جستجوی استاد راهنمای دانشگاهی

Barbara Sharanowskiمشاهده پروفایل

استاد

• Phylogenetic Systematics
• Insect Evolution
• Genomics
• +۳ مورد دیگر

Barbara Sharanowski is a Professor in the Department of Biology at the University of Central Florida (College of Sciences). Her research integrates phylogenetics, genomics, and ecology to study parasitic Hymenoptera evolution, particularly Braconidae wasps, with applications in biodiversity conservation and biocontrol. She directs an active lab developing mobile tools for integrated pest management. Education includes advanced degrees in Entomology/Evolutionary Biology. Research focuses on: Macroevolutionary patterns in parasitic wasps Genome evolution and viral symbionts in insects Biocontrol optimization using digital solutions Publications emphasize insect systematics, with recent work exploring genomic adaptations in parasitoids and field applications for sustainable agriculture. Awards include leadership of the International Society of Hymenopterists and NSF grants. Mentorship spans 6+ graduate students studying wasp taxonomy and tri-trophic interactions. Lab activities include international fieldwork and bioinformatics tool development.

Siavash Vahidiمشاهده پروفایل

استادیار

• Structural Biology
• Protein Degradation
• Mycobacterium tuberculosis Research
• +۴ مورد دیگر

Dr. Siavash Vahidi is an Assistant Professor in the Department of Molecular and Cellular Biology at the University of Guelph. His research focuses on understanding the structure, function, and dynamics of large biomolecular machines, particularly those involved in protein degradation in pathogens like Mycobacterium tuberculosis . He employs advanced techniques such as mass spectrometry (H/D exchange, native MS) and high-field NMR spectroscopy to study these systems. His lab is actively recruiting students and postdocs, emphasizing a commitment to diversity and training in cutting-edge methodologies. Education: BSc in Chemistry, National University of Iran, Tehran PhD in Chemistry and Biochemistry, University of Western Ontario CIHR Postdoctoral Fellowship, University of Toronto & The Hospital for Sick Children Research: Key interests include the M. tuberculosis proteasome system, ClpP proteases in human mitochondria, and the role of allostery in substrate selection. The lab’s integrative approach combines structural biology with biochemical and computational methods, aiming to identify novel drug targets for tuberculosis and other diseases. Awards: Paul de Mayo Award for Best PhD Thesis Lab & Training: The Vahidi Lab emphasizes interdisciplinary training, offering expertise in mass spectrometry, NMR, and computational tools (e.g., Python, Linux). Supported by grants and collaborations, the lab fosters an inclusive environment with a strong focus on mentorship. Lab Resources: Website: Vahidi Lab Twitter: @VahidiLab

Yang Xuمشاهده پروفایل

استادیار

• Plant Lipid Metabolism
• Microalgal Biotechnology
• Synthetic Biology
• +۳ مورد دیگر

Dr. Yang Xu is an Assistant Professor in the Department of Molecular and Cellular Biology at the University of Guelph. He holds a PhD in Plant Science from the University of Alberta, where he conducted postdoctoral research on plant lipid metabolism. Previously, he worked at Michigan State University and the University of Alberta, focusing on chloroplast lipid assembly and microalgal oil production. His research integrates synthetic biology and biotechnology to engineer lipid biosynthesis pathways in plants and microorganisms for applications in food, fuel, and materials. Dr. Xu’s work has been recognized with the 2021 Carl Douglas Prize from the Canadian Society of Plant Biologists. His research group investigates acyl lipid assembly, lipid trafficking, and synthetic biology approaches to enhance oil production. Key areas include understanding membrane lipid dynamics in photosynthetic organisms and designing pathways for value-added lipids. The lab employs genetics, biochemistry, and omics tools to address both fundamental and applied questions in lipid metabolism. Dr. Xu has mentored graduate students including Alyssa Clews, Brandon Ulch, and Monika Jesionowska, and his team collaborates on projects related to microalgal EPA/DHA synthesis and rice lipid genetics. Current and former lab members include researchers in biotechnology, plant genetics, and lipid biochemistry.

Azizur Rahmanمشاهده پروفایل

استاد

• Data Science
• AI and Machine Learning
• Statistics
• +۷ مورد دیگر

Professor Azizur Rahman is a leading academic in applied statistics and data science at Charles Sturt University (Australia), where he leads the Data Mining Research Group and directs the Data Analytics Lab. He holds a PhD in Economics and Applied Statistics from the University of Canberra, with postdoctoral experience at the University of Adelaide. His expertise spans statistical methodologies, microsimulation, AI/ML, and data-driven policy analysis. He has attracted over $4.03M in research funding and published over 200 scholarly works. Education: BSc (Honours) in Statistical Science, University of Chittagong (2000) MSc (Thesis) in Biostatistics, University of Chittagong (2003) PhD in Economics and Applied Statistics, University of Canberra (2010) Research Focus: Big data analytics and AI applications in health, environment, and policy Bayesian inference and multilevel modeling Spatial microsimulation for socioeconomic analysis Water quality modeling and environmental systems Public health challenges in developing nations Key Contributions: Developed novel computational methods for microsimulation modeling Founder of the Data Analytics Lab at CSU Editorial roles in journals like PLOS ONE and International Journal of Microsimulation Recipient of 2023 ANZRSAI Outstanding Service Award and Charles Sturt Excellence Awards Grants & Awards: $4.03M+ external research funding secured 30+ awards including travel grants, fellowships, and service recognitions Advising & Mentorship: Supervised 6 PhD/DIT students and co-supervised many more. Active in mentoring early-career researchers through editorial boards and conference organizing roles. Labs & Collaborations: Leads DaMRG (Data Mining Research Group) and collaborates with global institutions on interdisciplinary projects involving AI/ML, environmental science, and public health.

Jun Baiمشاهده پروفایل

استادیار

• Machine Learning
• Deep Learning
• Medical Image Analysis
• +۲ مورد دیگر

Jun Bai is an Assistant Professor in the Department of Computer Science at the University of Cincinnati's College of Engineering and Applied Science. His research focuses on Machine Learning, Deep Learning, Medical Image Analysis, AI-driven diagnostics for cancer and diseases, and drug discovery. He holds a Ph.D. in Computer Science and Engineering from the University of Connecticut (2023), an M.S. in Computer Science from the University of Dayton (2019), and an M.S. in Interdisciplinary Studies in Education (2015). His work emphasizes applying AI to healthcare challenges, such as robust mammogram classification, 3D biomedical image registration, and peptide generation for drug discovery. Recent studies include hybrid transformer models for medical imaging and weakly-supervised systems for prostate cancer diagnosis. His computational methods span molecular dynamics simulations and graph neural networks. Despite his prolific research output, no specific grants, advising roles, or lab affiliations are explicitly listed in the provided data. Contact: Rhodes Hall 891, Cincinnati, OH | Email: baiju@ucmail.uc.edu

Lane Harrisonمشاهده پروفایل

دانشیار

• Information Visualization
• Visual Analytics
• Human-Computer Interaction
• +۳ مورد دیگر

Lane Harrison is an Associate Professor in the Department of Computer Science at Worcester Polytechnic Institute (WPI), where he directs the Visualization and Information Equity lab (VIEW). His research leverages cognitive and perceptual principles to improve information visualization and visual analytics systems, with critical applications in cybersecurity and health-risk communication for high-stakes decision-making. His educational background includes a BS (2009) and PhD (2013) in Computer Science from the University of North Carolina, Charlotte. Prior to WPI, he was a Postdoctoral Researcher at Tufts University's Visual Analytics Lab. Harrison's research focuses on empirical evaluation of visualization techniques , investigating how cognitive principles can optimize user performance with visual representations. He develops design guidelines for effective visualizations in domains like cybersecurity and healthcare, while creating adaptive systems that integrate models of user abilities. His work bridges theoretical foundations with practical applications to address real-world challenges in data interpretation. Analysis of his 2016-2021 publications reveals consistent emphasis on user-centered evaluation methodologies , including crowdsourcing and controlled experiments. Key trends include quantifying exploration behaviors in web visualizations, addressing cognitive biases in data reproduction, and developing healthcare-focused analytics for drug interactions and risk communication. His research demonstrates strong interdisciplinary connections between visualization, cognitive science, and domain-specific applications. His scientific recognition includes: Best Paper Award at ACM CHI 2016 for adaptive interface research Harrison secures significant research funding including an NSF Grant (2022) for visualization studies and participates in an 11-school AI collaboration for intelligence professionals (2025). He teaches data visualization and web programming courses, mentoring students through the VIEW lab on projects applying visualization techniques to social issues and scientific domains. He directs the VIEW lab at WPI, which develops computational methods to understand how people engage with data visualizations while emphasizing equitable information access. Current projects integrate user modeling with visualization systems to optimize design for diverse cognitive abilities and application contexts.

Viktor Sowinski-Mydlarzمشاهده پروفایل

مدرس

• Cyber Security
• Data Analytics
• Machine Learning
• +۳ مورد دیگر

Dr. Viktor Sowinski-Mydlarz is a Lecturer in Computer Science and Applied Computing at the School of Computing and Digital Media, London Metropolitan University. He also serves as a post-doctoral researcher at the Cyber Security Research Centre. His research focuses on hybrid frameworks combining semantic and machine learning methods for data analysis, with recent projects involving cloud computing, bioinformatics (e.g., 3D modeling of Coronavirus variants), and cyber security threat intelligence. He teaches programming, software engineering, ethical hacking, computer networking, and cloud computing. His academic background includes a PhD in hybrid data processing frameworks. He has contributed to research funded by Lloyds Banking Group and Innovate UK, including Urban Data Space (air quality analysis) and Microsoft Azure Cluster (NHS prescription data statistics with Machine Learning). Dr. Sowinski-Mydlarz's work emphasizes software integration, containerization, and cloud-based data analytics orchestration. His publications span threat intelligence, cyber security policy validation, and AI-driven hybrid platforms.

• Ontologies and Terminologies
• Neuroinformatics
• Big Data Analytics
• +۳ مورد دیگر

Licong Cui, Ph.D., is an Associate Professor at the McWilliams School of Biomedical Informatics, University of Texas Health Science Center at Houston (UTHealth). She is also affiliated with the Center for Translational AI Excellence and Applications in Medicine (TEAM-AI) and the Texas Institute for Restorative Neurotechnologies (TIRN). Her research focuses on developing informatics methods to address biomedical data challenges, with expertise in ontologies, neuroinformatics, big data analytics, and clinical text mining. Dr. Cui has authored over 100 peer-reviewed publications and secured grants from NIH and NSF. Her work emphasizes ontology quality assurance, large language model applications in healthcare, and data integration frameworks. Notable contributions include developing the VaxBot-HPV chatbot for vaccine communication and advancing seizure frequency extraction methodologies using LLMs. Her honors include the 2022 AMIA New Investigator Award and 2021 NSF CAREER Award. Current projects involve enhancing NIH Common Data Elements with AI tools and improving EHR-based cohort querying through ontology-driven approaches. She collaborates on initiatives like the National Sleep Research Resource and Vaccine Ontology harmonization efforts.

Qian Liuمشاهده پروفایل

استادیار

• Machine Learning
• Deep Learning
• Applied Statistics
• +۵ مورد دیگر

Dr. Qian Liu is an Assistant Professor in the Department of Applied Computer Science at the University of Winnipeg . She holds a PhD in Individual Interdisciplinary Studies, integrating Computer Science, Statistics, and Medical Genetics. Her research focuses on advanced data science techniques for healthcare and material science, including multi-modal data integration and interpretable AI. Education : 2023: Postdoc, Western University, Canada 2023: PhD, University of Manitoba, Canada 2019: MSc, University of Manitoba, Canada Research Interests : Machine learning, deep learning, bioinformatics, computational biology, medical imaging, and their applications in healthcare and material informatics. Her lab develops tools for genomic integration, radiogenomics, and AI-driven biomarker discovery. Recent Contributions : Her work spans AI in nanomaterial classification, breast cancer biomarker discovery, and emotion recognition. Articles highlight innovations in graph neural networks, diffusion models, and interpretable deep learning. Awards : 2024 University of Manitoba Distinguished Dissertation Award 2019 Best Oral Paper at IEEE Bioinformatics Conference Grants : NSERC 2024 Discovery Grant (Principal Investigator) CIHR 2023 Project Grant (Co-Applicant) Labs/Teams : Active in interdisciplinary collaborations across computer science, statistics, and biomedical domains. Lab members include postdocs, PhD/MS students, and undergraduate researchers.

Russ Biagio Altmanمشاهده پروفایل

استاد

• Biomedical Informatics
• Pharmacogenomics
• Computational Biology
• +۷ مورد دیگر

Russ Biagio Altman is the Kenneth Fong Professor at Stanford University , with appointments in Bioengineering, Genetics, Medicine, Biomedical Data Science, and by courtesy, Computer Science. He serves as Senior Fellow at the Stanford Institute for Human-Centered AI and directs the Helix Research Group. Education: AB (summa cum laude) in Biochemistry and Molecular Biology from Harvard College (1983), PhD in Medical Information Sciences (1989), and MD in Medicine (1990) from Stanford University His research focuses on computational technologies in molecular biology and medicine , particularly pharmacogenomics, 3D structural analysis of biological molecules, and functional genomics using NLP and machine learning. Recent work includes AI-driven pharmacogenomic databases like PharmGKB , structural motif identification in proteins, and social media-based public health surveillance. Scientific awards include the U.S. Presidential Early Career Award , NSF CAREER Award , and fellowships in AAAS, AIMBE, and the National Academy of Medicine. He has received multiple teaching and mentorship awards and co-founded Personalis (NASDAQ: PSNL). His lab trains graduate and postdoctoral researchers across Biomedical Data Science , Genetics , and Bioengineering programs. Current projects include AI-guided CRISPR experiments, EHR integration for drug response prediction, and opioid epidemic monitoring via social media analytics.

Katie Marshallمشاهده پروفایل

استادیار

• Environmental Change
• Marine Biodiversity
• Population Ecology
• +۳ مورد دیگر

Katie Marshall is an Assistant Professor in the Department of Zoology at the University of British Columbia’s Faculty of Science. Her research focuses on the physiological and ecological mechanisms underlying species’ survival in cold environments, particularly in intertidal invertebrates and forest pest insects. She explores how low-temperature adaptation affects population growth and geographic range limits, aiming to predict climate change impacts on species distributions. Dr. Marshall is affiliated with the Biodiversity Research Centre and the Comparative Physiology Group, emphasizing interdisciplinary collaboration in ecology and evolutionary biology. Her work integrates biological and environmental disciplines, combining molecular studies with ecological observations. For instance, she investigates ice-binding proteins in marine invertebrates and metabolic adaptations in insects and mussels exposed to freezing conditions. She also utilizes advanced technologies like machine learning and DNA metabarcoding for species classification and ecosystem monitoring. Dr. Marshall’s research addresses key questions about why species have specific geographic ranges and how they might respond to environmental changes. Recent studies highlight themes such as the evolutionary origins of cold tolerance, the effects of temperature fluctuations on insect survival, and the interplay between climate variables and organismal physiology. She emphasizes understanding functional traits and eco-evolutionary dynamics to improve predictive models for species range shifts and ecosystem management. Though no specific scientific awards or grants are listed in the provided texts, her contributions to biodiversity and physiological ecology are evident through her active research and lab leadership. The Marshall Lab collaborates broadly within the Zoology Department and across UBC’s Biodiversity Research Centre to advance knowledge in these critical areas.

• Vertebrate Evolution
• Paleontology
• Evolutionary Biology
• +۳ مورد دیگر

Dr. Martin Brazeau is an Associate Professor of Vertebrate Evolution at Imperial College London's Department of Life Sciences (Silwood Park), part of the Faculty of Natural Sciences. He leads the Brazeau Lab, which focuses on the deep evolutionary history of vertebrates through fossil records and computational methods. His research explores the origins of vertebrate anatomical features like skulls, jaws, and hard tissues, using Paleozoic-era fossils and phylogenetic tools. Education: PhD in Evolutionary Biology from Uppsala University (2004–2009), BSc (First-class Honours) in Zoology from McGill University (2001–2004). He is affiliated with the Georgina Mace Centre for the Living Planet and the Silwood Park Campus. His work combines field expeditions, collections-based paleontology, and software development (e.g., Morphy for phylogenetic analysis). Research Interests: Evolutionary biology, vertebrate morphology, paleontology, and phylogenetics. His studies address how new traits emerge and their impacts on biodiversity. Key areas include placoderms, chondrichthyans, and early gnathostome evolution. Publications highlight advancements in understanding vertebrate jaw evolution, endoskeletal structures, and computational methods for analyzing fossil data. His lab's projects emphasize functional morphology and the integration of modern imaging techniques like X-ray computed tomography. Advising and Grants: No explicit details provided. Lab activities focus on collaborative projects and mentorship through PhD opportunities, such as the current opening on vertebrate reproductive modes. Labs/Teams: Brazeau Lab at Imperial College London, specializing in vertebrate evolution and morphological phylogenetics.

Auguste Genovesioمشاهده پروفایل

پژوهشگر

• Computational Bioimaging
• Bioinformatics
• Machine Learning
• +۷ مورد دیگر

Auguste Genovesio is a Research Director (DR INSERM) leading the Computational Bioimaging and Bioinformatics team at the Centre for Computational Biology within the École Normale Supérieure (ENS) in Paris. His work focuses on large-scale cellular morphology analysis, integrating machine learning, microscopy, and computational modeling to study cellular responses to perturbations. His team develops algorithms for analyzing high-dimensional biological data, with applications in drug discovery, functional genomics, and neuroscience. Education and Affiliations: Genovesio’s research is anchored at ENS and collaborates with institutions like Institut Curie, Collège de France, and ESPCI. His lab develops open-source tools such as PySpacell and ALFA , advancing spatial analysis and genomic data processing. Research Interests: His group combines deep learning, bioinformatics, and experimental biology to tackle challenges in cellular dynamics, morphological heterogeneity, and predictive modeling. Recent work includes applying diffusion models to reveal subtle phenotypes and optimizing microscopy image analysis pipelines. Key Projects: Cross-modal knowledge distillation for transcriptomics, latent diffusion models for small datasets, and super-resolution microscopy via StyleGAN regularization. Applications: Collaborations in drug screening, neurobiology (e.g., Drosophila memory studies), and cancer cell analysis. Publications: Over 50 peer-reviewed articles since 2007, including work in Nature Communications , Developmental Cell , and NeurIPS . Recent focus on generative AI for biological image analysis and self-supervised learning biases. Grants & Awards: While specific grants aren’t listed, his lab’s cutting-edge research suggests significant institutional and collaborative support. No explicit awards mentioned in texts. Labs/Teams: Director of the Computational Bioimaging group, part of the Functional Genomics section at ENS. Supervises PhD students and postdocs in AI-driven biology and computational microscopy.

Stewart Massonمشاهده پروفایل

پژوهشگر ارشد

• Metabolic Disease Genetics
• Insulin Resistance
• Cellular Trafficking Mechanisms
• +۳ مورد دیگر

Dr Stewart Masson is a Research Fellow at the Charles Perkins Centre, University of Sydney, within the Faculty of Science. His research focuses on the genetics of metabolic disease and insulin resistance, with a particular interest in the etiology of insulin resistance and intracellular trafficking mechanisms. He holds a PhD from the University of Auckland (2021), where he studied insulin signaling and β-catenin's role in skeletal muscle glucose transport under Prof. Peter Shepherd and Assoc. Prof. Troy Merry. His work now investigates genetic influences on metabolic health and aging in collaboration with Prof. David James' lab. Research Interests: Genetic determinants of metabolic disorders Insulin resistance mechanisms Cellular trafficking in metabolism Genetic diversity in metabolic responses Publications highlight advancements in understanding insulin signaling pathways, lipid metabolism, and the development of tools like Synteny for gene prioritization. His studies leverage mouse models to explore diet-related metabolic changes and identify therapeutic targets. Advising and Grants: While no students are listed, his research has been supported by projects investigating genetic and dietary influences on metabolic health. Collaborative efforts with the James Lab focus on metabolic cybernetics and systems biology approaches. Labs/Teams: Active member of the James Lab at the Charles Perkins Centre, contributing to the Metabolic Cybernetics research group.

Inga Prokopenkoمشاهده پروفایل

استاد

• Genetic Epidemiology
• Multi-Omics Analysis
• Type 2 Diabetes
• +۵ مورد دیگر

Professor Inga Prokopenko is a faculty member at the University of Surrey's School of Biosciences (Faculty of Health and Medical Sciences), specializing in e-One Health and leading the Statistical Multi-Omics group. Her research focuses on genetic epidemiology, multi-omics approaches, and the comorbidity between metabolic disorders like type 2 diabetes and conditions such as cardiovascular disease, cancer, and mental health disorders. She leads studies on genetic determinants of blood glucose regulation, obesity-related risks, and shared pathophysiological pathways between diseases. Key research areas include: Genetic links between type 2 diabetes and cancers (breast, pancreatic, etc.) Mechanisms of GLP-1 receptor signaling in diabetes treatment Causal relationships between depression and diabetes using Mendelian randomization Role of abdominal obesity in cancer susceptibility She supervises PhD/MSc students in biosciences and medicine programs and develops computational tools like comorbidPRS for polygenic risk score analysis. Her work integrates large-scale genomic data from cohorts like the UK Biobank and EPIC studies to uncover shared genetic mechanisms across diseases. Recent work highlights include: Systematic reviews on flavan-3-ols and cardiovascular health Meta-analyses of trans-ethnic diabetes GWAS data Epigenetic studies linking blood metabolites to cancer risk Her research bridges statistical genetics with clinical applications, emphasizing precision medicine strategies for complex diseases.