جست‌وجوی استاد و پژوهشگر

Mario Di Bernardoمشاهده پروفایل

استاد

• Nonlinear Systems and Control
• Complex Networks Analysis
• Piecewise-Smooth Dynamical Systems
• +۴ مورد دیگر

Mario Di Bernardo is a Professor of Automatic Control at the University of Naples Federico II and Honorary Professor of Nonlinear Systems and Control at the University of Bristol. He serves as Rector's Delegate for International Relations and coordinates the PhD Program in Modeling and Engineering Risk and Complexity at the Scuola Superiore Meridionale. His research focuses on nonlinear systems, complex networks, and control theory, with applications in engineering, robotics, and synthetic biology. He has received numerous awards, including the IEEE Fellow designation (2012) and the Cavaliere of the Order of Merit of Italy (2007). Di Bernardo leads international initiatives, including the IEEE Control Systems Society as Vice President for Technical Activities (2025–2026), and contributes to editorial roles in top journals like IEEE Transactions on Control of Network Systems . His research interests span complex systems control, multi-agent systems, and cyber-physical systems. Notable achievements include developing frameworks for controlling microbial consortia, analyzing crowd dynamics, and designing reinforcement learning strategies for robotic herding. He advises PhD students and postdoctoral researchers, with notable advisees winning awards like the IEEE Best Young Paper and Bristol’s Best PhD Thesis. Di Bernardo’s work integrates control engineering with synthetic biology, exemplified by projects like the EU-funded BeyondTheEdge and SHARESPACE . Di Bernardo has authored over 220 publications, with an h-index of 51 and 11,000+ citations. He leads research teams addressing societal challenges, such as pandemic modeling and sustainable energy systems. His labs and collaborations involve institutions like the Scuola Superiore Meridionale, University of Bristol, and international consortia.

• Combustion Diagnostics
• Laser-induced Incandescence
• Thermometry
• +۳ مورد دیگر

Qian Wang is a Researcher at the Department of Energy and Process Engineering, Faculty of Engineering, Norwegian University of Science and Technology (NTNU). Their research focuses on combustion diagnostics, laser-based measurement techniques, and advanced imaging systems for fluid dynamics analysis. Key areas include soot characterization, flame tomography, and the development of cost-effective diagnostic tools. Research interests encompass combustion kinetics, thermometry, and particle dynamics, with applications in propulsion systems and material science. Recent work emphasizes the use of machine learning and deep learning for data-driven diagnostics, particularly in turbulent combustion environments. Publications highlight innovations in volumetric reconstruction, time-resolved imaging, and optimization of optical systems under constrained conditions. No scientific awards are mentioned in the provided text. Qian’s work involves collaboration with engineering teams to advance combustion diagnostics through interdisciplinary approaches, though specific grants or advising roles are not detailed here.

• Materials Science
• Battery Technology
• Mechanics Informatics
• +۴ مورد دیگر

Wei Li is a Research Scientist in the Department of Mechanical and Industrial Engineering at Northeastern University. His research focuses on mechanics informatics, battery technology, and materials science, with applications in energy storage systems and failure analysis. He has been recognized in the 2024 and 2023 Stanford University Annual Assessments as a top 2% most-cited scientist in his field. His work combines computational modeling, machine learning, and experimental techniques to study electrochemical-mechanical interactions in batteries, particularly solid-state systems. Key areas include battery safety, interface stability, and material degradation mechanisms. He has developed novel methods like Mechanics Informatics and Mechano-Electro-Chemical Impedance Spectroscopy (MEIS) for advanced analysis. Awards: Top 2% most-cited scientist (2023, 2024) Research Themes: Solid-state batteries, phase-field modeling, physics-informed AI, and battery failure mechanisms His contributions bridge multi-scale modeling (from particle to electrode levels) and experimental validation, with applications in improving battery lifespan, safety, and performance. Collaborative efforts include digital twin frameworks for material design and optimizing test specimen design for constitutive modeling.

Sébastien LEMIEUXمشاهده پروفایل

دانشیار

• Bioinformatics
• Computational Biology
• Cancer Research
• +۵ مورد دیگر

Sébastien Lemieux is an Associate Professor in the Department of Biochemistry and Molecular Medicine at the Faculty of Medicine, Université de Montréal, and holds a joint appointment in the Department of Computer Science and Operations Research at the Faculty of Arts and Sciences. His research focuses on developing bioinformatics algorithms for analyzing high-throughput biological data, particularly in cancer genomics and precision medicine. He earned his PhD in Computer Science (2002), M.Sc. in Computer Science (1999), and B.Sc. in Microbiology (1998), all from Université de Montréal. He leads the Functional and Structural Bioinformatics Research Unit at the Institut de recherche en immunologie et en cancérologie (IRIC), directing the bioinformatics platform. His work integrates transcriptomics, proteomics, and high-throughput screening to address challenges in cancer research. Notable projects include developing AI-driven tools for drug discovery and analyzing genomic data in acute myeloid leukemia (AML). Dr. Lemieux has supervised over 20 graduate students and postdocs, focusing on computational methods for cancer biology. He has secured funding from major agencies like the Canadian Institutes of Health Research (CIHR), Genome Canada, and Fonds de recherche du Québec (FRQS). Key collaborations include work on personalized immunotherapy and chemogenomic tools for AML treatment. His research outputs span bioinformatics algorithm development, cancer biomarker discovery, and translational applications in clinical oncology. Current projects emphasize integrating multi-omics data to identify therapeutic targets and improve treatment outcomes for high-risk cancers.

Tom Horrocksمشاهده پروفایل

عضو هیئت علمی

• Machine Learning
• Geostatistics
• Geoscientific Data Mining
• +۳ مورد دیگر

Tom Horrocks is a Senior Research Fellow at the School of Earth and Oceans, University of Western Australia (UWA). He holds a dual degree in Engineering (Software) and Science (Physics/Applied Mathematics) from UWA, complemented by a research-focused doctoral program supported by the Robert and Maude Gledden Scholarship. His work bridges machine learning with geoscience challenges, focusing on predictive modeling, data mining, and software engineering solutions for resource exploration. Key research areas include machine learning applications in geophysics (e.g., neural networks for potential field modeling), hyperspectral imaging for mineral identification, and automated geological logging using FTIR spectra. His interdisciplinary approach often results in deployable software tools addressing real-world problems, such as the Rio Tinto Data Fusion project. Education : Bachelor of Engineering (Hons) in Software, UWA Bachelor of Science in Physics & Applied Mathematics, UWA Awards : Nick Rock Memorial Prize (2018) ASEG WA Branch Student Award (2015) UWA Vice Chancellor’s Impact & Innovation Award (2015) Current projects include the ChatGEO initiative for semantic document retrieval and collaboration with the Geodata Algorithms Team on predictive logging techniques. His research outputs span geostatistics, mineral exploration, and AI-driven geoscience solutions. Tom has contributed to major grants totaling over 5 years, including partnerships with industry leaders like Fortescue Metals Group and the Geological Survey of Western Australia. His work is disseminated through high-impact journals like Scientific Reports and Ore Geology Reviews , reflecting a strong focus on both theoretical innovation and practical application.

Jennifer Snyder-Cappioneمشاهده پروفایل

استادیار

• Immunology
• Virology
• Microbiology
• +۷ مورد دیگر

Jennifer Snyder-Cappione is an Assistant Professor at the Boston University Chobanian & Avedisian School of Medicine, Department of Virology, Immunology & Microbiology. She holds a PhD from the University of Rochester School of Medicine & Dentistry and a BS from Brandeis University. As Director of the BUMC Flow Cytometry Core Facility, she leads technical and research initiatives in immunophenotyping. Her research focuses on immune cell exhaustion, iNKT cell subsets, and ex vivo immune profiling in diseases like HIV, cancer, and autoimmunity. Education: PhD, University of Rochester School of Medicine & Dentistry (2006) MS, University of Rochester School of Medicine & Dentistry (2003) BS, Brandeis University (2000) Research interests include: (1) diagnostic/prognostic potential of immune profiling in inflammatory diseases, (2) lineage relationships of iNKT cell subsets, and (3) T cell exhaustion in chronic diseases. Key areas: HIV pathogenesis, cancer immunology, and age-related immunity. Grants/Projects: NIH/NIA (2020–2025): Gamma delta T cells in HIV/aging inflammation NIH/NIAMS (2017): Cell sorting infrastructure Labs/Teams: Director of Flow Cytometry Core; member of BU-BMC Cancer Center and Evans Center for Interdisciplinary Biomedical Research.

Tonio Buonassisiمشاهده پروفایل

استاد

• Materials Science
• Solar Photovoltaics
• Fusion Energy
• +۳ مورد دیگر

Tonio Buonassisi is a Professor at the Massachusetts Institute of Technology (MIT), affiliated with the Department of Mechanical Engineering . His research focuses on autonomous labs for solar photovoltaics and fusion energy , combining machine learning with materials discovery to address sustainability challenges. Key research areas include high-throughput experimentation , AI-driven semiconductor characterization , and robotics for materials science . Recent publications highlight his work on self-driving labs for rapid material screening Bayesian inference in experimental design AI and computer vision for semiconductor properties perovskite solar cells and fusion materials His work also emphasizes democratizing lab automation and knowledge-integrated machine learning to identify synthesizable materials. While no formal education, student, or award details are provided in the text, his research spans materials science , renewable energy , and lab-scale innovation .

Daniela Ruppمشاهده پروفایل

استادیار

• Nanostructure Physics
• X-ray Science
• Ultrafast Dynamics
• +۳ مورد دیگر

Prof. Daniela Rupp is an Assistant Professor at the Department of Physics at ETH Zürich, where she leads the Nanostructures and Ultrafast X-Ray Science group. Her research focuses on imaging and understanding transient states and dynamics in nanoscale systems using advanced X-ray and optical techniques. Key areas include coherent diffractive imaging of nanoparticles, ultrafast plasma dynamics, and the development of novel imaging frameworks like SPRING. Her work bridges physics, materials science, and computational methods to explore phenomena at femtosecond timescales and nanometer resolutions. Her laboratory, located at the Laboratorium für Festkörperphysik in Zürich, employs cutting-edge X-ray facilities to study systems such as helium nanodroplets, xenon clusters, and free nanoparticles. Research interests span structural dynamics, energy transfer mechanisms, and the interplay between light and matter at the nanoscale. Recent advancements include real-time visualization of nanoplasma formation and the application of machine learning for diffraction image analysis. Publications highlight innovations in imaging frameworks, ultrafast dynamics studies, and interdisciplinary approaches to photon science. Ongoing projects aim to advance single-shot diffraction techniques and explore applications in materials characterization. The group collaborates with Swiss and international research infrastructures to address future photon science needs through strategic roadmap initiatives.

• Data Science
• Geometric Data Analysis
• Topological Data Analysis
• +۲ مورد دیگر

Washington Mio is a Professor in the Department of Mathematics at Florida State University, specializing in data science, geometric and topological data analysis, and their scientific applications. His research bridges theoretical mathematics and practical implementations, focusing on complex data structures and geometric domains. Dr. Mio's primary research interests include developing methods for analyzing high-dimensional data using topological approaches, metric space analysis, and functional data on geometric domains. He works extensively on problems related to shape discrimination, persistent homology, and optimization techniques in metric spaces. His recent publications demonstrate a strong focus on topological data analysis, geometric statistics, and computational topology. Key trends include the development of unified frameworks for persistent homology, methods for analyzing functional data on geometric domains, and applications in materials science and biology. The research shows consistent innovation in mathematical approaches to complex data challenges.

Alexandra Paxtonمشاهده پروفایل

دانشیار

• Complex Dynamical Systems
• Communication and Social Interaction
• Data Science in Psychology
• +۴ مورد دیگر

Alexandra Paxton is an Associate Professor in the Department of Psychological Sciences at the University of Connecticut. She directs the Dyscord Lab, where she investigates communication and social interaction through the lens of complex dynamical systems. Her research leverages data-rich methods to analyze movement, language, and emotion in naturalistic contexts, with additional focus on ethical implications of human-derived data and big data theory-building in psychology. Dr. Paxton holds a Ph.D. in Cognitive and Information Sciences from the University of California, Merced (2015). Her research explores how context modulates interpersonal behavior, using dynamics-focused approaches to study multimodal coordination (e.g., gesture-speech synchronization) across diverse settings. Key interests include: Modeling interaction as a coupled system influenced by conversational constraints and environmental factors. Developing computational tools for analyzing linguistic and movement coordination. Ethical frameworks for naturally occurring data in psychological science. Her publications (2020-2024) predominantly analyze interpersonal dynamics using recurrence quantification, linguistic alignment, and dynamical systems modeling. Recent work emphasizes real-world applications—such as videoconferencing, autism communication patterns, and driving behavior—reflecting a consistent trend toward ecologically valid, interdisciplinary research bridging psychology, data science, and ethics. She leads the Dyscord Lab, which prioritizes open science practices and high-dimensional data analysis to understand social coordination. Dr. Paxton also serves in editorial roles at PLOS ONE, Cognitive Processing, and Behavior Research Methods.

Gayla Olbrichtمشاهده پروفایل

استاد

• Statistical Modeling of Genomic Data
• Epigenomic Analysis
• Biomedical Data Science
• +۳ مورد دیگر

Dr. Gayla Olbricht is a Professor of Statistics and Associate Chair in the Department of Mathematics and Statistics at Missouri University of Science and Technology. She holds a Ph.D. in Statistics from Purdue University (2010), an M.S. in Applied Statistics (2004), and a B.S. in Mathematics (2001) from Missouri State University. Her research focuses on statistical modeling of genomic, epigenomic, and biomedical data, with emphasis on explainable unsupervised learning methods. Key projects include lifespan modeling in Drosophila, traumatic brain injury biomarker analysis, and educational data science. Dr. Olbricht has received multiple awards, including the 2022 Missouri S&T Woman of the Year Award and the 2021 CASB Research Award. She has been a Co-Principal Investigator for SEQL (Science Education and Quantitative Literacy), a professional development program for K-12 educators funded by the Missouri Department of Higher Education (2014-2018). Her work has been supported by NIH and the Leonard Wood Institute. Her recent publications span statistical methodologies for genomic data, TBI biomarker analysis, and educational interventions. She has held leadership roles including Interim Director of Graduate Studies (2023) and has contributed to over 50 peer-reviewed articles. Teaching excellence is evident through her three Outstanding Teaching Awards at Missouri S&T.

• Biochemistry
• Cardiovascular Research
• Cell Biology
• +۳ مورد دیگر

Yang Li is an Associate Research Scientist in the Department of Cell Biology at Yale School of Medicine. They hold a PhD in Bioengineering from Clemson University (2020) and an MS in Optical Engineering from Tianjin University (2015). Their research focuses on integrating statistical methodologies with biomedical applications, particularly in cancer heterogeneity analysis, clinical trial design, and single-cell transcriptomics. Current work emphasizes model selection uncertainty, adaptive randomization techniques, and prior information integration in genomic studies. Education: PhD in Bioengineering, Clemson University (2020) MS in Optical Engineering, Tianjin University (2015) Research interests span: Statistical model development for biomedical data Cancer heterogeneity through gene expression networks Adaptive clinical trial methodologies Single-cell data analysis Their recent publications highlight innovations in covariate-adjusted randomization and penalized estimation techniques for imputed datasets. Lab affiliations include the Bewersdorf Lab, where they collaborate on spatial omics and multi-omics sequencing technologies. No awards explicitly listed, but their active grant-funded research includes NIH-supported projects on statistical methods for clinical trials and genomic analysis.

• Geostatistics
• Spatial Epidemiology
• Environmental Health
• +۴ مورد دیگر

Manuel Ribeiro serves as an Invited Assistant Professor in the Department of Mineral and Energy Resources Engineering at the School of Engineering, University of Lisbon, where he teaches Sampling and Environmental Analysis Methods, Multivariate Analysis of Regionalized Data, and Statistical Learning for Earth and Environmental Engineering. Affiliated with the CERENA research unit, his academic role bridges engineering and environmental health sciences through spatial statistics applications. His research program centers on geostatistical methodologies for environmental epidemiology, with pioneering work on lichen-based air quality indicators and spatial uncertainty quantification in health exposure studies. Core interests include multivariate geostatistics for ecological associations, spatiotemporal disease modeling, and high-resolution environmental risk mapping, particularly examining air pollution's impact on birth outcomes and pandemic dynamics. Analysis of his 15 most recent publications reveals a dominant focus on applying geostatistical simulation to public health crises (notably COVID-19) and environmental health questions. His work consistently integrates R-based open-source tools, with significant contributions to uncertainty visualization in disease mapping and agricultural resource management. The research demonstrates strong regional emphasis on Portuguese case studies while developing transferable methodological frameworks for spatial epidemiology. No scientific awards were documented in the source materials. As part of CERENA's research ecosystem, Ribeiro collaborates extensively on interdisciplinary projects involving environmental monitoring and health impact assessment, though specific advising roles or grant details were not provided in the source texts. His methodological innovations in spatial statistics directly support CERENA's mission in earth resources and environmental sustainability.

• Tribology
• Material Surface Characterization
• Dimensional Metrology
• +۳ مورد دیگر

Zane Trubiņa is a Researcher at Riga Technical University (RTU), affiliated with the Faculty of Civil and Mechanical Engineering. She is part of the Material Surface Characterization and Tribology Group and the Advanced Material Tribology and Surface Characterization Professor Group. Her primary affiliation is within the Tribology Research Laboratory and collaborative institutes like the Institute of High-Performance Materials and Structures. Her research focuses on experimental data analysis, metrology (dimensional measurement systems), and tribology. She actively contributes to scientific projects, participates in conferences, and co-authors publications. Zane holds a Bc. sc. ing. qualification and utilizes her expertise in data visualization and instrument-based measurements. Zane’s work aligns with RTU’s broader initiatives in sustainable building materials and high-performance structural systems. She collaborates with senior researchers and maintains an academic profile via her Scopus Author ID: 57291758900.

Na Zhangمشاهده پروفایل

پژوهشگر

• Neuroscience
• Medical Imaging
• Functional Neuroimaging
• +۳ مورد دیگر

Na Zhang is an Assistant Research Professor at the Department of Physics, Penn State University (University Park, PA). Holding a Ph.D. in Physical Chemistry from Peking University (2019) and a B.S. in Chemistry from the same institution (2014), Zhang specializes in neuroimaging and functional connectivity studies using rodent models. Education: Ph.D. in Physical Chemistry, Peking University (2019) B.S. in Chemistry, Peking University (2014) Zhang's research focuses on functional MRI (fMRI) applications in neuroscience, including brain activity mapping, neurostimulation effects (e.g., theta burst stimulation), and neurovascular coupling analysis. Their work also explores multimodal imaging techniques like ultrafast ultrasound and photoacoustic systems for hemodynamic monitoring, alongside machine learning applications in neuroimaging data interpretation. Recent studies investigate sex-specific neural responses, memory networks, and consciousness-related deactivation pathways. Zhang’s publications demonstrate expertise in rodent functional neuroimaging, with emphasis on longitudinal brain stimulation effects, resting-state connectivity analysis, and cross-modal signal validation. Key trends include translational models of psychiatric disorders (e.g., PTSD, schizophrenia), neurodegenerative disease mechanisms (Alzheimer’s), and innovative imaging methodologies. Based at the 205A Osmond Lab, Zhang contributes to awake animal neuroimaging frameworks and collaborates on protocols enhancing functional connectivity specificity (e.g., StandardRat initiative).