جست‌وجوی استاد و پژوهشگر

Kai Shimagakiمشاهده پروفایل

پژوهشگر

• HIV research
• virology
• immunology
• +۴ مورد دیگر

Kai Shimagaki is a Postdoctoral Associate at the University of Pittsburgh. His research focuses on HIV-1 evolution, coevolutionary processes between viruses and antibodies, and quantitative analysis of genetic interactions. He holds a Ph.D. from Sorbonne University (formerly Pierre et Marie Curie University), where his work centered on protein structure prediction and pathogen-immune system interactions. Education: Ph.D. in Computational Biology, Sorbonne University Research Interests: Quantitative analysis of viral evolution and immune system dynamics Computational modeling of fitness landscapes and epistasis Development of algorithms for deep mutational scanning and protein sequence analysis His work bridges virology, immunology, and computational methods to understand HIV adaptation mechanisms. Publications Overview: Recent work emphasizes HIV evolution modeling, epistasis detection, and computational tools for genetic fitness prediction. Key methods include Bézier interpolation, Boltzmann machine models, and higher-order covariance analysis. Scientific Awards: None listed. Advising & Funding: Supported by the Honjo International Scholarship Foundation during his Ph.D. No current advising roles noted. Labs/Teams: Affiliation details not specified in text.

• Super-Resolution Microscopy
• Cell Biology
• Virology
• +۴ مورد دیگر

Andrew Barentine is a Researcher in the Department of Chemistry at Stanford University. His work focuses on super-resolution microscopy, cellular organization of viral components, and nanoscale biological structures. He develops advanced imaging technologies to study viral replication organelles, stress granules in cancer cells, and endoplasmic reticulum dynamics. His research bridges biophysics, molecular biology, and engineering, with applications in virology and oncology. Key research interests include: Super-resolution imaging techniques (e.g., STED, localization microscopy) Nanoscale cellular architecture and viral pathogenesis Protein-RNA interactions in disease contexts Open-source tools for 3D microscopy data analysis Recent work has explored SARS-CoV-2 replication mechanisms, mutant U2AF1-driven stress granules in myeloid malignancies, and high-throughput nanoscopy platforms. His PYMEVisualize software facilitates exploratory analysis of super-resolution datasets.

• Biomedical Informatics
• Medical Imaging
• Medical Data Fusion
• +۳ مورد دیگر

Prof. Georgios Matsopoulos is a Professor at the Department of Information Transmission Systems and Materials Technology, School of Electrical and Computer Engineering, National Technical University of Athens (NTUA). He holds a Master's in Biomechanics and a PhD from the University of Strathclyde, Glasgow. As Director of the Biomedical Technology Laboratory, his expertise spans biomedical informatics, medical imaging, and healthcare data systems. His research focuses on diagnostic support systems, medical data fusion, biosignal processing, and AI-driven health technologies. Education: Bachelor's: School of Electrical & Computer Engineering, NTUA Master's: Biomechanics, University of Strathclyde PhD: University of Strathclyde Research Interests: Development of decision support systems, medical imaging analysis, health information systems, and AI applications in healthcare. He emphasizes large-scale medical data processing, radiomics, and clinical decision frameworks. His over 300 publications (2025-2021) highlight advancements in AI-driven diagnostics, medical robotics (e.g., HERON ontology), and telehealth systems. He leads projects like TeleRehaB DSS for stroke rehabilitation and the Retention project for heart failure monitoring. Grants/Projects: Extensive experience in managing research programs across public and private sectors, including EU-funded initiatives in medical AI and robotics. Labs & Teams: Oversees the Biomedical Technology Laboratory, focusing on translational research in biomedical engineering and digital health.

Xinzhong Liمشاهده پروفایل

استاد پژوهشی

• Bioinformatics
• Systems Biology
• Alzheimer's Disease
• +۵ مورد دیگر

Xinzhong Li is a Research Professor at Teesside University's SHLS Life Sciences Centre for Biodiscovery. He holds degrees from Xi'an Jiaotong University (BSc, MSc, PhD) and has prior experience at Imperial College London and Plymouth University. His research focuses on bioinformatics, systems biology, and neurodegenerative diseases like Alzheimer’s and brain cancer. He leads major grants, including the €3.9m TClock4AD project targeting circadian clock dysfunction in Alzheimer’s and the Net4Brain Action Network against brain cancer. Key collaborations include work with institutions in Berlin, Israel, and China. His methodologies span machine learning, next-generation sequencing, and biomarker discovery, with applications in diagnostics and drug repurposing. Projects highlight interdisciplinary approaches in healthcare technology and molecular diagnostics. Dr. Li’s research portfolio includes advanced imaging techniques (e.g., low-dose CT artifact suppression), marine genomics (sea anemone toxins), and business models for healthcare innovation. He has contributed to over 24 peer-reviewed articles and 7 datasets. His grants total millions in funding, emphasizing translational research and international collaboration. Current collaborations involve Prof. Illana Gozea (Tel Aviv University) and Prof. James Scott (Imperial College London). The Teesside lab under his leadership supports interdisciplinary teams addressing critical health challenges through cutting-edge bioinformatics and systems biology approaches. Labs/Teams: His work is anchored at the SHLS Life Sciences Centre, collaborating with global networks like the AiPBAND and BBDiag consortia. These platforms develop diagnostic tools and therapeutic strategies for brain cancers and Alzheimer’s, leveraging multi-omics data and AI-driven pipelines.

George Matsopoulosمشاهده پروفایل

استاد

• Nonlinear Image Processing
• Medical Applications
• Medical Image Registration
• +۴ مورد دیگر

George Matsopoulos is a tenured Professor at the Division of Information Transmission Systems and Material Technology, part of the School of Electrical and Computer Engineering at the National Technical University of Athens (NTUA). His research focuses on medical image processing, AI-driven healthcare systems, and telemedicine applications. He holds a diploma in Electrical Engineering from NTUA (1988), an M.Sc. (1989), and a Ph.D. (1993) from the Bioengineering Unit at the University of Strathclyde, U.K. His research portfolio includes over 300 peer-reviewed publications in journals, conferences, and books. Key areas include nonlinear image processing for medical diagnostics, 2D/3D medical image registration, computer vision applications in healthcare, and decision support systems for clinical workflows. He has pioneered frameworks for integrating AI into telemedicine, radiomics analysis for cancer detection, and robotic-assisted rehabilitation systems. Recent work emphasizes healthcare robotics ontologies (HERON), deep learning for MRI/PET analysis, and predictive models for neurological conditions like bipolar disorder. He leads interdisciplinary projects such as TeleRehaB DSS (balance rehabilitation) and the Retention Project (heart failure prediction). His contributions span clinical decision support, medical device design, and AI ethics in healthcare systems. Notable achievements include the development of automated segmentation algorithms for organs at risk in radiotherapy, machine learning models for early-stage disease prediction, and AR/VR tools for stroke rehabilitation assessment. His work bridges electrical engineering, computer science, and biomedical applications, addressing unmet clinical needs through technology innovation.

• Medical Imaging
• Artificial Intelligence
• Deep Learning
• +۳ مورد دیگر

Mena Shenouda is a Researcher in the Department of Radiology & Biomedical Imaging at Yale School of Medicine (Yale University). Their work focuses on advancing AI-driven medical imaging technologies to improve clinical outcomes, particularly in disease detection and diagnostic accuracy. Shenouda holds a Ph.D. in Medical Physics from the University of Chicago (2024) and a B.S. in Physics and Biophysics (Honors) from the University of Michigan (2019). They are also completing a Medical Physics Diagnostic Imaging Residency at Yale, set to conclude in 2026. Education: B.S. Physics, Biophysics (Honors), University of Michigan (2019) Ph.D. Medical Physics, University of Chicago (2024) Medical Physics Diagnostic Imaging Resident, Yale University (2026) Research Interests: Shenouda specializes in applying artificial intelligence and deep learning to medical imaging challenges. Their work includes developing algorithms for detecting diseases such as COVID-19 and pleural mesothelioma , optimizing radiomics features for clinical decision-making, and improving image reconstruction techniques. They emphasize the generalizability and ethical implementation of AI models in healthcare. Publications: Shenouda has authored peer-reviewed studies in high-impact journals like Journal of Medical Imaging and JCI Insight , focusing on deep learning applications in radiology and oncology. Their research bridges computational methods with clinical practice, addressing real-world challenges in imaging technology. Professional Contributions: As part of the Radiology & Biomedical Imaging team, Shenouda collaborates on translational research initiatives. Their projects align with Yale’s broader goals in precision medicine and healthcare innovation.

• Viral Evolution
• RNA Virus Genetics
• Experimental Evolution
• +۷ مورد دیگر

Jose Manuel Cuevas Torrijos is an Associate Professor in the Department of Genetics at the Faculty of Biological Sciences, Universitat de València, Spain. He is a leading member of the VIREVO (Virus Evolution Group) within the Institute for Biological Systems Integration (I2SYSBIO), and has previously contributed to the EVOSALUD research group. His academic career is deeply rooted in virology and evolutionary genetics. He earned his PhD from the Universitat de València in 2003 with a thesis on the experimental evolution of the Vesicular Stomatitis Virus (VSV), supervised by Dr. Santiago F. Elena and Dr. Andrés Moya Simarro. His research focuses on the evolutionary dynamics of RNA and DNA viruses, including mutation rates, fitness effects, host adaptation, and cross-species transmission. His research interests span viral evolution, experimental virology, viral metagenomics, and phylogenetics. He employs high-throughput sequencing, deep mutational scanning, and experimental evolution to study viruses such as HIV, hepatitis C virus, SARS-CoV-2, and various animal viruses. His work bridges molecular virology with population genetics to understand how viruses adapt and evolve. The analysis of his recent publications (2022–2025) reveals a strong focus on viral discovery in wildlife (particularly bats and rodents), the human blood virome, and the evolutionary mechanisms of RNA viruses. There is a clear trend toward metagenomic approaches and methodological improvements in viral nucleic acid recovery, alongside continued interest in mutation rate estimation and evolutionary constraints. A smaller subset of his work also touches on geothermal engineering, indicating interdisciplinary collaboration. While no formal scientific awards are listed, his extensive publication record and leadership in the VIREVO group underscore his scientific impact. He has supervised PhD students and mentored early-career researchers as part of his role in the VIREVO group, though specific names are not provided. His research is supported by institutional and national grants, particularly through his affiliation with I2SYSBIO and participation in collaborative projects. The VIREVO group, which he leads, focuses on experimental evolution of viruses, viral population genetics, and the origins of viral diversity. The team integrates computational biology, molecular virology, and evolutionary theory to study how viruses emerge, adapt, and spread.

Raman Srivatsanمشاهده پروفایل

دانشیار

• Biomolecular Folding & Interactions
• Chemical Biology & Enzymology
• Quantitative Biology
• +۷ مورد دیگر

Raman Srivatsan is an Associate Professor in the Department of Bacteriology at the University of Wisconsin-Madison, with affiliate appointments in the Department of Chemical and Biological Engineering. He maintains additional affiliations with the Great Lakes Bioenergy Research Center and the Center for Genome Science Innovation, reflecting the interdisciplinary nature of his research program that bridges biochemistry, microbiology, computation, and engineering. His educational background includes a B.S. from Baroda University in India, an M.S. from Missouri University of Science and Technology, a Ph.D. from the University of Washington, Seattle, and postdoctoral training at Harvard Medical School - Wyss Institute for Biologically-inspired Engineering. Dr. Srivatsan's research program develops technologies at the intersection of multiple disciplines to understand and engineer biological systems at protein-wide and genome-wide scales. His laboratory takes a systems and synthetic biology approach, leveraging computational protein design, next-generation DNA synthesis and sequencing technologies, and highly multiplexed selection and screening assays. A common theme in his work is devising experimental screens to make thousands to millions of mutational perturbations and measurements in biological systems in single-pot experiments, which reveal functional landscapes, uncover non-intuitive sequence-function relationships, and train machine learning models for engineering new functions. Analysis of his recent publication record shows a strong focus on bacteriophage engineering, protein allostery, and high-throughput approaches to understanding biological systems. His work combines deep mutational scanning with computational methods to address fundamental questions in biomolecular systems while maintaining close alignment with translational applications in areas like antibiotic resistance and microbiome engineering. Dr. Srivatsan's research spans several interconnected areas including protein allostery (molecular rules and engineering biosensors), bacteriophage host interactions (understanding interactions, phage-based antibacterial therapies, precision microbiome editing), human disease variants, and emerging themes in transporters, gene delivery systems, and vaccine design. His laboratory's approach enables the development of programmable synthetic phages, novel allosteric biosensors, and precision tools for microbiome editing with significant potential for addressing antibiotic resistance and other biomedical challenges.

Eugene Serebryanyمشاهده پروفایل

استادیار

• Protein Biophysics
• Chemical Biology
• Protein Misfolding Diseases
• +۵ مورد دیگر

Eugene Serebryany, PhD, is an Assistant Professor in the Department of Physiology and Biophysics at Stony Brook University. His research focuses on the chemical biophysics of protein misfolding in vivo, mechanisms of misfolding-associated diseases, and pharmacological targeting of non-native protein conformations. The lab develops innovative experimental methods at the intersection of molecular biophysics and chemical biology while collaborating with computational biophysicists. PhD from Massachusetts Institute of Technology (2016) Joint affiliations with the Laufer Center for Physical and Quantitative Biology and Department of Chemistry Research themes: protein aggregation mechanisms, redox-driven cataract pathology, metabolite-based therapeutics, and experimental method development The lab's work has revealed redox-driven 'inverse-prion' aggregation mechanisms in lens proteins and identified myo-inositol as a potential non-surgical cataract treatment. Their research also explores disulfide exchange dynamics and metal ion interactions in aging-related protein damage. Experimental approaches include high-throughput disulfide scanning, computational-experimental integration, and single-molecule protein analysis. The lab's findings challenge traditional views of lens crystallin biochemistry by demonstrating oxidoreductase activity in proteins previously considered inert.

Didier Devaursمشاهده پروفایل

مدرس

• Artificial Intelligence
• Machine Learning
• Deep Learning
• +۸ مورد دیگر

Didier Devaurs is a Lecturer in the Department of Computer and Information Sciences at the University of Strathclyde since June 2024. He holds a PhD in Artificial Intelligence from Université de Toulouse (2015) and has interdisciplinary expertise spanning computer science, mathematics, biology, and chemistry. His research focuses on computational modeling of biomolecular systems and robotics-inspired algorithms, with applications in structural bioinformatics, drug discovery, and personalized medicine. He serves on the editorial boards of Archives of Biochemistry and Biophysics , BMC Bioinformatics , and Biomolecules . Education : PhD in Artificial Intelligence, Université de Toulouse (2015) MSc in Computer Science, University of Lyon (2006) BSc in Computer Science, Université Clermont Auvergne (2004) Teacher Certification in Mathematics (2003) Research Interests : Interdisciplinary projects at the intersection of artificial intelligence and structural bioinformatics, including biomolecular AI, protein-ligand docking, molecular caging prediction, and hydrogen/deuterium exchange analysis. He develops algorithms for high-dimensional space exploration in robotics and computational biology, with applications in cancer immunotherapy and SARS-CoV-2 protein analysis. Publications & Impact : Over 50 peer-reviewed articles, including 15 first-author papers, with four highly cited works. His tools like DINC-COVID and MoMA-LigPath are widely used in computational biology. Recent work emphasizes clinical interpretation of genetic mutations using deep mutational scanning. Awards : Editor of Distinction Award (2025) Finalist, STEM for BRITAIN poster competition (2023) Teaching & Mentorship : Teaches bioinformatics and AI courses. Supervised 18 graduate/undergraduate students across institutions, including the University of Edinburgh and Rice University. Actively involved in mentoring researchers in interdisciplinary projects. Labs/Teams : Leads research groups focused on computational methods for biomedical applications, collaborating globally on projects funded by initiatives like the Faculty of Science Starter Grant (£300K+ in funding secured).

Joe Marshمشاهده پروفایل

استاد

• Protein Complexes
• Structural Bioinformatics
• Human Genetic Disease
• +۳ مورد دیگر

Professor Joe Marsh is a Personal Chair of Computational Protein Biology at the University of Edinburgh , affiliated with the Institute of Genetics and Cancer and leading the MRC Human Genetics Unit . His research focuses on computational methods to study protein complexes in normal biological processes and human disease. Education: BSc, University of British Columbia (2003) PhD, University of Toronto (2009) His research explores the molecular mechanisms of protein variants in disease, emphasizing gain-of-function and dominant-negative effects. He uses structural bioinformatics, computational variant prediction, and deep mutational scanning (in collaboration with the Kudla lab) to understand pathogenic mutations and improve variant effect prediction. His work also investigates protein complex assembly, regulation, and evolution through structural, genomic, and proteomic datasets. Recent publications highlight his group's contributions to structural biology, including studies on: Cotranslational assembly of protein complexes Differential effects of mutation types on protein structure Evolution of allostery in protein complexes Quaternary structure's role in disease mutations Binding site dynamics and stoichiometry in aneuploid cells Scientific recognition includes: 2018 - Lister Institute Research Prize 2019 - Patrick Neill Medal He advises PhD students : Hasan Çubuk Mohamed Fawzy Ankit Pathak Verena Obermuller Rowena Tao Yifei Shang Current projects include Protein variant interpretation (2023-2028) and collaborative efforts like Precision control of protein dosage in vivo funded by the Medical Research Council (MRC) and European Commission. His research themes span protein complexes , structural bioinformatics , genetic variation , and evolution .

Srivatsan Ramanمشاهده پروفایل

دانشیار

• Systems and Synthetic Biology
• Bacteriophage-Host Interactions
• Protein Allostery
• +۱۳ مورد دیگر

Srivatsan Raman is an Associate Professor at the University of Wisconsin–Madison , with appointments in the Department of Bacteriology and as an affiliate in the Department of Chemical and Biological Engineering . He is also affiliated with the Great Lakes Bioenergy Research Center and the Center for Genome Science Innovation . Education: B.S., Baroda University, India M.S., Missouri University of Science and Technology Ph.D., University of Washington, Seattle Postdoctoral, Harvard Medical School - Wyss Institute for Biologically-inspired Engineering His research lies at the intersection of systems biology , synthetic biology , and computational biology , with a focus on protein allostery , bacteriophage engineering , and high-throughput functional genomics . He develops and applies deep mutational scanning, chip-based DNA synthesis, and machine learning to understand and design biological systems at scale. His lab pioneers methods to reprogram phages for antibacterial therapy and microbiome editing, and to dissect the functional impact of human genetic variants. The recent publications reflect a strong trend in engineered biological systems , particularly in phage engineering , allostery , and functional genomics . These works span primary research and authoritative reviews in top journals, demonstrating both technical innovation and thought leadership. Scientific Contributions: Developed frameworks for engineering allostery and biosensors Pioneered high-throughput phage genome engineering Advanced deep mutational scanning for variant effect prediction Designed programmable phages with controllable infectivity Raman leads a multidisciplinary research program that bridges fundamental science with translational applications in health and biotechnology. His lab leverages cutting-edge technologies to train machine learning models for protein and genome design. While student names are not listed, his group clearly mentors trainees in synthetic biology and genomics. His work is supported by affiliations with major research centers, indicating substantial grant funding and collaborative networks. Laboratory & Research Themes: Raman Laboratory: Focuses on protein allostery, phage-host interactions, and human disease variants Technologies: Deep mutational scanning, pooled screening, computational design (Rosetta), single-cell RNA-seq Emerging Areas: Transporters, gene delivery systems, vaccine design

James Fraserمشاهده پروفایل

استاد

• Protein Structure
• Protein Conformational Dynamics
• X-ray Crystallography
• +۷ مورد دیگر

James Fraser is a Professor and Chair of the Department of Bioengineering and Therapeutic Sciences at the University of California San Francisco (UCSF) School of Pharmacy. He coordinates the school’s research efforts, focusing on collaborations across labs, departments, and institutions. His research centers on protein structure and conformational dynamics, particularly how mutations and ligand binding reshape protein ensembles to affect function and organismal fitness. He employs advanced computational and biophysical techniques such as X-ray crystallography, cryo-EM, and directed evolution to study these processes. UCSF School of Pharmacy Department of Bioengineering and Therapeutic Sciences Graduate Programs: Chemistry and Chemical Biology, Biophysics, Pharmaceutical Sciences and Pharmacogenomics Affiliations: Quantitative Biosciences Institute (QBI), Bowes Biomedical Investigator Program Fraser’s research combines structural biology, systems pharmacology, and computational modeling to address fundamental questions in protein function and evolution. His lab develops methods to extract maximal information from X-ray diffraction and cryo-EM data, enabling detailed analysis of protein conformational states. Recent work includes studying SARS-CoV-2 proteins for antiviral drug discovery and investigating resistance mechanisms in the MET receptor tyrosine kinase. His 15 most recent articles span structural biology, computational enzymology, and drug development, with a focus on SARS-CoV-2 and MET inhibitors. Keywords include Protein Dynamics , X-ray Crystallography , Cryo-EM , and Directed Evolution , reflecting his interdisciplinary approach. W.H. and W.L. Bragg Prize (2020), International Union of Crystallography Byers Award in Basic Science (2020), UCSF Packard Fellow (2014-2019), David and Lucille Packard Foundation Searle Scholar (2014-2017), Kinship Foundation Pew Scholar in the Biomedical Sciences (2014-2016), Pew Charitable Trusts Fraser mentors students and postdoctoral scholars, including Stephanie Wankowicz, Galen Correy, and Gabriella Estevam, who co-author his publications. His lab collaborates with institutions like the Weizmann Institute and QBI, integrating computational approaches with experimental validation. He advocates for open science and reproducibility, contributing to preprint peer review initiatives.

Hyebin Songمشاهده پروفایل

استادیار

• High-dimensional Statistics
• Statistical Learning
• Statistical Computing
• +۴ مورد دیگر

Hyebin Song is an Assistant Professor of Statistics at the Pennsylvania State University since 2020. She holds a PhD in Statistics from the University of Wisconsin-Madison (2020) and a BA in Applied Statistics from Yonsei University (2012). Previously, she worked as a Statistician at the Bank of Korea. Her research focuses on developing statistical methodologies for high-dimensional and complex datasets, with applications in neuroscience, systems biology, and computational protein modeling. Key areas include semi-parametric inference, statistical learning, and computational biology. She has developed influential software tools like PUlasso for high-dimensional variable selection and contributed to advancements in Markov chain autocovariance estimation. Her work has been recognized with the ASA SLDS Student Paper Competition Award (2018). Education: PhD in Statistics (UW-Madison, 2020), BA in Applied Statistics (Yonsei University, 2012). Current affiliations include the Department of Statistics at Penn State and collaborations with computational biology labs. She actively advises PhD students in statistical methodology and applications. Research interests span high-dimensional statistics, shape-constrained inference, and statistical computing, with applications to protein structure analysis and functional genomics. Notable software includes the 'momentLS' package for Markov chain estimation and 'pudms' for deep mutational scanning analysis.

Daniele Perloمشاهده پروفایل

پژوهشگر ارشد

• Artificial Intelligence
• Deep Learning
• Medical Imaging
• +۳ مورد دیگر

Daniele Perlo is a Postdoctoral Fellow at Luxembourg Institute of Health specializing in Brain Imaging & Neuro Epidemiology. His research bridges artificial intelligence and medical imaging to advance neuro-oncology diagnostics, with emphasis on brain tumor hypoxia detection and MRI analysis. His primary research interests include Artificial Intelligence , Deep Learning , Medical Imaging , and Neuroimaging , with specific focus on hypoxia detection in brain tumors using multiparametric MRI. He develops AI models for medical image synthesis, artifact correction, and tumor characterization, translating computational methods into clinical applications for improved cancer diagnostics. Analysis of Perlo's 15 recent publications (2021-2024) reveals a concentrated research trajectory in AI-driven medical imaging. Key themes include brain tumor hypoxia prediction through MRI-PET synthesis, MRI artifact correction via k-space deep learning, and creation of specialized medical datasets (Unitobrain, Unitochest, Unitopatho). His work demonstrates increasing emphasis on practical clinical implementations, particularly in neuro-oncology and multi-organ cancer imaging, leveraging large-scale deep learning frameworks. Perlo participated as a key contributor in the "Pseudo PET" research project (2022-2024), collaborating with principal investigators Keunen and Niclou to develop deep learning methods for synthesizing pseudo-PET images from MRI data to assess brain tumor hypoxia without radiation exposure. This HPC-intensive project utilized multiple GPUs for medical image processing in neuro-oncology applications. He operates within the Brain Imaging & Neuro Epidemiology research group at Luxembourg Institute of Health, focusing on AI applications for neuroimaging analysis and epidemiological studies of brain disorders through advanced computational techniques.