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Serge DarollesView profile

Professor

• Financial Econometrics
• Empirical Finance
• Quantitative Finance
• +5 more

Serge Darolles is a Professor of Finance at Paris-Dauphine University, where he has been teaching financial econometrics and empirical finance since 2012. Prior to joining Dauphine, he worked at Lyxor Asset Management from 2000 to 2012, developing mathematical models for investment strategies. He has also held consulting positions at Caisse des Dépôts et Consignations, Banque Paribas, and the Atomic Energy Commission. Darolles serves as a member of the AMF Scientific Advisory Board, contributing his expertise to financial regulation. His educational background includes a PhD in applied mathematics from the University of Toulouse and a DESS from ENSAE, Paris, providing a strong quantitative foundation for his research. These qualifications have enabled him to bridge theoretical econometrics with practical financial applications. Darolles specializes in financial econometrics with research interests spanning quantitative finance, hedge fund analysis, private equity, liquidity risk, ESG investing, and time series analysis. His work combines rigorous statistical methodology with practical financial insights, particularly in portfolio management, risk assessment, and market microstructure. He has developed innovative approaches to modeling financial time series and analyzing liquidity dynamics across various asset classes. Analysis of his recent publications reveals consistent focus on liquidity analysis, hedge fund performance evaluation, and quantitative investment strategies. Key themes include measurement of liquidity risk, dynamics of private equity performance, trend following strategies, and application of advanced time series models to financial data. His research demonstrates evolution with market developments, as evidenced by recent work on ESG factors and FinTech regulation. Darolles maintains an active presence in the academic finance community through regular presentations at international conferences including the French Finance Association meetings, Society for Financial Econometrics events, and specialized workshops on quantitative finance. His collaborative approach is evident through frequent co-authorship with researchers like G. Le Fol, C. Francq, and S. Ain Tommar.

Alina BazarovaView profile

Research Fellow

• Statistics
• Time Series Analysis
• Genomics
• +2 more

Alina Bazarova is a Research Fellow at the University of Warwick's Systems Biology Centre, working in Professor Nigel Burroughs's group on a BBSRC-funded project focused on genome-wide identification of priming sites for Okazaki fragments. She earned her PhD from Graz University of Technology (Austria) in 2014 with research on asymptotic properties of trimmed sums in analysis and statistics. Dr. Bazarova's research spans Statistics, Time Series Analysis, and Mathematical Analysis, with significant contributions to theoretical probability including trimmed sums, stable autoregressive processes, and lacunary series. Her recent work extends into Genomics and Behavioural Economics through collaborations with international researchers. Her publication trajectory from 2014-2024 shows evolving expertise from core statistical theory toward interdisciplinary applications, particularly in genomic sequence analysis and economic modeling. No scientific awards were documented in the source material. Currently supported by BBSRC funding for her genomics project, there is no indication of student advising in the provided information. She maintains active research collaborations with Professor Istvan Berkes (Graz University of Technology) in time series analysis and with Professor Steven Silver (San Jose State University) and Dr Marko Raseta (University of Keele) in behavioural economics. As a key member of Professor Burroughs's research group at the Systems Biology Centre, she participates in interdisciplinary genomic research while continuing theoretical work in probability and statistics.

William T. FreemanView profile

Professor

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +5 more

William T. Freeman is the Thomas and Gerd Perkins Professor of Electrical Engineering and Computer Science (EECS) at MIT and a member of the Computer Science and Artificial Intelligence Laboratory (CSAIL). Since 2015, he has also served as a research manager at Google Research in Cambridge, MA. Freeman previously held the role of Associate Department Head of EECS from 2011 to 2014. Research Interests: Mid-level vision, computational photography, 3D object structure, video synthesis, and vision-learning integration. Projects: Imaging privacy threats from ambient light sensors, Single-Image 3D Interpreter Network, Visual Dynamics, 3D-GANs, and motion magnification. His work spans foundational computer vision techniques and modern AI applications, emphasizing the importance of combining classical methods with neural networks. Recent projects include rapid video generation and ethical considerations in vision systems. Scientific Awards: 2020 Breakthrough Prize in Physics, 2019 PAMI Distinguished Researcher Award, multiple Outstanding Paper and Test-of-Time Awards. Freeman co-authored the 2024 textbook Foundations of Computer Vision , which bridges classical and deep learning-based approaches. He is actively involved in CSAIL and MIT’s Visual Computing and Vision Groups, impacting fields like cybersecurity, healthcare, and autonomous systems.

• Financial Econometrics
• Time Series Analysis
• Bayesian Inference
• +2 more

Richard Gerlach is a Professor at the University of Sydney, specializing in financial econometrics and time series analysis. His research focuses on developing Bayesian and semi-parametric models for forecasting and managing financial risk, with applications in volatility modeling, Value-at-Risk, and Expected Shortfall. He has published extensively in top-tier journals including the Journal of Financial Econometrics and Quantitative Finance . His research interests span: Financial econometrics and time series forecasting Bayesian methods for risk management Nonlinear heteroskedastic models High-frequency financial data analysis Volatility and tail risk modeling Gerlach's recent publications (2020-2025) predominantly explore Bayesian approaches to financial risk forecasting, with recurring themes in semi-parametric volatility modeling, tail risk quantification, and machine learning applications in finance. His work frequently integrates realized measures from high-frequency data to enhance predictive accuracy. He currently advises PhD students including Jacky LYU (working on semi-parametric financial risk forecasting) and Wen PENG (researching Bayesian neural networks for volatility dynamics). Notable grants awarded include: ARC Discovery Project: 'Deep learning based time series modeling and financial forecasting' (2020) Parametric and Semi-Parametric Financial Tail Risk Forecasting (Sydney Business School Pilot Research Scheme, 2020) ARC Discovery Project: 'Bayesian Inference for Flexible Parametric Multivariate Econometric modelling' (2008)

Jim E. GriffinView profile

Professor

• Bayesian Nonparametric Methods
• Regression Modelling with High-Dimensional Data
• Time Series Modelling in Econometrics and Finance
• +4 more

Jim E. Griffin is a Professor of Statistical Science at University College London's Department of Statistical Science. His research focuses on Bayesian Nonparametric Methods, Regression Modelling with High-Dimensional Data, and Time Series Modelling in Econometrics and Finance. He leads multiple collaborative projects including Bayesian Modelling of Macroeconomic Data (with Maria Kalli), Athletic Performance Passport for anti-doping detection (with James Hopker), and Efficient Computational Methods for Bayesian Variable Selection (with Sam Livingstone and Xitong Liang). Key research areas include applications in genomics, environmental science (via eDNA modelling with Eleni Matechou), and computational methods for model uncertainty visualization. Preprints and code for projects like Cartesian credible sets and tensor vector autoregression models are available via arXiv.org and GitHub repositories maintained by collaborators. His work bridges theoretical advancements in Bayesian statistics with practical applications in finance, ecology, and sports science. Collaborations span disciplines, with notable contributions to statistical methodologies for high-dimensional problems and model interpretability frameworks.

Ken SiuView profile

Professor

• Actuarial Science
• Quantitative Finance
• Risk Management
• +4 more

Ken Siu serves as Professor in the Department of Actuarial Studies and Business Analytics at Macquarie University, with affiliations to the Data Horizons Research Centre and Emerging Risks Research Centre. His research bridges theoretical and applied domains in quantitative risk analysis. Research interests span: Stochastic Processes in Financial Modeling Regime Switching and Markov Chain Applications Actuarial Risk Measurement Continuous-Time Pricing Models Esscher Transform Methodologies High-Frequency Financial Data Analysis Recent publications (2024-2025) demonstrate a strong focus on emerging financial and insurance challenges, including volatility modeling for cryptocurrency markets, pandemic risk quantification, and algorithmic approaches to claims reserving. His work consistently addresses model uncertainty and transaction cost implications across insurance and finance contexts. Professor Siu actively leads research initiatives including the ARC Discovery Project 'Two-Price Quantitative Finance' (2019-2022) and the current 'Climate Litigation Risk: AI-Enhanced Greenwashing Detection' project, demonstrating sustained grant acquisition capability. As a core member of Macquarie's Data Horizons Research Centre, he contributes to interdisciplinary collaborations analyzing complex risk landscapes through advanced computational and statistical frameworks.

Hanbat JeongView profile

Senior Lecturer

• Spatial Econometrics
• Panel Data Analysis
• Dynamic Game Theory
• +3 more

Hanbat Jeong serves as a Senior Lecturer in the Department of Economics within Macquarie University's Faculty of Business and Economics. He maintains active affiliations with both the Emerging Risks Research Centre and Health at Work Research Centre, demonstrating interdisciplinary research engagement. His research centers on advanced econometric methodologies with particular expertise in spatial econometrics and panel data modeling . Key focus areas include spatial autoregressive models for origin-destination flows, dynamic game theory applications in regional economics, and intertemporal optimization techniques. His work bridges theoretical econometrics with practical applications in public welfare and local government decision-making. Analysis of his publication trends (2019-2024) reveals consistent methodological innovation in spatial modeling techniques, with increasing emphasis on network-based economic analysis. His research demonstrates strong technical rigor while maintaining relevance to real-world policy challenges in regional development and government economics. Dr. Jeong completed his PhD in Economics in 2019, establishing a research trajectory characterized by high-impact publications in top econometrics journals including the Journal of Econometrics and Journal of Economic Dynamics and Control. His collaborative research network spans international institutions, with recent projects examining spillover effects in local government decision-making and spatial dynamic models for economic forecasting. Current work focuses on extending spatial autoregressive techniques to complex flow data applications.

Qianmei FengView profile

Professor

• Quality and Reliability Engineering
• Probability and Statistics
• Advanced Manufacturing Technology
• +4 more

Qianmei (May) Feng is a Professor and Graduate Program Director in the Department of Industrial and Systems Engineering at the University of Houston’s Cullen College of Engineering. She holds the Brij and Sunita Agrawal Faculty Fellowship. Her research focuses on quality and reliability engineering, stochastic processes, and their applications in advanced manufacturing, healthcare systems, and homeland security. She has led projects funded by NSF, DHS, and other agencies. Education: Ph.D., Industrial Engineering, University of Washington, 2005 M.S., Industrial Engineering, University of Washington, 2002 M.S., Management Science, Tsinghua University, 2000 B.S., Dual Degrees in Manufacturing & Industrial Engineering, Tsinghua University, 1998 Research Interests: Dr. Feng’s work emphasizes degradation-based reliability models, stochastic processes (e.g., Lévy processes), and their application to complex systems like MEMS, biomedical implants, and wind turbines. She also explores data-driven approaches for manufacturing process control and aviation security screening strategies. Grants & Awards: NSF Grant (2017): $348,077 for maintenance planning in dynamic environments Emerald Literati Network Award (2009) Featured in IE Magazine (2010–2014) Advising & Labs: Her research group advises over 15 students and collaborates on interdisciplinary projects. Key contributions include models for stent reliability, superconductor manufacturing, and aviation security. She leads the Feng Research Group , focusing on integrating quality, reliability, and statistical methods.

Mikko PakkanenView profile

Associate Professor

• Data Science
• Quantitative Finance
• Stochastic Processes
• +5 more

Mikko Pakkanen is an Associate Professor in Data Science and Quantitative Finance at the University of Waterloo, Canada, on leave from Imperial College London. He holds a PhD in Applied Mathematics from the University of Helsinki and has held academic positions at Aarhus University and Imperial College London. His research focuses on the intersection of data science, stochastic processes, and quantitative finance, with emphasis on high-frequency financial data, market microstructure, volatility modelling, and machine learning applications in epidemiology and finance. Education: PhD in Applied Mathematics, University of Helsinki, 2010 MSc in Mathematics, University of Helsinki, 2006 Research Interests: Statistical Modelling of Financial Markets Machine Learning in Finance and Epidemiology Stochastic Processes and Limit Theorems Market Microstructure Analysis Volatility Forecasting Articles Trends: Recent work emphasizes deep learning applications in financial markets, stochastic volatility estimation, and branching processes for epidemiological modelling. Key contributions include methodologies for high-frequency data analysis, GMM-based volatility roughness estimation, and unifying incidence-prevalence models in epidemics. Grants & Advising: Mikko has advised on projects involving reinforcement learning for hedging strategies and collaborated on epidemic models using time-varying branching processes. His GitHub repository includes numerical methods for integral equations applied to epidemiological research. Labs & Affiliations: Affiliated with the CFM-Imperial Institute of Quantitative Finance and the Department of Mathematics at Imperial College London while on leave. Active in interdisciplinary teams bridging data science and quantitative finance.

• Actuarial Science
• Demography
• Financial Risk Management
• +5 more

Jacie Liu is a Researcher at the Research School of Finance, Actuarial Studies & Statistics, Australian National University (ANU). Her work focuses on actuarial science, demography, and financial risk management. She has supervised research students and contributed to interdisciplinary studies involving econometrics, statistical modelling, and insurance applications. Her research interests include mortality forecasting, copula-based dependence modelling, board gender diversity impacts on banking performance, and longevity risk hedging. Recent projects combine machine learning techniques with demographic analysis, such as using manifold learning and neural networks for international life expectancy studies. Publications span peer-reviewed journals like Genus , Scandinavian Actuarial Journal , and Risks , addressing topics from ensemble mortality forecasts to Bayesian vine copulas for data breach losses. Collaborations include international scholars in statistics and actuarial science. Her work highlights methodological innovations in linking extreme value theory with demographic projections and applying vector autoregressive frameworks to mortality data.

Rüdiger WestermannView profile

Professor

• Computer Graphics
• Scientific Visualization
• Numerical Simulation
• +7 more

Prof. Rüdiger Westermann is a full Professor leading the Chair of Computer Graphics and Visualization at the Technische Universität München (TUM). His academic career includes roles at RWTH Aachen University (2001–2003) and research stays at Caltech and the University of Utah. He holds a doctorate from the University of Dortmund (1996) and has conducted foundational work in practical computer science, focusing on computer graphics, scientific visualization, and real-time numerical simulation. His research emphasizes algorithm development for interactive data exploration and physical simulation, particularly leveraging many-core architectures. Key research areas include volume visualization, multi-scale finite element simulation, and hierarchical data representation. Recent contributions involve stress-guided 3D design optimization, Bayesian imaging techniques, and GPU-accelerated visualization tools. His work bridges theoretical advancements with practical applications in meteorology, materials science, and medical visualization. Prof. Westermann’s academic journey includes a postdoctoral position under Prof. T. Ertl at Erlangen-Nuremberg (1998–2001) and prior research at GMD St. Augustin (1992–1997). He is actively involved in developing visualization tools for ensemble weather forecasts (e.g., Met. 3D) and structural design optimization for additive manufacturing. His publications span high-impact venues like IEEE Transactions on Visualization and Computer Graphics, emphasizing real-world applicability of visualization and simulation techniques. His research group at TUM collaborates on projects such as the Alpine benchmark for PDE emulators and GPU-based algorithms for large-scale data processing. Current efforts focus on neural fields for ensemble visualization, adaptive sampling techniques, and Bayesian methods for radio interferometry imaging.

Emmanuel IarussiView profile

Assistant Professor

• AI
• Computer Graphics
• Medical Imaging
• +2 more

Emmanuel Iarussi is an Assistant Professor at the Universidad Torcuato Di Tella and a researcher at CONICET in Buenos Aires. He holds a Ph.D. in Computer Graphics from INRIA Sophia Antipolis, supervised by Adrien Bousseau and George Drettakis, and completed a postdoc at IST Austria under Bernd Bickel. His engineering degree is from UNICEN University (2012). His research bridges AI, computer graphics, and medical imaging, focusing on generative models for 2D/3D content manipulation. Research Interests: His work spans AI-driven medical imaging, generative models for vascular structures, ultrasound simulation, and bone microstructure modeling. Key areas include autoregressive modeling, variational autoencoders, and applications in healthcare and biomedical engineering. Recent Trends in Articles: Recent publications emphasize combining AI techniques with medical applications, such as vascular geometry synthesis, melanoma detection reviews, and ultrasound realism enhancement. His work often intersects generative networks and clinical diagnostics. Grants & Recognition: Awarded a $69,960 grant from the Sloan Foundation’s Trust in AI initiative (2024). Active in workshops on AI ethics, public discourse, and open science. Labs & Teams: Leads projects like VisDecode (AI-driven scientific plot analysis) and contributes to repositories like generative3DSpongiosa for bone microstructure modeling.

João B. DuarteView profile

Associate Professor

• Macroeconomics
• Housing
• Monetary Policy
• +3 more

João B. Duarte is an Associate Professor at Nova School of Business and Economics in Carcavelos, Lisbon, Portugal. His research focuses on the intersection of Macroeconomics, Housing markets, Monetary Policy, and Economic Growth, with particular emphasis on structural transformation and productivity growth. Macroeconomic Modeling Monetary Policy Analysis Housing Market Dynamics Structural Transformation Labor Productivity Research His publications include methodological contributions using both theoretical and empirical approaches: Factor models for Euro Area monetary policy Technological change effects on inequality and taxation Credit cycle measurement frameworks Household leverage and welfare analysis Dr. Duarte supervises Ph.D. students working on topics such as: Luis T. Morais - Housing Finance and Public Finance Mariana Pires - Monetary Policy and Financial Integration Nuno Pereira - Structural Transformation and Productivity Pedro Estorninho - Fiscal Policy and Housing

Reza SameniView profile

Associate Professor

• Biomedical Signal Processing
• Machine Learning
• Artificial Intelligence
• +10 more

Reza Sameni is an Associate Professor at Emory University's Department of Biomedical Informatics and Adjunct Associate Professor at Georgia Institute of Technology's Wallace H. Coulter Department of Biomedical Engineering. He serves as Scientific Director of Emory's Medical Imaging, Informatics, and AI (MIIAI) Core. PhD in Biomedical Engineering (Sharif University of Technology) PhD in Signal Processing & Telecommunications (INPG) MSc in Biomedical Engineering (Sharif University) BSc in Electronics Engineering (Shiraz University) His research spans biomedical signal processing and model-based machine learning, with applications in cardiovascular diagnostics, maternal-fetal health, psychiatry, and global health equity. The Alphanumerics Lab develops explainable AI tools for multimodal physiological monitoring and open-access platforms like OSET. Current article trends focus on AI-driven diagnostics, cross-modal ECG/PCG analysis, edge computing for rural healthcare, and algorithmic reproducibility in biomedical applications. The lab actively recruits graduate students and postdocs. Georgia Institute of Technology (adjunct appointment) Emory University (tenured associate professor) Former Chair at Shiraz University Former Senior Researcher at GIPSA-lab Lab initiatives include: open-source toolboxes (OSET), crowdsourced AI frameworks for arrhythmia detection, and bias mitigation in clinical AI systems. Research integrates FPGA/ embedded systems for biomedical hardware development.

Tom McCoyView profile

Assistant Professor

• Computational Linguistics
• Cognitive Science
• Artificial Intelligence
• +5 more

Tom McCoy is an Assistant Professor in the Department of Linguistics at Yale University, with additional affiliations in the Department of Computer Science and the Wu Tsai Institute. His interdisciplinary research bridges computational linguistics, cognitive science, and artificial intelligence to understand the computational principles underlying human language. Dr. McCoy completed his PhD in Cognitive Science at Johns Hopkins University in 2021, where his graduate training emphasized interdisciplinary approaches to studying language acquisition from multiple theoretical and methodological perspectives. McCoy's research centers on two primary strands: language learning mechanisms investigating how humans acquire language from minimal data and how these capabilities can be replicated in machines, and linguistic representations exploring what computational architectures can effectively model language structure. His work frequently employs neural network language models while maintaining strong connections to linguistic theory and cognitive science principles. He has published influential work on hierarchical inductive biases in sequence-to-sequence networks and generalization capabilities of neural networks with respect to center embeddings. His recent publications reveal a research trajectory increasingly focused on the intersection of neural networks and cognitive science, examining inductive biases, Bayesian approaches to language learning, and the symbolic-subsymbolic debate in AI. His work appears in top venues including Nature Communications, Proceedings of the National Academy of Sciences, and leading AI conferences. McCoy actively mentors the next generation of researchers, currently considering PhD applicants for Fall 2026 and postdoctoral researchers for Fall 2026. He maintains a selective research group focused on computational linguistics and cognitive AI, prioritizing quality mentorship and methodological rigor. Though specific lab names aren't publicized, McCoy's research operates at the intersection of multiple departments at Yale, suggesting a collaborative environment where linguists, computer scientists, and cognitive scientists work together to tackle fundamental questions about language processing in both humans and machines.