory data and consumer acceptability. Dr. Phan's publication record demonstrates a consistent focus on food perception, consumption patterns, and cross-cultural differences. Her recent work examines socioeconomic factors affecting food safety perceptions, emotional responses to coffee consumption, and traditional food patterns across Southeast and East Asia. She has developed methodologies for studying eating motivations and food choice across different meal contexts, contributing to both academic literature and practical applications in food product development. Dr. Phan has extensive experience conducting both academic and industry projects, providing consulting services to help develop new food products while advancing knowledge in sensory and consumer science.

Michael ShieldsView profile

Professor

• Uncertainty quantification
• Machine learning
• Stochastic simulation
• +7 more

Michael Shields is a Professor in the Department of Civil and Systems Engineering at Johns Hopkins University, with secondary appointments in Materials Science and Engineering and affiliations with the Hopkins Extreme Materials Institute (HEMI) and the Data Science and AI Institute. He directs the Center on High-Throughput Materials Discovery for Extremes and co-leads the Center on Artificial Intelligence for Materials in Extreme Environments. Shields chairs the Whiting School of Engineering Faculty Senate and is an Ivy+ Provost Leadership Fellow. His research focuses on uncertainty quantification, machine learning, stochastic simulation, and reliability analysis for engineering systems. Key applications include extreme event modeling (earthquakes, blasts, impacts), materials science, and computational mechanics. His group develops open-source tools like UQpy for uncertainty quantification in physical systems. Recent publications (2024-2025) demonstrate strong trends in physics-informed machine learning, with neural operators and Bayesian methods dominating 80% of works. Surrogate modeling techniques appear in 60% of articles, while materials science applications feature in 40%. Natural hazards and computational efficiency are recurring themes. Awards & Honors: 2025 Early Achievement Research Award (IASSAR) DOE Early Career Award NSF CAREER Award ONR Young Investigator Award Johns Hopkins Catalyst Award He leads the Shields Uncertainty Research Group (SURG), funded by NSF, ONR, ARL, and national laboratories. Current projects emphasize real-time prediction of complex systems and reliability analysis under uncertainty.

Sarah DepaoliView profile

Associate Professor

• Bayesian Statistics
• Structural Equation Modeling
• Latent Variable Modeling
• +5 more

Sarah Depaoli is Associate Professor of Quantitative Psychology at the University of California, Merced and Visiting Distinguished Professor at the Methods Center of the Eberhard Karls University of Tübingen . She specializes in Bayesian statistics, structural equation modeling, and mixture modeling with applications in psychological and health-related research. PhD in Quantitative Methods (minor in Mathematical Statistics), University of Wisconsin (2010) MA in Quantitative Psychology, California State University-Sacramento (2007) BA in Psychology, California State University-Sacramento (2003) Her research focuses on: Bayesian estimation of latent variable and growth models Prior distribution selection in small-sample research Measurement invariance testing across populations Development of guidelines for model transparency (WAMBS-checklist) Psychological and health outcome modeling with Bayesian methods The most recent articles demonstrate expertise in: Bayesian longitudinal analysis (CushingQoL interpretation differences) Latent class enumeration in growth mixture models ML vs Bayesian estimation comparisons Health psychological outcomes (PTSD, Quality of Life) Cross-cultural measurement invariance Statistical software implementation (JAGS, Mplus) Scientific awards include: 2015 Association for Psychological Science Rising Star 2013 Hellman Fellow 2011 APA Division 5 Distinguished Dissertation Multiple travel awards from Psychometric Society and SMEP She has advised numerous graduate students and served as Associate Editor for Psychological Methods , Multivariate Behavioral Research , and Journal of Royal Statistical Society, Series A .

David J. FrancisView profile

Professor

• Reading Disabilities
• English Language Learners
• Quantitative Methods
• +8 more

Dr. David J. Francis holds the Hugh Roy and Lillie Cranz Cullen Distinguished Chair of Quantitative Methods in the Department of Psychology at the University of Houston. He serves as Director of the Texas Institute for Measurement, Evaluation, and Statistics (TIMES) and the Center for the Success of English Learners, a National Research and Development Center funded by the Institute of Education Sciences. His work focuses on statistical modeling, reading disabilities, and the education of English Language Learners. Ph.D. in Clinical-Neuropsychology from the University of Houston (1985), specializing in Quantitative Methods His research spans developmental disabilities , neuroscience (e.g., brain injury consequences), and educational equity for at-risk populations. He has pioneered methods for improving personnel selection using random effects models and explored interventions for seizure disorders via brain stimulation. Recent publications highlight his expertise in statistical modeling applied to education and neuroscience , including studies on academic vocabulary instruction, bilingual phonological awareness, and neuroimaging techniques for seizure localization. Collaborative grants from NICHD, NINDS, and the Office of Naval Research underscore his interdisciplinary impact. Scientific Awards : Hedges Lecture Award (2021) Albert J. Harris Award (2006) Esther Farfel Award (2008) Fellowships: APA Division 5, AERA, APS Dr. Francis has directed foundational centers like CREATE and the Texas Center for Learning Disabilities. His leadership roles include Chairman of the Psychology Department (2002-2014) and advisory positions on the National Board of Education Science and NRC committees. Key labs and teams include: Texas Institute for Measurement, Evaluation, and Statistics (TIMES) Center for the Success of English Learners Data Management and Statistics Core (PI, Texas Center for Learning Disabilities)

Camilla PegramView profile

Lecturer

• Veterinary Epidemiology
• Causal Inference Methods
• Clinical Intervention Evaluation
• +3 more

Dr. Camilla Pegram is a Lecturer in Veterinary Epidemiology within the Department of Pathobiology & Population Sciences at The Royal Veterinary College (RVC). Her research focuses on applying causal inference methods to veterinary clinical data, specifically through the VetCompass Programme, to evaluate real-world effectiveness of clinical interventions. She obtained her Bachelor of Veterinary Medicine (BVetMed) from RVC in 2015, followed by a Master of Research (MRes) in Companion Animal Epidemiology in 2017. She completed her PhD in 2021 on 'VetCompass eClinical Trials (VETs) – Generating Interventional Evidence from Observational Data' and continues to expand this work as faculty. Education: BVetMed (2015), MRes in Companion Animal Epidemiology (2017), PhD in Veterinary Epidemiology (2021) Research Focus: Causal inference in veterinary clinical records, breed-specific disorder analysis, urinary incontinence in bitches Teaching: Evidence-based veterinary medicine, epidemiology, and statistics for BVetMed, BSc, and MSc students Affiliations: VetCompass Causal Inference Research Programme, Clinical Residents (SCTS) at RVC Her recent publications demonstrate a consistent emphasis on causal modeling in veterinary observational data, with key collaborations with Drs. Dan O'Neill, David Brodbelt, and David Church. She contributes to the Distance Learning MSc in Veterinary Epidemiology and Public Health and works extensively with anonymized clinical records to improve canine health outcomes.