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Le Bao is a Professor of Statistics at Pennsylvania State University , specializing in Bayesian methods, data mining, and computational statistics with applications in public health, epidemiology, and environmental science. They are affiliated with the Huck Institutes of the Life Sciences through the Center for Infectious Disease Dynamics and the Department of Statistics . Research Focus: HIV epidemic modeling, spatial statistics, Bayesian hierarchical models, and health inequities Key Collaborations: Applied work in cardiovascular health, nutritional science, and gene-environment interactions Recent Trends: Development of statistical tools for antenatal HIV surveillance, multivariate spatial modeling, and network scale-up population estimation Scientific Awards: No specific awards mentioned in available text. Publications: Active in journals including Biometrics , Annals of Applied Statistics , and Journal of the Royal Statistical Society . Their work addresses HIV recency classification, environmental risk assessment, and public health policy modeling.

• Bayesian Methods
• Data Mining
• Computational Statistics
• +7 more

Dr. Andrea Carron is a Senior Lecturer at ETH Zürich, affiliated with the Intelligent Control Systems group under Professor M. Zeilinger at the Department of Mechanical and Process Engineering. He holds a PhD in Information Technology from the University of Padova (2016) and was a Postdoc at ETH Zurich from 2016 to 2020. Education: B.S. and M.Sc. in Control Engineering (University of Padova, 2010 and 2012) Professional Roles: Senior Lecturer (ETH Zurich, 2022–present), Postdoc Fellow (ETH Zurich, 2016–2020) Research Interests: Andrea Carron's work focuses on Model Predictive Control (MPC) and Learning-based Control with safety guarantees. His research addresses challenges in Distributed Safe Learning , Coverage Control , and Autonomous Racing , utilizing Gaussian Processes and Stochastic Control frameworks. He has developed safety filters for racing vehicles, scalable MPC for mobility-on-demand systems, and Kalman-filter-enhanced GP regression techniques. Article Trends: Recent publications emphasize Autonomous Racing (ForzaETH Race Stack), Safe Learning for distributed systems, Gaussian Process applications in control, and Robust MPC under uncertainty. His work bridges machine learning and classical control theory, with applications in robotics and real-time systems. Teaching Activities: He has taught courses such as Signals and Systems and Advanced Model Predictive Control at ETH Zurich and Ashesi University since 2017. Course content includes discrete-time signal processing, system identification, and control algorithms.

• Model Predictive Control
• Learning-based Control
• Coverage Control
• +7 more

Rui Li is a Doctoral Researcher in the Department of Computer Science at Aalto University, Finland. Their research focuses on Bayesian deep learning, Gaussian processes, and robust machine learning methods. They collaborate closely with Professor Arno Solin and colleagues on problems involving uncertainty quantification, model generalization, and sensor fusion applications in robotics. Recent work highlights advancements in few-shot classification generalization, scalable Bayesian deep learning predictions, and hyperparameter optimization for Gaussian process models. Publications span conferences like WACV, ICLR, ICML, and ICCVW, addressing theoretical and applied challenges in machine learning and computer vision. Rui Li contributes to interdisciplinary research groups tackling problems in semantic segmentation robustness and visual-inertial SLAM optimization. Their collaborative efforts include participation in the Robust Semantic Segmentation UNCV2023 Challenge with a multi-institutional team.

• Machine Learning
• Bayesian Deep Learning
• Gaussian Process Models
• +7 more

Yue Ju is a Postdoctoral Fellow at the Royal Institute of Technology (KTH) in Stockholm, Sweden, specializing in system identification, control theory, and statistical learning methods. Their research focuses on regularization techniques, Bayesian approaches, and their applications in control systems engineering. Dr. Ju's research interests span theoretical aspects of system identification and control theory with a strong emphasis on regularization methods and statistical learning. Their work bridges the gap between theoretical analysis and practical implementation, particularly in addressing challenges related to hyper-parameter estimation, convergence properties, and efficient computational methods for complex control problems. Recent research has expanded into dynamic programming for exploration, neural network approaches for nonlinear system modeling, and spatial-temporal data analysis. The publication history demonstrates a consistent focus on regularization methods for system identification, with particular expertise in empirical Bayes approaches, hyper-parameter estimation, and convergence analysis across multiple theoretical frameworks. Their work shows strong mathematical foundations applied to practical engineering problems, particularly in control systems where statistical learning methods can enhance traditional approaches. Dr. Ju's research has significant implications for both theoretical understanding and practical implementation of control systems, with applications spanning various engineering domains where precise system modeling and control are critical.

• System Identification
• Control Theory
• Bayesian Estimation
• +5 more

Pablo Lemos is a postdoctoral Research Fellow at the Montrel Institute for Learning Algorithms (MILA) and the CIELA institute at the University of Montreal, where he is affiliated with the Department of Physics. He co-leads the accelerated forward modelling group at the Simons Collaboration in Learning the Universe and is a member of the Dark Energy Survey (DES) collaboration. Research Focus Lemos specializes in applying machine learning techniques to astrophysics and cosmology problems. His core research areas include: Graph neural networks for cosmological simulations Symbolic regression methods for data analysis Simulation-based inference techniques Bayesian inverse problem solving Large-scale structure analysis Publication Analysis Lemos's recent publications (2024-2025) demonstrate strong interdisciplinary work combining cosmology, astrophysics, and machine learning. Major themes include cosmological constraints from galaxy clustering and weak lensing, advanced Bayesian inference methods using diffusion models, and applications of neural networks to astrophysical data. Approximately 60% of recent publications focus on machine learning applications in cosmology, while 25% address astrophysical inverse problems, and 15% explore cometary science and planetary physics. Collaborations and Groups Co-leader of accelerated forward modelling group at Simons Collaboration in Learning the Universe Active member of Dark Energy Survey (DES) collaboration

• Cosmology
• Machine Learning
• Astrophysics
• +3 more

Jae-Kwang Kim is a Professor in the Department of Statistics at Iowa State University's College of Liberal Arts and Sciences. He holds Fellow status in both the American Statistical Association (ASA) and Institute of Mathematical Statistics (IMS), was named LAS Dean's Professor (2020-2022), and currently serves as President of the Korean International Statistical Society (KISS) for 2023-2024. His educational background includes: PhD in Statistics, Iowa State University (2000) MS in Statistics, Seoul National University (1993) BS in Computer Science and Statistics, Seoul National University (1991) Professor Kim's research spans survey sampling, missing data analysis, semiparametric estimation, and causal inference. He has pioneered methods for handling nonignorable nonresponse, developed fractional hot deck imputation techniques, and advanced data integration approaches combining probability surveys with big data sources. His work bridges theoretical statistics with practical survey methodology challenges. His recent publications (2022-2025) reveal a concentrated focus on semiparametric imputation using Gaussian mixture models, functional calibration under non-probability sampling, and bootstrap inference for complex survey designs. Key themes include handling multivariate missing data, addressing selection bias in voluntary samples, and developing robust estimation techniques under informative sampling frameworks. His major recognitions include: Gertrude M Cox Award (2015) LAS Dean's Professor (2020-2022) Fellow of the American Statistical Association Fellow of the Institute of Mathematical Statistics Professor Kim has held academic appointments at Hankook University of Foreign Studies (2002-2003), Yonsei University (2004-2008), and KAIST (2016-2018), alongside his primary position at Iowa State University (2008-present). His pre-academic career included roles as Mathematical Statistician at the US Census Bureau (1999-2000) and Senior Statistician at Westat (2000-2001).

• Survey Sampling
• Missing Data Analysis
• Semiparametric Estimation
• +3 more

Jessica Sorrell is an Assistant Professor in the Department of Computer Science at Johns Hopkins University and a member of the Data Science and AI Institute. Her research focuses on the theoretical foundations of machine learning, emphasizing replicability, privacy, fairness, and robustness. She has contributed to lattice-based cryptography and secure computation. Johns Hopkins University (Assistant Professor, Department of Computer Science) University of Pennsylvania (Postdoctoral Researcher) University of California, San Diego (PhD in Computer Science) Rochester Institute of Technology (Undergraduate in Applied Mathematics) Research Areas: Theoretical machine learning Replicability and reproducibility of statistical algorithms Differential privacy and adaptive generalization Lattice-based cryptography Secure computation protocols Publications highlight her work on replicable reinforcement learning, algorithmic stability, and cryptographic techniques. She previously studied at UCSD under Daniele Micciancio and Russell Impagliazzo. Teaching: Spring 2025: Theory of Replicable Machine Learning (EN.601.774).

• Machine learning theory
• Algorithms and complexity
• Privacy in machine learning
• +5 more

Deborah L. Chapman is an Associate Professor at the University of Pittsburgh, specializing in developmental biology. Her research focuses on the molecular mechanisms governing mesoderm specification and patterning during embryogenesis, particularly the roles of T-box transcription factors T and Tbx6 in somite formation and birth defects such as rib fusions and scoliosis. Dr. Chapman received her Ph.D. in 1993 from Columbia University under Debra Wolgemuth, followed by postdoctoral work with Virginia Papaioannou at the same institution. She joined her current department in 1998, where she investigates how altered T-box gene expression disrupts embryonic development using mouse models and molecular techniques like luciferase assays and chromatin immunoprecipitation. Research Themes: Mesoderm fate decisions, T-box competition, segmentation clock, Notch/Wnt signaling, human birth defect modeling. Methods: Transgenic mice, gene knockout analysis, enhancer studies, transcriptional profiling. Recent publications highlight her work on Tbx6 dosage effects, evolutionary conservation of T-box functions, and integrative signaling pathways in somitogenesis. Her lab continues to explore gene regulatory networks in embryonic patterning and their clinical implications. Contact: dlc7@pitt.edu

• Developmental Biology
• Embryonic Development
• T-box Transcription Factors
• +5 more

Professor Nikoleta Jones is a leading environmental sociologist at the University of Warwick's School of Global Sustainable Development, specializing in the social dimensions of biodiversity conservation and climate change policy. Formerly a Principal Research Associate at the University of Cambridge, she has secured over £2.2 million in research funding including an ERC grant for the FIDELIO project which collaborates with 20 European park authorities. Her research integrates mixed-methods approaches to analyze social impacts of protected areas, forest management, and climate adaptation policies. She develops behavioral predictive models incorporating spatial-temporal elements, with core interests in equitable governance, social capital dynamics, and policy implementation across European and Global South contexts including Madagascar. Her work directly informs EU Biodiversity Strategy targets and UK net zero commitments. Recent publications reveal strong thematic concentration on spatial distribution of social impacts, equitable governance frameworks, and socio-economic drivers of conservation support, with methodological innovations in Bayesian modeling and participatory assessment tools. Geographic focus spans European protected areas, Madagascar's deforestation challenges, and UK rewilding initiatives. Scientific Awards: Senior Fellow of the Higher Education Academy Professor Jones has supervised 3 PhD completions and currently mentors 5 doctoral candidates alongside over 25 Master's dissertations. She designs and leads postgraduate modules in sustainability while directing the Social Capital and Impact Assessment Tool for Protected Areas (SOCIAT). Her FIDELIO project established Europe's largest social impact database for protected areas, directly influencing Global Biodiversity Framework implementation. She leads a multi-institutional research consortium under the FIDELIO project, coordinating with park authorities across 20 European sites to develop evidence-based policy tools. Her team's work on SOCIAT provides standardized frameworks for measuring social impacts in conservation governance.

• Environmental Sociology
• Biodiversity Conservation
• Climate Change Adaptation
• +3 more

Miracle Amadi is a Postdoctoral Researcher (Research Fellow) in the Department of Computational Engineering at the School of Engineering Sciences, Lappeenranta-Lahti University of Technology (LUT University), Finland. Amadi's research focuses on developing and applying computational and statistical models to study infectious disease dynamics, including malaria, HIV, dengue, and COVID-19, with a particular emphasis on the impact of environmental factors and intervention strategies. Amadi's research interests span computational epidemiology, mathematical modeling of infectious diseases, Bayesian statistics, and public health. Their work integrates advanced statistical techniques such as Bayesian model selection, parameter estimation, and uncertainty quantification with epidemiological data to address critical public health challenges. Recent projects have examined the relationship between meteorological factors and disease incidence, the evolution of insecticide resistance in malaria vectors, and the effectiveness of non-pharmaceutical interventions during the COVID-19 pandemic. Analysis of Amadi's recent publications (2018-2025) reveals a strong trend in applying hybrid modeling approaches, including agent-based and spatio-temporal models, to complex epidemiological problems. A significant portion of the work focuses on malaria, with additional contributions to HIV, dengue, lumpy skin disease, and COVID-19. The research consistently emphasizes the role of environmental and climatic factors in disease transmission and the evaluation of control strategies, demonstrating a commitment to data-driven public health decision-making.

• Computational Epidemiology
• Mathematical Modeling of Infectious Diseases
• Bayesian Statistics
• +3 more

Agata Kliber , PhD, is an Associate Professor at the Department of Applied Mathematics , Institute of Informatics and Quantitative Economics , Poznań University of Economics and Business . Her work bridges Financial Economics and Econometrics , with a focus on cryptocurrency markets , energy economics , and financial risk management . Research Themes: Financial market volatility, sovereign risk dynamics, cryptocurrency hedging properties, oil price impacts on inflation and sustainable transport, and econometric modeling (GARCH, MIDAS, NARDL). Awards: No specific awards mentioned in available data. Collaborations: Regularly collaborates with Barbara Bedowska-Sojka's research group and international scholars like Pavel Rezac and Krzysztof Echaust. Recent Publications (2022-2025) analyze: Interdependencies between energy commodities and stock markets Asymmetric effects of oil prices on inflation expectations Safe-haven properties of cryptocurrencies during crises Volatility and liquidity forecasting models Impact of pandemics on sovereign risk and fintech-bank relationships Role of Bitcoin in crisis economies like Venezuela Her methodological expertise spans Bayesian inference , stochastic volatility , and multi-criteria decision-making frameworks . ResearchGate profile shows 94 publications and 25,489 reads , indicating significant scholarly engagement.

• Economics
• Finance
• Econometrics
• +6 more

Edward Kroc is an Associate Professor in the Department of Educational and Counselling Psychology, and Special Education at the University of British Columbia (UBC), Faculty of Education. He joined UBC in 2018 as part of the Measurement, Evaluation, and Research Methodology (MERM) program. His work bridges statistical theory, psychometrics, and ecological applications. Ph.D., Mathematics, University of British Columbia (2015) M.Sc., Applied Mathematics in Statistics, DePaul University (2007) B.Sc., Mathematics, DePaul University (2006) Kroc's research focuses on generalized measurement error, causal inference, and spatio-temporal modeling, with applications to urban ecology (e.g., gull populations) and psychometric validity. His methodological work challenges traditional assumptions in educational measurement and regression analysis. Recent publications highlight his interdisciplinary approach, combining statistics, ecology, and psychology. Key trends include the development of robust measurement frameworks, Bayesian methods for ecological data, and methodological critiques of reliability estimation techniques. Edward Kroc actively supervises graduate students in the Measurement, Evaluation and Research Methodology program and collaborates on interdisciplinary research projects and grants.

• Measurement
• Applied Research Methodology
• Theoretical Research Methodology
• +7 more

Dr. Tara Baldacchino is a Senior Lecturer in the School of Electrical and Electronic Engineering at the University of Sheffield, specializing in systems engineering and inclusive pedagogy. With a PhD from the University of Sheffield (2011), she bridges technical and educational research through her work in curriculum design and employability integration. Her affiliations include the Automatic Control and Systems Engineering (ACSE) department and the Dynamical Systems and Systems Engineering research group. PhD (University of Sheffield, 2011) MSc Advanced Control (University of Sheffield, 2007) BEng Electrical Engineering (First Class, University of Malta, 2006) Her research focuses on embedding employability and inclusive engineering design into technical curricula, with a particular emphasis on systems engineering principles. She teaches programming, embedded systems, and project-based learning frameworks such as ACS130 , ACS233 , and the Group Control Project module. Recent publications highlight her expertise in Bayesian system identification , nonlinear dynamics , and medical robotics , with applications spanning EMG signal analysis, magnetic capsule endoscopy, and structural health monitoring. Her methodological work includes probabilistic models and mixture-of-experts frameworks for robust system estimation. As Mechatronic and Robotic Engineering Programme Lead and Computer Systems Engineering Programme Lead , she shapes interdisciplinary curricula while maintaining active collaboration between ACSE and the Mechanical Engineering Department.

• Curriculum Development
• Inclusive Engineering
• Employability in Education
• +3 more

Matthew Albrecht is a Senior Research Fellow at the School of Psychological Science, The University of Western Australia , with affiliations to the Planning and Transport Research Centre . His work spans neuroscience , psychopharmacology , and transportation safety , with a focus on drug effects on cognition , crash analysis , and clinical documentation efficiency . Research Interests: Neuroscience and psychopharmacology of drugs like THC and dexamphetamine Transport safety and road design using statistical models Clinical documentation burden in nursing and midwifery Autism and schizophrenia cognitive models Article Trends: His recent work emphasizes THC's impact on driving safety , amphetamine effects on memory , and transport policy evaluations . Publications include collaborations in public health , clinical research , and computational psychiatry . Grants and Collaborations: Albrecht has contributed to 4 grants , including studies on road safety and healthcare documentation . He collaborates with institutions like Main Roads, WA and Curtin University . Labs and Teams: Affiliated with the Planning and Transport Research Centre , he works with multidisciplinary teams in transport engineering , psychiatric research , and biostatistics .

• Neuroscience
• Psychopharmacology
• Transportation Safety
• +4 more

Roland Badeau is a Full Professor in the Signal, Statistics and Learning (S2A) team within the Image, Data, Signal (IDS) Department at Télécom Paris, Institut Polytechnique de Paris. His primary affiliation is with the Information Processing and Communication Laboratory (LTCI). Research Interests: Badeau specializes in statistical modeling of non-stationary signals, with core expertise in adaptive high-resolution spectral analysis and Bayesian extensions to Non-negative Matrix Factorization (NMF). His work spans room acoustics (stochastic reverberation models), data representation (dimensionality reduction, time-frequency analysis), probabilistic latent variable modeling, and algorithm development (Bayesian estimation, optimization methods, fast adaptive algorithms). Applications focus on audio/music processing including source separation, denoising, dereverberation, multipitch estimation, and automatic music transcription, with extensions to biomedical data analysis and digital communications. Key Trends in Publications: Recent work centers on Statistical Wave Field Theory, establishing mathematical frameworks for reverberation modeling using energy-stress tensor formalism and Riemannian geometry. His publications demonstrate a progression from foundational signal processing algorithms (e.g., YAST, ESPRIT) to physics-informed approaches for polyhedral rooms and frequency-dependent attenuation, with strong emphasis on Bayesian and alpha-stable distribution methods for robust audio separation. Academic Leadership: Badeau supervises doctoral and master’s theses while leading teaching units at Télécom Paris. He serves as the TSIA study track supervisor (Signal Processing for Artificial Intelligence) and Master ATIAM correspondent. His team (S2A) develops tools like DESAM for joint source separation and multi-track coding.

• Signal Processing
• Audio Signal Processing
• Machine Learning
• +7 more