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Ilya ShpitserView profile

Associate Professor

• Causal Inference
• Missing Data
• Graphical Models
• +5 more

Ilya Shpitser is a John C. Malone Associate Professor in the Department of Computer Science at Johns Hopkins University (JHU), within the Whiting School of Engineering. He is also a member of the Malone Center for Engineering in Healthcare and the Data Science and AI Institute. His research focuses on causal inference, missing data, graphical models, algorithmic fairness, and semi-parametric inference, with applications in healthcare, public health, and criminal justice. Shpitser holds a BA in Computer Science and Mathematics from UC Berkeley (1999), and MS (2004) and PhD (2008) in Computer Science from UCLA. Postdoctoral work included Harvard University and the University of Southampton. He has received awards such as the Causality in Statistics Education Award (2017) and the NSF CAREER Award (2020). His work addresses disparities in algorithmic bias, causal pathway analysis, and high-dimensional observational data. Key contributions include developing causal inference methods for missing data and fairness, as well as software tools like Ananke. He serves as an associate editor for the Journal of Causal Inference and the American Journal of Epidemiology. His research is funded by NSF, NIH, DARPA, and the Office of Naval Research. Shpitser advises numerous PhD students, many of whom have transitioned to academic and industry roles. His interdisciplinary collaborations span biostatistics, computer science, and public health, emphasizing real-world applications of causal methods.

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 .

• Causal Inference
• Precision Medicine
• Mendelian Randomization
• +5 more

Yunan Wu is an Assistant Professor at the Yau Mathematical Sciences Center, Tsinghua University (since September 2024). Previously, he served as an Assistant Professor at the University of Texas at Dallas (2020–2024) and completed a postdoctoral fellowship in Biostatistics at Yale University’s School of Public Health (2019–2020). He earned his PhD in Statistics from the University of Minnesota (2015–2019), supervised by Prof. Lan Wang, and holds a bachelor’s degree in Mathematics and Physics from Tsinghua University (Beijing, China). His research focuses on **causal inference in precision medicine**, **Mendelian randomization**, **non-parametric/semi-parametric methods**, and **high-dimensional statistical analysis**. He also explores robust machine learning techniques and incorrupted data methodologies. His work bridges theoretical advancements with practical applications in healthcare and biomedical research. Key contributions include the development of tuning-free robust regression approaches (e.g., the TFRE method) and inference frameworks for optimal treatment regimes. His GitHub repositories reflect these efforts, including implementations of robust regression and treatment regime estimation algorithms. He is affiliated with the Yau Mathematical Sciences Center, where he contributes to interdisciplinary research at the intersection of statistics, machine learning, and biomedical sciences. His email is wuyunan@mail.tsinghua.edu.cn , and his office is located in Shuangqing Complex Building A504.

Jiong TangView profile

Professor

• System Dynamics
• Control
• Sensing and Automation
• +7 more

Jiong Tang is a Pratt & Whitney Chair Professor in Design and Manufacturing at the University of Connecticut , where he also serves as Co-Director of the Management and Engineering for Manufacturing Program . He received his B.S. and M.S. in Applied Mechanics from Fudan University, China (1989 and 1992), and his Ph.D. in Mechanical Engineering from Pennsylvania State University (2001). Prior to joining UConn, he worked at the GE Research Center as a research engineer. Research Interests : System dynamics, control theory, smart materials, vibration suppression, uncertainty propagation, computational intelligence, and multi-physics system modeling. Current Projects : Digital twin development for aerospace materials, physics-informed machine learning in manufacturing, adaptive metasurface design, and optimization of cooperative robotics. Methodological Focus : Combines Bayesian deep learning , Gaussian process metamodeling , transformer-based architectures , and multi-fidelity data fusion for industrial applications. His work emphasizes smart sensing , electromechanical integration , and uncertainty-robust inverse analysis . Collaboration : Research funded by federal agencies and industrial partners , with particular emphasis on aerospace and manufacturing technologies. His recent publications highlight generative adversarial networks for defect detection , piezoelectric metamaterials , and physics-guided neural network architectures across mechanical, structural, and composite systems.

Marno VerbeekView profile

Professor

• Empirical Finance
• Mutual Funds
• Hedge Funds
• +5 more

Marno Verbeek is a Professor of Finance at the Rotterdam School of Management (RSM), Erasmus University Rotterdam, and currently serves as Head of the Department of Finance. He previously held roles as Dean of Research at RSM and Academic Director of the Erasmus Research Institute of Management (ERIM). He earned his PhD in Economics from Tilburg University in 1991. His research focuses on empirical finance, particularly mutual funds, hedge funds, asset pricing, investment strategies, and performance evaluation. He is renowned for his textbook A Guide to Modern Econometrics (5th ed, 2017) and Panel Methods for Finance (2022). His work has been published in top journals such as Management Science , Review of Finance , and Journal of Financial Economics . He has supervised numerous PhD students, including Xiaohong Huang, Patrick Verwijmeren, and Guillermo Baquero, among others. His contributions extend to editorial roles in academic journals and organizational leadership in conferences like the Conference on Professional Asset Management . Verbeek’s research emphasizes practical applications of econometric methods to financial markets, addressing issues like fund performance measurement, investor behavior, and market efficiency. His work bridges theoretical models with real-world financial decision-making.

Khurram NadeemView profile

Associate Professor

• Predictive Modeling
• Wildland Fire Occurrence Prediction
• Spatio-Temporal Processes
• +4 more

Khurram Nadeem is an Associate Professor of Statistics at the University of Guelph, specializing in predictive modeling of ecological and environmental systems. His research focuses on leveraging big data analytics to address challenges in wildland fire prediction, agricultural sustainability, and statistical bioinformatics. He leads a lab offering graduate research opportunities in these areas and collaborates with organizations like the Canadian Interagency Forest Fire Centre (CIFFC). Education: PhD in Statistics, University of Alberta (2013) MSc in Statistics, University of Karachi (2005) BSc (Honours) in Statistics, University of Karachi (2003) Research Themes: Wildland Fire Prediction: Developing spatiotemporal models to forecast fire occurrences in British Columbia, aiding fire management strategies. Agro-Environmental Science: Integrating big data from precision agriculture and genomics to optimize sustainable farming practices under climate change. Statistical Bioinformatics: Analyzing microbiome data in livestock and crops to improve disease prevention and crop resilience. Media & Partnerships: Featured in University of Guelph news for wildfire research and Food from Thought initiatives. Seeks industry partnerships in forest fire analytics, environmetrics, and quantitative ecology. Advising & Grants: Supervises graduate students in Statistics and Bioinformatics programs. Offers postdoctoral positions in applied statistics for climate-agriculture linkages and microbiome modeling. Labs & Teams: Hosts a multidisciplinary lab focusing on environmental and agricultural data science. Collaborates with the Canadian Forest Service and agricultural stakeholders.

• Biostatistics
• Clinical Trials
• Statistical Modeling
• +7 more

Erich J Greene is a Research Scientist in the Department of Biostatistics at the Yale School of Public Health , where he contributes to clinical trial methodology and statistical analysis. Yale Center for Analytical Sciences (YCAS) Yale Data Coordinating Center (YDCC) Education: PhD in Psychology (2003), Princeton University MA in Psychology (1997), Princeton University MS in Physics (1995), Cornell University AB in Physics (1991), Princeton University Research Interests span Biostatistics , Clinical Trial Design , Survival Analysis , and Public Health Data Science . His work focuses on: Competing risks and clustering in time-to-event data Electronic health record data validation Sex/gender disparities in cardiovascular outcomes Pragmatic trial methodologies Bayesian approaches for complex biomedical data Publication Trends reveal expertise in: Cluster-randomized trial statistical methods Fall injury prevention modeling Dementia care comparative effectiveness ICD-10 coding algorithms Non-proportional hazard scenarios Zero-inflated recurrent event modeling Collaborations include frequent partnerships with: James Dziura (9 co-publications) Can Meng (8 co-publications) David Ganz and Denise Esserman (6 each) Additional Activities: Served on the Board of Directors for the New Haven Theater Company (2011-2022).

• Econometrics
• Machine Learning
• Artificial Intelligence
• +8 more

Anna Simoni is a Senior Researcher at CNRS/CREST and Professor of Econometrics and Statistics at ENSAE and École Polytechnique. She is a CNRS Research Fellow and Fellow of Hi! Paris and Institut Louis Bachelier. Her research spans econometrics, machine learning, and AI, focusing on high-dimensional models and Bayesian inference. Education: PhD in Economics, Toulouse School of Economics (2009) Habilitation à Diriger de Recherche (HDR), Toulouse School of Economics (2017) Research Interests: Her work integrates econometrics with machine learning to develop statistical methods for big data, including Google search data for macroeconomic forecasting and causal inference with minimal assumptions. Grants and Awards: She received the CNRS Bronze Medal in 2019 and leads the ANR-funded project "Moment Conditions Models and Bayesian Inference for Policy Evaluation" (2021-2026).

• Quantum Information Science
• Quantum Computing
• Quantum Error Correction
• +6 more

Dr. Thinh Le is a Postdoctoral Research Fellow at the School of Computer Science , University of Technology Sydney , where he works at the Centre for Quantum Software and Information (since 2023). His academic journey includes research fellowships at the Institute for Quantum Optics and Quantum Information Vienna (2020-2023) and Leibniz University Hannover (2019-2020). He earned his PhD in Physics (2015) and BSc in Physics (2011) from the National University of Singapore . Thinh's research focuses on quantum information science , bridging theoretical foundations and experimental implementations. His work addresses critical challenges in quantum computing (error correction, fault tolerance), quantum tomography (device-independent characterization), and quantum software stack development. He investigates quantum randomness generation, Bell nonlocality, and entanglement distillation for quantum networks, with applications in quantum cryptography and secure communication. His recent publications examine advanced topics like trapped-ion lattice surgery (2025), quantum correlation boundaries (2025), and quantum backflow phenomena (2023). Earlier work established foundational limits for real quantum theory (2021) and developed practical entanglement distillation protocols (2018). Scientific awards include the Lise Meitner Postdoctoral Fellowship (2020-2023) and Humboldt Postdoctoral Fellowship (2019-2020). His funded research projects focus on quantum computing error correction and device-independent quantum tomography , aligning with his theoretical expertise guided by experimental applications.

Changbao WuView profile

Professor

• Survey Methodology
• Complex Surveys
• Empirical Likelihood Methods
• +6 more

Changbao Wu is a Professor and Chair of the Department of Statistics and Actuarial Science at the University of Waterloo, within the Faculty of Mathematics. His research focuses on complex survey design and analysis, with expertise in empirical likelihood methods, resampling techniques, and missing data problems. He has developed R packages to implement these methods and is a Fellow of the American Statistical Association and Elected Member of the International Statistical Institute, receiving the 2012 CRM-SSC Prize in Statistics. Education: PhD in Statistics (1999) from Simon Fraser University under Prof. Randy Sitter. He has been at Waterloo since 1999. Research Interests: Design and analysis of complex surveys Semiparametric/nonparametric methods Empirical likelihood methods Resampling (jackknife/bootstrap) Missing data and measurement error Survey sampling methodology Professional Roles: Associate Editor: Survey Methodology (2006–), Biometrika (2008–), Journal of Nonparametric Statistics (2011–) Member: Statistics Canada’s Advisory Committee on Statistical Methods Recent Publications Trends: Focus on non-probability survey samples, causal inference, and pseudo-empirical likelihood methods. Recent work addresses challenges in combining non-probability and probability samples, doubly robust estimation, and calibration techniques. Awards: Fellow of ASA (since 2020) Elected ISI Member (since 2012) CRM-SSC Prize (2012) Grants and Advising: Extensive grants related to survey methodology and statistical inference. Advises on the International Tobacco Control (ITC) China Survey and collaborates on large-scale studies like the Canadian Longitudinal Study on Aging (CLSA). Labs/Teams: Leads research in survey statistics and statistical methodology at the University of Waterloo, contributing to interdisciplinary projects involving health, economics, and social sciences.

Reetam MajumderView profile

Assistant Professor

• Spatial Statistics
• Extreme Value Theory
• Climate Modeling
• +3 more

Dr. Reetam Majumder is an Assistant Professor of Statistics in the Department of Mathematical Sciences at the University of Arkansas's Fulbright College of Arts & Sciences. Research Focus: Develops statistical methods for environmental extremes, including spatial modeling of climate extremes, wildfire risk assessment, and hydrological forecasting. Combines traditional statistics with deep learning approaches. Publications: Recent work demonstrates strong focus on environmental applications of extreme value theory, including streamflow modeling under climate change, spatial optimization for fire management, and climate model bias correction. Research frequently employs semi-parametric methods and neural network approximations for complex environmental systems.

Bin ZhangView profile

Associate Professor

• Prognostics and Health Management
• Fault Detection and Isolation
• Failure Prognosis
• +8 more

Dr. Bin Zhang is an Associate Professor in the Department of Electrical Engineering at the Molinaroli College of Engineering and Computing, University of South Carolina. With over 20 years of experience in prognostics and health management, intelligent systems and control, and robotics, he has established himself as a leading researcher in battery management systems, power electronics, and fault-tolerant control systems. Dr. Zhang's educational background includes: Ph.D. in Electrical Engineering from Nanyang Technological University, Singapore M.E. in Mechanical Engineering from Nanjing University of Science and Technology, China B.E. in Mechanical Engineering from Nanjing University of Science and Technology, China His research focuses on active approaches to achieve intelligent smart systems with self-situational-awareness and self-adapting capabilities. Primary interests include prognostics and health management (PHM), which covers fault detection and isolation, failure prognosis, and fault tolerance; robotics and unmanned systems; intelligent systems and control; and dynamic systems design, modeling, simulation and control. His work integrates physics-based models with data-driven techniques and computational intelligence, including pattern recognition and machine learning. Analysis of Dr. Zhang's recent publications reveals a strong emphasis on battery modeling (particularly lithium-ion batteries), power electronics control (including fractional order delay and virtual variable sampling techniques), and deep learning methods (including graph neural networks, deep residual convolutional neural networks, and deep belief networks). His research spans multiple application domains including power grids, batteries, aircraft, helicopters, and manned/unmanned vehicles. Dr. Zhang serves as Associate Editor for prestigious journals including IEEE Transactions on Industrial Electronics, IEEE Transactions on Systems, Man, and Cybernetics: Systems, and Neurocomputing. He is a Senior Member of IEEE and a member of ASME. As director of the Resilient Systems Laboratory, Dr. Zhang advises numerous graduate students working on cutting-edge research in battery technology, power cable insulation, and control systems. His lab is equipped with advanced facilities including an 8-channel ARBIN BT-Smart battery testing system, power electronics control systems, cable/wire testing systems, rotating machinery testing systems, and unmanned vehicles including quadrotors and hexacopters.

Hervé CardotView profile

Professor

• Functional Data Analysis
• Stochastic Algorithms
• Survey Sampling
• +10 more

Hervé Cardot is a Professor of Statistics and team leader of the Statistics, Probability, Optimization and Control (SPOC) group at the Institut de Mathématiques de Bourgogne (IMB) , University of Burgundy. His research spans functional data analysis, stochastic algorithms for robust estimation, survey sampling methodology, and applied statistics across diverse fields. Research Interests : Functional data analysis, online principal component analysis, zero-inflated regression, survey sampling, nonparametric estimation Key Applications : Agriculture, climatology, energy consumption, food science, remote sensing, medical imaging Recent Work includes: Developing statistical frameworks for analyzing categorical trajectories using multivariate functional PCA (2025) Advancing bias-robust estimation for functional data in survey sampling (2020) Creating online stochastic algorithms for high-dimensional robust estimation (2017) Applying semi-Markov models to sensory analysis (2018) Teaching at the University of Burgundy includes courses on statistical modeling, big data analysis, and applied statistics for economics and psychology. He supervises PhD students in statistics and co-edits Statistics and Probability Letters .

Roland SchmehlView profile

Associate Professor

• Wind Energy
• Aerospace Engineering
• Airborne Wind Energy Systems
• +4 more

Roland Schmehl is an Associate Professor at the Department of Aerospace Engineering, Delft University of Technology (TU Delft). He is a founding member of Airborne Wind Europe (since 2019) and serves on the advisory board of Kitepower (since 2016). His work focuses on airborne wind energy (AWE) systems, including their application in Martian habitats, noise analysis, and aero-structural modeling. Key Research Areas: Wind energy innovation, aerospace engineering, Martian habitat power systems, and robotics-assisted design. The 15 most recent articles highlight his contributions to AWE system design, flight pattern optimization, noise annoyance studies, and extraterrestrial energy applications. His scientific awards include the 2021 Innovation stamps and the 2021 Rhizome project prize for off-Earth habitat development. Schmehl’s projects include MERIDIONAL (multi-scale wind farm design), Rhizome (autonomous Mars habitats), and EFRO (unmanned systems research). He has led editorial activities for journals like Wind Energy Science and Energies and participated in international conferences.

Aljaž BožičView profile

Researcher

• Computer Vision
• Computer Graphics
• Artificial Intelligence
• +7 more

Aljaž Božič is a Research Scientist at Meta Reality Labs Research , focusing on neural rendering, 3D reconstruction, and AI-driven geometry modeling. He earned his Ph.D. in Computer Science from the Technical University of Munich (TUM) and holds a Master's in Computer Science from TUM and a Bachelor's in Mathematics from the University of Ljubljana . His research spans computer vision, graphics, and artificial intelligence , with a focus on neural rendering , generative AI , and 3D deformable object modeling , targeting applications in VR/AR and robotics. His work includes time-consistent dynamic scene reconstruction (SceNeRFlow), volumetric hair appearance modeling, and high-fidelity walkable VR spaces (VR-NeRF), alongside efficient NeRF distillation and calibration methods (Neural Lens Modeling). Key article trends include Transformer-based monocular reconstruction (TransformerFusion), neural parametric shape models (NPMs), and self-supervised non-rigid tracking (Neural Deformation Graphs). He has contributed to open-source projects like the TransformerFusion GitHub repository , emphasizing MIT-licensed tools for scene reconstruction. At TUM, he served as a Teaching Assistant for courses such as 3D Scanning and Spatial Learning and 3D Vision Seminar , bridging academic instruction with research innovation. His work integrates advanced neural networks with practical optimization techniques, advancing fields like RGB-D reconstruction (DeepDeform) and variational SLAM.