جستجوی استاد راهنمای دانشگاهی

Yannis Paschalidis is a Distinguished Professor at Boston University with appointments in Electrical and Computer Engineering, Systems Engineering, Biomedical Engineering, and Computing & Data Sciences. He serves as Director of the Rafik B. Hariri Institute for Computing and Computational Science & Engineering. He holds a PhD (1996) and MS (1993) in Electrical Engineering and Computer Science from MIT, and a Diploma (1991) from the National Technical University of Athens. His interdisciplinary research spans optimization, control systems, machine learning, and data science with applications in healthcare, autonomous systems, and networks. Key focus areas include developing algorithms for autonomous navigation, healthcare analytics for clinical decision support, energy demand optimization, and computational biology for protein interaction modeling. Recent publications demonstrate strong focus on AI robustness (adversarial defenses, distributional robustness), healthcare applications (cognitive impairment detection, epidemic control), and sustainable systems (power networks, ecological forecasting). Methodological innovations center on reinforcement learning, distributionally robust optimization, and geometric analysis of classical algorithms. CAREER Award (NSF) IEEE Fellow (2014) IFAC Fellow (2022) IBM/IEEE Smarter Planet Award IEEE Computer Society Crowd Sourcing Prize IMIA Best Paper Award Charles DeLisi Award (2020) Distinguished Professor of Engineering As primary advisor to 35 PhD graduates, he leads the Network Optimization & Control (NOC) Lab. His research is funded by NSF, NIH, DoD, ARPA-E, and industry partners, including major grants on Neuro-Autonomy (ONR MURI), pandemic preparedness (ARPA-E NewRAMP), and healthcare AI (NIH QuBBD). He directs the Network Optimization & Control Lab focusing on optimization, learning, and control for autonomous systems, healthcare, and networks. The lab develops fundamental methodologies with applications in robotics, computational medicine, and infrastructure systems.

• Optimization
• Control Systems
• Stochastic Systems
• +۸ مورد دیگر

Anna Gottard is an Associate Professor of Statistics at the University of Florence, where she leads the Department of Statistics, Computer Science, and Applications. She directs the Florence Center for Data Science (FDS) and participates in the Technical Scientific Committee of the Tuscan Center for Big Data, Data Science, and AI (CBDAI). Her research focuses on multivariate statistical models, particularly graphical models, and extends to statistical machine learning, fair models, and directional data analysis. She is an Associate Editor for the Journal of the Royal Statistical Society Series A (JRSSA) and Statistical Methods & Applications (SMA). Her recent work includes Bayesian approaches for mixed graphical models, uncertainty-aware classification trees, and methodological advancements in latent uncertainty models. Her contributions span theoretical developments and applied research in interdisciplinary areas like biostatistics and sustainability. Her research interests emphasize bridging statistical theory with practical applications, including fairness in machine learning, interpretable models, and tree-based methodologies. She has actively contributed to open-source software, notably the Mix3Trees R package for mixed-effect tree models. Her work addresses challenges in variable selection, graphical model inference, and ethical AI practices. Current projects explore Bayesian frameworks for complex data structures and methodological improvements in graphical model interpretability. Anna has advised on interdisciplinary collaborations, such as studies on GDPR compliance in biobanking and epidemiological modeling of the SARS-CoV-2 pandemic in Tuscany. She collaborates with institutions like the CBDAI to advance data science applications in regional policy and healthcare. Her research trajectory reflects a commitment to both foundational statistical theory and real-world problem-solving across diverse domains.

• Multivariate Statistical Models
• Graphical Models
• Statistical Machine Learning
• +۵ مورد دیگر

Professor Alexandra M. Schmidt is a leading academic in Biostatistics at McGill University, holding an endowed University Chair. She specializes in spatial and spatio-temporal modeling, particularly in epidemiology and environmental health. Previously, she served as a Full Professor at the Federal University of Rio de Janeiro (2012–2016). Her research focuses on Bayesian methodologies for analyzing complex processes, including disease spread, environmental hazards, and socio-economic disparities. She has authored influential books such as Spatio-Temporal Methods in Environmental Epidemiology with R (2023) and contributed to over 150 peer-reviewed articles. Key awards include the ISBA Fellowship (2024), ASA Fellowship (2020), and the Abdel El-Shaarawi Award (2008). Education: PhD in Statistics (2001, University of Sheffield, UK), MSc and BSc in Statistics (Federal University of Rio de Janeiro, Brazil). Research interests span Bayesian inference, spatial statistics, and environmental epidemiology. She has advised numerous PhD/MSc students and collaborated on projects linking statistical methods to public health challenges, such as modeling dengue outbreaks and air pollution impacts. Active in academic service, she has chaired major conferences (e.g., 2022 ISBA World Meeting) and serves on editorial boards of top journals like Bayesian Analysis and Canadian Journal of Statistics . Teaching includes advanced courses on generalized linear models, spatial epidemiology, and Bayesian analysis. Her work bridges theoretical statistics with practical applications, addressing global health issues through innovative spatio-temporal modeling techniques.

• Biostatistics
• Bayesian Inference
• Spatial Statistics
• +۳ مورد دیگر

Pan Xu is a tenure-track assistant professor with joint appointments in the Department of Biostatistics & Bioinformatics, Department of Computer Science, and Department of Electrical & Computer Engineering at Duke University. Previously, Xu was a Postdoctoral Scholar Research Associate at Caltech's Department of Computing and Mathematical Science and earned a Ph.D. in Computer Science from UCLA. Xu's research focuses on developing computationally- and data-efficient machine learning algorithms with strong empirical performance and theoretical guarantees. Xu's research interests center around Machine Learning with broad applications in Artificial Intelligence, Data Science, Optimization, Reinforcement Learning, and High Dimensional Statistics. The research specifically targets real-world problems in Bioinformatics and Healthcare, with recent work emphasizing distributionally robust decision making, efficient exploration strategies, and multi-agent systems. Xu has developed novel algorithms that address the challenges of exploration in sequential decision making and robustness to distributional shifts between training and deployment environments. Xu's recent publications demonstrate a strong trend toward developing theoretically grounded yet practical algorithms for reinforcement learning and bandit problems, with particular emphasis on distributionally robust methods, efficient exploration techniques, and applications to healthcare. The work spans both theoretical analysis (providing minimax optimal regret bounds) and practical implementations (validated on benchmarks like Atari games and real healthcare datasets). Whitehead Scholar award from Duke University School of Medicine (2023) Best Paper Award at ACM FAccT 2023 for Queer In AI paper PIMCO Postdoctoral Fellowship in Data Science (2022) TMLR Featured Certification (2023) NSF award on approximate sampling based exploration (2023) Xu actively mentors multiple Ph.D. students across Duke's Biostatistics & Bioinformatics, Computer Science, and Electrical & Computer Engineering programs, with several alumni now pursuing doctoral studies at top institutions. The research group has secured competitive funding including an NSF award for approximate sampling based exploration for sequential decision making. Xu serves as an action editor for TMLR and as an area chair for major conferences including ICML, NeurIPS, AAAI, ICLR, and AISTATS. Xu leads a dynamic research group focused on sequential decision making, with projects spanning theoretical algorithm development, implementation of practical systems, and applications to healthcare and bioinformatics. The group maintains active collaborations across Duke's medical and engineering schools, with recent work applying machine learning to epidemic forecasting during the pandemic.

• Machine Learning
• Artificial Intelligence
• Data Science
• +۷ مورد دیگر

Dr. Manish Kumar is a Professor in the Department of Mechanical Engineering at the University of Cincinnati, leading the Cooperative Distributed Systems (CDS) Laboratory and the UAV MASTER Lab. He specializes in robotics, unmanned aerial systems (UAVs), and swarm systems, with a focus on decision-making, control in complex systems, and multi-sensor data fusion. His research has been supported by grants from the National Science Foundation, Department of Defense, and industry partners. Education: Ph.D. in Mechanical Engineering, Duke University (2004) M.S. in Mechanical Engineering, Duke University (2002) Research Interests: Dr. Kumar's work addresses challenges in autonomous systems, including UAV coordination, swarm robotics, and emergency response technologies. He integrates machine learning, control theory, and optimization to develop robust systems for applications such as wildfire management, telehealth, and industrial automation. Grants & Labs: Directed projects funded by federal agencies (NSF, DoD) and industry, totaling over $5M. Co-directs the Collaboratory for Medical Innovation and Implementation and the UAV MASTER Lab. Key Contributions: Pioneered algorithms for UAV path planning and swarm coordination. Developed PDE-based models for epidemic spread prediction and control.

• Unmanned Aerial Vehicles
• Robotics
• Swarm Systems
• +۴ مورد دیگر

Claire Donnat is an Assistant Professor in the Department of Statistics at the University of Chicago, specializing in statistical and machine learning methods for graph-structured and high-dimensional data. Her work bridges theoretical innovation with applications in biomedical research, environmental science, and public health. Education: B.S. and M.S. in Applied Mathematics from Ecole Polytechnique; Ph.D. in Statistics from Stanford University (2020). Her research focuses on three methodological directions: (1) statistical foundations for graph neural networks (GNNs), (2) structured estimation with graph constraints, and (3) multimodal data integration with uncertainty quantification. Key applications include thermotolerance in photosynthetic microbes, family network analysis for child welfare, and spatial transcriptomics. The 15 most recent publications highlight her work in GNNs, CCA, tensor modeling, and epidemic analysis, with keywords spanning statistics, machine learning, and network science. Her methodological contributions address challenges in sparsity, graph topology, and heterogeneous data fusion. Scientific Awards: Facebook Research Award (2021), C3.AI COVID Grand Challenge winner (2020), Lumiata hackathon winner (2020), Stanford Centennial Award (2019), and others. Claire's research group actively recruits postdocs and students for projects involving graph-based modeling, data integration, and biomedical applications. She also provides research consulting in statistical methodology and graph modeling for life sciences.

• Statistics
• Machine Learning
• Graph Neural Networks (GNNs)
• +۷ مورد دیگر

Professor Paul Fearnhead is a leading academic in Statistics at Lancaster University 's School of Mathematical Sciences . His research focuses on Bayesian and Computational Statistics , with applications in Anomaly Detection , Continuous-time Markov Processes , and Changepoint Analysis . Department: Mathematics and Statistics Academic Rank: Professor Email: p.fearnhead@lancaster.ac.uk His work bridges theoretical statistics and computational efficiency, notably through pruning techniques for change detection and novel Monte Carlo methods. Current projects include AI Hub initiatives, probabilistic AI foundations, and real-time anomaly detection in streaming data. Research outputs span Bayesian Analysis , Time Series Modeling , and Scalable Statistical Algorithms , with applications in fields like astronomy and epidemiology. Recent publications emphasize simulation-based composite likelihoods and efficient distributed changepoint detection. Scientific contributions include leadership roles in the STOR-i Centre for Doctoral Training and Data Science Institute (DSI) projects such as CoSInES and Statscale. He supervises PhD students including Dylan Bahia, Yuntang Fan, and Ziyang Yang.

• Bayesian Statistics
• Computational Statistics
• Change Point Detection
• +۵ مورد دیگر

Aditya Prakash is an Associate Professor and Associate Chair for Academic Affairs in the School of Computational Science and Engineering at Georgia Tech. He holds a PhD from Carnegie Mellon University (2012) and a B.Tech from IIT Bombay (2007). His research focuses on data science, machine learning, and AI applied to epidemiology, healthcare, security, and urban computing. His work has led to impactful tools used by organizations like CDC, ORNL, and Walmart. Notable awards include the NSF CAREER Award (2018) and IEEE's 'AI Ten to Watch' (2017). Education: PhD (Computer Science, CMU, 2012), B.Tech (IIT Bombay, 2007) Research Interests: Epidemic forecasting, network analysis, healthcare informatics, and large-scale data-driven solutions for societal challenges. He has authored over 80 papers and holds two patents. His lab develops methods for disease modeling, urban infrastructure analysis, and cybersecurity. Key projects include the NSF-funded BEHIVE initiative for pandemic prediction and collaborations with MIDAS network for infectious disease modeling. Awards: NSF CAREER, Facebook Faculty Award, IEEE AI Recognition, and multiple best-paper awards. His group advises students across PhD, MS, and undergraduate levels, with notable alumni in academia and industry. Labs & Affiliations: Core faculty at ML@GT (Machine Learning Center) and IDEaS (Institute for Data Engineering and Science). Active in organizing conferences like AAAI, KDD, and SIGMOD.

• Data Science
• Machine Learning
• Artificial Intelligence
• +۶ مورد دیگر

Professor Joseph Wu is a faculty member at The University of Hong Kong , specializing in mathematical and statistical modeling of diseases. His research focuses on developing practical analytics for disease control, translating findings into public health policies, and addressing global health challenges through AI technology and tools. He leads the Laboratory of Data Discovery for Health (D²⁴H) and has directed significant educational initiatives, including HKU’s Epidemics MOOC and the Croucher Summer Course in Vaccinology . His research spans epidemiology , infectious disease modeling , and public health policy . He has contributed to understanding COVID-19 , influenza , and yellow fever dynamics, with a focus on vaccine hesitancy and epidemic forecasting. His work has been published in high-impact journals such as Nature Medicine and The Lancet . Scientific Awards: Fellow of the UK Faculty of Public Health Professor Wu serves as co-editor-in-chief of Epidemics and associate editor for PLOS Computational Biology and PLOS Neglected Tropical Diseases . He contributes to global health through membership in the WHO Advisory Committee on Immunization and Vaccines-related Implementation Research (IVIR-AC) , the MIT SOLVE Challenge Leadership Group , and the MIT HK Innovation Node .

• Epidemiology
• Infectious Disease Modeling
• Public Health Policy
• +۵ مورد دیگر

Max Lau is an Assistant Professor in the Department of Biostatistics and Bioinformatics and the Department of Epidemiology at Emory University. His research focuses on integrating machine learning and computational methods with epidemiological and genomic data to study infectious disease dynamics. He teaches courses such as BIOS 790R (Advanced Seminar in Biostatistics) and DATA 534 (Applied Machine Learning). Dr. Lau's work emphasizes scalable Bayesian inference, graph neural networks, and stochastic modeling to address challenges in disease transmission, outbreak control, and pathogen evolution. His recent research includes developing tools like ScITree and Epilearn, and he has contributed to understanding measles dynamics, tuberculosis treatment, and livestock disease management. His academic contributions span over 30 publications since 2010, with a particular focus on phylodynamics, epidemic modeling, and vaccine strategy evaluation. His interdisciplinary approach bridges computational methods with public health applications, aiming to enhance disease prediction and intervention efficacy.

• Infectious Disease Dynamics
• Machine Learning
• Artificial Intelligence

Ying MacNab is an Associate Professor in the Department of Statistics at the University of British Columbia (UBC), Vancouver Campus. She holds an additional affiliation as an Associate Member in the School of Population and Public Health (SPPH). Her research focuses on Bayesian hierarchical modeling, spatial epidemiology, and disease mapping with applications to public health surveillance and aging populations. She has contributed extensively to methodological advancements in Gaussian Markov random fields and spatiotemporal modeling frameworks. Her work bridges statistical theory and practical health challenges, including pandemic-related stress in older adults, opioid treatment outcomes, and infectious disease forecasting. MacNab has collaborated on projects involving mental health assessments (e.g., sleep dysfunction, anxiety/depression in iOAT patients) and has developed novel statistical tools for analyzing spatially and temporally correlated health data. Her research also addresses methodological gaps in coregionalized multivariate models and constrained Bayesian estimation. MacNab's publications reflect a multidisciplinary approach, integrating epidemiological theory with advanced computational methods. Recent trends in her work emphasize dynamic modeling of infection risks, mediation analysis in aging populations, and validation of psychometric scales for health-related stress. She has maintained an active research agenda since the early 2000s, with notable contributions to neonatal health outcomes, injury surveillance, and healthcare quality improvement.

• Biostatistics
• Bayesian Methods
• Spatial Epidemiology
• +۳ مورد دیگر

Alison Galvani is the Burnett and Stender Families Professor of Epidemiology at Yale School of Public Health and Yale School of Medicine, where she serves as founding director of the Center for Infectious Disease Modeling and Analysis (CIDMA). Her interdisciplinary work bridges epidemiology, evolutionary ecology, and health economics to inform public health policies for diseases including HIV, Ebola, influenza, and COVID-19. Her research focuses on optimizing vaccination strategies and healthcare interventions through mathematical modeling. Recent studies examine SARS-CoV-2 transmission dynamics, RSV vaccine impact, and integration of social determinants into infectious disease models. She has pioneered frameworks for conflict-induced migration analysis and pharmaceutical policy evaluations. Notable scientific awards include the Bellman Prize, Blavatnik Award for Young Scientists, and Guggenheim Fellowship. Her publications span top journals like The Lancet , Nature Communications , and PNAS , with media coverage in major outlets and policy references.

• Infectious Disease Modeling
• Health Economics
• Epidemiology Methods
• +۷ مورد دیگر

Richard A. Davis is the Howard Levene Professor of Statistics at Columbia University's Faculty of Arts and Sciences. He is affiliated with the Data Science Institute (DSI) and the Financial and Business Analytics Center. His research focuses on applied probability, time series analysis, stochastic processes, and extreme value theory, with applications to financial data and spatial modeling. He co-founded the Space-Time Aquatic Resources Modeling and Analysis Program (STARMAP), supported by an EPA-STAR grant. Education details are not explicitly provided in the text, but his academic roles indicate advanced qualification in statistics. His work combines theoretical advancements with practical applications, such as analyzing financial time series models (e.g., GARCH) and spatial environmental data. Recent research emphasizes high-dimensional extremes, sparsity, and privacy-preserving methods. His articles explore cutting-edge topics like kernel PCA for multivariate extremes, quantile treatment effects, and goodness-of-fit testing for time series. He has also contributed to applications in healthcare imaging and disaster economics. His collaborative projects aim to bridge statistical theory with environmental and societal challenges. Key contributions include the STARMAP initiative and grants focused on extreme value analysis. His work often integrates advanced statistical techniques with real-world data challenges, reflecting a commitment to both methodological innovation and interdisciplinary impact.

• Applied Probability
• Time Series Analysis
• Stochastic Processes
• +۵ مورد دیگر

Ross Meentemeyer is a Professor and Director of the Center for Geospatial Analytics at North Carolina State University, affiliated with the Department of Forestry and Environmental Resources in the College of Natural Resources. He holds a Ph.D. in Geography from the University of North Carolina, Chapel Hill (2000) and a B.S. in Geography from the University of Georgia (1993). His research focuses on geospatial analytics, ecological forecasting, biological invasions, and forest health, with a strong emphasis on integrating geospatial data and modeling to address environmental challenges. Dr. Meentemeyer's work includes developing decision-support tools for pest management, climate adaptation, and land-use planning. Notable projects involve forecasting invasive species spread via international trade, creating open-source geospatial platforms, and modeling floodplain development risks. He collaborates extensively with federal agencies like USDA, DOI, and NPS to translate research into practical solutions. His grants include multi-million dollar NSF and USDA-funded initiatives addressing plant disease pandemics, agricultural pest threats, and geospatial infrastructure development. Key outcomes include the PAdb system for pandemic prediction and the FUTURES model for urbanization forecasting. He also leads efforts to enhance stakeholder engagement through participatory modeling tools like Tangible Landscape. Research contributions span 20+ years, with over 100 peer-reviewed articles on topics like viewscape modeling, river water dynamics, and wildfire-epidemic interactions. His work bridges ecological and social sciences, emphasizing actionable solutions for sustainable land management and climate resilience.

• Geospatial Analytics
• Ecological Forecasting
• Biological Invasions
• +۳ مورد دیگر

Louis Du Plessis is a Lecturer at ETH Zürich's Department of Biosystems Science and Engineering in Basel, Switzerland. His research focuses on computational evolution with particular emphasis on infectious disease dynamics and genomic analysis. He maintains an active research profile with numerous high-impact publications in top-tier journals. Dr. Du Plessis completed his doctoral studies at ETH Zürich in 2016 with a thesis titled 'Understanding the spread and adaptation of infectious diseases using genomic sequencing data,' building upon his 2011 Master's work on evolutionary rate variation. His current research sits at the intersection of computational biology, epidemiology, and evolutionary genetics. His research interests span computational epidemiology, phylodynamics, viral evolution, and infectious disease modeling. He has made significant contributions to understanding pandemic dynamics, particularly regarding influenza and SARS-CoV-2, using genomic and epidemiological data integration. His methodological work includes developing computational approaches for estimating epidemic dynamics and viral transmission patterns. Analysis of his recent publications reveals a strong focus on how pandemics disrupt normal viral circulation patterns, with particular attention to influenza evolution during the 2009 H1N1 and COVID-19 pandemics. His work often combines phylogenetic analysis with epidemiological modeling to extract maximum information from genomic and case count data. Dr. Du Plessis has received research funding from European Commission projects including 'From Foundations of Phylodynamics to new applications in Cell Biology' (grant 101001077) and 'MOnitoring Outbreak events for Disease surveillance in a data science context' (grant 874850). He is actively involved in developing computational tools for analyzing pathogen genomic data and has contributed to several software packages used in the field. His work has significant implications for public health surveillance and pandemic preparedness.

• Computational Epidemiology
• Phylodynamics
• Viral Evolution
• +۳ مورد دیگر