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Ali Gooya is a Senior Lecturer (Associate Professor) in Machine Learning at the School of Computing Science, University of Glasgow, UK. His research focuses on probabilistic deep learning applied to medical imaging, particularly in cardiology and oncology, emphasizing semi/unsupervised methods due to sparse expert annotations. He holds a PhD in medical image analysis from the University of Tokyo (2007) and has held academic positions at the University of Leeds and Sheffield before joining Glasgow in 2022. Affiliations: Senior Lecturer in Machine Learning, University of Glasgow (2022–present) Lecturer in Computing, University of Leeds (2018–2022) Lecturer in Computing, University of Sheffield (2016–2018) Postdoctoral Researcher, University of Pennsylvania (2008–2011) Research Interests: Deep learning for medical imaging, probabilistic modeling, cardiac and cancer imaging, computational anatomy, and marker discovery. Key applications include motion analysis, segmentation, and predictive modeling in healthcare. Key Achievements: Won prestigious fellowships including Allen Touring Institute (2022), JSPS Short-Term (2020), Marie-Curie IIF (2014), and JSPS-PDRA (2008). Pioneered Bayesian deep learning frameworks for cardiac motion assessment and generative models in medical imaging. Grants & Supervision: EPSRC Impact Acceleration Award (PI) EPSRC New Investigator Grant (EP/S012796/1) Actively supervising PhD students in areas like Bayesian deep atlases for cardiac motion analysis. Labs & Teams: Leads research in medical AI within the School of Computing Science, collaborating on projects integrating imaging and patient metadata for clinical decision support.

• Machine Learning
• Computer Vision
• Medical Imaging
• +7 more

Anna Kuparinen is a Professor at the University of Jyväskylä's Faculty of Mathematics and Science, Department of Biological and Environmental Science. Her research group, EcoEvoAqua, focuses on aquatic species and their ecosystems, particularly eco-evolutionary dynamics. Key research areas include ecological modeling, fisheries-induced evolution, food web stability, and environmental impacts on species survival. Recent work examines predator reintroduction effects, climate-driven selection pressures, and parasite-host interactions in aquatic systems. Publications span theoretical ecology, conservation biology, and fisheries management. Her research emphasizes interdisciplinary approaches to address complex ecological challenges. Education details are not explicitly listed, but her academic career reflects extensive contributions to aquatic ecology and conservation. Awards are not mentioned in the text, though her prolific publication record indicates recognition in her field. She leads the EcoEvoAqua research team, which integrates computational models with empirical data to study ecosystem resilience and biodiversity. Ongoing projects include modeling Lake Oulujärvi dynamics and exploring manganese toxicity in fish populations.

• Ecological Modeling
• Fisheries Science
• Aquatic Ecosystems
• +3 more

Manuel Arellano is Professor of Economics at the Center for Monetary and Financial Studies (CEMFI) in Madrid since 1991, with prior appointments at the University of Oxford (1985-89) and London School of Economics (1989-91). A leading econometrician specializing in panel data analysis, his work bridges theoretical econometrics and labor economics applications. He earned his undergraduate degree from the University of Barcelona and Ph.D. from the London School of Economics. Arellano's research focuses on econometric methodology for panel data, particularly dynamic models with heterogeneity. His seminal book Panel Data Econometrics (2003) established foundational frameworks for nonlinear and dynamic panel estimation. Current work extends to distributional analysis of random coefficients and robust inference under uncertainty, maintaining consistent emphasis on labor market applications like unemployment duration and policy evaluation. His publication history reveals a 30-year trajectory advancing panel data econometrics, evolving from specification testing (1987-1995) to sophisticated dynamic and nonlinear models (2003-2014), with persistent focus on practical implementation and labor economics applications. Major honors include: President of the Econometric Society (2014) Foreign Honorary Member of the American Academy of Arts and Sciences (2014) Rey Jaime I Prize in Economics (2012) ISI Highly Cited Researcher status (2010) Fellow of the Econometric Society (2002) No information on student advising or research grants appears in the source materials. Similarly, details about research laboratories or collaborative teams are not documented in the provided texts.

• Econometrics
• Panel Data Analysis
• Labour Economics
• +1 more

Dr. Swati Biswas is a Professor and Associate Department Head in the Department of Mathematical Sciences at the University of Texas at Dallas (UTD). She holds a Ph.D. in Biostatistics from The Ohio State University (2003) and has held academic positions at UTD since 2012, including roles at the University of North Texas Health Science Center prior to that. Her research focuses on biostatistical methods for genetic epidemiology, cancer genetics, and risk prediction modeling, with a particular emphasis on Bayesian approaches. Education: B.Sc. (1994), M.Sc. (1996) in Statistics from the University of Delhi, followed by a Ph.D. in Biostatistics from The Ohio State University (2003). She completed a postdoctoral fellowship at MD Anderson Cancer Center (2004–2005). Research interests include statistical genetics, rare haplotype analysis, Bayesian clinical trials, and personalized risk prediction models for diseases like breast cancer and substance use disorders. She has developed tools such as CBCRisk for contralateral breast cancer prediction and Bayesian hierarchical models for pathway analysis. Her publications span over 40 peer-reviewed articles, focusing on methodological advancements in genetic association studies and clinical risk modeling. Notable awards include the 2016 Young Researcher Award from the International Indian Statistical Association and the 2011 President’s Award for Educational Excellence from UNT HSC. Dr. Biswas has secured grants totaling over $10M, including NIH funding for projects like multifrequency ultrasound imaging for breast cancer monitoring and Bayesian meta-analysis of cancer risk. She mentors doctoral students and has advised nine completed dissertations, with several current students.

• Biostatistics
• Statistical Genetics
• Genetic Epidemiology
• +3 more

Andrea Meilán-Vila is an Assistant Professor in the Department of Statistics at Universidad Carlos III de Madrid since 2021, holding a Juan de la Cierva Fellowship since 2023. She earned her PhD in Statistics from Universidade da Coruña (2021) and previously served as a Postdoctoral Fellow at Universidade de Santiago de Compostela's Department of Statistics, Mathematical Analysis and Optimisation. Her research focuses on nonparametric methods for analyzing complex data types, including directional, spatial, and functional data. Key areas include kernel smoothing techniques, goodness-of-fit testing for regression models, and spatial trend estimation. She serves as an Associate Editor for the Journal of Nonparametric Statistics . Recent work emphasizes applications in climate science (temperature curve modeling), fluid dynamics (wake flow control), and biomedical imaging (hippocampus shape analysis). Her methodologies address challenges like sparse data estimation and spatial correlation in regression frameworks. Key Projects: STENED (Stein-based goodness-of-fit tests for non-Euclidean data) Awards: Juan de la Cierva Fellowship (2023) Publications span journals like Journal of Fluid Mechanics , Statistical Papers , and TEST , with a focus on methodological advancements in statistical modeling and computational validation.

• Nonparametric Estimation
• Directional Statistics
• Kernel Smoothing
• +3 more

Weipeng Zhou is a Postdoctoral Associate at the Yale School of Medicine within the Department of Biomedical Informatics and Data Science . Working under Professor Hua Xu , he specializes in pre-training and evaluating large medical language models using electronic health records and medical claims data. His work bridges Natural Language Processing , Biomedical Data Science , and Clinical Informatics to address critical healthcare challenges. PhD in Medical Informatics from the University of Washington (2025) Bachelor's in Computer Science and Statistics from the University of Wisconsin (2019) His research involves NLP/LLM applications in healthcare domains such as: Long COVID characterization and prediction Cardiovascular disease analysis Suicide prevention through clinical text mining Clinical note section identification Emerging water contaminant detection via PubMed article analysis His publications focus on model transferability , automated cohort discovery , and contextual health research tools . Notable collaborations include work with teams at University of Washington and Yale .

• Biomedical Informatics
• Natural Language Processing
• Large Language Models
• +3 more

Jeff M Phillips is a Professor in the Kahlert School of Computing at the University of Utah, specializing in algorithms for big data analytics, computational geometry, and machine learning. He holds a BS in Computer Science and Mathematics from Rice University (2003) and a PhD in Computer Science from Duke University (2009). He serves as Director of the Utah Center for Data Science, Director of the Data Science Program in the Kahlert School of Computing, and Faculty Co-Director of the One U Data Science Hub. His research focuses on geometric data analysis, coresets, sketches, and handling uncertainty in data. Education: BS/BA (Rice University, 2003), PhD (Duke University, 2009) CI Postdoctoral Fellow at University of Utah (2009–2011) His research interests include algorithms for big data analytics, computational geometry, machine learning, spatial statistics, and AI. He has led NSF-funded projects on spatial data analysis, cosmic origins via AI, and reactive flow data modeling. Phillips has advised numerous PhD and master’s students, contributing to topics like trajectory classification and bias mitigation in word embeddings. His publications span computational geometry, data science, and machine learning. Notable work includes coresets for kernel density estimates, bias mitigation in language models, and scalable spatial scan statistics. Phillips is also active in academic service, serving as co-PC chair for SoCG 2024 and on program committees for major conferences like NeurIPS and ICML.

• Algorithms for Big Data Analytics
• Computational Geometry
• Machine Learning
• +4 more

Professor Dino Sejdinovic is a faculty member in the School of Computer and Mathematical Sciences at the University of Adelaide, part of the Faculty of Sciences, Engineering and Technology. Previously, he held positions as Lecturer and Associate Professor at the University of Oxford's Department of Statistics (2014–2022). His academic qualifications include a PhD in Electrical and Electronic Engineering from the University of Bristol (2009) and a Diplom in Mathematics and Theoretical Computer Science from the University of Sarajevo (2006). His research focuses on the intersection of statistical methodology and machine learning, encompassing large-scale nonparametric methods, robust machine learning, multiresolution data fusion, and measures of dependence. He has contributed to kernel methods, Bayesian inference, causal discovery, and applications in climate science, quantum computing, and social science data analysis. Education: PhD in Electrical and Electronic Engineering, University of Bristol (2009) Diplom in Mathematics and Theoretical Computer Science, University of Sarajevo (2006) Sejdinovic's work emphasizes bridging theoretical foundations with practical applications, such as cloud type classification using vision transformers and machine learning-driven quantum device optimization. His recent publications explore topics like kernel-based causal inference, Bayesian neural networks, and uncertainty quantification in statistical models. Advising and grants: Eligible to supervise Masters and PhD students in machine learning and statistics, though specific grants or student advisees are not explicitly listed in the provided texts.

• Large-scale nonparametric and kernel methods
• Robust and trustworthy machine learning
• Multiresolution data and data fusion
• +3 more

Noel Cressie is a Distinguished Professor of Statistics at the University of Wollongong (UOW), Australia, affiliated with the School of Mathematics and Applied Statistics and the National Institute for Applied Statistics Research Australia (NIASRA). He is also the Director of the Centre for Environmental Informatics (CEI). His academic journey includes a PhD from Princeton University (1975) and a B.Sc. with First Class Honours from the University of Western Australia (1972). His research focuses on spatial and spatio-temporal statistics, Bayesian methods, environmental informatics, and applications in climate science. Notable projects include work on atmospheric CO2 flux inversion (WOMBAT framework), Antarctic environmental research (SAEF initiative), and statistical remote sensing for NASA. He has secured over $20 million in research funding and authored four influential books, including Statistics for Spatial Data . Cressie has received prestigious awards such as the COPSS R.A. Fisher Award (2009), Pitman Medal (2014), and Fellowship of the Australian Academy of Science (2018). He leads interdisciplinary teams addressing global challenges like carbon cycle dynamics and biodiversity modeling. His contributions to statistical methodology and environmental science have been recognized through international collaborations and advisory roles.

• Spatial Statistics
• Environmental Informatics
• Bayesian Statistics
• +3 more

Professor Alexandros Taflanidis holds a concurrent faculty position as Professor in the Department of Civil and Environmental Engineering and Earth Sciences and the Department of Aerospace and Mechanical Engineering at the University of Notre Dame's College of Engineering. He serves as the Director of Graduate Studies for CEEES. His research focuses on uncertainty quantification, disaster risk reduction, Bayesian model updating, and enhancing the sustainability and resilience of civil infrastructure systems, particularly in natural hazard contexts like hurricanes and earthquakes. His work integrates computational statistics and surrogate modeling to improve real-time emergency response and long-term risk mitigation strategies. Prof. Taflanidis earned a Ph.D. from the California Institute of Technology (2007), and M.S. and B.S. degrees in Civil and Environmental Engineering from Aristotle University of Thessaloniki (2003 and 2002). He leads projects such as the Coastal Hazards System (CHS) for Louisiana and Puerto Rico, advancing probabilistic coastal hazard analysis frameworks. His research also explores storm surge emulation, seismic response estimation, and innovative protective device designs for structures. He won the ASCE Huber Prize for his contributions to community resilience through scientific computing. His collaborative efforts include advancing machine learning for data imputation in coastal hazards and developing lifecycle assessment workflows for resilient buildings. Current research trends in his publications emphasize computational efficiency, multi-fidelity modeling, and adaptive strategies for real-time predictions. Prof. Taflanidis's work bridges academic and practical domains, addressing challenges such as climate change impacts on coastal regions and earthquake early warning systems. His lab focuses on integrating interdisciplinary approaches to create actionable solutions for infrastructure resilience.

• Uncertainty Quantification
• Disaster Risk Reduction
• Bayesian Model Updating
• +3 more

Boris Shor is an Associate Professor of Political Science at the Hobby School of Public Affairs, University of Houston, where he conducts research on state legislatures, political polarization, representation, and health policy. He is associated with multiple interdisciplinary initiatives and contributes to major data infrastructure in political science. Education: Ph.D. and M.A. in Political Science, Columbia University B.A., Princeton University Shor specializes in American politics with a focus on legislative behavior, ideological measurement, and health policy. His work combines quantitative methods, roll call analysis, and survey data to understand polarization and representation across U.S. states. He has developed one of the most comprehensive datasets on state legislative ideology, influencing both academic research and policy analysis. His recent publications span top journals such as American Political Science Review , American Journal of Political Science , and Political Analysis , with a thematic focus on state-level political dynamics, methodological innovation, and health policy politics. The research consistently explores how partisanship, ideology, and institutional structures shape policy outcomes. Scientific Awards and Honors: Robert Wood Johnson Scholar in Health Policy, UC Berkeley Fellow, Center for the Study of Democratic Politics, Princeton University Shor has secured major external funding from the National Science Foundation and the Russell Sage Foundation . He has advised undergraduate, master’s, and doctoral students throughout his career. His ongoing book project examines the politics of health policy in the American states, building on his interdisciplinary background in political science and public health. He is affiliated with research centers focused on democratic governance and public policy, contributing to collaborative academic networks across institutions.

• Political Science
• State Legislatures
• Political Polarization
• +7 more

Mathangi Gopalakrishnan, PhD, MPharm, is an Associate Professor in the Department of Practice, Science, and Health Outcomes Research at the University of Maryland School of Pharmacy. She is actively engaged in research, mentoring, and academic scholarship, with a focus on quantitative clinical pharmacology and data-driven therapeutic optimization. Education: PhD, Statistics, University of Maryland, Baltimore County MS, Statistics, University of Maryland, Baltimore County MPharm, Birla Institute of Technology & Science, Pilani, Rajasthan, India BPharmacy (Honors), Birla Institute of Technology & Science, Pilani, Rajasthan, India Her research interests center on pharmacometrics, precision therapeutics, predictive analytics, real-world data, and drug development . She integrates principles of clinical pharmacology, advanced frequentist and Bayesian statistical methods, and artificial intelligence/machine learning to enhance patient outcomes, particularly among vulnerable populations. Her lab's work includes designing prospective clinical pharmacokinetic trials for anti-epileptics and antimicrobials in patients on continuous renal replacement therapy, leveraging real-world data from electronic health records to optimize dosing in neonatal opioid withdrawal syndrome and pediatric anticoagulation, and developing models for disease progression in conditions like schizophrenia and binge-eating disorders. The trends in her recent publications reflect a consistent focus on model-informed precision dosing, real-world evidence generation, pharmacokinetic-pharmacodynamic (PK/PD) modeling in special populations, and methodological innovation in clinical trial design . Her work spans diverse therapeutic areas including critical care, neonatology, psychiatry, and maternal health, demonstrating a broad impact of quantitative pharmacology. Dr. Gopalakrishnan is accepting applications for postdoctoral positions in pharmacometrics at the Center for Translational Medicine, indicating active research funding and team leadership. Her collaborations extend to academic medical institutions nationwide, underscoring her role in multi-center research initiatives. She is involved in collaborative research with academic medical institutions across the country and has presented her work at major conferences including the Joint Statistical Meetings (JSM) and the American Conference on Pharmacometrics (ACoP).

• Pharmacometrics
• Precision Therapeutics
• Predictive Analytics
• +9 more

Etienne Mémin is a Research Director (Full Professor status) at Inria and leads the Odyssey research group, which is affiliated with multiple institutions including University of Rennes, IRMAR, Ifremer, LOPS, UBO, IMT Atlantique, and Lab-STICC. He serves as a Visiting Professor at the Department of Mathematics, Imperial College London (2020–2026) and is the Principal Investigator of the ERC STUOD grant. His research spans the intersection of geophysical sciences, fluid mechanics, computational sciences, and applied mathematics, focusing on stochastic modeling of fluid flows, data assimilation, and uncertainty quantification. He has developed frameworks for stochastic geophysical flows, coarse-scale simulations, and robust motion estimation techniques. Recent publications highlight his work on stochastic Navier-Stokes equations, ensemble forecasting, and data assimilation for ocean and atmospheric models. He has applied these methods to numerical weather prediction, turbulence analysis, and real-time flow reconstruction using sparse measurements. Scientific Awards: ERC STUOD grant PhD Students: Francesco Tucciarone (ERC STUOD, NEMO code) Benjamin Dufée (Ensemble Kalman filters, ATER position) Berenger Hug (Stochastic Navier-Stokes analysis, teaching) Antoine Moneyron (Stochastic ocean models) Collaborations: Imperial College London (D. Crisan, S. Laizet), Zhejiang University (S. Cai, C. Xu), MétéoFrance (P. Arbogast, O. Pannekoucke), Ifremer (B. Chapron), IRSTEA Lyon (L. Pénard), IRMAR (R. Lewandovsky), University of Buenos Aires (G. Artana), ISSI Beijing (T. Corpetti).

• Stochastic Modeling
• Geophysical Flows
• Fluid Mechanics
• +3 more

Andrew Ng is an Adjunct Professor at Stanford University's Computer Science Department and a globally recognized leader in AI. He is the Founder of DeepLearning.AI, Executive Chairman of LandingAI, General Partner at AI Fund, and Co-Founder of Coursera. His work has revolutionized machine learning and online education, with over 200 research papers in AI, robotics, and related fields. He was named to the 2023 Time100 AI list of most influential figures in AI. Ng's research focuses on machine learning, deep learning, reinforcement learning, and their applications in robotics and education. He pioneered the development of massive open online courses (MOOCs), notably through Stanford's early experiments in 2011 that attracted hundreds of thousands of learners. His contributions include foundational work in algorithms like Latent Dirichlet Allocation (LDA) for text analysis and advancements in spectral clustering and inverse reinforcement learning. His publications span topics from robotic hand design to scalable deep learning systems, emphasizing practical and scalable solutions. Ng's educational initiatives, such as the Machine Learning and Deep Learning Specializations, have educated millions worldwide. He advocates for accessible AI education and ethical AI development, emphasizing collaboration between academia and industry.

• Artificial Intelligence
• Machine Learning
• Robotics
• +4 more

Lori Graham-Brady is a Professor in the Department of Civil and Systems Engineering at Johns Hopkins University's Whiting School of Engineering. She serves as Vice Dean for Faculty and directs the Center on AI for Materials in Extreme Environments (CAIMEE), while also holding secondary appointments in Mechanical Engineering and Materials Science and Engineering. Her research focuses on stochastic mechanics, multiscale modeling, and machine learning applications for understanding material variability under extreme conditions. Research areas include probabilistic mechanics, AI-driven materials design, and fragmentation modeling. Leadership roles: Director of CAIMEE, former Director of Center for Materials in Extreme Dynamic Environments, founding Director of HT-MAX, and founding Associate Director of HEMI (2012-2024). Education: PhD in Civil Engineering and Operations Research from Princeton University. Her recent work emphasizes AI for multiscale mechanics, error propagation in material characterization, and digital microstructure generation. Publications highlight stochastic modeling of ceramics, composites, and metals under impact and high-strain-rate loading. Scientific awards include the Presidential Early Career Award, Huber Civil Engineering Research Prize, and Fellowships in ASCE EMI and USACM. She led NSF IGERT programs and serves as Associate Editor for the ASCE Journal of Engineering Mechanics.

• Stochastic finite element methods
• Probabilistic mechanics
• Stochastic simulation of material properties
• +5 more