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Chao ChenView profile

Assistant Professor

• High performance computing
• Numerical linear algebra
• Numerical PDE
• +3 more

Chao Chen is an Assistant Professor in the Department of Mathematics at North Carolina State University (NC State), where he has been since 2023. His research focuses on developing efficient algorithms for matrix computations, with applications ranging from solving partial differential equations to analyzing large high-dimensional datasets. Prior to this, he was a postdoctoral researcher at The University of Texas at Austin’s Oden Institute, working with George Biros and Gunnar Martinsson. He earned his PhD in Computational and Mathematical Engineering (ICME) from Stanford University in 2018, advised by Eric Darve. He also holds an MS from Stanford’s ICME (2014) and a BS in Information and Computational Mathematics from Nankai University (2012). Education: PhD in Computational and Mathematical Engineering, Stanford University, 2018 MS in Computational and Mathematical Engineering, Stanford University, 2014 BS in Information and Computational Mathematics, Nankai University, 2012 Research Interests: Chen’s work emphasizes high-performance computing, numerical linear algebra, and randomized algorithms. He develops methods to reduce computational complexity and enhance performance on modern hardware. His applications span computational physics, data science, and machine learning. Notably, he has contributed to fast multipole methods, hierarchical solvers, and preconditioners for large-scale systems. Teaching: Chen teaches courses such as Linear Analysis (MA 303), Mathematics of Scientific Computing (MA 402), Linear Transformations and Matrix Theory (MA 523), and Theory of Matrices (MA 723). His courses emphasize theoretical foundations and practical applications, often incorporating computational tools like MATLAB and LaTeX. Presentations: He has presented at conferences worldwide, including SIAM Annual Meetings, Supercomputing Conferences, and specialized workshops on numerical linear algebra and fast solvers.

• Financial Econometrics
• Asset Pricing
• Statistical Methods in Finance
• +7 more

Paul Schneider is a Full Professor in the Faculty of Economic Sciences at the University of Italian Switzerland (USI), where he has been a faculty member since 2012. He is affiliated with the Institute of Finance (IFin) and the Euler Institute (EUL), contributing to interdisciplinary research in quantitative finance and econometrics. His research focuses on financial econometrics, asset pricing, and statistical methods in finance, with an emphasis on extracting latent market information under minimal assumptions. He integrates techniques from engineering, mathematics, and data science to develop robust models for financial markets. His work spans risk premia, ambiguity in investment decisions, nonlinear pricing, and model-free recovery methods. His recent publications (2023–2024) in journals such as Review of Finance , Management Science , and SIAM Journal on Mathematics of Data Science highlight trends in adaptive learning, empirical scenario generation, constrained likelihood estimation, and optimal investment under ambiguity . These reflect a strong focus on data-driven, computationally efficient, and theoretically sound approaches to financial modeling. Adaptive joint distribution learning Fast empirical scenarios Optimal Investment under Ambiguity Constrained polynomial likelihood Dispersion of Beliefs and Sentimental Recovery Scientific Awards: No specific awards or fellowships are mentioned in the provided text. Advising and Grants: While no formal list of advisees is provided, Paul Schneider has collaborated extensively with researchers such as Damir Filipovic, Fabio Trojani, and Christian Wagner, suggesting a strong mentorship and collaborative role. He has contributed to funded research projects, particularly in financial modeling and econometrics, though specific grant names are not detailed. Labs and Research Teams: He is actively involved with the Institute of Finance (IFin) and the Euler Institute at USI, which support interdisciplinary research in finance, mathematics, and data science. He has also developed computational tools such as the KDM R package for kernel density machines, indicating engagement with data science and open research practices.

Kseniia MarcqView profile

Research Fellow

• Educational Assessment
• Item Response Theory
• Large-Scale Educational Studies
• +3 more

Kseniia Marcq is a Research Fellow at the Centre for Educational Measurement (CEMO) at the University of Oslo. Her primary role involves postdoctoral research focusing on educational assessment methodologies. She holds a PhD in Educational Sciences (expected 2025) and an MSc in Measurement, Assessment, and Evaluation from the University of Oslo. Her academic background includes roles as a research assistant and teaching assistant in various educational measurement courses. Her research interests center on large-scale educational assessments, particularly the Programme for International Student Assessment (PISA), with a focus on item format analysis, nonresponse patterns, and psychometric methodologies. She explores cross-country comparisons, gender disparities in educational metrics, and improving the reliability of assessment instruments. Her publications analyze PISA 2018 data, examining item nonresponse, cognitive domain measurement, and methodological advancements in kernel equating and IRT models. She contributes to projects like the 'Early and Adapted Assessment for Language Skills' (SEALS) and collaborates with the Frontier Research in Educational Measurement (FREMO) group. Teaching responsibilities include courses on data science, measurement models, linear models, and survey methodology. She supervised a Master’s thesis on PISA’s mathematical literacy scores and actively participates in academic discourse through peer-reviewed journals. Her work bridges theoretical psychometrics with practical applications in international educational evaluations.

• Theoretical Quantum Physics
• Quantum Information Theory
• Open Quantum Systems
• +7 more

Maarten Wegewijs is an Associate Professor (apl. Prof.) at RWTH Aachen University and a permanent staff member at the Peter Grünberg Institute (PGI-2) for Theoretical Nanoelektronik at Forschungszentrum Jülich, Germany. He leads research in theoretical quantum physics with a focus on quantum information, open quantum systems, and quantum transport. He is actively involved in teaching the Quantum Technology Master program at RWTH Aachen and collaborates with leading research groups across Europe. Research Interests: Theoretical and mathematical foundations of quantum information Quantum dynamics and non-Markovianity Quantum thermodynamics and heat transport Fermionic duality and exact symmetries Driven quantum systems and geometric effects Quantum transport in nanoscale and molecular junctions His recent research, reflected in 15 key themes from 2012–2023, shows a strong trend toward foundational aspects of quantum theory, particularly in information recovery, higher-order operations, and thermodynamic consistency in open systems. The work bridges mathematical physics, quantum information, and condensed matter theory. Scientific Awards and Recognition: No explicit awards listed for Maarten Wegewijs. Former student Konstantin Nestmann is a Marie Skłodowska-Curie Fellow, indicating high-quality mentorship. Advising and Research Collaborations: He has advised PhD students including Konstantin Nestmann and collaborates with prominent groups such as Mario Berta (RWTH Aachen), Janine Splettstoesser (Chalmers), Dirk Schuricht (Utrecht), and Herre van der Zant (Delft). His research has been supported through positions at the Helmholtz Association and European Commission networks. Labs and Research Teams: He leads the theoretical research group within PGI-2 at Forschungszentrum Jülich and maintains a strong affiliation with the Institute of Quantum Information at RWTH Aachen. His former Helmholtz Young Investigator Group focused on single-molecule quantum transport, continuing through international collaborations.

• Financial Econometrics
• Time Series Modelling
• Volatility Models
• +6 more

Dr. Chao Wang is a Senior Lecturer at the University of Sydney's Sydney Business School. He holds a PhD in Econometrics from the same university, along with master's degrees in Machine Learning & Data Mining (Helsinki University of Technology) and Mechatronic Engineering (Beijing Institute of Technology). His research focuses on financial econometrics, time series modeling, and volatility analysis, particularly using Bayesian methods and high-frequency data. He teaches courses like Quantitative Business Analysis (BUSS1020) and Machine Learning for Business (QBUS6840). Dr. Wang's research interests include parametric/non-parametric volatility models, Bayesian MCMC estimation, and applications of machine learning in finance. His work addresses microstructure noise in high-frequency data and integrates realized measures like variance and range. He has supervised students on topics such as spatiotemporal volatility forecasting and financial technology-based risk management. Grants: Machine learning and high-frequency data-based risk forecasting (2022), financial tail risk forecasting with deep learning (2021), and parametric tail risk forecasting (2020). Key collaborations: With Prof. Robert Gerlach and Minh-Ngoc Tran on Bayesian frameworks and realized measures. Publications: Over 15 peer-reviewed articles in top journals like Quantitative Finance and Journal of Financial Econometrics , focusing on risk forecasting methodologies. His work bridges econometric theory and practical financial applications, emphasizing robust risk prediction under volatile market conditions.

Robert DouglasView profile

Lecturer

• Applied Mathematical Analysis
• Optimal Mass Transfer
• Weather Front Formation
• +4 more

Robert Douglas is a Lecturer in the Department of Mathematics at Aberystwyth University since February 2000. His research focuses on applied mathematical analysis, particularly in optimal mass transfer, weather front formation, and relaxation spectrum recovery. He holds a PhD in applied mathematical analysis from Bath University, specializing in nonlinear analysis of vortices in ideal fluid flow. His career includes positions as a Research Fellow at the Universities of Cambridge and Reading, and a Visiting Scientist at the UK Meteorological Office. Key research interests include proving rigorous results using measure theory and convex analysis, with notable contributions to the Cullen-Norbury-Purser conjecture and polar factorization theory. Collaborations include work with H.R. Whittle Gruffudd on relaxation spectra and G.R. Burton on uniqueness conditions for polar factorizations. His work spans 1994–2019 with 9 peer-reviewed articles, addressing topics like semigeostrophic equations, viscoelastic fluid dynamics, and inverse problems. His research contributes to UN Sustainable Development Goals through advancements in mathematical modeling of environmental systems. Dr. Douglas actively participates in the Analysis cluster of the Wales Institute for Mathematical and Computational Sciences. His expertise combines theoretical rigor with applications in geophysical fluid dynamics and materials science.

• High Performance Computing
• Parallel Computing
• Heterogeneous Computing
• +12 more

Anne C. Elster is a Professor in the Department of Computer Science at the Norwegian University of Science and Technology (NTNU), within the Faculty of Information Technology and Electrical Engineering. She is the founder and director of the HPC-Lab, a leading research group in heterogeneous and parallel computing. She also maintains a long-standing affiliation with the Oden Institute at the University of Texas at Austin as a Senior Visiting Scientist until Summer 2025. Research Interests: Her work spans high-performance computing (HPC), GPU computing, parallel algorithms, auto-tuning, performance optimization, and machine learning applications in scientific computing. She leads research in heterogeneous architectures and has contributed significantly to compiler and runtime systems for GPUs and accelerators. Publications Trends: Her recent publications (2021–2024) focus on GPU acceleration, auto-tuning frameworks (e.g., BAT, LS-CAT), performance modeling (Roofline), machine learning integration in HPC, and applications in geophysical and scientific computing. There is a strong emphasis on empirical evaluation, benchmarking, and practical optimization techniques. Scientific Awards and Recognition: IEEE Senior Member (2000) IEEE Computer Society Distinguished Contributor Charter member, NTNU's Board (2021) Distinguished Speaker, IEEE Computer Society (2019–2022) Advising and Grants: She has advised over 100 master’s students and several PhD students. She has led major funded projects including the RCN SFI Centre for Geophysical Forecasting, EU H2020 CloudLightning and TICOH, and NFR FRINATEK on Computational Microscopy. She has served on numerous international program committees and evaluation boards. Labs and Teams: She leads the HPC-Lab at NTNU, which includes postdocs, PhDs, and master’s students, and collaborates with international researchers. The lab is a hub for innovation in GPU computing, auto-tuning, and HPC applications.

Masashi SugiyamaView profile

Professor

• statistical machine learning
• weakly supervised learning
• learning from noisy supervision
• +6 more

Masashi Sugiyama is a Professor at the Department of Complexity Science and Engineering, Graduate School of Frontier Sciences, The University of Tokyo, where he has been serving since 2014. He concurrently serves as the Director of the RIKEN Center for Advanced Intelligence Project (AIP) since 2016, leading research groups focused on fundamental AI technologies, AI applications, and social issues of AI. His academic journey began at Tokyo Institute of Technology, where he earned his Bachelor, Master, and Doctor of Engineering degrees in Computer Science in 1997, 1999, and 2001 respectively, before becoming an Assistant Professor and later Associate Professor at the same institution. Sugiyama's research primarily focuses on statistical machine learning, with particular expertise in weakly supervised learning, learning from noisy supervision, and learning under distribution shift. His work has established important theoretical frameworks for density ratio estimation, covariate shift adaptation, and non-stationary environments. He has developed numerous practical algorithms including KLIEP (Kullback-Leibler importance estimation procedure), uLSIF (unconstrained least-squares importance fitting), and LSDD (least-squares density difference), which have become standard tools in the machine learning community. Analysis of his recent publications reveals a strong emphasis on reliable and robust machine learning techniques that can handle imperfect supervision and data distribution changes. His work spans theoretical foundations, algorithm development, and practical applications across various domains including bioinformatics, anomaly detection, and dimensionality reduction. The recurring themes in his research include direct density ratio estimation methods, importance weighting techniques, and mutual information-based approaches for various machine learning tasks. Award for Science and Technology from the Japanese Minister of Education, Culture, Sports, Science and Technology (2022) Japan Academy Medal (2017) Japan Society for the Promotion of Science Award (2017) Faculty Award from IBM (2007) Nagao Special Researcher Award from the Information Processing Society of Japan (2011) Young Scientists' Prize for the Commendation for Science and Technology by the Minister of Education, Culture, Sports, Science and Technology Japan (2014) Sugiyama has served as program co-chair for major machine learning conferences including NeurIPS 2015, AISTATS 2019, and ACML 2010 and 2020. He has received prestigious fellowships including the Alexander von Humboldt Foundation Research Fellowship (2003-2004) and the European Commission Program Erasmus Mundus Scholarship (2006). He is the (co-)author of influential machine learning monographs including Machine Learning in Non-Stationary Environments (MIT Press, 2012), Density Ratio Estimation in Machine Learning (Cambridge University Press, 2012), Statistical Reinforcement Learning (Chapman & Hall, 2015), and Machine Learning from Weak Supervision (MIT Press, 2022). At RIKEN AIP, Sugiyama leads research initiatives focused on developing robust AI systems that can operate reliably in real-world conditions with imperfect data. His laboratory maintains an active software development effort, providing open-source implementations of many of his research contributions, which has significantly influenced both academic research and practical applications of machine learning.

Haohan WangView profile

Assistant Professor

• Trustworthy Machine Learning
• Computational Biology
• Healthcare Applications
• +5 more

Haohan Wang is an Assistant Professor at the University of Illinois Urbana-Champaign's School of Information Sciences, with affiliations to the Carl R. Woese Institute for Genomic Biology and the National Center for Supercomputing Applications. His research focuses on developing trustworthy machine learning methods for computational biology and healthcare applications , emphasizing robustness , causality , and interpretability in vision-based models. Recent research trends in his work include: Large Language Model interactions with biomedical challenges (GenoAgent, GenoTex) Adversarial security in language and vision models (Guard, Jailbreakzoo) Genomic data analysis through robust machine learning frameworks (Precision Lasso, Kernel Mixed Models) Interactive toolkits like Robustar for data annotation and model training Scientific awards include recognition as Baidu's Top 50 AI+X Rising Young Scholars (2022), Best Paper Honorable Mention at WSDM 2023, and Broad Institute's Next Generation status (2019). Current projects explore AI-made scientists for biomedical discovery and Robustar development for GUI-based robust vision learning.

Damek DavisView profile

Visiting Associate Professor

• Optimization
• Machine Learning
• Signal Processing
• +4 more

Damek Davis is a Visiting Associate Professor at the Wharton Department of Statistics and Data Science at the University of Pennsylvania and an Associate Professor of Operations Research at Cornell University (currently on leave). He held NSF Postdoctoral Fellowships and completed his PhD in Mathematics at UCLA under Wotao Yin and Stefano Soatto. His research bridges optimization , machine learning , statistics , and signal processing , with a focus on convergence guarantees for stochastic and nonsmooth optimization methods. He has developed accelerated gradient descent algorithms, characterized SGD behavior in nonconvex settings, and formalized avoidance of strict saddle points in proximal methods. Key publications include exponential accelerations of gradient-based techniques and first guarantees for SGD on weakly convex functions. His work has been recognized by the Sloan Research Fellowship INFORMS Optimization Society Young Researchers Prize NSF CAREER Award SIAM Activity Group on Optimization Best Paper Prize Davis actively advises Penn graduate students and serves as an associate editor at Mathematical Programming and Foundations of Computational Mathematics . He emphasizes clear technical writing and public communication, maintaining a blog and lecture notes on optimization theory.

Marc HOFFMANNView profile

Professor

• Statistics
• Nonparametric Statistics
• Stochastic Processes
• +3 more

Marc HOFFMANN is a Professor at Université Paris Dauphine-PSL and holds the Fundamental Chair at the Institut Universitaire de France since 2024. His academic journey includes roles at institutions such as INRIA (2020-2022), École Polytechnique (2007-2015), and Université Gustave Eiffel (2003-2012). Research Focus: Statistics of random processes, nonparametric statistics, and applications in financial modeling and population biology. Key Contributions: Adaptive estimation, confidence bands, inverse problems, rough volatility modeling, and growth-fragmentation processes. Advising: Supervised 19 PhD students with topics spanning statistical inference, Hawkes processes, and stochastic volatility. Collaborations include CIFRE grants with EDF, SCOR, and Banque de France. Scientific Awards : Fundamental Chair at Institut Universitaire de France (2024).

• Machine Learning
• Natural Language Processing (NLP)
• Political Science
• +4 more

Victor Kristof is a researcher at École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the INDY2 laboratory. His work spans interdisciplinary domains, combining Natural Language Processing (NLP) , Machine Learning , and Social Process Modeling to analyze legislative dynamics, vote prediction, and environmental perception. Research Focus: Kristof develops interpretable models for democratic transparency, including aligning interest group positions with parliamentary speeches. He pioneered methods for predicting legislative edit acceptance using matrix factorization and NLP. His work on Swiss referendum prediction integrates historical data with real-time analysis via the Predikon platform . Methodological Contributions: He applies Bayesian statistics , time-dynamic pairwise comparison models , and active learning algorithms to diverse problems, from carbon footprint perception to sports analytics. His War of Words framework reveals ideological patterns in EU law-making, while his Player Kernel model improves football match prediction. Labs & Collaborations: Based at EPFL's Laboratory of Dynamic Information and Networks (INDY2) , he collaborates with researchers like Matthias Grossglauser and Patrick Thiran. His datasets on legislative edits and carbon perception have advanced transparency studies.

Feliks NüskeView profile

Research Professor

• Data-driven modeling
• Molecular simulation
• Koopman operator theory
• +6 more

Dr. Feliks Nüske is a Max Planck Group Leader at the Max Planck Institute for Dynamics of Complex Technical Systems in Magdeburg, leading the Data-driven Modeling of Complex Physical Systems research group. He also holds a position as Guest Professor at Freie Universität Berlin (2023-2024). His research bridges applied mathematics, data science, and molecular simulation to develop novel algorithms for understanding complex physical systems at the molecular level. Dr. Nüske's educational background includes: Ph.D. in Mathematics from Freie Universität Berlin (2012-2017) Postdoctoral research at Universität Paderborn (2019-2022) and Rice University (2017-2019) His research focuses on developing data-driven methods that combine physical insights with machine learning to extract meaningful information from molecular simulation data. Key areas include Koopman operator theory for analyzing nonlinear dynamical systems, dimensionality reduction techniques, tensor methods for efficient computation, and kinetically consistent coarse-graining approaches. His work enables more efficient modeling of complex molecular processes that would otherwise be computationally prohibitive. Dr. Nüske's publication record shows a consistent trajectory of advancing both the theoretical foundations and practical applications of data-driven modeling in molecular science. His recent work emphasizes error analysis for data-driven models, control of stochastic systems, tensor-based dimensionality reduction, and methods to preserve kinetic properties in coarse-grained models. These contributions address critical challenges in scaling molecular simulations to biologically relevant timescales and system sizes. Dr. Nüske actively collaborates with researchers worldwide and has established partnerships with leading institutions including Freie Universität Berlin, Rice University, and TU Ilmenau. His collaborative network spans multiple disciplines, connecting mathematicians, chemists, and computational scientists. Dr. Nüske advises several Ph.D. students at the Max Planck Institute, including Vahid Nateghi, Lei Guo, Minakshi Verma, and Hauke Sprink. He has organized workshops on Uncertainty Quantification for molecular systems and regularly presents at major international conferences including SIAM MS, MTNS, and IMSI workshops. His research group continues to push the boundaries of what's possible in computational molecular science through innovative mathematical approaches.

Yuanhua FengView profile

Professor

• Econometrics
• Financial Econometrics
• Time Series Analysis
• +5 more

Prof. Dr. Yuanhua Feng leads the Chair of Econometrics and Quantitative Methods at the University of Paderborn 's Faculty of Business Administration and Economics , focusing on Financial and Economic Data Science . His research includes semiparametric modeling of seasonal time series, long memory processes, spatial time series, and machine learning hybrids. Doctoral supervision for Sebastian Letmathe (2023), Bastian Schäfer (2022), Xuehai Zhang (2018), and others Teaching courses: Econometrics, Financial Econometrics & Quantitative Risk Management Research areas emphasize: Semiparametric GARCH/ACD models for risk management Non-negative financial process forecasting Spatial time series with long memory Hybrid time series/machine learning models Recent publications focus on: Risk measurement with long-memory GARCH Spatial volatility modeling Box-Cox transformed models DeSeaTS seasonal decomposition Notable collaborations include DFG projects and joint PhD supervision with Chinese institutions. Teaching materials integrate R packages like smoots , rugarch , and custom tools for VaR/ES applications.

• Statistics
• Applied Mathematics
• Systems Biology
• +2 more

Dr. Hannes Matuschek is a Researcher in the Department of Applied Mathematics at the University of Potsdam, Germany, with office space 2.09.1.24 and contact email hannes.matuschek@uni-potsdam.de. He actively participates in the institute's academic events including working group seminars and colloquia. Research Interests: His work spans Statistics, Applied Mathematics, Systems Biology, and Biomechanics. Key contributions include statistical methodology for linear mixed models (addressing Type I error/power tradeoffs and interaction effects), smoothing spline ANOVA for eye movement analysis in reading, stochastic modeling of gene regulatory networks, and vector field manipulations on spherical domains. His research bridges theoretical frameworks with applications in cognitive science, systems biology, and fraud detection. Publication Trends: His 12 publications (2012-2019) reveal a trajectory from computational systems biology (stochastic biochemical kinetics tools like iNA) toward statistical methodology development. Early work focused on noise approximation in gene networks, evolving into eye-tracking analysis and vector field mathematics, demonstrating consistent integration of advanced statistics with domain-specific challenges across biological and cognitive sciences. Scientific Awards: No awards were mentioned in the provided materials. Advising and Grants: No information was provided regarding student supervision, grant funding, or research collaborations. Labs and Teams: He operates within the Applied Mathematics research group at the University of Potsdam, contributing to seminars and events in analysis and interdisciplinary applications as evidenced by his institutional presence.