Find Academic Supervisors

• Quantitative Finance
• Credit Risk
• Financial Engineering
• +3 more

Roles and Affiliations: Alexander Herbertsson is a Senior Lecturer in Statistics and Quantitative Finance at the University of Gothenburg, affiliated with the CFF-Centre for Finance. He is based in the Department of Economics with Statistics, located at Vasagatan 1, Gothenburg. His work focuses on financial risk management, credit risk modeling, and quantitative finance methodologies. Education: Ph.D. in Economics: Quantitative Finance (2007), University of Gothenburg Licentiate of Engineering in Industrial Mathematics (2005), Chalmers University of Technology M.Sc. in Engineering Physics (Applied Mathematics specialty) (2001), Chalmers University of Technology Research Interests: Herbertsson's research emphasizes applied financial mathematics and statistical methods in finance. Key areas include credit risk modeling (default contagion, systemic risks), financial engineering, and the development of dynamic models for portfolio credit risk. His work often integrates Markov chain models, phase-type distributions, and stochastic processes to analyze dependency structures and pricing of credit derivatives. Recent studies explore saddlepoint approximations for portfolio risk analysis and risk management under exogenous shocks. Teaching: He teaches advanced courses in credit risk modeling, quantitative finance, and applied probability theory, emphasizing practical applications in risk management and financial markets. Grants and Labs: While specific grants are not detailed in the text, his affiliation with the CFF-Centre for Finance suggests involvement in collaborative financial research projects. His work often addresses real-world applications of theoretical models in systemic risk and portfolio hedging.

Marten WegkampView profile

Professor

• Applied Mathematics
• Probability and Statistics
• Mathematical Statistics
• +4 more

Marten Wegkamp is a Professor of Mathematics and Professor of Statistics & Data Science at Cornell University, located in Ithaca, NY. He holds dual affiliations within the College of Arts and Sciences, contributing to both the Department of Mathematics and the Department of Statistics & Data Science. His research focuses on applied mathematics, probability, and statistics, with a strong emphasis on high-dimensional statistics, statistical learning theory, and empirical process theory. He has developed methodologies in latent factor regression, sparse topic models, and interpretable statistical frameworks. His work frequently addresses challenges in high-dimensional data analysis and machine learning. Education: PhD in Mathematics from Leiden University (1996). Research Interests: Wegkamp’s research spans mathematical statistics, empirical process theory, and the development of novel statistical learning techniques. He explores areas such as latent factor models, high-dimensional inference, and the theoretical foundations of machine learning algorithms. His contributions include advancements in prediction methods, discriminant analysis, and optimal estimation strategies for complex data structures. Publications: His recent work includes studies on latent factor regression, sparse topic models, and interdisciplinary applications in genomics and multi-omic data analysis. Key themes across his publications involve high-dimensional data analysis, latent structure discovery, and algorithmic optimization for statistical models. Professional Contributions: He is affiliated with the Statistical Learning and High Dimensional Inference Group at Cornell, and his research has led to software packages like STRS, LOVE, and LoveER, which implement his methodologies. He teaches advanced courses such as Statistical Learning Theory (MATH 7740) and supervises research projects.

Göran KauermannView profile

Professor

• Nonparametric Models
• Semi- and nonparametric analysis
• Generalized Linear Models
• +2 more

Prof. Dr. Göran Kauermann is a Full Professor of Statistics at the Ludwig-Maximilians-University Munich , holding the Chair of Applied Statistics in Social Sciences, Economics and Business . His research spans nonparametric models, generalized linear models, and network data analysis, with applications in economics, epidemiology, and data science. Education: Diplom in Economic Mathematics (1991, TU Berlin), PhD in Statistics (1994), Habilitation (Venia Legendi) in Statistics (2000) Kauermann’s research interests focus on penalized regression , network analysis , and statistical modeling in economics, social sciences, and public health. Recent work explores label uncertainty in machine learning , spatio-temporal conflict diffusion , and dynamic network models for economic and social data. Scientific trends in his publications include penalized splines for nonlinear modeling, network flow estimation in social and economic contexts, and label variation analysis in machine learning. His collaborations span climate zone classification , Covid-19 mortality modeling , and smart city parking analytics . Scientific Awards: Bruce Russett Award (2020) for political network research Leadership Roles: He served as Dean of the Faculty of Mathematics, Informatics and Statistics (2019–2021), Speaker of the Elite Master Program in Data Science (2016–2026), and Chair of the German Statistical Society (2005–2013). He also held editorial roles in journals like AStA Advances in Statistical Analysis and Statistical Modelling .

Stephen RamseyView profile

Associate Professor

• Computational Systems Biology
• Bioinformatics
• Precision Medicine
• +6 more

Stephen Ramsey, an Associate Professor at Oregon State University, holds dual appointments in the School of Electrical Engineering and Computer Science (College of Engineering) and the Department of Biomedical Sciences (Carlson College of Veterinary Medicine). With a PhD in Physics from the University of Maryland, his postdoctoral training in computational genomics at the University of Washington, and professional experience at the Institute for Systems Biology and Center for Infectious Disease Research, Ramsey bridges computational methods with biomedical applications. Education : Ph.D., Physics, University of Maryland; M.S., Physics, University of Maryland; Sc.B., Mathematical Physics, Brown University Ramsey specializes in computational systems biology , focusing on bioinformatics , biomedical knowledge graphs , and precision medicine . His research integrates machine learning , gene regulatory network modeling , and multi-omics data analysis to address challenges in rare disease diagnostics , drug monitoring , and inflammatory disease mechanisms . Current work includes AI-driven biomedical translation and electrochemical biosensor development for non-invasive diagnostics . Recent publications highlight knowledge graph applications in translational biomedicine , causal network inference in clinical-environmental data integration , and cross-species cancer transcriptomics . His team develops tools like RTX-KG2 and PloverDB to standardize biomedical data sharing and semantic reasoning . Scientific Awards : 2019 Zoetis Award (Carlson College of Veterinary Medicine) 2016 NSF CAREER Award 2016 PhRMA New Investigator Award 2010 NIH K25 Mentored Quantitative Research Award Ramsey advises in computational biology courses (CS 446/546) and contributes to biomedical AI through projects like mediKanren for rare disease diagnostics . His NSF-funded research explores gene expression noise and regulatory network dynamics , while NIH and PhRMA grants support his translational medicine initiatives. He leads the Ramsey Laboratory , which develops graph-based reasoning tools for biomedical data translation and multi-omics integration . The lab's work spans comparative oncology models, electrochemical biosensors , and knowledge graph infrastructure for clinical decision support .

• Population Studies/Demography
• Marriage and the Family
• Health, Illness and Mortality
• +1 more

Dr. Bali Ram serves as an Adjunct Research Professor in the Department of Sociology and Anthropology at Carleton University, a position held since 1995. He retired from Statistics Canada in October 2009 after 34 years of service where he held leadership roles including Chief of the Demographic Characteristics Section and Senior Research Advisor in the Demography Division. His professional prominence includes serving as President of the Research Committee on Sociology of Population (International Sociological Association, 2003-2006) and President of the Federation of Canadian Demographers (1999-2002). His educational background features demography studies at Mumbai's International Institute for Population Sciences (1966-68), an M.A. in Sociology from the University of Western Ontario (1971), and a Ph.D. in Sociology from Ohio State University (1975). International Institute for Population Sciences, Mumbai (1966-68) University of Western Ontario, M.A. Sociology (1971) Ohio State University, Ph.D. Sociology (1975) Dr. Ram's research centers on Population Studies/Demography with specialized focus on Marriage and the Family, Health, Illness and Mortality, and Internal/International Migration. His current projects examine Economic Well-Being of Canadian Women in the Early 20th Century and Fertility Decline and Family Change in India, reflecting his dual geographic expertise in Canadian and Indian demographic contexts. His methodological approach combines quantitative analysis with comparative frameworks across national settings. His 2007-2010 publications reveal consistent thematic patterns across demography and sociology, particularly examining migration impacts, health disparities, and family dynamics through Canadian datasets. Key trends include longitudinal analysis of immigrant integration, adolescent health determinants, and fertility transitions, often employing multivariate statistical techniques with co-authors Teresa Abada and Feng Hou. His work bridges theoretical demography with policy-relevant findings for Canadian population studies. At Carleton University, Dr. Ram has taught core sociology courses including Sociological Methodology, Research Design and Data Analysis/Social Statistics, and Population Studies. His academic service includes editorial leadership as Guest Editor for Canadian Studies in Population's special issue on Low Fertility.

Yildiz YilmazView profile

Associate Professor

• Survival Analysis
• Statistical Genetics
• Genetic Epidemiology
• +4 more

Yildiz Yilmaz serves as Associate Professor of Statistics and Deputy Head of Statistics at Memorial University of Newfoundland's Department of Mathematics and Statistics within the Faculty of Science. Holding a PhD from the University of Waterloo, she maintains an active research program at the intersection of statistical methodology and biomedical applications. Education PhD in Statistics, University of Waterloo (2009) MSc in Statistics, Middle East Technical University (2004) MSc in Computer Engineering, Middle East Technical University (2004) BSc in Statistics with minor in Computer Engineering, Middle East Technical University (2002) Her research focuses on developing advanced statistical methods for survival analysis, genetic epidemiology, and causal inference. Key programs include: (1) novel methods for genome-wide prognosis studies of time-to-event phenotypes, (2) genetic association methods using joint/directional models of multiple phenotypes, (3) evaluation of response-dependent sampling designs, and (4) models for multivariate survival times. Her work addresses critical challenges in biomedical research through innovative statistical theory. Recent publications demonstrate consistent focus on colorectal cancer genomics, statistical genetics methodology, and survival analysis applications. Trends show increasing emphasis on multi-phenotype analysis, SNP interaction networks, and time-varying effects in genetic association studies, with significant contributions to copula-based joint modeling approaches. Research Support Natural Sciences and Engineering Research Council (NSERC) Canadian Statistical Sciences Institute (CANSSI) Faculty of Medicine, Memorial University Research and Development Corporation (RDC) of Newfoundland and Labrador Dr. Yilmaz actively mentors students across multiple programs including PhD, MSc, MAS, and undergraduate levels. Her current research group comprises seven students working on statistical genetics and survival analysis projects, building on a track record of successfully supervising numerous graduate students and post-doctoral fellows since 2013.

Richard J. VaccaroView profile

Professor

• Array Signal Processing
• Direction Finding (DOA Estimation)
• Statistical Modeling of Inertial Sensors
• +3 more

Richard J. Vaccaro is a Professor in the Department of Electrical, Computer and Biomedical Engineering at the University of Rhode Island. His research focuses on subspace estimation in sensor array signal processing, statistical modeling of inertial sensors, and control system design. Education : Ph.D., Electrical Engineering, Princeton University, 1983 M.S., Electrical Engineering, Drexel University, 1979 B.S., Electrical Engineering, Drexel University, 1979 Research Interests include array signal processing, direction-of-arrival (DOA) estimation, inertial sensor modeling, and control theory. His work explores subspace perturbation expansions, sensor fusion for drift reduction, and robust underwater acoustic localization. Recent Publications highlight advancements in subspace-based parameter estimation, optimal sensor array design, and geometric approaches to signal processing. Key trends involve applications in radar, underwater acoustics, and mobile robotics. Grants include collaborations with MIT (2023) and funding from the Office of Naval Research (2021). His patented technologies address sensor array calibration and noise reduction in inertial measurement units.

José E. ChacónView profile

Professor

• Nonparametric Kernel Smoothing
• Cluster Analysis
• Mathematical Statistics
• +3 more

José E. Chacón is a Professor of Statistics at the Department of Mathematics, University of Extremadura, Spain. He is also a member of the Institute of Mathematics at the same university. His research focuses on nonparametric kernel smoothing, cluster analysis, and mathematical statistics. He earned his PhD in Statistics from the University of Extremadura in 2004. Chacón’s work emphasizes methodological advancements in density estimation, clustering algorithms, and statistical theory. His recent publications address topics like geodesic distributions, Bayesian taut splines for mode estimation, and bump detection via density curvature. He has contributed to applied areas such as animal home range estimation and data science for pandemic analysis. His articles often explore cross-validation techniques, bandwidth selection, and mixture model clustering. He co-authored the textbook Multivariate Kernel Smoothing and Its Applications (2018), consolidating his expertise in kernel-based methods. Chacón’s research bridges theoretical statistics with practical applications, influencing both academic and applied domains.

• Data Analytics
• Statistical Learning
• Computational Science
• +4 more

Giuseppe Brandi is an Assistant Professor in Data Science at Northeastern University London and an Honorary Research Fellow at Imperial College London's Centre for Climate Finance & Investment. His research focuses on DataMetrics, a discipline blending Data Analytics, Statistical Learning, and Computational Science to analyze complex systems, particularly in finance and climate contexts. He emphasizes adapting models to data rather than forcing data into pre-existing theoretical frameworks. Dr. Brandi's work spans Climate Data Science, Econometrics, and Quantitative Finance. He critiques rigid theoretical models and advocates for interdisciplinary methods to extract insights from multidimensional data. His recent publications explore multiscaling in financial systems, tensor-based decomposition for multidimensional data, and liquidity contagion in interbank markets. His articles highlight trends in financial risk assessment, systemic risk modeling, and the application of tensor methods to complex datasets. Notably, he develops tools like the DisaggregateTS package for time-series analysis and visual warning systems for financial anomalies. Despite these contributions, no scientific awards are explicitly mentioned in the provided texts. Dr. Brandi's advising and grant activities remain undocumented here, though his academic roles suggest involvement in student mentorship and collaborative research projects. His affiliation with Northeastern's global network positions him to engage with interdisciplinary teams across multiple campuses.

Christos DiouView profile

Associate Professor

• Machine Learning
• Artificial Intelligence
• Healthcare Applications
• +3 more

Christos Diou is an Associate Professor of Artificial Intelligence and Machine Learning at the Department of Informatics and Telematics, Harokopio University of Athens, Greece. His academic career spans over 15 years of participation in national and international research projects, with a focus on machine learning algorithms, domain generalization, causal inference, and bias mitigation. He earned a BSc and Ph.D. in Electrical and Computer Engineering from Aristotle University of Thessaloniki. His research emphasizes the application of machine learning to healthcare, addressing challenges such as visual bias mitigation, causal effect estimation from observational data, and fairness-aware representation learning. Notable projects include REBECCA and RELEVIUM , both EU-funded, and MELIORA , targeting lifestyle interventions for breast cancer risk reduction. He has published extensively in top-tier venues like IEEE TPAMI, CVPR, and ICCV. Christos is a leading voice in AI ethics and healthcare innovation, with over 150 publications and best paper awards at IEEE Big Data Service 2023 and AIAI 2022. His work includes developing platforms like Effector for feature effects and Beam for behavior studies. He collaborates with institutions such as Karolinska Institutet and CERTH/ITI, and his students include PhD candidates Ioannis Sarridis and Aristotelis Ballas.

Emily BergView profile

Associate Professor

• Statistics
• Small Area Estimation
• Agricultural Statistics
• +3 more

Emily Berg is an Associate Professor in the Department of Statistics at Iowa State University. Her research focuses on small area estimation, Bayesian hierarchical models, and statistical applications in agriculture and environmental science. She holds a Ph.D. in Statistics from Iowa State University (2010), an M.S. in Statistics (2008), and a B.A. in Mathematics from Middlebury College (2005). Education: Ph.D. Statistics, Iowa State University, 2010 M.S. Statistics, Iowa State University, 2008 B.A. Mathematics, Middlebury College, 2005 Her research interests include developing statistical methodologies for small area estimation, analyzing agricultural and environmental data, and addressing challenges in informative sampling and missing data. She has contributed to applications in transportation safety, veterinary practices, and environmental monitoring. Dr. Berg’s work emphasizes practical solutions for data integration and estimation under complex scenarios, including the use of Bayesian models and quantile regression techniques. She collaborates on projects such as the Conservation Effects Assessment Project and has developed web applications for data visualization and quality assessment. No scientific awards have been explicitly mentioned in the provided materials. She has advised or collaborated on numerous statistical methodologies but no specific student names are listed. Her research extends to agricultural economics, environmental modeling, and public health surveillance.

• Stochastic Processes
• Mathematical Statistics
• Financial Mathematics
• +3 more

Marko Voutilainen is a University Lecturer in the Department of Accounting and Finance at the University of Turku. His research focuses on stochastic processes, time series analysis, and statistical modeling with applications in finance and mathematical statistics. He has published extensively on topics such as stationary processes, AR models, and generalized Ornstein-Uhlenbeck processes. His work emphasizes theoretical developments in stochastic modeling, including Lamperti transformations, spatial sign autocovariance matrices, and parameter estimation techniques. Recent contributions explore discrete and continuous-time stationary processes, as well as applications of ARCH models in financial contexts. Voutilainen frequently collaborates with researchers like Pauliina Ilmonen, Lauri Viitasaari, and Ciprian Tudor, contributing to peer-reviewed journals like Stochastic Models , Theory of Probability and Mathematical Statistics , and Scandinavian Journal of Statistics . Despite his prolific output, no scientific awards or grants are explicitly mentioned in the provided materials. No doctoral students are listed, though his research likely engages graduate-level collaboration.

Ben DuanView profile

Academic

• Reinforcement Learning
• Graph Neural Networks
• Intelligent Transportation Systems (ITS)
• +4 more

Ben Duan is an Adjunct Lecturer at the Department of Data Science & AI, Faculty of Information Technology, Monash University. He holds a PhD in Big Data and Electronics from the University of Technology Sydney. Previously, he was a Research Fellow at the Hong Kong University of Science and Technology. His research focuses on reinforcement learning, graph neural networks, intelligent transportation systems (ITS), and fintech. He teaches courses such as FIT5221: Intelligent Image and Video Analysis, FIT5215: Deep Learning, and FIT5217: Statistical Data Modeling at Monash’s Suzhou campus. Dr. Duan’s work aligns with UN Sustainable Development Goals related to sustainable cities and communities (SDG 11) and industry innovation (SDG 9). His recent research explores AI-driven solutions for traffic congestion, stock trend prediction using graph neural networks, and spiking neural networks for neural activation control. He has contributed to over 28 publications and a patent with the Monash Suzhou Research Institute. He supervises PhD students interested in reinforcement learning, graph neural networks, or ITS, offering full scholarships at Monash Suzhou. Key collaborations span transportation systems, fintech, and data-driven industrial processes.

Richard A. DavisView profile

Professor

• Applied Probability
• Time Series Analysis
• Stochastic Processes
• +4 more

Richard A. Davis is a Professor of Statistics at Columbia University and a Hans Fischer Senior Fellow at the Technical University of Munich's Institute for Advanced Study (TUM-IAS). He holds the Howard Levene Professorship in Statistics. His research focuses on applied probability, time series analysis, stochastic processes, and extreme value theory. Davis has contributed to nonlinear time series models, spatial modeling, and financial econometrics. He has held academic positions at Colorado State University and MIT, and has been recognized with awards including the Koopmans Prize, IBM Faculty Award, and fellowships from the Institute of Mathematical Statistics and American Statistical Association. His education includes a B.A. and Ph.D. in Mathematics from the University of California, San Diego. He co-authored influential textbooks like Time Series: Theory and Methods and has advised numerous papers on topics ranging from count time series to spatial extremes. Current research explores machine learning applications in time series and environmental risk modeling through collaborations like the STARMAP program.

Joseph RomanoView profile

Professor

• Nonparametric Statistics
• Bootstrap Methods
• Multiple Testing
• +7 more

Joseph Romano is a Professor of Statistics and Economics at Stanford University, where he has served since 1986. He holds dual appointments in both the Department of Statistics and the Department of Economics. His research focuses on theoretical statistics, nonparametric methods, bootstrap techniques, multiple testing, and their applications in econometrics, climate science, genetics, and clinical trials. He has received numerous awards, including the LGBTQ+ Scientist of the Year (2021) and the Presidential Young Investigator Award (1989–1994). Education: Ph.D. in Statistics, University of California, Berkeley (1986) M.S. in Statistics, University of California, Berkeley (1983) B.A. in Statistics, Princeton University (1982), Summa Cum Laude Research interests include developing robust statistical methodologies for high-dimensional data, controlling errors in multiple testing, and applying resampling techniques to complex datasets. He has authored over 100 papers, focusing on topics like permutation tests, bootstrap methods, and econometric modeling. Notable works include advancements in fixed sequence testing and randomized inference for modern data challenges. Awards and Recognition: Fellow, Institute of Mathematical Statistics (2000) Fellow, International Association of Applied Econometrics (2020) Canadian Journal of Statistics Award (1989) Romano has advised numerous students, including doctoral candidates in statistics and economics. He has held administrative roles at Stanford, such as Associate Chair and member of faculty committees. His grants include NSF-funded projects on resampling methods, multiple testing, and econometric theory. He is a vocal advocate for LGBTQ+ visibility in academia and has contributed to initiatives like 500 Queer Scientists. Beyond academia, he enjoys music, tennis, cooking, and architecture.