جست‌وجوی استاد و پژوهشگر

• System Informatics
• Big Data Analytics
• Data Fusion
• +۹ مورد دیگر

Dr. Kaibo Liu is the Grainger STAR Professor in the Department of Industrial and Systems Engineering at the University of Wisconsin-Madison and serves as Associate Director of the UW-Madison IoT Systems Research Center. He earned his B.S. from the Hong Kong University of Science and Technology (2009), and M.S. and Ph.D. from Georgia Tech (2011/2013). His research focuses on system informatics, big data analytics, and data fusion for process modeling, monitoring, and decision-making. He has been funded by NSF, ONR, DOE, and industry partners. Notable awards include the 2024 Hromi Medal (ASQ), 2021 IISE Technical Innovation Award, and multiple early-career recognitions. Recent work emphasizes real-time cyber-physical security, reinforcement learning for data streams, and Bayesian methods for prognosis. He edits IEEE Transactions on Automation Science and Engineering and IISE Transactions on Data Science.

• Healthy Ageing
• Pharmacology
• Pharmacoepidemiology
• +۳ مورد دیگر

Dr. Kenji Fujita is a Research Fellow at the Kolling Institute, University of Sydney, specializing in ageing, pharmacology, and pharmaceutical care quality. He holds a PhD, MScMed (ClinEpid), and BPharm from the University of Sydney. Previously a pharmacist in Japan, he leads international initiatives in the Pharmaceutical Care Network Europe (PCNE) and the International Pharmaceutical Federation (FIP). His work focuses on deprescribing strategies, polypharmacy, and developing quality indicators for healthcare services. Dr. Fujita’s research integrates big data analysis, statistical modeling, and machine learning to address challenges in geriatric pharmacotherapy. Current projects include validating frailty indices, evaluating deprescribing interventions in older adults, and developing NLP tools for clinical documentation analysis. He has also contributed to assessing organizational readiness for guideline implementation in community pharmacies. Awards include the 2023 Outstanding Poster Presentation Award at the IAGG Asia/Oceania Congress and 2022 Best Poster at the Japanese Society of Social Pharmacy. He has secured grants such as the 2024 electronic frailty index project and the 2021 systems-approach to medication review initiative. Dr. Fujita collaborates with global networks like J-HOP (Japan Home Visiting Pharmacy Association) and leads multidisciplinary teams across Australia and Europe. His lab focuses on translating data-driven insights into actionable clinical practices to improve medication safety and quality in ageing populations.

Stratis Ioannidisمشاهده پروفایل

استاد

• Distributed systems
• Networking
• Machine learning
• +۳ مورد دیگر

Stratis Ioannidis is a Professor in the Electrical and Computer Engineering Department at Northeastern University, with a courtesy appointment in the Khoury College of Computer Sciences. His research focuses on distributed systems, networking, machine learning, big data, and privacy. He earned his B.Sc. from the National Technical University of Athens, and M.Sc. and Ph.D. from the University of Toronto. Prior to Northeastern, he worked at Technicolor and Yahoo Labs. Education: B.Sc. in Electrical and Computer Engineering (2002, National Technical University of Athens); M.Sc. and Ph.D. in Computer Science (2004, 2009, University of Toronto). Research interests span machine learning, distributed systems, optimization, and privacy. Key projects include the NSF AI Institute for Future Edge Networks and Distributed Intelligence (AI-EDGE), and work on federated learning, continual learning, and privacy-preserving algorithms. His research has been supported by NSF, Google, and Facebook grants. Recent publications emphasize federated learning, continual learning, and edge computing. Awards include the NSF CAREER Award, Søren Buus Outstanding Research Award, and multiple best paper awards. He advises numerous PhD students and collaborates across disciplines, including healthcare and wireless networks. Lab activities include SPIRAL and WIoT labs, focusing on machine learning at the edge and network optimization. Grants include the NSF AI Institute and multiple collaborative projects with industry and academia.

• Bayesian Statistics
• Ecological Statistics
• Capture-Recapture Modeling
• +۵ مورد دیگر

Prof. Ruth King is the Thomas Bayes’ Professor of Statistics at the University of Edinburgh’s School of Mathematics. Her research focuses on applying Bayesian statistical methods to ecological and public health challenges, including population estimation for hidden groups (e.g., injecting drug users, modern-day slaves) and wildlife conservation. She develops computationally efficient techniques for analyzing large datasets, such as spatial capture-recapture models for animal populations and spatio-temporal abundance models for hidden human populations. Key projects include estimating survival rates of guillemots (30,000 individuals) and improving capture-recapture models to account for animal movement dynamics. Her work bridges statistical methodology with real-world applications, emphasizing rigorous inference and scalable algorithms. King’s academic contributions span Bayesian modeling frameworks, parameter clustering in neuroscientific data, and hierarchical centering in random effects models. She collaborates with biologists and policymakers to address conservation and public health issues. Notable recent projects include incorporating memory effects into spatial capture-recapture models and developing semi-complete data augmentation for state-space models. Her interdisciplinary approach addresses challenges in ecology, epidemiology, and computational statistics, with a focus on methodological innovation for large-scale data. Her scientific contributions are highlighted through over 100 peer-reviewed articles, including work on integrated population models, animal movement dynamics, and hidden Markov models for seabird behavior. King emphasizes the importance of statistics in uncovering hidden information within datasets, advocating for robust methodologies that ‘stand up in court’ when applied to critical real-world problems.

Simon Makمشاهده پروفایل

استادیار

• Statistical Modeling
• Bayesian Statistics
• Gaussian Process
• +۵ مورد دیگر

Simon Mak is an Assistant Professor of Statistical Science at Duke University and a Faculty Network Member of the Duke Institute for Brain Sciences. His educational background includes: Ph.D. in Statistics, Georgia Institute of Technology (2018) M.S. in Statistics, Georgia Institute of Technology (2018) B.S. in Statistics, Simon Fraser University (2013) Dr. Mak's research focuses on advanced statistical methodologies for complex scientific problems. His expertise spans statistical modeling , Bayesian inference , Gaussian process emulation , and uncertainty quantification . He applies these methods to nuclear physics (heavy-ion collisions), engineering (engine control systems), and music information retrieval, emphasizing scalability and interpretability in scientific computing. Analysis of his 2023-2025 publications reveals dominant trends in scalable Gaussian process methods for massive datasets and multi-fidelity simulations, particularly applied to high-energy physics and engineering systems. He has pioneered innovations in Bayesian optimization for expensive simulators and developed novel frameworks for online change-point detection in streaming data, demonstrating exceptional cross-disciplinary impact. Dr. Mak leads multiple significant research initiatives: Collaborative Research: Cost-Efficient and Confident Sampling for Modern Scientific Discovery (2023-2026) Science-Integrated Predictive modeLing (SCINPL) for scalable scientific computing (2022-2025) The X-SCAPE collaboration for statistically advanced nuclear collision modeling (2020-2025) These projects fund his development of statistical frameworks for scientific discovery in complex systems. He actively contributes to the JETSCAPE collaboration, developing multi-stage frameworks for studying jet quenching in heavy-ion collisions, and applies statistical methods through the Duke Institute for Brain Sciences to advance neuroscience research.

Lars Linsenمشاهده پروفایل

استاد

• Interactive Visual Analysis
• Medical Visualization
• Scientific Visualization
• +۴ مورد دیگر

Prof. Dr.-Ing. Lars Linsen is a full Professor of Computer Science at the Westfälische Wilhelms-Universität (WWU) Münster, leading the VISualization & graphIX (VISIX) group. His primary affiliation is the Institute of Computer Science within the Faculty of Mathematics and Computer Science. He holds adjunct professorships at Jacobs University, Bremen, and has held previous academic roles including Full Professor at Jacobs University (2012–2017) and Associate/Assistant Professor roles in Germany and the U.S. His research focuses on interactive visual analysis, medical visualization, and scientific visualization, with applications in life sciences and engineering. Education: PhD (Dr.-Ing.) in Computer Science from Universität Karlsruhe (2001), M.Sc. (Diplom) in Computer Science (1997), B.Sc. (Vordiplom) in Computer Science (1994). Awards: IEEE Visualization Design Contest Winner (2008, 2022, 2018), Preis des Fördervereins des Forschungszentrum Informatik (2002). Research Highlights: Develops visualization tools for medical imaging (e.g., mass spectrometry imaging, MRI data analysis) and physical simulations (e.g., wildfire spread analysis, asteroid impact modeling). Active in EU-funded projects like Pig-Pro-QuO (surface coatings) and cells-in-motion initiatives. Supervised over 20 PhD/MS advisees, including notable graduates in medical visualization and simulation ensemble analysis. Publications: Over 100 peer-reviewed articles in top venues like IEEE Transactions on Visualization and Computer Graphics, Computers & Graphics, and EuroVis. Key works include SciVis contest-winning wildfire analysis frameworks and medical visualization tools for stenosis detection. Teaching: Offers courses on visualization, computer graphics, and computational science. Actively involved in thesis supervision and curriculum development at both WWU Münster and Jacobs University. Grants & Collaborations: Principal investigator on DFG-funded projects (e.g., hemodynamics simulations, ensemble visualization) and industry collaborations (e.g., Tascon GmbH for coating quality analysis). Member of the Cells-in-Motion Interfaculty Centre and CDH board at WWU.

Ravi Dharمشاهده پروفایل

استاد

• Consumer Behavior
• Branding
• Marketing Strategy
• +۴ مورد دیگر

Ravi Dhar is the George Rogers Clark Professor at the Yale School of Management and holds an affiliated appointment as a Professor of Psychology at Yale University. He serves as Director of the Center for Customer Insights , focusing on consumer behavior, branding, and marketing strategy through psychological and economic frameworks. Ph.D. in Marketing, University of California at Berkeley (1992) MS, University of California at Berkeley (1990) MBA, Indian Institute of Management (1987) BTech, Indian Institute of Technology (1986) His research examines preference formation, self-regulation, and the interplay of conflicting goals in consumer decisions. Recent work explores sustainability, mobile commerce, and how guilt paradoxically enhances consumer pleasure. He has published over 50 articles and advised Fortune 100 companies across industries. Key trends in his publications include behavioral economics , eco-conscious consumption , and technology-mediated decisions . His studies address choice overload, goal systems, and the psychological drivers of indulgence versus self-control. Distinguished Scientific Contribution Award (Society for Consumer Psychology, 2012) Yale SOM Alumni Teaching Award (2012) William O'Dell Award Finalist (2004, 2008, 2012) AMA Doctoral Consortium Fellow (1991) Dhar consults firms on customer insights and has held visiting roles at HEC Paris , Erasmus University , and Stanford/NYU . He edits top journals like Journal of Consumer Research and Marketing Science , shaping academic and industry discourse.

• Marketing
• Applied Economics
• Econometrics
• +۱۰ مورد دیگر

Alan Montgomery is a Professor of Marketing at Carnegie Mellon University's Tepper School of Business, where he has held a tenured position since 2018 (previously as Associate Professor from 2005-2017). He also maintains an affiliation with the Machine Learning Department at CMU's School of Computer Science, demonstrating his interdisciplinary research approach at the intersection of marketing, economics, and computational methods. Dr. Montgomery earned his educational credentials from prestigious institutions: Ph.D. in Marketing/Economics, University of Chicago (1994) MBA, University of Chicago (1994) BS in Economics, University of Illinois at Chicago (1989) His research focuses on applying advanced quantitative methods to marketing problems, with particular expertise in consumer behavior modeling, clickstream data analysis, pricing strategies, and micro-marketing. Dr. Montgomery's work bridges traditional marketing theory with computational approaches, making significant contributions to both academic literature and practical business applications. His research often involves large-scale data analysis to uncover patterns in consumer decision-making processes, with recent work exploring mental accounting, bandit algorithms, and the impact of digital phenomena like movie piracy on traditional markets. Dr. Montgomery has received notable recognition including the 1999 Mitchell Prize from the American Statistical Association for his paper "Estimating Price Elasticities with Theory-based Priors." His work has been published in top-tier journals across marketing, economics, and computer science disciplines, demonstrating the interdisciplinary impact of his research. As an educator and mentor, Dr. Montgomery has advised numerous PhD students and collaborated extensively with researchers across multiple institutions. His interdisciplinary approach has led to collaborations with computer scientists studying web browsing behavior and economists examining consumer decision frameworks. His research has been supported by various grants throughout his career, enabling extensive data collection and analysis projects. Dr. Montgomery's work spans multiple research environments, including collaborations with the Machine Learning Department at CMU's School of Computer Science. His research group likely focuses on applying computational methods to marketing problems, particularly in the areas of consumer behavior modeling, clickstream analysis, and data-driven marketing strategies. His recent work shows increasing integration of machine learning techniques with traditional marketing research methodologies.

Christine Eckertمشاهده پروفایل

استاد

• Marketing Analytics
• Quantitative Modeling
• Consumer Financial Decisions
• +۴ مورد دیگر

Christine Eckert is a Professor of Marketing Analytics at the TUM School of Management (Technische Universität München). She holds a doctorate in economics from Goethe University Frankfurt am Main and has previously held academic positions at University of Technology Sydney and EBS University of Business and Law. Education: Mathematics (Johannes Gutenberg University Mainz, Christian-Albrechts University Kiel); Economics (Goethe University Frankfurt am Main) Her research focuses on quantitative modeling of market participants' decisions, spanning consumer financial behavior, strategic innovation decisions, and corporate social responsibility. She also explores methodological advancements in management research, particularly causal inference techniques. Notable contributions include serving as co-editor for Big Data and Business Analytics (Journal of Business Research) and receiving an Australian Research Council Discovery Grant (2019-2021). Her work has been recognized with the Center for Financial Planning's Best Paper Award (2021). Key journals: Journal of the Academy of Marketing Science, Journal of Management, Journal of Marketing Research She contributes to academic governance through roles like Panel Member for New Zealand's Performance Based Research Fund (2018) and advisory board membership with Super Consumers Australia (since 2022).

• Geographical Psychology
• Regional Personality Differences
• Mobility and Migration
• +۶ مورد دیگر

Dr. Friedrich Götz is an Associate Professor of Psychology in the Department of Psychology at the University of British Columbia (Faculty of Arts). His research focuses on geographical psychology, exploring the causes and consequences of regional personality differences through an interdisciplinary Big Data approach. PhD, University of Cambridge (UK), 2021 MPhil, University of Cambridge (UK), 2017 BSc, University of Konstanz (Germany), 2016 Dr. Götz’s work bridges social and personality psychology with behavioral science, examining topics like mobility, migration, wanderlust, courage, and entrepreneurship. He co-developed large-scale survey studies with TIME Magazine, attracting over 3 million participants. His research often involves experience sampling methods and open science. His recent publications emphasize geographical psychology, personality-environment interactions, and methodological advancements. Articles span 2020–2025, with key themes including regional personality differences, misinformation susceptibility, and the psychological impact of environmental features. Rising Star Award (Association for Psychological Science), 2025 SAGE Emerging Scholar Award (Society for Personality and Social Psychology), 2025 Top 40 under 40 – Germany (CAPITAL Magazine), 2024 President’s New Researcher Award (Canadian Psychological Association), 2024 Best Dissertation Prize (German Psychological Society), 2021 Leading Scholar (Green College), 2021 Dr. Götz teaches undergraduate and graduate courses, including Personality Psychology and Contemporary Conceptual Issues in Personality, with a focus on geographical dimensions. He is based in the Personality and Geographical Ambiance (PANGEA) Lab, which prioritizes inclusivity and collaboration in studying person-environment interactions.

Dmitri Krioukovمشاهده پروفایل

دانشیار

• Network Science
• Complex Networks
• Mathematical Physics
• +۷ مورد دیگر

Prof. Dmitri Krioukov is an Associate Professor in the Department of Physics at Northeastern University and holds an affiliated faculty position in Electrical and Computer Engineering. He directs the DK-Lab at the Network Science Institute, focusing on theoretical aspects of complex networks, including latent network geometry, random geometric graphs, and navigation in networks. His work bridges mathematical physics and applied network science, with applications to real-world data such as the Internet's structure. Research interests revolve around the interplay between network topology and geometry, including studies of causal sets, graph curvature, and dynamics in complex systems. He has pioneered frameworks linking network growth to hyperbolic geometry, enabling efficient routing algorithms. Notable contributions include the discovery of latent geometric structures underlying real-world networks and their implications for navigation and scalability. He has been recognized for high-impact publications, including multiple Stanford University Annual Assessments placing him among the top 2% most-cited scientists in his field (2024, 2023, 2022). His lab's interdisciplinary approach integrates principles from physics, mathematics, and computer science to address fundamental questions in network science.

John Wakeleyمشاهده پروفایل

استاد

• Theoretical Population Genetics
• Mathematical Biology
• Evolutionary Biology
• +۳ مورد دیگر

John Wakeley is a Professor of Organismic and Evolutionary Biology at Harvard University's Faculty of Arts and Sciences. He leads the Wakeley Lab, focusing on theoretical population genetics, mathematical models of genetic variation, and evolutionary processes. His research integrates analytical and computational methods to study contemporary and historical factors shaping genetic diversity. As of 2023, he is not accepting new graduate students for the academic year 2023-2024. Wakeley's work emphasizes coalescent theory, population structure, and evolutionary game theory. Notable contributions include developing statistical tools for analyzing ancient DNA and advancing models of ancestry reconstruction. Recent projects explore topics such as recurrent mutation in rare variants and the implications of big family effects on coalescence patterns. His lab members include researchers like Louis Fan, Jack Edwards, and Erin Ciccone, collaborating on diverse projects in theoretical and applied population genetics. Key scientific outputs include studies on iterated survival games and genomic analyses of butterfly radiation.

Sylvia Richardsonمشاهده پروفایل

استاد پژوهشی

• Biostatistics
• Bayesian Statistics
• Spatial Modeling
• +۷ مورد دیگر

Sylvia Richardson is an MRC Investigator at the MRC Biostatistics Unit and holds a Research Professorship at the University of Cambridge, where she served as Director of the Biostatistics Unit from 2012 to 2021. She is affiliated with the Cambridge Mathematics of Information in Healthcare Hub (CMIH) at the Centre for Mathematical Sciences. Her work bridges advanced statistical methodology with critical healthcare applications, particularly in the analysis of complex biomedical data. Richardson's research spans multiple domains of biostatistics with a strong emphasis on Bayesian approaches. Her work has significantly advanced spatial modeling and disease mapping techniques, developed sophisticated methods for handling measurement error in epidemiological studies, and pioneered mixture and clustering models for integrative analysis of heterogeneous data sources. Her research addresses fundamental challenges in analyzing longitudinal health data, multimorbidity patterns, and complex disease trajectories. Her publication record demonstrates consistent methodological innovation applied to pressing healthcare challenges. Recent work focuses on traumatic brain injury outcomes, multimorbidity progression, genomic analysis, and statistical approaches to pandemic data. The articles reveal a strong pattern of methodological development driven by real-world healthcare challenges, with particular attention to longitudinal analysis, Bayesian computation, and integrative modeling approaches that can handle diverse and complex data structures. While specific awards are not detailed in the available information, Richardson's leadership as Director of the MRC Biostatistics Unit for nearly a decade and her continued Research Professorship reflect significant recognition of her contributions to the field. Her work with major international consortia like CENTER-TBI demonstrates her role in large-scale collaborative research efforts addressing critical health challenges. Richardson's research has substantial implications for healthcare policy and practice, particularly in understanding disease progression, developing predictive models for patient outcomes, and creating methodological frameworks that can integrate diverse data sources to generate meaningful clinical insights. Her work continues to influence both statistical methodology and healthcare applications through ongoing research and leadership in the field.

Yves-Alexandre de Montjoyeمشاهده پروفایل

دانشیار

• Computational Privacy
• AI Safety
• Machine Learning
• +۳ مورد دیگر

Yves-Alexandre de Montjoye is an Associate Professor of Applied Mathematics and Computer Science at Imperial College London, where he leads the Computational Privacy Group. He holds a joint affiliation between the Department of Computing and the Data Science Institute. His roles include serving as a Special Adviser on AI and Data Protection to the EC Justice Commissioner Didier Reynders, a Parliament-appointed Commissioner for the Belgian Data Protection Agency, and a Special Adviser to EC Competition Commissioner Margrethe Vestager, co-authoring the 'Competition Policy for the Digital Era' report. He earned his PhD from MIT in 2015 under Alex 'Sandy' Pentland. His master's degrees include an M.Sc. in Applied Mathematics from UCLouvain, an M.Sc. (Centralien) from École Centrale Paris, and an M.Sc. in Mathematical Engineering from KU Leuven. He also holds a B.Sc. in Engineering from UCLouvain. His research interests focus on computational privacy, anonymization techniques, AI safety, and machine learning attacks. He develops methods to 'red team' AI systems and create privacy-preserving mechanisms. His work addresses vulnerabilities such as membership inference, attribute inference, and re-identification risks in datasets, with applications to location tracking, synthetic data, and LLMs. His articles analyze adversarial attacks against privacy systems, emphasizing robustness and practical guarantees. He advocates for privacy-by-design approaches in big data analytics and has explored ethical AI, competition policy in digital markets, and humanitarian uses of mobile data. While no scientific awards are explicitly listed, his contributions have been widely covered in media. He is currently recruiting motivated PhD students for his group at Imperial College. His advising and grants narrative includes work on privacy-preserving technologies and policy implications of AI, with collaborations across academia and public institutions. He is affiliated with the Computational Privacy Group and contributes to platforms like OPAL for privacy analytics. His office is in the ACE Extension building (ACEX 259), accessible via Exhibition Road.

• Forecasting
• Time Series Analysis
• Applied Econometrics
• +۵ مورد دیگر

George Athanasopoulos is Professor and Head of the Department of Econometrics and Business Statistics at Monash University, a position he has held since 2022. He was appointed Professor in 2019 and has established himself as an internationally recognized expert in forecasting, time series analysis, and applied econometrics. He serves as Past President (since 2024) and former Director (2014-2024) of the International Institute of Forecasters, and is Associate Editor of the International Journal of Forecasting since 2014. His research focuses on hierarchical and grouped time series forecasting, where he has pioneered methods for forecast reconciliation and cross-temporal coherence. His work has significantly influenced forecasting practices across diverse fields including national statistics offices, energy markets, and public health. He is particularly renowned for his contributions to tourism forecasting and macroeconomic modeling in big data environments. Awarded the Australian Awards for University Teaching in 2022 for outstanding contributions to student learning, Professor Athanasopoulos has also received multiple Dean's Awards from Monash Business School for research excellence, teaching innovation, and publication quality. His research output includes over 49 publications and leadership of six major research projects, including the ARC-funded 'Macroeconomic forecasting in a Big Data world' and the RACE for 2030 CRC project on clean energy forecasting. His work contributes to UN Sustainable Development Goals through applications in economic forecasting, energy modeling, and sustainable tourism development. He has supervised numerous research students and collaborated extensively with institutions including Australian National University, Griffith University, and international partners across multiple continents.