Find Academic Supervisors

Aiping XuView profile

Assistant Professor

• Functional Data Analysis
• Statistical Learning
• Mathematics Education
• +3 more

Aiping Xu serves as Assistant Professor Academic at Coventry University's Mathematics Support Centre (LIB), actively supervising PhD students while leading institutional mathematics education initiatives. Her research bridges advanced statistical methodology with practical educational applications across engineering, data science, and higher education contexts. Her academic foundation includes: Doctorate in Signal Processing and Telecommunication from Research Institute of Computer Science and Random Systems (awarded 2002) MSc in Operational Research and Control Theory from Shandong University (awarded 1998) Bachelor's degree in Mathematics from Shandong University (awarded 1995) Dr. Xu's research program demonstrates significant evolution from early work on nonlinear system fault diagnosis using adaptive observers (2001-2004) to contemporary expertise in functional data analysis, where she develops principal curve clustering methods and Gaussian process regression frameworks. Her fingerprint analysis confirms dominant specialization in Functional Data Mathematics (100%), Principal Curve Mathematics (100%), and Forecasting (100%), with strong contributions to mathematics education through support centre evaluations and mathematical modeling competitions. Recent publications (2017-2022) reveal a strategic pivot toward applying statistical learning in educational contexts, particularly through her leadership in Coventry University's Mathematics Support Centre. This work integrates machine learning techniques with pedagogical innovation to address student challenges in mathematical comprehension and application. As an active PhD supervisor, Dr. Xu contributes to academic development through the Mathematical Contest in Modelling and institutional support services. Her research outputs show consistent international collaboration, particularly with Newcastle University, spanning statistical methodology, educational practice, and interdisciplinary applications. The Mathematics Support Centre under her involvement operates as a critical academic infrastructure unit, providing cross-disciplinary mathematical assistance to students across Coventry University through structured tutoring, resource development, and pedagogical research initiatives.

• Functional Data Analysis
• Statistical Modeling of Neuronal Spike Trains
• Nonparametric Methods on Point Processes
• +7 more

Wei Wu is a Professor in the Department of Statistics and an Associate Graduate Faculty member in the Program in Neuroscience at Florida State University (FSU). He holds a Ph.D. in Applied Mathematics from Brown University (2004). His research focuses on interdisciplinary statistical methods for neuroscience, functional data analysis, and computational statistics. Key contributions include frameworks for spike train analysis, statistical depth in point processes, and registration of functional data using Fisher-Rao metrics. Affiliations: Department of Statistics, Program in Neuroscience Education: Ph.D. in Applied Mathematics, Brown University (2004) Research Interests: Functional data registration, nonparametric methods for point processes, neural spike train modeling, statistical depth analysis, and applications in birdsong production and computational neuroscience. He develops tools for analyzing complex data structures in neuroscience and biostatistics, emphasizing geometric and Bayesian approaches. His work spans statistical modeling of neural coding, protein structure comparison, and signal processing under compositional noise. Recent publications include advancements in stochastic models for time warping and depth-based statistical inference in point processes. Collaborations: Active with researchers in neuroscience, mathematics, and bioinformatics at FSU and other institutions.

• Statistics
• Bioinformatics
• Biostatistics
• +7 more

Marepalli B. Rao is a Professor in the Department of Environmental Health at the University of Cincinnati's College of Medicine. He holds an Emeritus Professor title at North Dakota State University (NDSU), where he served from 1987–2003. His academic journey includes roles as Visiting Professor at the University of Pittsburgh and Lecturer at the University of Sheffield. Rao earned his B.S. and M.S. in Statistics from Madras University and a Ph.D. in Statistics from the Indian Statistical Institute. His research focuses on statistical methodologies with applications in bioinformatics, biostatistics, epidemiology, and genetic studies. Notable areas include damage models, survival analysis, and statistical genetics. Rao has contributed to over 200 peer-reviewed publications and serves on several editorial boards. His work often intersects with clinical and public health challenges, such as asthma epidemiology and environmental exposure analysis. Rao has led or collaborated on numerous grants, including NIH-funded studies on statistical genomics, tissue engineering, and pediatric allergy research. He has held leadership roles in academic committees and has mentored numerous Ph.D. students. His expertise in statistical design and analysis is applied across disciplines, from biomedical engineering to environmental health.

• Bayesian Statistics
• Statistical Modeling
• Applied Mathematics
• +3 more

Luca LA ROCCA is an Associate Professor in the Department of Education and Humanities at the University of Modena and Reggio Emilia. His research focuses on Bayesian statistics, graphical models, complex systems analysis, and statistical applications in education and social sciences. He teaches courses such as Applied Mathematics and Social Statistics for engineering and pedagogy programs. His research spans topics including Bayesian inference for directed acyclic graphs, non-local priors, and statistical methods for analyzing complex systems. Notable contributions include work on detecting pairwise interactions in biological networks and assessing skewness in financial markets. He has also developed methodologies for structural learning in heterogeneous data and seismic hazard assessment. LA ROCCA has published extensively in top journals and conferences, with recent works emphasizing the interplay between statistical theory and real-world applications. His teaching responsibilities include courses on applied statistics, probability, and regression analysis.

• Machine Learning
• Cyber-Physical Systems
• Intelligent Embedded Systems
• +5 more

Cesare Alippi is a Professor at Politecnico di Milano, Italy, and Università della Svizzera italiana, Switzerland, with visiting appointments at the University of Kobe (Japan) and Guangzhou (China). His research focuses on machine learning in nonstationary environments, intelligent embedded systems, and cyber-physical systems. Alippi holds IEEE Fellow status and has received notable awards, including the 2016 Gabor Award and the 2018 IEEE CIS Outstanding Computational Intelligence Magazine Award. He has authored a monograph, 6 edited books, and over 200 papers. Alippi's academic roles include Vice-President for Education of the IEEE Computational Intelligence Society and Distinguished Lecturer (2014-2016). He has organized major conferences like IJCNN and IEEE SSCI and contributed to initiatives promoting education in computational intelligence. His research addresses challenges in adaptive systems, embedded intelligence, and fault diagnosis. Key projects include deploying wireless sensor networks for environmental monitoring and developing machine learning algorithms for real-world applications like landslide prediction and network security. Alippi's awards reflect his contributions to neural networks and adaptive learning, including the IBM Faculty Award (2013) and IEEE Young Engineer Award (2004).

Eduardo G. AltmannView profile

Professor

• Complex Systems
• Data Science
• Network Analysis
• +5 more

Eduardo G. Altmann is a Professor in the School of Mathematics and Statistics at the University of Sydney. His research focuses on mathematical models and computational methods applied to complex systems, data science, and statistical laws. He is part of the Complex Systems and Data Science group and the Computational Social Science Lab, and contributes to interdisciplinary collaborations in areas like urban scaling and language dynamics. Altmann teaches courses such as MATH3076/3976/4076 (Mathematical Computing) and DATA5441 (Networks and High-Dimensional Inference). He serves on editorial boards for journals including the Journal of Statistical Mechanics and New Journal of Physics. His recent work includes studies on Monte Carlo methods for manifold triangulations, generative models for network communities, and statistical laws in complex systems. He has authored a monograph, 'Statistical Laws in Complex Systems,' published by Springer Nature, and frequently engages in academic outreach via platforms like Bluesky.

Germain Van BeverView profile

Research Fellow

• Nonparametric Inference
• Statistical Depth
• Functional Data Analysis
• +3 more

Germain Van Bever is a Research Fellow (FNRS) at Université Libre de Bruxelles (2016–2018) and a former Research Associate at The Open University (2014–2016). He is affiliated with the Namur Institute for Complex Systems. His expertise spans statistical depth, functional data analysis, and high-dimensional classification. Education: Doctor of Science in Contributions to Nonparametric and Semiparametric Inference based on Statistical Depth from Université Libre de Bruxelles (2013). Research interests include statistical depth measures, asymptotic solutions, and applications in functional data. Recent projects focus on imperfect data analysis and high-dimensional classification methods. He has contributed to over 22 research outputs, with notable work on influence functions and functional data prediction. Awards: None explicitly mentioned beyond academic degrees. Grants: Active in projects like 'IMAL: Imperfect Data: From Mathematical Foundations to Applications in Life Sciences' (2020–2025) and 'High-dimensional and functional classification' (2020–2021). Labs/Teams: Collaborates with the Namur Institute for Complex Systems and co-leads research initiatives in statistical foundations and applications.

Mohamed AbdalmoatyView profile

Researcher

• Data-Driven Modelling
• Control Systems
• Signal Processing
• +5 more

Dr. Mohamed Abdalmoaty is a Researcher at ETH Zürich's Institut für Automatik, specializing in the Department of Automatic Control. His work focuses on Data-Driven Modelling and Control , with expertise in system identification, stochastic systems, and optimal control. He holds an affiliation within the Professorship for Complex Systems Control. His research interests emphasize data-driven approaches for predictive control, frequency-domain identification, and nonlinear dynamical systems. He has contributed to advancements in Kalman filter formulations, stochastic Wiener models, and cybersecurity in control systems, particularly in medical applications like the artificial pancreas. Abdalmoaty’s recent publications (2022–2024) highlight innovations in time-varying normalizing flows, privacy-preserving network control, and robust parameter estimation under uncertainty. His work bridges theoretical control systems with practical applications in machine learning and biomedical engineering. He has developed software tools for system identification and simulation, including implementations for frequency-domain analysis and nonparametric closed-loop identification. His research aligns with emerging trends in hybrid machine learning-control frameworks and resilient cyber-physical systems.

Jens Joachim RogmannView profile

Researcher

• Educational Psychology
• Conflict Resolution
• Mediation
• +4 more

Dr. Jens Joachim Rogmann is a researcher and academic coordinator at the University of Hamburg's Faculty of Education, affiliated with the Department of General, Intercultural and International Comparative Education and Educational Psychology (EW 1). He holds roles as Post-Doc in Pedagogical Psychology, Coordinator of Interdisciplinary Studies modules in the BA Educational Science program, Module Leader for PM-2 'Key Competencies', and Head of the FüB-WP Area for Educational Psychology/Interdisciplinary Studies. He also serves as Ombudsman in the Faculty's Complaints Office for Examination Matters (per §66 HmbHG). His research focuses on critical and ethical learning processes, ordinal statistical methods, higher education pedagogy, communication psychology, and conflict mediation. Notable contributions include work on 'communicative resilience' in education and the development of R packages for ordinal dominance statistics. He teaches courses on scientific methodology, ethical competence, and conflict mediation, and oversees the 'Konfliktberatung und Mediation' postgraduate program since 2006.

• Bayesian Inference
• Spatial Statistics
• Biostatistics
• +5 more

Maura Mezzetti is a Full Professor of Statistics at the Department of Economics and Finance, Università degli Studi di Roma II (Tor Vergata), Rome, Italy. Her career spans institutions such as Harvard School of Public Health, Università Bocconi, and European Institute of Oncology. She holds a PhD in Methodological Statistics from Università di Trento (1997) and a Laurea in Mathematics from Università di Milano (1992). Current Position: Full Professor (2021–Present) Former Roles: Associate Professor (2010–2021), Assistant Professor (2004–2010, Tor Vergata; 2000–2005, Bocconi) Research Interests: She specializes in Bayesian statistical methods for spatially correlated data, hierarchical modeling, survival analysis, and biostatistics. Her work addresses public health challenges like cancer incidence modeling, socioeconomic disparities in disease burden, and psychometric/motor behavior analysis. She has developed Bayesian compartmental models for chemical exposure studies and contributed to epidemiological research on breast and colorectal cancer risk factors. Scientific Awards: Yamagiwa-Yoshida Fellowship (2014) ICRETT Fellowships (2012) Excellence in Research (2005) from Bocconi AIRC Fellowships (1998, 1993) Teaching Contributions: She has taught Statistics at undergraduate and graduate levels across Tor Vergata and Bocconi, including specialized courses in medicine, European economy, and data analysis. Her pedagogical efforts include authoring educational materials like "Statistica Descrittiva: Esercizi risolti e guida alla soluzione usando Excel" (2002).

Quanjun LangView profile

Assistant Professor

• Quantum Information Theory
• Applied Mathematics
• Interacting Particle Systems
• +3 more

Quanjun Lang is an Assistant Research Professor of Mathematics at Duke University's Trinity College of Arts & Sciences since 2023. He holds a Ph.D. from Johns Hopkins University (2023). His research focuses on quantum information theory, applied mathematics, and statistical mechanics, particularly in kernel learning for operators, interacting particle systems, and non-Markovian dynamics. Lang teaches courses in probability (MATH 230/STA 230) and differential equations (MATH 353/753), emphasizing foundational mathematical concepts. His recent work includes developing methods for quantum channel learning and memory kernel estimation in generalized Langevin equations. Research Interests : Lang's work bridges theoretical mathematics and applied sciences, addressing challenges in inverse problems and data-driven modeling. Key areas include: Quantum system characterization via low-rank matrix sensing Nonparametric estimation of interaction kernels in particle systems Regularization techniques for stochastic processes Teaching : Recent courses include Probability (Fall 2023-2024), Ordinary and Partial Differential Equations (Spring 2025), and graduate-level Probability (Spring 2024). These reflect his expertise in stochastic processes and mathematical analysis. Research Trends : His publications (2020–2025) demonstrate progression from foundational work on Dirichlet spaces to cutting-edge quantum information applications, showcasing interdisciplinary impact. Current trends focus on unifying measurement design with machine learning for complex dynamical systems.

Scott MonroeView profile

Associate Professor

• Educational Measurement
• Psychometrics
• Item Response Theory (IRT)
• +5 more

Scott Monroe is an Associate Professor at the University of Massachusetts Amherst in the Department of Educational Policy, Research & Administration (EPRA). His research focuses on latent variable modeling, item response theory (IRT), and structural equation modeling, with applied work in state testing and student growth evaluation. He holds a Ph.D. from UCLA's Graduate School of Education & Information Sciences and an M.S. in Statistics from UCLA. B.A., University of California San Diego, 2000 M.S., Brooklyn College, 2007 M.S., University of California Los Angeles, 2013 J.D., University of California Los Angeles, 2004 Ph.D., University of California Los Angeles, 2014 His research interests emphasize methodological advancements in IRT model evaluation, teacher evaluation frameworks, and statistical computing. He has contributed to global discussions on educational measurement, including work published in Multivariate Behavioral Research and Educational and Psychological Measurement . Prior to academia, Monroe was a high school mathematics teacher in Brooklyn, New York, and Culver City, California. His recent projects include examining learning progressions in middle-school mathematics and evaluating educational interventions like the Green Dot Locke Transformation Project. His work on IRT model fit evaluation and student growth modeling has been influential in shaping assessment practices. He has collaborated on CRESST reports addressing topics like MIRT-based student growth percentiles and diagnostic classification models.

Rui TuoView profile

Assistant Professor

• Data Science
• Computer Experiments
• Uncertainty Quantification
• +4 more

Rui Tuo is an Assistant Professor in the Department of Industrial & Systems Engineering at Texas A&M University. He is also affiliated with the Department of Statistics and serves as a Texas A&M Institute of Data Science Career Initiation Fellow. His research focuses on data science, computer experiments, uncertainty quantification, probabilistic machine learning models, and nonparametric statistics. Dr. Tuo holds a Ph.D. in Statistics from the University of Chinese Academy of Sciences (2013). His work emphasizes methodological advancements in statistical modeling and computational efficiency, particularly in high-dimensional and complex systems. His research interests include Gaussian process models, computer model calibration, kernel methods, and their applications in engineering and data-driven decision-making. Recent publications explore topics like multi-fidelity optimization, privacy-aware machine learning, and distributed Gaussian processes. Key achievements include the Career Initiation Fellowship (2022) and contributions to foundational works in calibration theory and uncertainty quantification. His research bridges statistical theory and practical applications, addressing challenges in simulation-based inference and scalable algorithms.

Robert ShermanView profile

Professor

• Econometrics
• Statistics
• Asymptotic Methods
• +4 more

Robert Sherman is a Professor of Economics and Statistics at the California Institute of Technology (Caltech), affiliated with the Division of Humanities and Social Sciences. He holds a Ph.D. in Statistics from Yale University (1991) and joined Caltech in 1996 as an Assistant Professor, advancing to his current rank in 2005. His research focuses on asymptotic methods, semiparametric estimation, and addressing data deficiencies such as misreporting or nonresponse. Sherman has contributed to econometric theory, panel data analysis, and environmental pollutant modeling. Education: B.A. in Philosophy and Latin (Marquette University, 1978), M.A. in Mathematics (University of Louisville, 1985), M.Phil. in Statistics (Yale, 1987), and Ph.D. in Statistics (Yale, 1991). Prior to Caltech, he was at Bellcore's Statistics and Economics Research Group (1990–1996). Research Interests: Asymptotic theory, causal effects estimation in regression models, correlated random coefficient models, and applications to environmental and educational data. His work emphasizes large-sample behavior of estimators and robustness to data contamination. Publications span econometric theory journals (e.g., Econometrica, Journal of Econometrics) and interdisciplinary topics like network traffic analysis (e.g., IEEE/ACM Transactions on Networking). Notable contributions include methodologies for bounding treatment effects and analyzing biased survey data. Advising and Grants: While formal advisee names are unspecified, his research collaborations include co-authors such as Jeff Dominitz, Roger Klein, and Martin S. Taqqu. His work has been supported by grants related to econometric modeling and data analysis.

• Bayesian Statistics
• Probability Theory
• Mathematical Physics
• +3 more

Emanuele Dolera is an Associate Professor at the University of Pavia, affiliated with the Probability and Statistics research group. His work focuses on Bayesian nonparametric methods, probability theory, and mathematical physics, with a particular emphasis on statistical mechanics and optimal transport frameworks. He has contributed to foundational research in posterior contraction rates, stochastic processes, and the analysis of complex models like the Boltzmann equation and Ewens-Pitman sampling schemes. His research interests span Bayesian predictive inference, asymptotic theory, and the interplay between probability and partial differential equations. Notable contributions include studies on credible intervals in nonparametric estimation, exponential convergence in kinetic equations, and counterexamples in stochastic operator theory. Dolera has also edited special issues honoring prominent statisticians like Eugenio Regazzini. His publications reflect a strong theoretical orientation, addressing topics such as optimal transport applications, count-min sketches in data streams, and the asymptotic behavior of statistical estimators. While no specific grants, awards, or advisees are listed, his extensive bibliography underscores his role as a leading figure in advanced statistical methodology and mathematical analysis.