Find Academic Supervisors

• Weak Dependence
• Limit Theorems for Stochastic Processes
• Statistical Inference for Mixed Moving Average and Ambit Fields
• +4 more

Dr. Imma Valentina Curato is a researcher specializing in probability theory, financial econometrics, and stochastic processes. She holds a DFG-funded research grant focused on PAC Bayesian bounds for light cone and trajectory data, with a particular emphasis on high-frequency financial modeling and statistical inference for complex random fields. Education: PhD in Mathematics, University of Pisa (2013) MSc in Mathematics, University of Florence (2009) BSc in Mathematics, University of Florence (2006) Research Focus: Weak dependence in stochastic processes Limit theorems for spatio-temporal models Fourier and Laplace transform methods for volatility estimation Bayesian learning in ambit field models Notable Contributions: Developed Fourier-based analysis for stochastic leverage effects Advanced central limit theorems for mixed moving averages Created nonparametric volatility estimation techniques Scientific Recognition: DFG Research Grant recipient Published in top-tier journals like Annals of Applied Probability and Quantitative Finance Academic Engagement: Regular participant in international conferences (SPA, European Meeting of Statisticians, AMASES) Invited speaker at institutions like University of Florence, Imperial College London, and Aarhus University Educational Leadership: Teaches courses on statistical learning, financial engineering, and stochastic processes Organizes seminars on Gaussian processes and high-frequency econometrics

Kateryna MorozovskaView profile

Researcher

• Computational Methods for Energy Systems
• Physics-Informed Machine Learning
• Renewable Energy Integration
• +3 more

Kateryna Morozovska is a Researcher at the KTH Royal Institute of Technology , affiliated with the Division of Decision and Control Systems and the School of Electrical Engineering and Computer Science . Her work bridges computational methods and energy systems development, focusing on physics-informed machine learning for renewable energy integration and power transformer optimization. Education B.S. and M.S. in Electrical Mechanics from Zaporizhzhya National Technical University (2013) European Energy Masters program with mobility at DTU (Denmark), TU Delft (Netherlands), and NTNU (Norway) PhD in Electrical Engineering from KTH (2020) on 'Dynamic rating for applications in renewable energy' Licentiate from KTH (2019) Research Focus Kateryna's research emphasizes Physics-Informed Neural Networks (PINNs) for power system optimization, including transformer thermal modeling, cellulose degradation analysis, and renewable energy integration. Her projects explore dynamic rating techniques to enhance grid efficiency and sustainability, funded by Vinnova and applied in PV-power plants and wind farms. Scientific Contributions She has developed frameworks for transformer cost analysis, wind farm sizing, and sensor placement optimization using PINNs and MILP. Her work addresses environmental trade-offs in wind energy, such as raw material mining impacts, and investigates thermodynamic challenges in nanocellulose and power systems. Affiliations Kateryna collaborates with industry partners through the PINN Summer School and contributes to SweGRIDS and Mendeley communities. Her teaching includes hands-on PINN training and multi-GPU machine learning.

Chen ZhaoView profile

Assistant Professor

• Fairness-Aware Machine Learning
• Domain Generalization
• Graph Learning
• +5 more

Dr. Chen Zhao serves as an Assistant Professor in Computer Science at Baylor University, specializing in machine learning, data mining, and AI systems with emphasis on fairness-aware modeling and domain generalization. His work addresses critical challenges in building robust AI for dynamic real-world environments. Education: PhD from The University of Texas at Dallas (2021) MS in Computer Science from University at Albany, State University of New York (2016) MS in Biomedical Informatics from Albany Medical College (2016) Research Interests: Dr. Zhao pioneers techniques in Fairness-Aware Machine Learning to mitigate algorithmic bias, Domain Generalization for distribution shift resilience, and Graph Learning for complex relational data. His cross-cutting work integrates Causal Learning and Uncertainty Quantification to develop trustworthy AI systems that maintain performance under changing conditions and novel domains. Publication Trends: Analysis of 2023-2025 publications reveals dominant focus on fairness under distribution shifts (68% of works), with significant contributions to graph-based OOD detection (24%) and causal fairness models (18%). Recent innovations increasingly leverage diffusion models and LLM agents, indicating strategic expansion into generative AI for robustness verification while maintaining core commitment to ethical machine learning frameworks.

• Bayesian statistics
• Machine learning
• Neuroimaging
• +5 more

François Laurent is a Researcher at the Bioinformatics and Biostatistics Hub of the Pasteur Institute in Paris, France. Their work focuses on computational methods for analyzing biological data, particularly in Drosophila behavior , single-molecule tracking , and Bayesian inference in heterogeneous environments. They lead or co-lead software projects such as TRamWAy , Themis , and Japet , which address biostatistical modeling, decision-making algorithms, and medical applications. Education : Engineering Degree in Biomedical Engineering, Université de Technologie de Compiègne (1989) DEA in Control of Complex Systems, Université de Technologie de Compiègne (1990) PhD in Computational Neuroscience (unspecified year) Their research spans interdisciplinary domains including Bayesian statistics , machine learning , and neuroimaging . Recent projects involve single-molecule localization microscopy (SMLM) analysis, force inference in biological systems, and synaptic receptor dynamics . They contribute to open-source software development and collaborate on methodological innovations for high-throughput biological data . Key software contributions include: TRamWAy : Trajectory analysis for SMLM data SHADE : Experimental design support for biologists Japet : Accelerated neural network training platform François Laurent participates in advising PhD students and is involved in projects like Mapping Receptor Dynamics In Synapses and Dynamique des Décisions de Développement chez la Drosophile . Their work integrates statistical modeling , computational physics , and artificial intelligence to address questions in cellular biophysics and neuroscience . They also contribute to the Hub de Bioinformatique et Biostatistique 's mission of providing computational biology support to research units at the Pasteur Institute.

Sancho Salcedo-SanzView profile

Professor

• Signal Processing
• Machine Learning
• Wireless Communications
• +4 more

Sancho Salcedo-Sanz is a Full Professor in the Department of Signal Theory and Communications at the University of Alcalá's Higher Polytechnic School. He serves as the Deputy Director of his department and is the Director of the GHEODE Research Group at the University of Alcalá (UAH). With extensive experience in academia and research, he has made significant contributions to the fields of signal processing, machine learning, and optimization algorithms. His educational background includes a Full Professor Accreditation (2013), Associate Professor Accreditation (2005), a Ph.D. in Telecommunication Engineering from Universidad Carlos III de Madrid (2002), and a Bachelor's degree in Physics from Universidad Complutense de Madrid (1998). Professor Salcedo-Sanz's research spans multiple domains within computational intelligence and signal processing. His work focuses on developing innovative optimization algorithms like the Coral Reefs Optimization algorithm, with applications in renewable energy forecasting, wireless communications, and environmental modeling. He has pioneered approaches that combine machine learning techniques with evolutionary computation to solve complex real-world problems across various domains including energy, telecommunications, and environmental science. His recent publications demonstrate a strong focus on practical applications of computational intelligence, with particular emphasis on solar radiation forecasting, wind power prediction, structural vibration control, and optimization of smart grid systems. The interdisciplinary nature of his work bridges engineering, computer science, and environmental applications. VI Premio Accenture en Economía y Gestión de la Innovación, Finalists, 2015 Premio Sacyr Hacemos lo Imposible, in the category of Proyectos Ciudadanos, 2011 Premio a la mejor noticia científica in the I Certamen de Divulgación Científica de la Universidad de Alcalá, 2011 Premio del Consejo Social de la Universidad de Alcalá, 2011 Premio Fundación 3M a la Innovación, in the category of Medio Ambiente, 2010 Premio Jóvenes Investigadores de la Universidad de Alcalá en CC. Experimentales, 2009 Professor Salcedo-Sanz leads the GHEODE Research Group at UAH, which focuses on Geospatial and High-dimensional Data Optimization and Engineering. His team works on cutting-edge research in computational intelligence, with particular emphasis on the development and application of novel metaheuristic algorithms to solve complex optimization problems in various domains including renewable energy, telecommunications, and environmental modeling. The group has secured numerous research projects and maintains strong collaborations with international institutions.

• Statistics
• Biostatistics
• Time Series Analysis
• +3 more

Josh Patrick serves as a Senior Lecturer at Baylor University, where he maintains an active research program spanning theoretical statistics and applied biostatistics. His core research domains include: Theoretical statistics: Matrix quadratic forms, Wishart distributions, and covariance matrix structures Time series analysis: Functional-coefficient autoregressive models and forecasting methodologies Biostatistics: Trauma research, survival analysis for ECMO patients, and combat casualty care Spatio-temporal modeling: Applications in renewable energy data such as solar irradiance Publication trends from 2009-2021 reveal consistent methodological innovation, with recent work (2020-2021) emphasizing matrix distributions and medical applications, while earlier contributions (2013-2016) focus on time series and environmental modeling. His approach integrates semiparametric techniques, Bayesian inference, and simulation-based validation across disciplines. No scientific awards or honors were documented in the source materials. Details regarding graduate student supervision, research funding, or laboratory affiliations remain unspecified in available information.

Ben SpycherView profile

Associate Professor

• Childhood Cancer Epidemiology
• Pediatric Respiratory Epidemiology
• Spatial Epidemiology
• +3 more

Ben Spycher serves as Associate Professor and Head of Research Group at the Institute of Social and Preventive Medicine (ISPM), University of Bern, Switzerland. He leads the Environmental & Spatial Epidemiology group while contributing to the Paediatric Cancer Epidemiology and Pediatric Respiratory Epidemiology groups. His academic credentials include: PhD in Biostatistics/Epidemiology, University of Bern (2006-2010) MSc in Agricultural Economics, ETH Zurich (1992-1997) BSc in Statistics, Freie Universität Berlin (2002-2005) Dr. Spycher's research centers on childhood cancer epidemiology (spatial clustering, population mixing, risk factors) and pediatric respiratory disorders (phenotypes, natural history), employing advanced spatial statistics and cohort study methodologies. His work bridges environmental exposure assessment with clinical outcomes through rigorous epidemiological frameworks. Recent publications (2024-2025) reveal strong thematic continuity in spatial cancer epidemiology (UV radiation, transformer proximity), pediatric growth/obesity metrics, and respiratory phenotyping, while expanding into methodological innovations for missing data and non-Gaussian modeling. The SwissPedHealth initiative demonstrates his leadership in national data infrastructure development. His career progression shows consistent institutional commitment: Research Fellow at ISPM (2012-2025), Postdoctoral Fellow at University of Bristol (2010-2011), and current Associate Professor since 2025. Fieldwork spans Switzerland, UK, and international collaborations including Papua New Guinea.

• Evolutionary Ecology
• Quantitative Genetics
• Population Dynamics
• +4 more

Paul Acker is an evolutionary ecologist at the Norwegian University of Science and Technology (NTNU), focusing on how biodiversity responds to environmental change. His work integrates empirical studies with conceptual frameworks to understand interactions between natural selection, phenotypic plasticity, and genetic variation in wild populations. Affiliation: Department of Biology, NTNU Projects: Spatio-Seasonal Eco-Evolutionary Dynamics (ERC-funded), Early-life eco-evolutionary dynamics of migration (NFR-funded), Centre for Biodiversity Dynamics (CBD) Research interests center on eco-evolutionary responses to environmental variability, with key traits including: Seasonal migration Survival and reproduction strategies Phenotypic plasticity in discrete traits Quantitative genetic analysis of threshold traits Population dynamics under climate change His methodology combines Bayesian statistics, capture-recapture models, and quantitative genetics to analyze long-term ecological monitoring data. Teaching includes PhD courses on: Quantitative genetics in wild populations (BI8091) Natural selection analysis via Bayesian capture-recapture models Plasticity in adaptive evolution (BI8083) He previously taught capture-recapture methodologies at the University of Aberdeen. Supervision involves co-guiding five PhD students across institutions in Western Britanny, NTNU, and Montpellier, focusing on migration phenology, plumage trait selection, and metapopulation dynamics. He has supervised eight past master's students. Scientific awards include ERC Advanced Grant (via Prof. Jane Reid) SQUID Student Fellowship ESEB Hewitt Mobility Award Mobility grants from UBO and ISBLUE Collaborations span institutions in UK (University of Aberdeen), France (Montpellier SupAgro), and Norway (NTNU, CBD). He contributes to EURING, ESEB, and BOU conferences.

• Measurement
• Applied and Theoretical Research Methodology
• Spatio-Temporal Modelling
• +3 more

Dr. Ed Kroc is an Associate Professor at the University of British Columbia (UBC) within the Department of Educational and Counselling Psychology, and Special Education in the Faculty of Education. Specializing in the Measurement, Evaluation, and Research Methodology (MERM) program, he joined UBC in 2018 after completing his PhD in Mathematics at UBC, M.Sc. in Applied Mathematics in Statistics, and B.Sc. in Mathematics at DePaul University. Education: PhD (UBC, 2015), M.Sc. (DePaul, 2007), B.Sc. (DePaul, 2006) Courses Taught: Experimental Designs, Correlational Analysis, Meta-Analysis, Causal Inference, Bayesian Methods, Multivariate Analysis His scholarly interests focus on measurement theory, causal inference, spatio-temporal modeling, and urban ecology. Recent research explores the life cycles of urban-nesting Glaucous-winged Gulls, measurement error models, and statistical methodology. His publications span psychology, ecology, mathematics, and interdisciplinary research. Dr. Kroc’s work emphasizes methodological rigor, including reliability estimation, polynomial regression techniques, and assessment protocols for motor skill evaluation. He advocates for multivariate thinking in subscale cutoff determination and challenges traditional measurement frameworks like Stevens’ scales. Contact: ed.kroc@ubc.ca | Office: Scarfe Office Block 2527

Danfei XuView profile

Assistant Professor

• Robotics
• Machine Learning
• Robot Learning
• +3 more

Danfei Xu is an Assistant Professor at the School of Interactive Computing, College of Computing, Georgia Institute of Technology, and a part-time Research Scientist at NVIDIA AI. He leads the Robot Learning and Reasoning Lab (RL2), focusing on developing adaptable robot intelligence through machine learning approaches. His research spans robot learning from human data, long-horizon reasoning with generative models, and full-stack robot learning systems. Education: Ph.D. in Computer Science, Stanford University (2015-2021) B.S. from Columbia University (SEAS'15) Research interests center on creating robots that learn from human demonstrations using wearable devices, developing compositional generative models for long-horizon planning, and building open-source robotic systems. His work integrates neuro-symbolic methods with deep learning to enable flexible task execution in diverse environments like homes, factories, and healthcare settings. Publications demonstrate strong focus on imitation learning (38%), generative models (25%), and robotic systems development (20%), with applications spanning manipulation (42%), planning (33%), and human-robot interaction (17%). Key trends include diffusion models for planning, neural fields for representation, and multi-task learning frameworks. Awards: NSF CAREER Award (2025) IEEE RA-L Best Paper Honorable Mention (2023) ICRA Best Conference Paper (2024) CoRL Best Paper Finalist (2023) CoRL Best Systems Paper Finalist (2023) Leads RL2 lab with 21 members (11 PhD, 7 MS, 3 undergraduates). Secured research funding from NSF (CAREER, generative models), Meta (human data research), Autodesk (manipulation), and Samsung (home robots). Lab maintains multiple robotic platforms including Unitree G1, Franka arms, and custom EVE system.

Yao XieView profile

Professor

• Statistics
• Machine Learning
• Signal Processing
• +5 more

Yao Xie is the Coca-Cola Foundation Chair and Professor in the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Institute of Technology (Georgia Tech), where she joined as an Assistant Professor in 2013. She also serves as Associate Director of Machine Learning at the Center for Machine Learning and is an Adjunct Professor in the School of Electrical and Computer Engineering (ECE). Her academic career includes holding the Harold R. and Mary Anne Nash Early Career Professorship (2017–2023) and prior roles as a Research Scientist at Duke University (2012–2013). Education : Ph.D. in Electrical Engineering with Mathematics minor (Stanford, 2012), M.S. in Electrical and Computer Engineering (University of Florida, 2006), and B.S. in Electrical Engineering and Computer Science (USTC, 2004). Her research spans statistics, machine learning, and optimization , focusing on sequential data analysis , change-point detection , spatio-temporal modeling , and robust learning . Recent work emphasizes generative models for conformal prediction , deep spatio-temporal point processes , and applications in healthcare and urban systems . The 15 most recent publications highlight her expertise in spatio-temporal modeling , generative AI , and uncertainty quantification . Key themes include flow-based conformal prediction , Hawkes processes for event modeling , and deep learning for change detection in robotics, energy systems, and healthcare. Scientific honors include the INFORMS Donald P. Gaver, Jr. Early Career Award (2022), CWS Woodroofe Award (2024), NSF CAREER Award (2017), and multiple scholarships and fellowships. She co-mentored Shixiang Zhu (Woody) , whose work was recognized with the INFORMS Wagner Prize Finalist (2021). Her grants include NSF CMMI-2015787 for data-driven policing, NSF SCALE MoDL Program (2022–2024) for reliability in deep learning, and contributions to the NSF AI Institute for Advances in Optimization . She has collaborated with institutions like the City of Atlanta , City of South Fulton , and the Center for Disease Control (CDC) .

• Spatial Statistics
• Spatio-Temporal Statistics
• Bayesian Hierarchical Models
• +6 more

Matthew Thomas is a Researcher at the Department of Infectious Disease Epidemiology within the Faculty of Medicine at Imperial College London . His work focuses on integrating statistical methodologies with public health and environmental applications. His research interests include: Spatial and spatio-temporal statistics Bayesian hierarchical models Environmental health and air quality Disease burden estimation Prior to joining Imperial, he completed his PhD at the University of Bath , where he developed Bayesian models for global air quality assessment and collaborated with organizations like the World Health Organization and the Department of Pharmacy & Pharmacology at the University of Bath.

Marc SturrockView profile

Lecturer

• Biophysics
• Computational Biology
• Gene Regulatory Networks
• +6 more

Dr. Sturrock is a Lecturer in Biophysics and Computational Biology in the Department of Physiology. He holds a B.Sc. in Applied Mathematics (2009) and a Ph.D. (2013) from the University of Dundee, focusing on spatio-temporal models of gene regulatory networks. His postdoctoral work includes research at The Ohio State University (macromolecular crowding, cell polarization) and Imperial College London (stochastic gene expression, synthetic Turing patterns). B.Sc., Applied Mathematics, University of Dundee (2009) Ph.D., University of Dundee (2013) His research centers on computational modeling of biological systems, including gene regulation, cell polarization, and cancer dynamics. Publications highlight the role of stochasticity in gene networks, spatial effects in signaling, and applications to glioblastoma and synthetic biology. Topics span mathematical biology, systems biology, and biophysics. Recent articles emphasize machine learning integration for genomic selection, Bayesian network inference, spatio-temporal cancer modeling, and stochastic simulations. Key methodologies include agent-based modeling, synthetic data generation, and compartmentalized systems analysis. He has collaborated with institutions such as the Mathematical Biosciences Institute at Ohio State and synthetic biology groups at Imperial College London, though no current lab affiliations are detailed in the provided text.

Cici BauerView profile

Associate Professor

• Bayesian spatiotemporal modeling
• Small area estimation
• Hierarchical models for complex survey data
• +8 more

Cici Bauer is an Associate Professor of Biostatistics and Data Science at the University of Texas Health Science Center at Houston (UTHealth) and serves as the Founding Director of the Center for Spatial Temporal Modeling for Applications in Population Sciences (CSMAPS) . PhD in Statistics, University of Washington Seattle (2012) Her research focuses on advanced statistical methodologies for public health applications, including: Bayesian spatiotemporal modeling Small area estimation Hierarchical models for complex survey data Statistical analysis of data from wearable devices She contributes to interdisciplinary research areas such as Big Data, biostatistics, border health, cancer epidemiology, GIS/spatio-temporal data analysis, infectious disease, and social determinants of health.

Nikolay BliznyukView profile

Associate Professor

• Statistical Machine Learning
• Bayesian Statistics
• Spatial and Spatio-temporal Modeling
• +9 more

Nikolay Bliznyuk serves as an Associate Professor at the University of Florida with joint appointments across four departments: Agricultural and Biological Engineering Biostatistics Statistics Electrical and Computer Engineering His research centers on three methodological thrusts: (i) statistical machine learning for predictive modeling, (ii) Bayesian modeling strategies for integrative informatics and uncertainty quantification, and (iii) modeling for dependent data including spatial, temporal, and spatiotemporal structures. Applications span digital health technologies using sensor networks, smart agriculture for natural resource optimization (water/nutrients), and infectious disease modeling. He leads the Bliznyuk Lab for Applied Statistics in IFAS (UF/IFAS BLAST), developing cutting-edge statistical tools for environmental and life sciences data. Dr. Bliznyuk teaches graduate courses including STA6703 Statistical Machine Learning and STA6348 Bayesian Analysis, contributing to UF/IFAS AI initiatives and training interdisciplinary data scientists for real-world challenges.