جست‌وجوی استاد و پژوهشگر

Changjae Ohمشاهده پروفایل

مدرس

• Computer Vision
• Robotics
• Machine Learning
• +۴ مورد دیگر

Dr. Changjae Oh is a Lecturer in Multimedia at the School of Electronic Engineering and Computer Science (EECS), Queen Mary University of London (QMUL). He is affiliated with the Centre for Multimodal AI, Centre for Intelligent Sensing, Computer Vision Group, and Advanced Robotics at Queen Mary. His research focuses on embodied agents, combining robotics and computer vision to enable precise environmental perception and action execution. Current research interests include human-to-robot handover, 3D computer vision, robust machine learning, and vision-language models for robot perception. Research group includes PhD students working on topics like adversarial policy learning, vision-language models, and federated learning for malaria prediction. Recent publications emphasize stereo vision, object pose estimation, and transformer-based architectures. Grants include funding from the Royal Society and Korea Institute for Advancement of Technology for projects on generative models and multi-modal foundational models.

Alejandra Avalos-Pachecoمشاهده پروفایل

استادیار

• Bayesian Statistics
• Probabilistic Machine Learning
• Applied Statistical Modeling
• +۵ مورد دیگر

Alejandra Avalos-Pacheco is a Tenure Track Assistant Professor at the Institute of Applied Statistics , Johannes Kepler University Linz, Austria. She holds affiliations as an Affiliated Member at Harvard University's Harvard-MIT Center for Regulatory Science. Her academic journey includes a PhD in Statistics from the joint Warwick-Oxford CDT program (OxWASP), postdoctoral work at Harvard-MIT, and research fellowships at the University of Florence and TU Wien. Education PhD in Statistics (2019), University of Warwick & University of Oxford (OxWASP) MSc in Statistics (2014), University of Warwick Her research focuses on interpretable Bayesian models for complex data , particularly in medical applications (cancer, clinical trials). Key areas include high-dimensional inference, data integration, and probabilistic machine learning . She collaborates across institutions like Harvard-MIT, University of Florence, and Dana-Farber Cancer Institute. Notable awards include the 2019 Savage Award for her PhD thesis and multiple ISBA travel awards . She co-leads a €130,000 EULiST-funded project on Bayesian spatio-temporal modeling and contributes to the COMBINERS EU grant for network analysis. Scientific Leadership Co-PI, Bayesian inference for water hyacinth dynamics (2024-2027) External Collaborator, BayesMeCOS (European Union-funded) Senior Associate Editor, ACM Transactions on Probabilistic Machine Learning

Gustavo Coelhoمشاهده پروفایل

استادیار

• Hydrology
• Flood Forecasting
• Water Resources Management
• +۳ مورد دیگر

Dr. Gustavo Coelho is an Assistant Professor of Water Resources at Furman University since 2022. He holds a Ph.D. from George Mason University and professional licenses in environmental engineering from Brazil and France. His research focuses on hydrology, flood forecasting, and climate adaptation, with a strong emphasis on integrating weather and climate science to enhance water resource management and disaster resilience. He has extensive industry experience in Brazil as a water resources engineer, specializing in watershed management and flood mitigation. Educational Background: Ph.D. in Civil, Environmental, and Infrastructure Engineering (George Mason University) M.S. in Civil, Hydraulic Engineering (University of São Paulo) B.S. in Sanitary and Environmental Engineering (Federal University of Santa Catarina) Professional License in Environmental Protection (ENGEES, France) Research Interests: Dr. Coelho’s work addresses water quantity/quality challenges and natural hazards, particularly leveraging climate science for resilient urban planning. Key areas include real-time flood forecasting, sea level rise impacts, and socio-economic vulnerability assessments. Key Awards: 2022 AGU Frontiers in Hydrology Travel Grant 2021 Summer Research Fellowship (George Mason University) 2021 Best Hackathon Project Award (DesignSafe Academy) Advising & Grants: His professional activities include roles as a volunteer scientist with AGU’s Thriving Earth Exchange (2018–2022) and participation in CUAHSI’s National Water Center program. His teaching philosophy emphasizes experiential learning through real-world case studies in courses like Global Water Issues and Hydrogeology . Labs/Teams: Collaborates with interdisciplinary teams on projects funded by federal and institutional grants, focusing on flood resilience, climate adaptation, and urban water systems.

• Algorithms
• Theory & Logic
• Programming Languages & Verification
• +۴ مورد دیگر

Ali Zarezade is a researcher at the Max Planck Institute for Software Systems (MPI-SWS), focusing on interdisciplinary research spanning algorithms, machine learning, and social computing. His work integrates theoretical foundations with practical applications in cyber-physical systems, distributed networks, and human-centric AI. Key research areas include human-in-the-loop machine learning, optimal control strategies for social networks, and spatio-temporal modeling of social media activity. His methods often combine stochastic control theory, sparse regression techniques, and online algorithm design to address challenges in education technology, security, and information diffusion. Prior contributions involve developing algorithms like RedQueen and Cheshire for optimizing social network activity, probabilistic models for hyperspectral unmixing, and visual tracking systems resilient to occlusions. His research bridges computational theory with real-world systems, emphasizing scalable solutions for distributed and networked environments.

Haoran Shiمشاهده پروفایل

پژوهشگر

• river hydrodynamics
• environmental fluid mechanics
• physical limnology
• +۳ مورد دیگر

Dr. Haoran Shi is a Postdoctoral Research Officer at the School of Ocean Sciences, Bangor University. He holds a PhD in Civil and Environmental Engineering from École Polytechnique Fédérale de Lausanne (EPFL), Switzerland (2023). His research focuses on river hydrodynamics, environmental fluid mechanics, and physical limnology, with recent work exploring subsurface heatwaves in lakes, hyperpycnal river plumes, and multivariate extremes in aquatic systems. His methodologies include experimental studies, numerical modeling, and spatio-temporal analysis. Education: PhD in Civil and Environmental Engineering, EPFL (2023); prior degrees not detailed in the text. Research collaborations span international teams addressing coastal sediment dynamics, lake climatology, and vegetation effects on flow patterns. Key contributions include the development of analytical models for vegetated channel hydraulics and improved parameterization of environmental fluid dynamics processes. Dr. Shi’s publications (n=15+) demonstrate expertise in fluid dynamics applications across riverine, lacustrine, and coastal environments. His work bridges fundamental fluid mechanics with real-world challenges like climate impacts on water resources and sediment transport modeling. No awards or grants are explicitly listed in the provided materials. Research activities include laboratory experiments on plume dynamics, field observations of lake thermal regimes, and computational simulations of flow-vegetation interactions. Current projects likely involve integrating remote sensing data with in-situ measurements for environmental monitoring.

Sergey Zelikمشاهده پروفایل

استاد

• Partial Differential Equations
• Dynamical Systems
• Mathematical Analysis
• +۳ مورد دیگر

Professor Sergey Zelik is a faculty member in the School of Mathematics and Physics at the University of Surrey. His research focuses on partial differential equations (PDEs), dynamical systems, and mathematical analysis, with a particular emphasis on nonlinear dynamics, reaction-diffusion systems, and attractor theory. He has made significant contributions to the study of global attractors, inertial manifolds, and chaos in PDEs. Notably, he was awarded China’s Highest Foreign Research Honour in 2019. His work spans theoretical and applied mathematics, including studies on the Cahn-Hilliard equation, Ginzburg-Landau equations, and wave equations. Recent research includes the dynamics of multi-pulse interactions and the validity of Whitham modulation equations in Sobolev spaces. Zelik’s publications explore topics like Poincaré inequalities, interpolation inequalities, and the long-time behavior of dissipative systems. His findings have been published in top-tier journals such as SIAM Journal on Applied Dynamical Systems , Nonlinearity , and Communications in Partial Differential Equations .

Christian Rohrbeckمشاهده پروفایل

مدرس ارشد

• Extreme Value Analysis
• Spatial Statistics
• Environmental Statistics
• +۳ مورد دیگر

Dr. Christian Rohrbeck is a Senior Lecturer in the Department of Mathematical Sciences at the University of Bath. He holds a PhD in Statistics and Operational Research from Lancaster University and leads projects in statistical climatology through the EPSRC Centre for Doctoral Training in Statistical Applied Mathematics and Centre for Climate Adaptation & Environment Research. His research combines extreme value theory, spatial statistics, and Bayesian methods to analyze environmental and financial data. Current projects focus on flood risk modeling, climate extremes standardization, and monotonic regression techniques. Dr. Rohrbeck accepts doctoral students in extreme value analysis, flood risk, and climate adaptation statistics.

• Mathematical Modelling
• Dynamical Systems
• Pattern Formation
• +۳ مورد دیگر

Pranali Roy Chowdhury is a Research Fellow at the University of Alberta's Department of Mathematical and Statistical Sciences, where she leads toxic-based ecosystem modelling research in the Interdisciplinary Lab for Mathematical Ecology and Epidemiology (ILMEE) under Prof. Hao Wang. Previously, she held a postdoctoral position at the Indian Institute of Technology (IIT) Bombay in the Department of Mathematics, contributing to the National Disease Modelling Consortium's work on Respiratory Syncytial Virus (RSV) burden estimation in India. She earned her PhD in Mathematics from IIT Kanpur in 2023. Pranali completed her PhD in Mathematics at the Indian Institute of Technology Kanpur (IITK), India, in 2023. Her doctoral research focused on canards, relaxation oscillations, and transient spatiotemporal patterns in ecological systems. Prior to this, she briefly held a postdoctoral position at IIT Bombay. Her research interests revolve around mathematical modelling in ecological and epidemiological contexts, with a focus on dynamical systems and pattern formation. She explores these topics through interdisciplinary approaches, particularly examining the impact of toxins on ecosystems, slow-fast systems in predator-prey interactions, and spatio-temporal dynamics in fragmented habitats. Her work integrates both theoretical analysis and applied mathematical techniques to address real-world challenges in environmental and public health domains. Her publications reflect a strong emphasis on slow-fast dynamical systems and their applications in ecology and epidemiology. Recent work includes examining methane's ecological impact, oxygen minimum zones in plankton dynamics, and bifurcation analysis in predator-prey models. She also investigates transient dynamics and pattern formation in spatio-temporal systems, highlighting the role of time scales in ecological stability and disease spread modeling. In terms of advising and grants, Pranali Roy Chowdhury is currently leading research projects within ILMEE and previously contributed to the National Disease Modelling Consortium. While she has not listed formal advisees, her work involves collaborations and supervision within these interdisciplinary research environments. Details about specific grants or funding are not explicitly provided in the text. She is affiliated with the Interdisciplinary Lab for Mathematical Ecology and Epidemiology (ILMEE) at the University of Alberta and was part of the National Disease Modelling Consortium during her time at IIT Bombay.

Prathyush Sambaturuمشاهده پروفایل

پژوهشگر ارشد

• Computational and Spatial Epidemiology
• Network Science
• Combinatorial Optimization
• +۱ مورد دیگر

Dr. Prathyush Sambaturu is a Research Fellow in the Department of Biology at the University of Oxford, affiliated with the Kraemer research group. His work focuses on computational and spatial epidemiology, network science, combinatorial optimization, and complex systems. He designs agent-based network models to study infectious disease dynamics and develops surveillance strategies like genomic or wastewater monitoring to detect outbreaks and infer epidemiological parameters such as the reproduction number. His research interests include understanding how human mobility and population structure influence disease spread, as well as creating scalable algorithms for epidemic control. He also explores the impacts of climate change on migration patterns and associated disease risks. His articles span advanced computational methods applied to public health challenges, including graph-based active learning, fair disaster containment policies, and GPU-optimized intervention strategies. No scientific awards are explicitly mentioned. His research advisory role and grants information are not detailed in the provided texts. He is part of the Kraemer group, contributing to interdisciplinary projects at the intersection of epidemiology, network science, and algorithm design.

Paolo Favaroمشاهده پروفایل

استاد

• Computer Vision
• Computational Photography
• Machine Learning
• +۴ مورد دیگر

Prof. Dr. Paolo Favaro is a full Professor and Head of the Computer Vision Group (CVG) at the University of Bern's Institute of Computer Science. He holds a Laurea (BSc+MSc) from the University of Padova, Italy, and M.Sc. and Ph.D. in Electrical Engineering from Washington University in St. Louis. His career includes postdoctoral roles at UCLA and Cambridge University, followed by work in medical imaging at Siemens Corporate Research. From 2006–2011, he was Lecturer and Reader at Heriot-Watt University, and Honorary Fellow at the University of Edinburgh before joining Bern in 2012. His research focuses on computer vision , computational photography , machine learning , and signal/image processing , with contributions to inverse problems and variational techniques. His work spans theoretical advancements and practical applications, such as video diffusion models, unsupervised segmentation, and image restoration. He is an active member of the IEEE Society. Recent research trends emphasize generative models (e.g., diffusion models, video generation) and self-supervised learning , addressing challenges in real-world image/video denoising and sparsity-driven methods. His articles explore topics like Kalman-based optimization, multimodal world models, and spatio-temporal representation learning. Prof. Favaro leads the CVG, fostering interdisciplinary research at the intersection of computer vision and AI. His lab develops cutting-edge solutions for 3D reconstruction, medical imaging, and autonomous systems.

Fredrik Lindstenمشاهده پروفایل

دانشیار

• Machine Learning
• Statistical Machine Learning
• Approximate Bayesian Inference
• +۷ مورد دیگر

Fredrik Lindsten is a Senior Associate Professor in Machine Learning and Head of the Division of Statistics and Machine Learning at Linköping University's Department of Computer and Information Science (IDA). His research focuses on statistical machine learning, emphasizing probabilistic modeling and uncertainty quantification in methods such as approximate Bayesian inference, representation learning, and graph-based approaches. Applications span weather forecasting, materials science, biochemistry, and automotive industry challenges. Education: MSc (2008), PhD (2013) in Automatic Control from Linköping University; Postdoctoral roles at University of Cambridge, UC Berkeley, and University of Oxford. Affiliations: WASP (Wallenberg AI, Autonomous Systems and Software Program), ELLIIT (Lab for Information and Communication Technology). Research Interests: Lindsten’s work bridges statistical methodology and machine learning, particularly in quantifying uncertainty in predictions. His team explores method development across diverse applications, including spatio-temporal models and graph-based techniques. Recent projects include probabilistic weather forecasting with graph neural networks and cryo-EM reconstruction techniques. Publications: Over 25+ peer-reviewed articles, with recent highlights in Nature Methods , NeurIPS , and Physical Review Materials , focusing on Bayesian methods, generative models, and computational statistics. Awards: Ingvar Carlsson Award (Swedish Foundation for Strategic Research), Benzelius Award (Royal Society of Sciences in Uppsala). Grants & Teams: Supervises 7+ PhD students; active in collaborative projects funded by WASP, ELLIIT, and VR (Swedish Research Council). Labs/Teams: Leads the Division of Statistics and Machine Learning (STIMA) at IDA, fostering interdisciplinary research in probabilistic machine learning.

Arul Earnestمشاهده پروفایل

استاد

• Biostatistics
• Health Economics
• Clinical Registries
• +۴ مورد دیگر

Professor Arul Earnest is a leading academic research biostatistician at Monash University's School of Public Health & Preventive Medicine, where he serves as Deputy Head of the Clinical Outcomes Data Reporting and Research Program (CORRP). He holds a Professor (Research) appointment in Biostatistics, Data Analytics/Modelling, and Health Economics. His expertise spans Bayesian spatio-temporal modelling, machine learning applications in healthcare, and clinical registries analysis. He coordinates the MPH5270 unit on Advanced Statistical Methods for Clinical Research and developed the 'Clinical Registries Data Analysis using Stata' course. His research collaborations include government bodies, patient advocacy groups, and international organizations, with a focus on improving healthcare data quality and patient outcomes. Professor Earnest has authored over 245 peer-reviewed publications, a textbook on biostatistical collaboration, and earned 19 scientific awards. He manages a statistical team supporting ~30 clinical registries and leads national projects on ovarian cancer management, real-time patient outcome measures, and chronic disease priority-setting strategies. His work contributes to UN Sustainable Development Goals addressing health and well-being (SDG 3), and he actively participates in grant review panels, ethics committees, and statistical conferences.

Christina Davyمشاهده پروفایل

دانشیار

• Biodiversity Conservation
• Wildlife Pathogens
• Aeroconservation
• +۳ مورد دیگر

Christina Davy is an Associate Professor in the Department of Biology at Carleton University (located on unceded Algonquin Anishnaabeg territory). Her research focuses on biodiversity conservation under environmental threats like habitat loss, climate change, and wildlife pathogens. She leads the Davy Lab, emphasizing collaboration among researchers, governments, and Indigenous communities. Key areas include aeroconservation and functional connectivity of migratory species. Educations: B.Sc. from University of Guelph M.Sc. from Western University Ph.D. from University of Toronto (affiliated with Royal Ontario Museum) Research Themes: Integrates ecological theory with applied conservation science to address species persistence. Current projects investigate synergistic threats (habitat modification, invasive species, pathogens) on amphibians, reptiles, and bats. Strong emphasis on Indigenous-led conservation stewardship and public science communication. Publications: Recent work highlights urbanization impacts on migratory bats, reptile-roads interactions, and white-nose syndrome in bats. Research spans ecological networks, functional connectivity, and pathogen-driven declines. Lab & Fieldwork: Collaborates across Turtle Island territories (Michi Saagiig, Omàmìwininìwag, Haudenosaunee lands), acknowledging Indigenous stewardship. Focuses on improving conservation partnerships and science outreach.

Silvia Liveraniمشاهده پروفایل

دانشیار

• Bayesian Statistics
• Spatial Modelling
• Computational Statistics
• +۳ مورد دیگر

Silvia Liverani is a Reader in Statistics and Head of the Centre in Data Science, Statistics and Probability at Queen Mary University of London (School of Mathematical Sciences). Her research focuses on Bayesian methods, spatial-temporal modeling, and computational statistics, with applications in epidemiology, environmental health, and genetics. She leads projects on biodiversity analysis and spatial health data, collaborating with institutions like the Royal Botanical Gardens, Kew and the Alan Turing Institute. Education: PhD in Bayesian Clustering from the University of Warwick (2009). Grants & Initiatives: Awarded £1,000 (2025) for an LGBTQ+ Mathematics Open Day; led a Data Study Group at the Alan Turing Institute (2023). Labs/Teams: Director of the Centre in Data Science, developer of R packages PReMiuM and CARME for Bayesian clustering and spatial modeling. Key Research Themes: Dirichlet process mixture models, spatial misalignment correction, and interdisciplinary collaborations in health and environment. Current work includes predicting biodiversity loss under climate change and analyzing socio-economic health disparities through spatio-temporal frameworks.

Nicholas Meyerمشاهده پروفایل

مدرس

• Geomatic Engineering
• Civil Engineering
• Environmental Monitoring
• +۳ مورد دیگر

Nicholas Meyer is a Lecturer at the Department of Civil, Environmental and Geomatic Engineering at ETH Zürich. His research focuses on advanced geodetic measuring technologies, including terrestrial laser scanning (TLS) and 3D displacement estimation for structural and environmental monitoring. Key applications include deformation analysis of hydroelectric dams, long-range TLS in alpine environments, and vibration monitoring of bridges. His work emphasizes high-fidelity numerical simulations and multi-scale analysis, targeting infrastructure resilience and natural hazard assessment. He leads projects integrating TLS with RGB data for enhanced geomonitoring and employs self-supervised machine learning for debris flow analysis. Contact details: nicholas.meyer@geod.baug.ethz.ch. No scientific awards are explicitly listed in the provided materials. Research interests span geomatic engineering, environmental monitoring, and civil infrastructure diagnostics. Advising and grants sections remain unspecified, though his research group actively publishes in structural health and geospatial technologies.