جست‌وجوی استاد و پژوهشگر

• Infectious Disease Ecology
• Malaria Vector Ecology
• Vector-Borne Disease Control
• +۵ مورد دیگر

Professor Heather M Ferguson is a leading researcher in infectious disease ecology at the University of Glasgow, specializing in malaria vector ecology and control. She holds a professorship within the Institute of Biodiversity, Animal Health and Comparative Medicine (BAHCM) in the College of Medical, Veterinary and Life Sciences. Her educational background includes a BSc (Honours) in Zoology from the University of Toronto, an MSc in Zoology (conservation biology) from the University of British Columbia, and a PhD from the University of Edinburgh focused on malaria vector-parasite interactions. She completed postdoctoral research at the University of Edinburgh and the Ifakara Health Institute in Tanzania before joining the University of Glasgow as a BBSRC David Philips Research Fellow in 2006. Professor Ferguson's research focuses on the ecology and behavior of African malaria vectors, surveillance and control of mosquito vectors of malaria and arboviruses, the impact of environmental change on vector-borne diseases, and assessing the risk of mosquito-borne disease emergence in Scotland. Her recent publications demonstrate a strong emphasis on vector genetics, insecticide resistance mechanisms, mosquito behavior, and innovative surveillance methods including spectroscopy and geospatial modeling. Her work spans multiple continents with significant research conducted in Tanzania, Ecuador, Malaysia, and Scotland. She maintains a close research partnership with the Ifakara Health Institute in Tanzania where she holds a Visiting Scientist appointment. Her research has significant implications for malaria control programs in endemic regions and contributes to understanding potential vector-borne disease risks in non-endemic areas like Scotland. Professor Ferguson's work bridges fundamental vector ecology with practical applications for disease control, making substantial contributions to both scientific understanding and public health practice.

Liam Brierleyمشاهده پروفایل

پژوهشگر ارشد

• Virology
• RNA Virus Ecology
• Computational Biology
• +۷ مورد دیگر

Dr. Liam Brierley is a Research Fellow in Virology and Affiliate Researcher at the School of Biodiversity, One Health & Veterinary Medicine, University of Glasgow. His work focuses on applying computational methods to understand RNA virus cross-species transmission, leveraging statistical modeling, machine learning, and text mining tools. He holds a MRC Career Development Award (2024-2029) and previously received a MRC Skills Development Fellowship (2019-2022). His research spans ecological modeling of host-virus relationships, viral sequence analysis, and pandemic preparedness frameworks. Research interests include: Machine learning for RNA virus trait prediction (genotype-to-phenotype) Systematic data mining for host-virus relationship analysis Large-scale viral sequence analysis Ecological drivers of zoonotic transmission Key contributions include modeling RNA virus discovery patterns, preprint analysis during the pandemic, and computational tools for ctDNA variant detection. He has published over 20 peer-reviewed articles, including work in Proceedings of the Royal Society B , PLOS Pathogens , and Molecular Biology and Evolution . He serves on editorial boards for Scientific Data and Frontiers in Virology , and is a Fellow of the Royal Statistical Society. His outreach includes science communication through Edinburgh Fringe Festival performances and Bright Club Glasgow stand-up comedy events.

Youyi Fongمشاهده پروفایل

عضو هیئت علمی

• Biostatistics
• Machine Learning
• Computer Vision
• +۷ مورد دیگر

Youyi Fong is an Affiliate Professor in the Department of Biostatistics at the University of Washington. He holds a PhD in Biostatistics from the University of Washington and dual PhDs in Molecular, Cellular, and Developmental Biology and Computer Science from Indiana University. His research focuses on statistical methods for vaccine efficacy trials, immune correlates analysis, machine learning applications in medical imaging, and computational biology. He leads the Fong Group, developing tools for cell segmentation and phenotyping using deep learning and computer vision techniques. Key areas of research include analyzing clinical trial data for HIV, SARS-CoV-2, and dengue vaccines, as well as developing statistical frameworks for immune response modeling. His work integrates biostatistical methodologies with cutting-edge computational approaches, such as autoencoder-based unsupervised learning and kernel-based random effects models. Collaborations span infectious disease research, cell imaging analysis, and public health policy. Education: MS in Computer Science, Indiana University PhD in Molecular, Cellular, and Developmental Biology, Indiana University PhD in Biostatistics, University of Washington Research interests emphasize vaccine development, threshold regression models, and computational tools for biomedical imaging. His lab has contributed software like TSP (The Seattle Pipeline) for cell and tissue imaging analysis, which includes cell segmentation, spatial analysis, and statistical methods for multiplexed data. Publications span topics such as immune correlates of protection, vaccine durability, and statistical methodologies for clinical trials. His work bridges biostatistics, machine learning, and translational medicine to address global health challenges.

• Bioinformatics
• Systems Biology
• Biotechnology
• +۳ مورد دیگر

ÓSCAR MANUEL LIMA DIAS is an Auxiliary Researcher at the University of Minho's Centre of Biological Engineering (CEB) since 2016 and CEO of OMNIUMAI, a company specializing in AI and data sciences. He holds a BSc in Biological Engineering, MSc in Informatics, and PhD in Chemical and Biological Engineering from the University of Minho. His academic roles include serving on the Steering Committee of the Master's in Bioinformatics and contributing to the Scientific Steering Committee of the Department of Biological Engineering. Roles: Auxiliary Researcher (2016–present), CEO of OMNIUMAI (2020–present), Member of Multiple Scientific Committees Education: PhD (2013), MSc (2008), BSc (2005) from the University of Minho Research Interests: Bioinformatics, Systems Biology, Metabolic Modeling, Machine Learning applications in biology, and Biotechnology. His work spans microbial metabolism, synthetic biology, and computational tools for metabolic network analysis. Publications & Projects: Over 57 peer-reviewed publications, leading roles in projects like iPlants.pt and Methane Mitigation Strategies. Collaborations with institutions in Denmark, Germany, Netherlands, Spain, UK, and USA. Labs & Teams: Active in the Centre of Biological Engineering, Labbels, and the Associate Laboratory on Biotechnology. Contributions to frameworks like merlin and TranSyT for metabolic modeling.

Yongmann Chungمشاهده پروفایل

دانشیار

• Computational Fluid Dynamics
• Turbulence Modeling
• Flow Control and Drag Reduction
• +۴ مورد دیگر

Yongmann Chung is an Associate Professor at the School of Engineering, University of Warwick. His research focuses on Computational Fluid Dynamics (CFD), turbulence modeling, and flow control with applications in aerospace engineering and electrochemistry. He specializes in Large Eddy Simulation (LES) and Direct Numerical Simulation (DNS) of complex flows, including micro-fluid dynamics in electrochemical systems and drag reduction strategies. His work spans unsteady turbulent flows, heat transfer, and aerodynamic phenomena in systems like flying cars and quadcopters. Teaching interests include fluid mechanics (ES2A7), computational fluid dynamics (ES440), and scientific computing (MA5P9). Recent publications explore battery health prediction using graph neural networks, vortex ring state analysis, and computational models for droplet dispersion in public spaces. Chung’s research also addresses industrial challenges such as cavitation erosion monitoring and turbulent flow control via Lorentz force actuation. Office hours are Fridays 10 am–12 pm during term time. He advises projects and grants in fluid dynamics and thermal engineering, with a focus on both fundamental turbulence research and applied energy systems. His work extends to bio-inspired design, exemplified by studies on dolphin kick swimmers, and environmental health modeling for airborne disease transmission.

• Adaptive Immunity
• Bioinformatics
• Computational Biology
• +۵ مورد دیگر

Lindsay Cowell is a Professor in the Department of Population and Data Sciences at UT Southwestern Medical Center, with a secondary appointment in the Department of Immunology within the School of Biomedical Sciences. She directs research in adaptive immunity and bioinformatics, affiliated with the Immunology, Cancer Biology, and Computational and Systems Biology graduate programs. Her educational background includes an M.S. in Biomathematics (1995) and Ph.D. in Biomathematics (2000), both from North Carolina State University, followed by a postdoctoral fellowship at Duke University Medical Center. She joined UT Southwestern in 2010 after serving as an Assistant Professor in Duke's Department of Biostatistics and Bioinformatics. Dr. Cowell's research focuses on adaptive immune receptor repertoires, leveraging high-throughput sequencing and computational methods to study immune responses in cancer, autoimmune diseases (e.g., multiple sclerosis), and infections (e.g., HIV, SARS-CoV-2). Her group develops innovative approaches for representing immune repertoires as antigen specificity landscapes and detecting diagnostic patterns. Recent work emphasizes clinical applications through projects like VDJServer and AIRR Community initiatives. Analysis of her 15 most recent publications reveals a strong trend toward translational immunology, with significant contributions to SARS-CoV-2 complications, cancer biomarker discovery, and immune repertoire data standards. Her work bridges computational biology with clinical immunology, particularly in HPV-related cancers and autoimmune diagnostics. As a principal investigator, she leads collaborative projects across multiple institutions and serves on editorial boards for immunoinformatics resources. Her lab maintains active involvement in the AIRR Community, driving standards for immune repertoire data sharing and analysis.

Farnoush Farahpourمشاهده پروفایل

پژوهشگر

• Computational Biology
• Immunology
• Microbial Ecology
• +۳ مورد دیگر

Dr. Farnoush Farahpour is a Researcher at the University of Duisburg-Essen, affiliated with the Faculty of Biology and Faculty of Medicine. She leads the Junior Group Farahpour Lab, focusing on computational methods to understand complex biological systems. Her research integrates data analysis and mathematical modeling to study microbial communities, T-cell receptor repertoires, and immune responses in diseases like cancer and diabetes. Key areas include developing tools for interaction inference and multi-level modeling of systems biology problems. Research Interests: Overcoming complexity in biological systems through computational approaches, microbial ecology, immune system dynamics, systems biology, and the application of stochastic models. Recent work explores gut microbiota’s role in HIV, SARS-CoV-2 pathogenesis, and therapeutic effects of BRAF/MEK inhibitors in melanoma. Recent Trends in Publications: Dr. Farahpour’s work emphasizes computational tools (e.g., scBubbletree for single-cell RNA-seq visualization) and microbiome studies, linking microbial diversity to disease outcomes. Her publications reflect a focus on translational research, bridging computational biology with clinical applications in oncology and infectious diseases. Grants & Labs: Operates the Junior Group Farahpour Lab, actively involved in funded projects on oncology and immunology. Collaborates widely, evidenced by co-authorships across multidisciplinary teams. Ongoing projects aim to expand computational frameworks for analyzing complex biological interactions and their clinical implications.

Antonio Martinez Sanchezمشاهده پروفایل

پژوهشگر

• Artificial Intelligence
• Bioinformatics
• Image Processing
• +۵ مورد دیگر

Antonio Martinez Sanchez is a Ramón y Cajal Researcher at the University of Murcia's Faculty of Informatics, Department of Information and Communication Engineering. His research focuses on computational methods for cryo-electron tomography, artificial intelligence, and bioimaging. He holds a PhD from the University of Almería (2013), specializing in differential geometry-based computational techniques for electron tomography. Education : PhD in Computer Science, Universidad de Almería (2013) His research interests include deep learning applications in microscopy, membrane protein analysis, and high-performance computing for 3D imaging. Recent work emphasizes template matching, orientation estimation, and automated pipelines for cryo-electron tomography. He leads the AIKE (Artificial Intelligence and Knowledge Engineering) research group and contributes to tools like ScipionTomo and MemBrain. Articles highlight advancements in computational methods for structural biology, such as fast cross-correlation algorithms and deep learning-driven orientation estimation. These techniques address challenges in analyzing cellular architecture and viral interactions. His work bridges computer science and biology, advancing tools for subcellular imaging and data reproducibility. Labs/Teams : AIKE: Artificial Intelligence and Knowledge Engineering research group.

PAOLO CRIPPAمشاهده پروفایل

دانشیار

• Biomedical Signal Processing
• Embedded Systems
• Neural Networks
• +۵ مورد دیگر

Prof. Paolo Crippa is an Associate Professor at the Department of Information Engineering (DII) of the University of Ancona (Università Politecnica delle Marche), Italy. His research focuses on biomedical signal processing, embedded systems, and wearable technology for healthcare applications. He specializes in neural networks, sensor design, and nanoelectronics, with notable contributions to graphene-based biosensors and real-time gesture recognition systems. His work bridges electronics, machine learning, and healthcare, addressing challenges in disease diagnosis, activity monitoring, and assistive living technologies. Prof. Crippa’s publications emphasize the development of low-power, high-accuracy systems for applications such as EEG-based disease classification, wearable sensors for activity recognition, and SARS-CoV-2 detection using advanced materials. His recent work includes optimizing LSTM neural networks for embedded platforms and integrating multimodal sensors (PPG, IMU, sEMG) for health monitoring. His research trends reveal a strong emphasis on applying machine learning to biomedical signals, improving sensor synchronization, and leveraging nanotechnology for diagnostic tools. He also explores open-source hardware/software co-design frameworks for system-on-chip (SoC) development. Prof. Crippa’s contributions extend to international collaborations and interdisciplinary projects, reflecting his role in advancing both theoretical and applied aspects of information engineering.

ALDO FRANCO DRAGONIمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +۳ مورد دیگر

Prof. Aldo Franco Dragoni is an Associate Professor at the Department of Information Engineering, Polytechnic University of Marche (UNIVPM). He specializes in artificial intelligence applications for healthcare, cybersecurity, and smart systems. His research spans machine learning, computer vision, and multi-agent systems, with notable projects like CLAUDIA (Alzheimer's diagnosis via 3D MRI) and Cloud-YLung (lung cancer histology classification). He leads initiatives in medical imaging analysis, violence detection, and IoT-based systems. Research interests include AI-driven medical diagnostics, cyber-physical systems, and BDI agent architectures for ambient assisted living (e.g., Virtual Carer project). His work integrates cloud computing, neural networks, and multi-agent reasoning for healthcare solutions. Publications focus on AI applications in medical imaging, surveillance, and smart environments. He contributes to academic publications and conference proceedings, co-authoring papers on topics like deep learning for violence detection, face recognition systems, and blockchain in multi-agent negotiations.

• Computational Physics
• Molecular Modelling
• Atomistic Simulations
• +۳ مورد دیگر

Dr. Umberto Terranova serves as a Lecturer in Chemical Physics at the University of Buckingham's School of Health Sciences since 2019. His expertise combines computational physics and molecular modelling to address sustainable energy challenges, with particular focus on catalytic systems for CO 2 conversion and renewable fuel production. Academic background: BSc Physics, Università di Pisa MSc Physics (with internship at National Enterprise for nanoScience and nanoTechnology, Italy) PhD Physics, University College London (2013; research at National Institute of Materials Science, Japan) Dr. Terranova specializes in atomistic simulations using density functional theory and classical force fields to model complex systems from inorganic materials to proteins. His research centers on two interconnected themes: (1) metalloenzymes for CO 2 reduction, particularly iron-sulfur clusters in viral and biological contexts, and (2) oxide perovskites like SrTiO 3 for photocatalytic hydrogen evolution. This work bridges quantum chemistry, biochemistry, and materials science to develop sustainable catalysts for energy applications. Analysis of his 2020-2025 publications reveals consistent focus on computational catalysis, with emerging integration of machine learning methods. Key research threads include CO 2 hydrogenation, oxygen evolution reaction mechanisms, and chirality-spin interactions, demonstrating interdisciplinary impact across virology, energy storage, and space medicine. Research Funding: HPC-Europa3 programme (2019): Collaborative project with University of Barcelona on artificial photosynthesis. As a lecturer, Dr. Terranova contributes to teaching within the School of Health Sciences and likely supervises computational research projects, though specific student advisees are not documented in the provided materials. His work shows strong translational potential for carbon capture technologies and renewable energy systems.

Kristian Svendsenمشاهده پروفایل

دانشیار

• Pharmacoepidemiology
• Adverse Drug Reactions
• Antibiotic Use Patterns
• +۴ مورد دیگر

Kristian Svendsen is an Associate Professor in Pharmacoepidemiology at the Department of Pharmacy, Faculty of Health Sciences, UiT The Arctic University of Norway. He holds a part-time position as a Pharmacoepidemiologist at NordicRWE in Oslo since 2022. His research focuses on rational medication use, adverse drug reactions (ADRs), antibiotic utilization, and elderly medication safety. He is actively involved in projects such as the IMMENSE study (improving medication safety in geriatric patients) and the Tromsø Study (population-based health research). Key methodologies include network analysis, machine learning for predicting hospital admissions, and time-and-motion studies in emergency departments. He has collaborated with institutions like the European Medicines Agency and led projects on medication reconciliation and pharmacist roles in acute care settings. His work integrates clinical pharmacy practices with epidemiological data analysis to address healthcare challenges such as antibiotic resistance and medication-related hospitalizations. Notable contributions include analyzing ADR reporting systems, studying SARS-CoV-2 outbreaks on cruise ships via network analysis, and evaluating the impact of clinical pharmacists in emergency teams. He serves on the editorial board of the Norwegian Pharmaceutical Journal and contributes to international networks addressing medicines shortages and healthcare efficiency.

• Learning and Memory Formation
• Ethanol Tolerance
• Exploration and Responses to Novel Stimuli
• +۳ مورد دیگر

Gregg Roman is a Research Professor in the Department of BioMolecular Sciences at the University of Mississippi's School of Pharmacy, and holds an additional role as Research Professor in the Research Institute of Pharmaceutical Sciences. He directs the GlyCORE imaging CORE and previously co-directed the Brain-Wellness Constellation Program. Roman's career includes faculty roles at Baylor College of Medicine and the University of Houston, where he founded the Biology of Behavior Institute. His research focuses on behavioral plasticity mechanisms, particularly ethanol tolerance and exploration behaviors in Drosophila melanogaster. Education: Ph.D. in Biology from the University of Pennsylvania (1995). Research interests include learning/memory formation, ethanol tolerance, and exploration responses, utilizing genetic, physiological, and behavioral approaches in Drosophila. Key topics involve how brains adapt to alcohol exposure and the genetic basis of exploratory behavior. Recent work advances imaging technologies and machine learning applications for genotype prediction from behavior. Scientific achievements include awards such as the Dean Scholar Award for Research Excellence and the Henry and William Test Investigator recognition. His lab pioneered tools like SCAMP for circadian rhythm analysis and PySmooth for genotyping error correction. Leadership roles include directing imaging facilities and co-developing interdisciplinary programs. Ongoing projects address neuroHIV applications via nanoparticle delivery and explore genetic interactions in alcohol sensitivity.

Ahmad Hosseinizadehمشاهده پروفایل

استادیار

• Computational Biophysics
• Machine Learning in Biology
• Structural Dynamics of Biomolecules
• +۳ مورد دیگر

Dr. Ahmad Hosseinizadeh is an Assistant Professor of Physics at the University of Wisconsin–Milwaukee (UWM), where he has been affiliated since 2011. He holds a Ph.D. in theoretical physics from Laval University (2010) and a Master's in theoretical astrophysics from the Institute for Advanced Studies in Basic Sciences, Iran. His research focuses on computational biophysics, developing machine learning algorithms to analyze large-scale biological datasets and study ultrafast structural dynamics of biomolecules at high resolution. He is a core member of BioXFEL, an NSF-funded center leveraging X-ray Free Electron Lasers (XFEL) for biological research. His work emphasizes data-driven methods applied to XFEL, electron microscopy, and NMR data, with a focus on structural biology and dynamics. Collaborations include developing novel techniques for time-resolved crystallography and single-particle imaging. His research bridges theoretical physics, computational science, and experimental biology, addressing challenges in understanding molecular mechanisms at atomic scales. Dr. Hosseinizadeh's group, the Computational Biophysics Lab, creates algorithms to extract information from XFEL and other high-dimensional datasets, advancing structural biology and interdisciplinary applications. His contributions have been published in journals like Nature , Nature Methods , and Structural Dynamics .

Anna Tarakanovaمشاهده پروفایل

استادیار

• Molecular Modeling
• Multiscale Simulation
• Biological Materials
• +۳ مورد دیگر

Anna Tarakanova is an Assistant Professor in the Department of Mechanical Engineering at the University of Connecticut. She holds a B.S. from Cornell University and M.S./Ph.D. from MIT, followed by postdoctoral training at MIT. Her research group focuses on advancing molecular and multiscale modeling methods to study biological materials and disease mechanisms. Research Focus Prof. Tarakanova's research integrates computational approaches to understand structure-function relationships in biological systems. Key areas include: Molecular dynamics of proteins and biomaterials Mechanobiology of aging tissues Computational design of bioinspired materials Disease mechanism modeling at multiple scales Publications Her research output demonstrates strong interdisciplinary focus combining mechanics, materials science, and biology. Recent publications show increasing emphasis on medical applications including cancer diagnosis, cardiovascular mechanics, and viral protein dynamics. Methodological innovations consistently integrate machine learning with multiscale modeling approaches. Research Group Leads an active laboratory with multiple PhD students and postdoctoral researchers working on biomolecular simulations and computational materials design. The group develops frameworks for predictive materials design and disease mechanism investigation.