جست‌وجوی استاد و پژوهشگر

Alexandre Bonvinمشاهده پروفایل

استاد

• Computational Structural Biology
• Biomolecular Interactions
• NMR Spectroscopy
• +۵ مورد دیگر

Prof. Alexandre Bonvin is a Professor of Computational Structural Biology and Scientific Director of the Bijvoet Center for Biomolecular Research at Utrecht University. He leads the Computational Structural Biology group, focusing on developing integrative computational methods to study biomolecular interactions. His work emphasizes predicting protein complex structures using experimental data and computational models, with applications in drug discovery and structural biology. Bonvin pioneered the HADDOCK software suite, a widely used platform for modeling biomolecular complexes. Education: Studied Chemistry at the University of Lausanne, obtained a PhD from Utrecht University (1993), followed by postdocs at Yale University and ETH Zurich. Joined Utrecht University in 1998, becoming a full professor in 2009. He has held leadership roles, including Director of Chemical Education and Vice Head of the Chemistry Department. Research Themes: Biomolecular interactions, computational modeling, NMR spectroscopy, and grid computing. He coordinates EU projects like WeNMR and BioExcel, advancing e-infrastructure for structural biology. His lab’s contributions include software tools like HADDOCK, DisVis, and PowerFit, serving thousands of researchers globally. Awards: 2006 VICI grant from NWO, HPC Innovation Award (2020). Over 275 peer-reviewed publications, including high-impact work on teixobactin mechanism and antibody modeling. Active in training through EMBO courses and mentoring postdocs/students.

Fereydoun Hormozdiariمشاهده پروفایل

دانشیار

• Computational Genomics
• Structural Variation Discovery
• Genome Assembly
• +۳ مورد دیگر

Dr. Fereydoun Hormozdiari is an Associate Professor in the Department of Biochemistry and Molecular Medicine at the University of California, Davis School of Medicine. His research focuses on developing computational methods for analyzing genomic data, particularly in structural variation discovery, genome assembly, and disease prediction using omics data. He leads a lab that integrates machine learning and combinatorial algorithms to address challenges in understanding complex disorders like autism and cancer. Key research areas include: Structural variation discovery and genotyping using next-generation sequencing technologies. Development of novel algorithms for de novo genome assembly. Identification of genetic modules and pathways linked to neurological disorders. Prediction of complex disorders through integrated analysis of genomic and transcriptomic data. Recent work includes advancements in early-stage cancer detection using cell-free RNA biomarkers and AI, as well as tools for tumor phylogeny reconstruction and structural variant genotyping. His contributions span over 80 publications in high-impact journals like Nature, Cell, and Genome Research. Lab and team collaborations involve interdisciplinary approaches, combining computational biology with clinical applications. Ongoing projects emphasize translating genomic insights into predictive models for personalized medicine and disease intervention.

Iman Azimiمشاهده پروفایل

مدرس ارشد

• Brain Cancer
• Cellular Aging
• Calcium Signaling Pathways
• +۳ مورد دیگر

Iman Azimi is a Senior Lecturer and Research Group Head in the Pharmacology Department at Monash Biomedicine Discovery Institute, Monash University. His academic journey includes a PhD from the University of New South Wales (2011), postdoctoral research at The University of Queensland (2011–2017), and a Lecturer role at the University of Tasmania (2018–2023). He leads research focused on brain cancer, cellular aging, calcium signaling, and drug discovery. His work is supported by grants from Cancer Council Tasmania, Brain Foundation, and others. Education: PhD in Pharmacology, University of New South Wales, 2011 Postdoctoral Fellowship, The University of Queensland, 2011–2017 Research Interests: Identifying therapeutic targets for brain cancers (e.g., medulloblastoma) Unraveling cellular aging mechanisms Calcium signaling in cancer and senescence High-throughput drug screening assay development Publications: Recent articles focus on LIN28 proteins in medulloblastoma, T-type calcium channel inhibitors, and calcium signaling pathways. His work bridges basic science and translational applications in cancer therapy. Awards: Includes the ASCEPT New Investigator Award (2016), Oxygen Club of California Award (2015), and multiple grants and scholarships. Leadership & Service: Chair of Institutional Biosafety Committees, Guest Editor for Cancers and Frontiers in Pharmacology , and grant reviewer for NHMRC and international agencies. Labs/Teams: Leads a research group at Monash focused on cancer and aging biology, with expertise in cell culture, fluorescence microscopy, and high-content imaging.

Asolina Braunمشاهده پروفایل

پژوهشگر ارشد

• Skin Immunology
• Autoimmunity
• Psoriasis
• +۷ مورد دیگر

Dr. Asolina Braun is a Senior Research Fellow at the Department of Biochemistry & Molecular Biology, Monash University. She actively supervises PhD students and contributes to research in Skin Immunology Psoriasis Pathogenesis Immunopeptidomics Tissue-Resident Memory T Cells . Education : Biology, Hannover Medical School Doctoral Degree in Immunology (Hannover Medical School, Germany, 2011–2014) Postdoctoral Fellowship with Francis Carbone and Thomas Gebhardt (Germany, 2014–2018) Research Focus : Her work explores HLA-Cw6-dependent antigen presentation and TRM cell survival factors in psoriasis, aiming to develop tolerization strategies for autoinflammatory skin diseases. She also pioneered tactile science communication methods for psoriasis outreach. Key Projects : Leading Psoriasis Antigen Triggers (2023–2025) Investigating Influenza Immune Cross-Protection (2024–2027) Validating HLA-Cw6 Antigen Presentation (2020–2022) Awards & Roles : DFG Fellowship Treasurer, Australian and New Zealand Society for Immunology (2018–2022) Executive Committee Member Collaborations span institutions in Australia, Germany, and Denmark, with expertise in flow cytometry , mass spectrometry , and MHC-peptide interactions .

Mihaela Perteaمشاهده پروفایل

دانشیار

• Computational Genomics
• Gene Annotation
• Transcriptome Assembly
• +۳ مورد دیگر

Mihaela Pertea, PhD, is an Associate Professor in the Department of Biomedical Engineering at Johns Hopkins University, with affiliations in Genetic Medicine and the Computer Science department. She leads the Pertea Lab, focusing on computational methods for analyzing genomic and transcriptomic data to understand cellular function, particularly in gene finding, alternative splicing, and RNA-seq analysis. Her work integrates machine learning and statistical techniques to improve gene annotation and maintain genomic catalogues. Education: PhD in Computer Science (Johns Hopkins University, 2001); multiple degrees from University of Bucharest (BS in Psychology, BS and MS in Computer Science). Affiliations: Center for Computational Biology at Johns Hopkins. Research interests include developing algorithms for transcriptome assembly, splice site prediction (e.g., Splam tool), and tools like StringTie for RNA-seq analysis. Her lab’s work has contributed to genome projects of malaria parasites, nematodes, and other pathogens. Recent achievements include an AIMBE College of Fellows recognition (2025) and innovations in AI-driven gene splicing analysis. Publications span over 70 peer-reviewed articles, emphasizing computational biology tools and genomic studies. Notable collaborations include work on the ENCODE project and genome sequencing of Plasmodium falciparum. Her lab’s tools (e.g., CHESS, GFF Utilities) are widely used in genomic research.

Herna Viktorمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Cybersecurity
• +۷ مورد دیگر

Herna Viktor is a Full Professor and Director of the School of Electrical Engineering and Computer Science at the University of Ottawa. She holds a Ph.D. and has extensive administrative leadership experience within the university's Faculty of Engineering. Research Interests : Dr. Viktor specializes in Artificial Intelligence (AI) with a focus on data-driven discovery across diverse domains. Her work emphasizes applications in healthcare (e.g., medical outcome prediction), cybersecurity (e.g., threat detection via semi-supervised learning), finance (e.g., stock market analysis with temporal Transformers), and computational biology (e.g., protein structure generation using diffusion models). She also explores advanced machine learning techniques like lifelong learning, ensemble methods, and interpretability measures. Her research integrates geometric deep learning, quantum computing, and hybrid models to address complex problems in structural proteomics and molecular dynamics. Recent Articles : Her recent publications (2023–2025) highlight trends in protein generation via diffusion models, adversarial robustness of NLP systems, and financial time series predictions. She consistently bridges theoretical AI advancements with real-world applications in medicine, cybersecurity, and quantum computing. Advising & Grants : Currently supervises Paul Kiyambu Mvula . While no grants are explicitly listed, her research demonstrates significant interdisciplinary impact. Her work on SCUT-DS and DynaQ reflects ongoing engagement with imbalanced data and online learning challenges. Labs/Teams : No specific lab or team is mentioned, but she collaborates on projects involving hybrid quantum neural networks and molecular dynamics simulations.

Jean (Jiayu) Wenمشاهده پروفایل

دانشیار

• RNA Biology
• Computational Biology
• MicroRNA Regulation
• +۵ مورد دیگر

Associate Professor Jean (Jiayu) Wen holds positions at The Australian National University (ANU), including Group Leader of The Wen Group, ARC Future Fellow, and Deputy Director of The Shine-Dalgarno Centre for RNA Innovation. She specializes in computational and molecular biology, focusing on RNA regulation, gene expression, and cancer genomics. Her affiliations include ANU’s Division of Genome Sciences and Cancer, and the Centre for Computational Biomedical Sciences. Education: BEng in Electronic Engineering (Beijing), MSc in Computer Science (Lakehead University), PhD in Computational Biology (ANU). Postdoctoral training at Copenhagen University and Memorial Sloan-Kettering Cancer Center. Research interests span RNA structures, microRNA biogenesis, transcriptome dynamics, and epigenetic regulation. Her work addresses intragenomic conflicts, cancer mechanisms, and neural development. Notable projects include RNA-based machine learning models for RNA-RNA interactions and immune cell differentiation studies. Publications highlight contributions to RNA interference pathways, tumor development, and Drosophila genetics. Awards include the ARC Future Fellowship. She leads interdisciplinary teams advancing computational and experimental approaches in genomics and systems biology.

Alison Rodgerمشاهده پروفایل

استاد

• Biophysical Chemistry
• Physical Chemistry
• Analytical Science
• +۳ مورد دیگر

Prof Alison Rodger is a Professor in the Research School of Chemistry at The Australian National University, where she leads research in biophysical spectroscopy. Formerly at Macquarie University (2017–2024) and the University of Warwick (1990s–2017), she specializes in developing advanced spectroscopic techniques for biomacromolecule analysis. Her work integrates circular dichroism, linear dichroism, and Raman methods to study nucleic acids, proteins, and membrane systems. She co-directs the ARC-funded Industrial Transformation Training Centre in Facilitated Advancement of Australia’s Bioactives (FAAB) and runs an open-access biophysical spectroscopy lab. Key awards include Fellowships from the Australian Academy of Science (2021) and Royal Society of Chemistry (2000), and recognition in the Analytical Science Power List (2015). Education: BSc, PhD, DSc (Sydney University) MA (Oxford) DSc (Warwick) BA (Chester) Research Interests: Development of polarized-light spectroscopies for biomacromolecule analysis, including electronic/circular dichroism, Raman spectroscopy, and hybrid techniques. Applications span protein-DNA interactions, membrane biophysics, and biopharmaceutical characterization. She invented five spectroscopic techniques, including micro-volume Couette flow linear dichroism and fluorescence-detected linear dichroism. Awards & Roles: Fellow of the Australian Academy of Science Fellow of the Royal Society of Chemistry Emeritus Professor (University of Warwick) Recipient of Science Teachers of NSW Dedicated Service Award Consultant to European Science Foundation CASPER project Advising & Grants: Supervises PhD students in interdisciplinary biophysical chemistry. Led the EPSRC-funded Molecular Organisation and Assembly in Cells DTC at Warwick. Currently co-directs the ARC FAAB Centre, focusing on bioactive product characterization. Labs & Collaborations: Operates an open-access biophysical spectroscopy lab supporting academic and commercial users. Collaborations span mathematics, medicine, and engineering, with projects on DNA knotting, antimicrobial peptides, and nanomaterials for biosensing.

Deyou Zhengمشاهده پروفایل

استاد

• Computational Genomics
• Bioinformatics
• Gene Regulatory Networks
• +۳ مورد دیگر

Dr. Deyou Zheng is Professor in the Saul R. Korey Department of Neurology, Department of Genetics, and Dominick P. Purpura Department of Neuroscience at Albert Einstein College of Medicine. His research specializes in computational genomics and bioinformatics, analyzing high-throughput genomic data to understand genome structure, transcription regulation, and evolutionary dynamics during development and disease. Core research areas include: (1) Neural system development and neuropsychiatric disorders using iPSC-derived neurons and single-cell transcriptomics; (2) Genetic bases of congenital heart diseases; (3) Cancer genomics focusing on tumor microenvironment and immunotherapy resistance. Collaborative projects employ iPSC technology, RNA-seq, and CRISPR-Cas9 to model disorders like autism and schizophrenia. Recent publications emphasize: Single-cell integration algorithms for transcriptomic data Cancer-associated fibroblasts in glioblastoma/osteosarcoma CHD8 mutations in neurodevelopmental disorders Allele-specific expression in human brain cells

Zhengdong Zhangمشاهده پروفایل

استاد

• Genetics of complex human diseases
• Systems biology
• Aging
• +۵ مورد دیگر

Zhengdong Zhang is a Professor in the Department of Genetics at Albert Einstein College of Medicine. His research focuses on the genetics of aging, Alzheimer's disease, and complex human diseases, combining computational methods with clinical data to uncover genetic mechanisms. His lab develops novel algorithms for genetic analysis, including post-GWAS gene prioritization, Bayesian statistical methods for genomic data, and systems biology approaches. Education details are not explicitly listed, but his professional background includes significant contributions to computational genetics and aging research. Key research areas include method development for genetic analysis, aging-related genetic networks, and disease genetics of Alzheimer's and schizophrenia. His lab has pioneered studies on long-lived species' genomic features, such as naked mole rats and beavers, to understand longevity and disease resistance. Research highlights include identifying rare coding variants linked to schizophrenia risk in 22q11.2 deletion syndrome patients, developing frameworks for Alzheimer's immune pathway analysis, and exploring DNA repair mechanisms in long-lived species. His work emphasizes interdisciplinary approaches integrating data science with clinical insights. Zhengdong Zhang's lab is located in the Michael F. Price Center at Einstein College of Medicine, focusing on translational research to bridge genetic discoveries with clinical applications.

Sandra Zillesمشاهده پروفایل

استاد

• Computational Learning Theory
• Artificial Intelligence
• Machine Learning
• +۳ مورد دیگر

Sandra Zilles is a Professor and Canada Research Chair (Tier 1) in Computational Learning Theory at the University of Regina's Department of Computer Science. She holds adjunct appointments at the University of Waterloo and collaborates with the Alberta Machine Intelligence Institute (Amii). Her research focuses on theoretical computer science and artificial intelligence, particularly interactive learning models, formal language theory, and heuristic search algorithms. Her research integrates computational learning theory, formal language theory, and discrete artificial intelligence structures. Key interests include: Machine teaching with limited data Learnability of pattern languages and automata Graph-theoretic approaches in AI Her work bridges theoretical frameworks with applications in medical imaging, bioinformatics, and game theory. Zilles has received numerous honors including: NSERC Canada Research Chair (Tier 1, 2022-2029) Royal Society of Canada College membership Best Paper Awards (KI 2012, ALT 2003, COLT 2002) She mentors over 50 students and postdocs through her research group. Current projects explore symbolic automata, collaborative learning, and geometric teaching models. Her lab maintains international collaborations with institutions in Germany, Canada, and the US.

Tiago Mendes Ferreiraمشاهده پروفایل

پژوهشگر

• Computational Chemistry
• Biological Membranes
• Molecular Dynamics Simulations
• +۳ مورد دیگر

Tiago Mendes Ferreira is a Ramón y Cajal Researcher at the University of Santiago de Compostela (USC), affiliated with the Center for Research in Biological Chemistry and Molecular Materials (CIQUS) and the Department of Physical Chemistry. His research focuses on computational chemistry, molecular dynamics simulations, and solid-state NMR spectroscopy to study lipid membranes and their interactions with guest molecules. He previously held a Junior Research Group Leader position at Martin Luther University Halle-Wittenberg (MLU), Germany, and received a DFG grant for his work on combining MD simulations with NMR experiments. Education : Bachelor's in Theoretical Chemistry from the University of Coimbra, Portugal. PhD in Computational Chemistry (Portugal), funded by the Portuguese Foundation for Science and Technology. Postdoctoral research at the University of Paderborn and MLU, Germany. Research Interests : His work bridges computational and experimental methods to elucidate molecular mechanisms in biological membranes. Key themes include lipid membrane structure, cholesterol effects, and the integration of solid-state NMR data with molecular simulations. He develops novel methodologies for characterizing membrane systems, such as long n-alkanes and bacterial membranes. Key Contributions : Advances in ssNMR methodology for lipid systems. Development of the NMRlipids Databank for biomembrane analysis. Studies on lipid phase behavior and protein-membrane interactions. Awards & Grants : Ramón y Cajal Fellowship (2024–present). DFG Temporary Principal Investigator Grant (Germany). Labs & Teams : He leads the QTEC Group (Theoretical and Computational Chemistry) , which focuses on interdisciplinary approaches to membrane science. His group collaborates internationally on NMR-based biomembrane studies.

Viji Ahanathapillaiمشاهده پروفایل

استادیار

• Biomedical Signal and Image Processing
• Wearable Technology for Health Monitoring
• Women's Health
• +۳ مورد دیگر

Dr. Viji Ahanathapillai is an Assistant Professor in Biomedical Engineering at the University of Warwick's School of Engineering, specializing in the Systems and Information Engineering stream. She holds a PhD in biomedical signal and image processing from the University of Strathclyde and has extensive industry experience in roles such as Image Processing Algorithms Engineer at Lucid Software Ltd and Development Scientist at Malvern Panalytical Ltd. Her academic career includes post-doctoral research at Warwick's Institute of Digital Healthcare and a Senior Lecturer position at Birmingham City University. Education: B.Eng (India) → PhD (University of Strathclyde). Research focuses on Biomedical Signal/Image Processing , Wearable Health Monitoring , Women's Health , and AI in Healthcare . Recent work includes developing algorithms for venous dislodgement detection, AI-driven healthcare data analysis, and wearable devices for real-time health monitoring. Teaching: Course leader for MSc Diagnostics Data and Digital Health, and module leader for Research Project modules. Provides weekly math support to first-year students. Labs/Teams: Active contributor to the Systems & Information Engineering team and the Institute of Digital Healthcare at Warwick. Editor of IET Healthcare Technology Letters.

Ralph Fyfeمشاهده پروفایل

استاد

• Environmental Science
• Archaeology
• Climate Change
• +۵ مورد دیگر

Professor Ralph Fyfe, currently at the University of Plymouth , serves as Professor in Geospatial Information and Associate Dean of Research for Science and Engineering. His research focuses on reconstructing past environmental change, with applications to conservation and climate change mitigation. Interdisciplinary expertise in archaeology, ecology, and climate science Lead Leverhulme Trust projects: "Deforesting Europe," "Transforming the Face of the Mediterranean," and "Reclaiming Exmoor" Contributed to over 115 academic papers and book chapters Ralph’s work combines pollen-landscape calibration datasets to quantify historical land cover changes, revealing human-driven transformations dating back to the Neolithic and Bronze Age periods. His research informs modern conservation strategies and has influenced UK National Park Authorities and COP26 climate policies. Recent publications highlight collaborations with international teams on Holocene vegetation dynamics, megafauna extinction impacts, and climate modeling. Key outlets include Global Change Biology and Scientific Reports . Fellow of the Royal Geographical Society Fellow of the Higher Education Academy Fellow of the Royal Society of Arts He teaches GIS skills , palaeoecology , and employability skills , integrating fieldwork and problem-based learning in locations like Iceland and Dartmoor.

• Algorithmic Graph Theory
• Computational Complexity
• Structural Graph Theory
• +۳ مورد دیگر

Narmina Baghirova is a Researcher at the Department of Informatics within the Interfaculty Informatics Department at the University of Fribourg. She holds a bachelor’s degree in Mathematics from the University of Primorska (Slovenia), a master’s in Mathematical Sciences from the same institution, and has completed exchange semesters at Universidad del País Vasco (Spain) and Eötvös Loránd University (Hungary). She is currently pursuing a PhD under Prof. Bernard Ries and Dr. David Schindl. Her research focuses on Algorithmic Graph Theory, Computational Complexity, and Combinatorial Optimization. Key areas include k-community structure detection in graphs, proportionally dense subgraphs (PDS), and parameterized complexity analysis. Recent work explores efficiently solvable cases for these problems and their applications in social network modeling. Her publications (2022–2025) span theoretical computer science, discrete mathematics, and bioinformatics, addressing graph partitioning, phylogenetic algorithms, and structural graph properties. She has contributed to both journal articles and conference proceedings. Baghirova collaborates with international researchers and is affiliated with the Department of Informatics at the University of Fribourg, where she contributes to research on computational complexity and graph-theoretic problems.