جست‌وجوی استاد و پژوهشگر

• Statistics
• Machine Learning
• Bioinformatics
• +۴ مورد دیگر

Ping Ma is a Professor of Statistics with a courtesy appointment in Computer Science at the University of Georgia. His research focuses on developing innovative statistical and machine learning methodologies for complex high-dimensional data, with applications spanning bioinformatics, computational biology, social network analysis, and anomaly detection in power systems. Research interests include: Statistical Methodology : Nonparametric modeling, optimal transport theory, subsampling techniques, and functional regression for large-scale data Computational Biology : Spatial transcriptomics analysis, single-cell data integration, virology classification, and gene regulatory networks Machine Learning Innovations : Knowledge distillation for LLMs, tensor analysis, quantum-inspired algorithms, and ensemble learning for model robustness His recent publications demonstrate a strong trend toward interdisciplinary applications, particularly in developing AI/statistical tools for biomedical research (47% of recent papers), advancing foundational machine learning techniques (33%), and solving engineering challenges like power grid security (20%). Methodologically, 67% focus on novel algorithm development while 33% refine existing techniques for scalability.

• Algebraic Geometry
• Algebraic Topology
• Moduli Spaces
• +۳ مورد دیگر

Dan Petersen is a Professor of Mathematics at Stockholm University, specializing in the intersection of algebraic geometry and algebraic topology with a focus on moduli spaces. His research explores topics such as cohomology theories, homological stability, and geometric structures. He has advised PhD students including Erik Lindell, Louis Hainaut, Josefien Kuijper, and Oliver Lindström. His work frequently intersects with geometric topology, number theory, and representation theory, as seen in his recent publications on handlebody groups, Mumford conjectures, and configuration spaces. Collaborations with postdocs such as Johan Alm, Marcel Rubió, and Sylvain Douteau highlight his involvement in advanced research networks. His contributions to algebraic structures and topological methods have advanced understanding in both pure and applied mathematics contexts.

Tomoko Matsuoمشاهده پروفایل

دانشیار

• Atmospheric Sciences
• Space Physics
• Data Assimilation
• +۴ مورد دیگر

Tomoko Matsuo is an Associate Professor in the Ann and H.J. Smead Department of Aerospace Engineering Sciences at the University of Colorado Boulder. She holds an affiliate faculty position in the Department of Applied Mathematics and is a member of the Colorado Center for Astrodynamics Research (CCAR). A H. Joseph Smead Faculty Fellow, her research focuses on data assimilation, predictability of geophysical systems, and space physics. She earned a PhD in Atmospheric Science from SUNY Stony Brook (2003), MS in Physics from Nagoya University (1998), and BS in Geophysics from Hokkaido University (1995). Affiliations: CCAR, Smead Aerospace, Department of Applied Mathematics Awards: NSF CAREER Award (2019), multiple teaching awards, and fellowships Education: PhD, Atmospheric Science, SUNY Stony Brook, 2003 MS, Physics, Nagoya University, Japan, 1998 BS, Geophysics, Hokkaido University, Japan, 1995 Research Interests: Advanced methods in data assimilation for space weather forecasting, ionospheric dynamics, and geophysical predictability. Her work bridges atmospheric sciences, space physics, and statistical learning. Current projects include the DYNAMIC mission concept (NASA-selected Phase-A study) and the AMGeO data science platform. Grants & Collaborations: NSF CAREER Award (2019), NASA-funded projects (e.g., DYNAMIC mission), and collaborations with JHU APL, NCAR, and international institutions. Advises students on remote sensing and STEM outreach programs. Labs/Teams: Leads her own research group and contributes to CCAR. Develops tools for assimilative mapping of geospace observations (AMGeO). Active in citizen science initiatives like Aurorasaurus.

Alain Trouvéمشاهده پروفایل

استاد

• Shape Spaces and Applications
• Imaging Processing and Computational Vision
• Computational Biology
• +۳ مورد دیگر

Alain Trouvé is a Professor at the Center for Mathematics and Their Applications (CMLA) within the Ecole Normale Supérieure de Cachan , France. His research focuses on Shape Spaces , Computational Anatomy , Imaging Processing , and Biological Imaging , with applications in medical and computational fields. He directs the Mathematics Department at ENS Cachan and contributes to neuroanatomical studies through diffeomorphometry techniques. Key roles include membership in the Conseil National des Universités (Section 26) and teaching responsibilities such as courses on Geometry and Shape Spaces and Probability Theory . His work spans from theoretical frameworks (e.g., Hamiltonian modeling of shape evolution) to practical applications like 3D cell imaging and white matter fiber analysis. Publications emphasize interdisciplinary methods, including diffeomorphic registration, varifold-based image analysis, and stochastic shape evolutions. Current projects explore multi-scale modeling of biological systems and AI-driven medical diagnostics. Key Research Themes: Diffeomorphic mappings, computational vision, and functional shape analysis. Teaching: Courses on geometric modeling and probability at undergraduate and graduate levels. Tools Developed: xIV-LDDMM Toolkit for multi-modal biomedical data analysis.

Rémi Flamaryمشاهده پروفایل

استاد

• Machine Learning
• Optimal Transport
• Domain Adaptation
• +۷ مورد دیگر

Rémi Flamary is a Professor in the Applied Mathematics department at École Polytechnique, France, and a member of the CMAP Laboratory. Previously, he held an Associate Professor position at Université Côte d'Azur in the Department of Electronics and Lagrange Laboratory. He completed his PhD at Rouen University under Alain Rakotomamonjy, focusing on statistical signal processing and optimization. His research interests span machine learning, optimal transport, domain adaptation, and their applications in biomedical engineering, energy, and remote sensing. He leads the development of the POT (Python Optimal Transport) library and contributes to projects like SKADA for domain adaptation. Education: PhD in Applied Mathematics, Rouen University (LITIS Laboratory) Research Focus: Flamary's work emphasizes leveraging optimal transport theory for machine learning tasks such as graph prediction, signal normalization, and cross-domain adaptation. His contributions include novel algorithms for unbalanced transport, semi-relaxed Gromov-Wasserstein distances, and end-to-end graph generation frameworks. He actively collaborates on applications in neuroscience, astronomy, and energy systems. Professional Activities: He has presented at NeurIPS and other top conferences, supervised PhD students like Cédric Vincent-Cuaz, and contributed to open-source software. His teaching includes courses on signal processing and machine learning at École Polytechnique.

Andrew Tatemمشاهده پروفایل

استاد

• Demography
• Epidemiology
• Geospatial Data Analysis
• +۳ مورد دیگر

Professor Andrew Tatem is a Personal Chair in Spatial Demography and Epidemiology at the University of Southampton, leading the WorldPop research group. His work focuses on integrating traditional demographic data with geospatial technologies like satellite imagery and mobile phone data to map population distributions, health dynamics, and pathogen transmission. He leads major initiatives funded by the Bill & Melinda Gates Foundation, Wellcome Trust, and others. Research interests include: Mapping population demographics and mobility patterns Modeling disease spread (e.g., malaria, polio, Zika) Supporting public health interventions via high-resolution data Urbanization and environmental health impacts Notable projects involve global vaccination coverage mapping, polio eradication support, and disaster preparedness. His work has been recognized with awards like the Turing Fellowship and Geovation Innovation Award. He supervises four PhD students in Geography and collaborates internationally with UN agencies and governments. Awards include: AGI Award for Excellence with Impact (2024) Geovation International Geospatial Innovation Award (2023) Breheny Prize (2021) Global Mobile Award for Humanitarian Work (2016) His research outputs emphasize geospatial data fusion, mobility-driven disease modeling, and SDG tracking, with recent focus on post-pandemic health systems and climate adaptation strategies.

Michael B. Yaffeمشاهده پروفایل

استاد

• Cancer Biology
• Signal Transduction
• Precision Medicine
• +۷ مورد دیگر

Michael B. Yaffe is the David H. Koch Professor of Science and Professor of Biology and Biological Engineering at MIT, where he has been a faculty member since 2000. He serves as Director of the MIT Center for Precision Cancer Medicine and the KI Clinical Investigator Program. Clinically, he is an attending surgeon and intensivist at Beth Israel Deaconess Medical Center, specializing in injury and surgical oncology. Education: MD-PhD from Case Western Reserve University Training: Residency at University Hospitals of Cleveland; Fellowship at Harvard-Longwood Critical Care Program Dr. Yaffe's research focuses on protein kinase signaling pathways activated by DNA damage, cell injury, and inflammation. His work explores how these pathways regulate cancer behavior and treatment response through integrated experimental and computational approaches, with translational applications in systems pharmacology and precision medicine. His 15 most recent publications span DNA damage responses, mitotic stress, immunogenic cell injury, kinase signaling atlases, and reproducibility in science. Key trends include targeting pathway crosstalk for therapeutic synergy, leveraging R-loops in DNA repair-deficient tumors, and understanding immune activation post-chemotherapy. Scientific Awards: HHMI Physician-Scientist Fellowship Burroughs-Wellcome Fund Award MacVicar Faculty Fellow (2021) Association of American Physicians (2021) American Surgical Association membership Dr. Yaffe advises trainees in scientific career development and leads the Yaffe Lab at MIT, which pioneers novel technologies like peptide libraries and multiplex kinase reporters. His work bridges benchtop experiments with clinical applications in chemotherapy, radiation, and surgery, while also addressing inflammation-cancer connections.

Henrik Jeldtoft Jensenمشاهده پروفایل

استاد

• Complexity Science
• Statistical Mechanics
• Mathematical Physics
• +۷ مورد دیگر

Henrik Jeldtoft Jensen is a Professor of Mathematical Physics and leads the Centre for Complexity Science at Imperial College London. His work spans multiple disciplines, focusing on the statistical mechanics of complex systems, with applications in physics, biology, neuroscience, and finance. Professor, Mathematical Physics Leader, Centre for Complexity Science Institution: Imperial College London His research interests lie at the intersection of theoretical physics and complex systems. He is best known for developing the Tangled Nature Model of evolving ecosystems, which has been extended into financial modeling through the Tangled Finance approach. His work in brain dynamics involves analyzing fMRI and EEG data using tools from statistical physics. He has made significant contributions to self-organized criticality and stochastic dynamics of complex systems, particularly in condensed matter and evolutionary contexts. The recent publications reflect a strong trend toward interdisciplinary complexity science, integrating concepts from physics, biology, economics, and neuroscience. Keywords across these works include complexity, statistical mechanics, dynamical systems, and network theory, with subfields ranging from neural avalanches to financial instability and biodiversity modeling. Henrik Jensen is the author of two influential books: Self-Organized Criticality and Stochastic Dynamics of Complex Systems (with Paolo Sibani), which have been widely cited across disciplines. He has supervised numerous PhD and postdoctoral researchers through the Centre for Complexity Science, though specific names are not listed. His research has been supported by grants from UK research councils and international collaborations, particularly in interdisciplinary complexity projects. He is affiliated with the Centre for Complexity Science, a multidisciplinary research hub at Imperial College London that brings together physicists, mathematicians, biologists, and social scientists to study complex adaptive systems.

Domenico Cicchellaمشاهده پروفایل

دانشیار

• Geochemistry
• Environmental Monitoring
• Soil Contamination
• +۷ مورد دیگر

Domenico Cicchella is an Associate Professor in the Department of Science and Technology at the University of Sannio, Italy, specializing in Geochemistry and Volcanology (GEO/08). With over two decades of academic experience, he has established himself as a leading researcher in environmental geochemistry, particularly in soil and water contamination studies in the Campania region of southern Italy. His research interests span multiple critical areas of environmental geochemistry: Urban and regional geochemical mapping Heavy metal and potentially toxic element (PTE) analysis in soils Environmental risk assessment methodologies Application of advanced statistical techniques to geochemical data Water pollution monitoring and assessment Integration of compositional data analysis with machine learning approaches Geochemical background determination Analysis of his publication record reveals a strong focus on developing innovative methodologies for environmental assessment, particularly through the "Campania Trasparente" project - a comprehensive multimedia monitoring initiative. His work increasingly incorporates advanced data analysis techniques including compositional data analysis, multivariate statistics, and machine learning applications to address complex environmental challenges in urban and regional settings. His research spans from fundamental geochemical studies to practical applications for environmental management and public health protection. Professor Cicchella maintains an active research profile with numerous collaborations across Italy and internationally, contributing significantly to our understanding of environmental contamination patterns and developing practical approaches for risk assessment and management. His work has appeared in leading journals including Journal of Geochemical Exploration, Applied Geochemistry, and Science of the Total Environment, demonstrating both academic rigor and practical relevance to environmental challenges.

• Geometric Group Theory
• Topology
• Low-Dimensional Topology
• +۴ مورد دیگر

Henry Wilton is a Professor of Topology and Group Theory at the University of Cambridge's Department of Pure Mathematics and Mathematical Statistics (DPMMS), and a Fellow of Trinity College. His research focuses on geometric group theory, topology, and low-dimensional topology, with particular emphasis on hyperbolic groups, 3-manifold groups, and residual properties of groups. He has contributed extensively to understanding limit groups, profinite completions, and decision problems in geometric topology. Wilton has taught courses including Part III Geometric Group Theory (2024), Part II Riemann Surfaces (2023), and Part III Mapping Class Groups (2021). He co-organizes the Groups and Geometry in the South East seminar series and actively contributes to academic communities through blogs (e.g., Low-Dimensional Topology) and collaborations with institutions like MSRI. His recent work addresses topics such as the congruence subgroup property for mapping class groups, rational curvature invariants in 2-complexes, and coherence of one-relator groups. He has published in high-impact journals like the Journal of the American Mathematical Society, Inventiones Mathematicae, and Duke Mathematical Journal. Wilton's research also explores profinite rigidity, virtual properties of 3-manifolds, and algorithmic problems in geometric topology. He collaborates with leading mathematicians such as Martin Bridson, Daniel Groves, and Pavel Zalesskii on projects spanning geometric group theory, hyperbolic geometry, and algebraic topology.

• Medical Imaging
• Artificial Intelligence
• Nuclear Medicine
• +۵ مورد دیگر

Priv. Doz. Dr. Kuangyu Shi is an Associate Professor and Senior Lecturer at the Chair for Computer-aided Medical Procedures, School of Computation, Information and Technology (CIT), Technical University of Munich (TUM). He also serves as Chief Medical Physicist and Head of the Lab for Artificial Intelligence & Translational Theranostics at the Department of Nuclear Medicine, Inselspital, University of Bern. His research focuses on translational molecular imaging computing, AI in nuclear medicine, digital twin technology, and computational biology for theranostics. Dr. Shi holds a Ph.D. from the Max-Planck Institute for Computer Science (2008) and an Habilitation from TU Munich (2018). He teaches courses including Introduction to Artificial Intelligence in Medical Imaging , Clinical Decision Support , and Advanced Medical Imaging . He is actively involved in professional organizations like the European Association of Nuclear Medicine (EANM) and the International Commission on Radiological Protection (ICRP), and serves on editorial boards for journals such as Eur J Nucl Med Mol Imaging . His research projects include the DHM (German Heart Center Munich) lab, NARVIS Lab, and computational surgineering. He supervises student projects in AI-driven treatment planning, low-dose imaging, and early diagnosis of neurodegenerative diseases. His work bridges clinical needs with advanced computational methods, emphasizing AI and medical imaging innovations.

• Robotics
• Human-Robot Interaction (HRI)
• Artificial Intelligence
• +۳ مورد دیگر

Marc Hanheide is a Professor of Intelligent Robotics and Interactive Systems at the University of Lincoln 's School of Computer Science. With a career spanning EU projects like VAMPIRE, COGNIRON, CogX, and STRANDS, his work focuses on long-term robotic behavior, human-robot spatial interaction, and cognitive system architectures. He has secured over 12 major grants from organizations including EPSRC, BBSRC, and the European Commission. Key Research Areas : Autonomous robotics, HRI, AI, cognitive systems, agricultural robotics Current Projects : STRANDS (long-term behavior), AgriFoRwArdS (robotics training), NCNR (nuclear robotics) Major Contributions : Human-aware navigation modules, topology optimization for robot fleets, causal analysis frameworks Scientific Awards: While no specific awards are listed, his numerous EPSRC grants and leadership in multi-institutional projects highlight his impact. He has over 172 publications and collaborates with institutions like CoR-Lab and CITEC.

Andreas Huthمشاهده پروفایل

پژوهشگر

• Ecological Modeling
• Vegetation Dynamics
• Biodiversity Modeling
• +۴ مورد دیگر

Andreas Huth is a prominent researcher at the Helmholtz Centre for Environmental Research (UFZ) in Leipzig, Germany, where he works within the Department of Ecological Systems Analysis. His research focuses on developing and applying computational models to understand complex ecological systems, with particular emphasis on vegetation dynamics, biodiversity patterns, and environmental interactions. Huth is actively involved in several major research initiatives including the FORMIND project, which simulates forest growth using individual-based vegetation models, and contributes to the UFZ's research program on Smart Models and Monitoring. Huth's research interests span ecological modeling, vegetation dynamics, biodiversity assessment, and environmental system analysis. He specializes in developing sophisticated computational frameworks that integrate atmospheric, ecological, and spatial data to model complex environmental processes. His work often involves coupling different modeling approaches, such as connecting atmosphere and vegetation radiative transfer models to study cloud-vegetation interactions. Huth is particularly interested in understanding spatial patterns in ecological systems, species-habitat relationships, and the impacts of environmental change on biodiversity. His methodological expertise includes agent-based modeling, individual-based modeling, and developing digital twin technologies for environmental systems. Analysis of Huth's recent publications reveals a strong focus on integrating different modeling approaches to address complex environmental questions. His work demonstrates a progression from basic ecological modeling toward more sophisticated integrative frameworks that connect atmospheric processes with vegetation dynamics, and social systems with ecological outcomes. A notable trend is his increasing involvement in digital twin technology for environmental systems, particularly for biodiversity monitoring and prediction. His research spans multiple scales - from microbial ecosystems to continental-scale species distributions - while maintaining a consistent methodological thread of spatially explicit modeling and causal analysis in complex systems. Huth collaborates extensively with researchers across the UFZ and internationally, as evidenced by his co-authorship on numerous interdisciplinary publications. While specific awards are not mentioned in the available information, his consistent publication record in high-impact journals and leadership in major modeling initiatives like FORMIND indicate significant recognition within the ecological modeling community. His work has practical applications for environmental policy, conservation planning, and climate change adaptation strategies. Huth is an active member of the Ecological Systems Analysis research group at UFZ, which develops and applies computational models to investigate key mechanisms in ecological systems. He contributes to several major research platforms including FORMIND (an individual-based vegetation model for species-rich forests) and participates in the UFZ's broader research initiatives on environmental monitoring and sustainable technologies. His work is integrated within the UFZ's research program on Smart Models and Monitoring, which aims to develop innovative approaches for understanding and predicting environmental change.

Vahab Khoshdelمشاهده پروفایل

استادیار

• Machine Learning
• Deep Learning
• Computer Vision
• +۶ مورد دیگر

Vahab Khoshdel is an Assistant Professor in the Department of Electrical and Computer Engineering at the Price Faculty of Engineering, University of Manitoba. His research bridges machine learning, deep learning, computer vision, robotics, and medical imaging, with applications in rehabilitation robotics, microwave/ultrasound imaging, stored grain monitoring, and precision agriculture. Education: 2021, Ph.D. Biomedical Engineering, University of Manitoba 2017, Ph.D. Mechanical Engineering, Ferdowsi University of Mashhad 2013, M.Sc. Mechatronic Engineering, University of Shahrood 2011, B.Sc. Robotics Engineering, University of Shahrood Research Interests: Khoshdel specializes in applying generative AI, neural networks, and optimization techniques to medical imaging and robotics. His work includes microwave/ultrasound breast imaging, impedance control for rehabilitation robots, and AI-driven agricultural monitoring systems. Publication Trends: His recent articles emphasize machine learning workflows for medical diagnostics, deep learning in multimodal imaging, and neural networks in rehabilitation robotics. Key subfields include 3D imaging, inverse scattering, tissue classification, and sEMG signal analysis. Contact: Vahab.Khoshdel@umanitoba.ca

Nicole Kalmsمشاهده پروفایل

استاد

• Urbanism
• Feminism
• Gender and the City
• +۱۲ مورد دیگر

Nicole Kalms is a Professor in the Department of Design at Monash University’s Faculty of Art, Design and Architecture, where she also serves as Associate Dean of Research. She is the founding director of the XYX Lab, a pioneering research group focused on Gender and Place, leading national and international projects on gender equity in urban environments. PhD in Architecture, Monash University (2017) Masters in Landscape Architecture, RMIT University (2006) Bachelor of Architecture (Honours), RMIT University (1998) Nicole Kalms’ research lies at the intersection of urbanism, feminism, spatial practice, and media. Her work critically examines how gender, sexuality, and power shape urban spaces, advocating for design as a tool for social justice. She emphasizes interdisciplinary and co-design approaches to address systemic inequities in cities, particularly for women and marginalized communities. Her recent publications and projects reveal a strong thematic focus on gender-sensitive urban design, digital interventions for public engagement, and inclusive mobility. The 15 most recent works include books, chapters, and research outputs centered on feminist urbanism, crowd-sourced safety mapping, visual communication of gender inequality, and participatory design. These contributions reflect a consistent commitment to translating academic research into public discourse and policy impact. Dean's Award for Research Impact (2018) Premier's Design Awards Finalist – 'SHEcity' (2019) Good Design Award Gold Winner – 'Yours, Mine, Ours' (2020) AGDA Award Distinction – 'HyperSext City' (2021) Designers Australia Award Winner – 'HyperSext City' (2021) Nicole Kalms actively supervises research and leads externally funded projects such as the Safer Cities Crowd Mapping Project and PETAL: Pedalling Towards Active Living. She has secured grants from government bodies and international partners, and her work informs public policy on urban safety, transport, and inclusivity. She regularly serves on advisory boards and contributes to public discourse through media and lectures. She leads the XYX Lab, a dynamic research team dedicated to gender and place, which produces innovative design-led research, public exhibitions, and policy recommendations. The lab fosters collaboration across disciplines and with community stakeholders to co-create equitable urban futures.