جستجوی استاد راهنمای دانشگاهی

• Infectious diseases
• Gene transcription
• Omics
• +۳ مورد دیگر

Gustavo Sganzerla Martinez is a Research Fellow at the Department of Microbiology & Immunology, Faculty of Medicine, Dalhousie University. His research focuses on infectious diseases, gene transcription, omics, bioinformatics, and computational biology. He holds a BSc in Computer Science (2016), and a Master's and PhD in Biotechnology (2018), both from Universidade de Caxias do Sul, Brazil, with additional studies at California State University, Northridge (USA). His work integrates artificial intelligence with genomic and epidemiological data, particularly in viral pathogens like SARS-CoV-2, Mpox, and Rift Valley Fever Virus. Key projects include developing predictive tools for bacterial promoters (CDBProm), antigen epitope prediction (PoxiPred), and analyzing mobility data to model disease spread. He contributes to open-source bioinformatics tools like MPP and MLDSPP. His recent publications (2023–2025) address viral evolution, immune biomarker classification in ICU patients, and the impact of public transportation on pandemic dynamics. His GitHub profile showcases computational biology projects, reflecting his expertise in software development and data analysis.

• Genetics
• Aquaculture
• Quantitative Trait Loci (QTL)
• +۳ مورد دیگر

Herman W. Raadsma is a researcher at James Cook University's School of Marine and Tropical Biology. His work spans genetics, aquaculture, and genomics, focusing on shrimp breeding (Penaeus monodon, Litopenaeus vannamei) and bovine genetics. He collaborates extensively on studies involving quantitative trait loci (QTL), linkage disequilibrium, and genomic selection. His publications highlight advancements in Shrimp disease resistance (Gill-associated virus) Genomic tools for aquaculture breeding programs Conservation genetics in koalas and African wild dogs High-density SNP mapping and haplotype blocks Raadsma contributes to both experimental and computational approaches in genetics, including RAD-Seq assays, genome-wide selection models, and network visualization tools for population structure analysis. He has no listed scientific awards in this dataset.

• Pulmonary Medicine
• Critical Care Medicine
• Hospital Administration
• +۷ مورد دیگر

Jonathan Siner, MD, is an Associate Professor at Yale School of Medicine, specializing in Pulmonary, Critical Care & Sleep Medicine within the Department of Internal Medicine. He serves as Clinical Section Chief, Associate Chief Safety Officer for Yale New Haven Health, and Medical Director of the Tele-ICU. His work integrates hospital administration, critical care, and safety management. Education: MD from University of Pittsburgh School of Medicine (1998), BA from Swarthmore College (1992). Dr. Siner’s research focuses on translational and biomedical studies in critical care, including low-tidal volume ventilation for ARDS and swallowing dysfunction post-ventilation. His publications highlight applications of artificial intelligence in early warning scores, sepsis prediction, and clinical deterioration tools, reflecting trends in technology-driven patient safety initiatives. Yale Medicine Quality and Safety Award (2018) Senior Award in Medicine (American College of Physicians, 1998) Alpha Omega Alpha Honor Medical Society (1997) Sigma Xi Scientific Research Society (1992) He led initiatives to reduce central line-associated bloodstream infections by 50% at Yale New Haven Hospital and collaborates with multidisciplinary teams on ARDS, sepsis, and healthcare system improvements. His clinical leadership spans interstitial lung disease, tele-ICU operations, and integration of innovative tools across large health systems.

• Clinical Informatics
• Machine Learning
• Biomedical Data Science
• +۳ مورد دیگر

Hobart Patrick Young, PhD, is an Associate Research Scientist in the Department of Laboratory Medicine at Yale School of Medicine, Yale University. He holds a PhD in Geology and Geophysics from Yale (2016), an MS (2009) and BS (2007) in Earth Science from Rice University. Previously, he worked at Prometheus Research LLC on clinical data registry systems and at ExxonMobil on geostatistical modeling. Dr. Young's research integrates machine learning , clinical informatics , and quantitative methods to advance precision medicine. He leads architecture development for Yale New Haven Health System's Computational Health Informatics Platform (CHIP) and contributes to COVID-19 studies exploring immunological phenotypes, biomarker discovery, and health disparities. His recent publications focus on health informatics innovations , including quantum computing applications, AI-driven clinical architectures, and predictive modeling using real-world data. Studies frequently involve multi-institutional collaborations, such as the IMPACC cohort, to dissect disease mechanisms and outcomes. Awards & Honors: Best Paper Award – Industry Track (IEEE ICHI 2024) Bateman Fellow, Yale University (2009–2010) President’s Volunteer Service Award (AmeriCorps, 2003) He collaborates with the Schulz Lab and interdisciplinary teams (e.g., IMPACC consortium) on NIH-funded projects. Current work emphasizes scalable AI infrastructure for biomedical research and pandemic response analytics.

• Neuroscience
• Immunology
• Gut Microbiome
• +۳ مورد دیگر

Christopher Fields is an Associate Research Scientist at the Yale School of Medicine. He holds a PhD in Neuroscience from Georgia State University, an MS from Rockefeller University, and a BA in Biochemistry from Washington University. His research focuses on the intersection of gut microbiome, immunology, and behavioral neuroscience, particularly in understanding mechanisms linking gut dysbiosis to anxiety, compulsive behaviors, and movement disorders. He employs machine learning techniques to quantify behaviors in animal models and integrates sociological frameworks into addiction research. Recent work explores gut barrier dysfunction’s role in type 2 immunity and compulsive behavior, as well as sex-specific shifts in microbiota due to vasopressin deletion. He actively participates in academic discussions, including chairing the Social Neuroscience Discussion Series and presenting at conferences like the Society for Neuroscience. Key research interests include neuroimmunology, microbiome-brain interactions, and the application of computational methods to behavioral analysis. His publications span peer-reviewed journals in neuroscience, immunology, and virology, reflecting interdisciplinary approaches to understanding complex biological systems.

• 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.

• Topological Data Analysis
• Machine Learning
• Bioinformatics
• +۵ مورد دیگر

Xiang Liu is a Visiting Assistant Professor in the Department of Mathematics at Michigan State University, located in Wells Hall. His research focuses on integrating topological data analysis with machine learning to address challenges in molecular modeling, drug design, and complex systems analysis. He specializes in applying advanced mathematical frameworks like persistent homology, hypergraph theory, and sheaf models to study biological systems, protein interactions, and climate-related ecological networks. Key research interests include topological approaches for predicting protein-ligand binding affinities, modeling high-order interactions in complex systems, and analyzing networks in aging and microbiota dynamics. His work bridges algebraic topology, computational biology, and physics-informed modeling to develop novel algorithms for drug discovery and systems biology. Xiang’s recent publications highlight themes such as torsion graph neural networks for molecular property prediction, hypernetwork modeling for complex systems, and topological analysis of soil microbiota under global warming. His methods combine multiscale topological indices with machine learning to tackle problems in virology, metabolomics, and environmental science. No scientific awards or grants are explicitly listed in the provided information. He currently advises no students, though his research team likely collaborates on interdisciplinary projects within the Department of Mathematics and affiliated institutions.

• Structural Bioinformatics
• Bioinformatics
• Computational Biology
• +۳ مورد دیگر

Professor Charlotte Deane is a Professor of Structural Bioinformatics at the University of Oxford, leading the Oxford Protein Informatics Group in the Department of Statistics. She previously served as Deputy Executive Chair of the Engineering and Physical Sciences Research Council (EPSRC) and Response Director for UKRI’s Covid-19 Research. Her work bridges computational methods with biological problems, emphasizing interdisciplinary collaboration between academia and government. Her research interests include structural bioinformatics, computational biology, and leveraging AI to address public health challenges. She transitioned from chemistry to bioinformatics, advocating for effective communication between researchers and policymakers. This interview highlights her contributions to the UK’s rapid response to the pandemic and her advice for early-career researchers. No specific awards or grants are listed here, though her leadership roles reflect significant contributions to research policy. She heads a prominent research group focused on protein informatics and statistical methodologies in biology.

• Molecular Recognition
• Binding Promiscuity
• Protein-Ligand Interactions
• +۷ مورد دیگر

Rafaël Najmanovich is a Full Professor at Université de Montréal, affiliated with the Faculty of Medicine's Department of Pharmacology and Physiology and Department of Biochemistry and Molecular Medicine. His research focuses on molecular recognition, binding promiscuity, and drug design, with applications in understanding protein-ligand interactions, drug metabolism, and antibiotic development. He leads multiple grants, including projects funded by CRSNG, Génome Québec, and FRSQ. His expertise spans structural biology, systems biology, and computational methods for drug discovery. He has advised numerous students, including Master’s and PhD candidates, focusing on topics like SARS-CoV-2 spike glycoprotein dynamics and computational drug repurposing. His work bridges structural biochemistry with clinical applications, aiming to predict protein functions and design innovative therapies. Key research areas include studying binding promiscuity in proteins, developing computational tools for protein design, and understanding molecular mechanisms in diseases like neurodegenerative disorders and viral infections. His lab integrates structural studies with AI-driven approaches to tackle challenges in drug development and molecular medicine.

Katrin Ackermannمشاهده پروفایل

پژوهشگر ارشد

• EPR Spectroscopy
• Biophysics
• Structural Biology
• +۳ مورد دیگر

Katrin Ackermann is a Senior Research Fellow at the University of St Andrews, affiliated with the School of Chemistry and the Institute of Behavioural and Neural Sciences. Her work focuses on EPR (Electron Paramagnetic Resonance) spectroscopy, applied to structural biology and biomolecular interactions. She has contributed to over 50 peer-reviewed publications and leads a research project on Cu(II)-19F distance measurements for structural biology. Her research interests include protein dynamics, membrane channel structure, and metal-binding sites in biomolecules. Notable contributions involve using pulse EPR techniques to study multimeric systems, such as mechanosensitive membrane channels and viral proteins. She collaborates internationally on projects spanning EPR method development and applications in biochemistry. Recent work highlights include characterizing copper-binding sites in human serum albumin and investigating the conformational flexibility of biomolecules using advanced EPR methods. Her research also explores the role of cyclic nucleotides in CRISPR defense mechanisms and the application of machine learning to chronobiological analyses. Ackermann has created over 25 datasets and software tools, including spin-labeling methodologies and protocols for low-concentration EPR studies. She actively engages in grant-funded projects and has supervised collaborations across multiple universities.

Regina Barzilayمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Healthcare AI
• +۵ مورد دیگر

Regina Barzilay is the School of Engineering Distinguished Professor of AI & Health at MIT, affiliated with the Computer Science and Artificial Intelligence Lab (CSAIL). She leads the Jameel Clinic for Machine Learning in Health, focusing on AI applications in oncology and drug discovery. Her work bridges machine learning, natural language processing, and medical imaging, with significant contributions to breast cancer detection and drug design. Education: Ph.D. in Computer Science (Columbia University, 2000). Notable academic roles include Ross Career Development Professor (2006) and Delta Electronics Professor (2016). Research: Pioneers in AI for healthcare, developing algorithms for early cancer detection via mammograms and molecular design for drug discovery. Her team created CellVi, a tool for tumor analysis, and advanced neural networks for deciphering ancient scripts. Awards: MacArthur Fellow (2017), AAAI Squirrel AI Award (2021), and multiple fellowships including ACL and AAAS memberships. Her lab collaborates across disciplines, with projects spanning clinical AI, generative models for proteins, and ethical AI frameworks. She advises over 15 students, advancing both theoretical and applied machine learning.

Francesco Bravoمشاهده پروفایل

دانشیار

• Econometric Theory
• Statistical Theory
• Nonparametric Methods
• +۳ مورد دیگر

Dr. Francesco Bravo is a Reader in the Department of Economics at the University of York. His research focuses on econometric theory, statistical theory, and applied econometrics, with particular emphasis on nonparametric and semiparametric methods, higher-order asymptotic theory, and missing data analysis. He holds a Laurea from the University of Udine, an MSc from the University of Southampton, and a PhD from the University of Southampton. His research interests include nonparametric likelihood, generalized empirical likelihood methods, and the development of robust statistical techniques for complex models. He has contributed to advancements in econometric methods for time series and panel data analyses, particularly in handling dependent data structures and model misspecification. Dr. Bravo has published widely in top-tier journals such as the Annals of the Institute of Statistical Mathematics , Journal of Time Series Analysis , and Statistics & Probability Letters . His work often bridges theoretical contributions with practical applications, addressing challenges in economic and health-related data analysis. He has secured research funding, including a project on Sieve non-parametric methods for unit root tests funded by the Economic and Social Research Council (ESRC). He teaches undergraduate courses in Probability and Statistics, Econometric Methods for Research, and graduate-level econometrics. His office hours are Wednesday 11:00-13:00, and he can be reached at francesco.bravo@york.ac.uk for appointments.

Susan Schiffmanمشاهده پروفایل

استاد مدعو

• Sensory Science
• Environmental Health
• Food Chemistry
• +۳ مورد دیگر

Susan Schiffman is an Adjunct Professor at North Carolina State University. Her research focuses on sensory science, environmental health, and toxicology, with a particular emphasis on artificial sweeteners, odor measurement standards, and the impact of medications on taste and smell. She has contributed to the development of IEEE standards for electronic olfaction devices and conducted extensive studies on sucralose's biological effects. Her work spans interdisciplinary areas including food chemistry, environmental engineering, and biomedical applications. Schiffman explores how sensory perception changes with aging, medication use, and disease, with applications in improving nutritional status for vulnerable populations. She has also investigated environmental odors from industrial swine operations and their health implications, employing advanced sensor technologies like field asymmetric ion mobility spectrometry. Key contributions include analyzing the interaction between artificial sweeteners and gut microbiota, evaluating odor dispersion modeling, and assessing health risks associated with airborne contaminants. Her research integrates engineering, biology, and public health, with a focus on translating scientific findings into practical solutions for regulatory and clinical domains.

Marie Erikssonمشاهده پروفایل

استاد

• Statistics
• Epidemiology
• Clinical Trials
• +۳ مورد دیگر

Marie Eriksson is a Professor of Statistics at the Umeå School of Business, Economics and Statistics (USBE) , specializing in statistical applications in medicine. She also holds an associate professorship (docent) in experimental medicine and is affiliated with the Department of Public Health and Clinical Medicine at Umeå University. Her work focuses on improving healthcare quality through methodological advancements in register studies, epidemiology, and clinical trials. Key affiliations include leadership roles in the Stat4Reg research group and the Swedish National Quality Register for Stroke Care (Riksstroke) . Her research addresses socioeconomic disparities in healthcare outcomes, particularly in stroke care, using large-scale register data. Notable projects include developing machine learning models for predictive analysis in complex diseases and mediating analysis of stroke outcomes. She defended her thesis in Public Health and Clinical Medicine at Umeå University in 2008, became an associate professor in 2012, and was appointed Professor in Statistics in 2017. Her 20+ years of experience include leading the MONICA survey in northern Sweden and consulting in biostatistics. Current grants span innovative statistical methods for register data and integrative machine learning in healthcare analytics. Her interdisciplinary projects examine access and outcomes across demographic factors like sex, age, and socio-economic status. Recent work highlights stroke recurrence risks, post-COVID syndrome analysis, and disparities in myocardial infarction care. She collaborates internationally on register-based studies and clinical trials.

Zeny Fengمشاهده پروفایل

استاد

• Statistics
• Bioinformatics
• Artificial Intelligence
• +۳ مورد دیگر

Dr. Zeny Feng is a Professor of Statistics and Graduate Coordinator in the Department of Mathematics and Statistics at the University of Guelph. Her research focuses on statistical methodologies applied to epidemiology, healthcare policy, and computational biology. She holds a B.Sc. in Statistics from York University (1999), an M.Math. in Biostatistics from the University of Waterloo (2000), and a Ph.D. in Statistics from the University of Waterloo (2005). Her research interests span statistical modeling for infectious diseases (e.g., hepatitis C elimination strategies), cost-effectiveness analyses of novel therapies (e.g., CAR T-cell treatments), and bioinformatics applications including metabolomics and pathogen genomics. She develops computational tools like Pathogen.jl for disease transmission modeling and has contributed to agent-based simulations for public health decision-making. Her work integrates statistical theory with real-world challenges, bridging epidemiological surveillance systems and health utility assessments. Recent studies address disparities in patient care outcomes and optimize resource allocation for chronic disease management in diverse settings. Publications from 2022–2025 highlight methodological advancements in mixed-type data imputation, agent-based modeling for viral eradication, and precision medicine applications. Her interdisciplinary approach addresses global health challenges through rigorous statistical frameworks.