جست‌وجوی استاد و پژوهشگر

Lennart Martensمشاهده پروفایل

استاد

• Bioinformatics
• Biomedical Analytics
• Predictive Modelling
• +۵ مورد دیگر

Lennart Martens is a Professor of Systems Biology at Ghent University and serves as Group Leader and Associate Director at the VIB-UGent Center for Medical Biotechnology . He leads the Computational Omics and Systems Biology Group (CompOmics), focusing on Machine Learning applications in high-throughput omics data. His research involves developing AI-driven algorithms for proteomics data analysis, including tools like DeepLC for retention time prediction and MS2Rescore for improved peptide identification. He contributes to open-source proteomics software and FAIR data practices. Fellow of the Royal Society of Chemistry Prometheus Award for Research Excellence (Ghent University, 2014) Juan Pablo Albar 'Proteomics Pioneer' Award (European Proteomics Association, 2015) He has authored over 220 research papers, co-authored two Wiley textbooks on mass spectrometry bioinformatics, and organized Dagstuhl Seminars on computational proteomics. His editorial roles include membership on the boards of Molecular & Cellular Proteomics , Molecular BioSystems , and PeerJ .

Per Svenningssonمشاهده پروفایل

استاد

• Neurology
• Neurosciences
• Parkinson's Disease
• +۴ مورد دیگر

Per Svenningsson is a Professor of Neurology and Senior Physician at the Department of Clinical Neuroscience, Karolinska Institutet, and leads the Neuropharmacology research group focusing on movement disorders, particularly Parkinson's disease. He holds appointments at Karolinska University Hospital and is affiliated with the Biopark cohort and multiple collaborative initiatives. His research integrates clinical, translational, and experimental approaches to understand disease mechanisms and develop biomarkers. Education: MD (1996) and PhD (1998) from Karolinska Institutet. Research expertise spans neuropharmacology, neurodegeneration, and psychiatric comorbidities like depression. He leads projects on Parkinson's non-motor symptoms, biomarker development, and clinical trials for disease-modifying therapies. Research interests include: Pathophysiology of Parkinson's disease Biomarkers and early diagnosis Neurochemical changes in L-DOPA-induced dyskinesia Role of glial cells and lipid homeostasis Novel drug targets using mass spectrometry imaging Key publications focus on parkinsonism models, computational memory capacity, and molecular mechanisms in depression. His work has been supported by grants from the Swedish Research Council, Wallenberg Foundation, and international initiatives like ASAP for Parkinson's research. Advising includes supervision of PhD students Behzad Iravani and Lovisa Brodin. Collaborations involve teams across neurology, psychiatry, and bioinformatics, emphasizing multidisciplinary approaches to neurodegenerative diseases. Labs/Teams: Neuropharmacology - Movement Disorders group at Karolinska Institutet, contributing to the Biopark cohort and international clinical trials (e.g., Montelukast for Parkinson's neuroinflammation).

Wenyi Wangمشاهده پروفایل

استاد

• Cancer Genomics
• Bioinformatics
• Machine Learning in Healthcare
• +۵ مورد دیگر

Dr. Wenyi Wang is a Professor in the Department of Bioinformatics and Computational Biology at The University of Texas MD Anderson Cancer Center. She leads the Statistical Bioinformatics Lab, focusing on advancing computational methods for cancer genomics, transcriptomics, and risk modeling. Her work integrates statistical rigor and AI to address challenges in understanding tumor heterogeneity, cancer evolution, and clinical translation. She holds adjunct professorships at Rice University and Texas A&M University. Research Interests: Multi-omic deconvolution, tumor microenvironment dynamics, cancer risk prediction using Bayesian and machine learning models, and development of tools for mutation calling (e.g., MuSE, DeMixT). Her lab’s current directions include DNA–RNA dynamics in cancer and spatial transcriptomics to map tumor architectures. Publications: Dr. Wang’s recent work includes high-impact studies on deconvolution methods (e.g., DeMixSC), TP53 mutation annotation, and clinical risk prediction models. Her lab’s tools like CliPP and DeMixNB advance precision medicine by enabling accurate analysis of tumor evolution and heterogeneity. Awards: While personal awards are not explicitly listed, her research has garnered institutional recognition, including inclusion on MD Anderson’s Wall of Science for contributions to cancer discovery. Grants and Collaborations: Collaborates with clinicians and experimental biologists to translate computational insights into clinical practice. Her lab trains students across interdisciplinary programs, emphasizing training in quantitative sciences and translational research. Labs/Teams: The Statistical Bioinformatics Lab fosters a community dedicated to bridging bioinformatics and clinical oncology, with ongoing projects supported by grants and collaborations across institutions.

Juannan Zhouمشاهده پروفایل

استاد

• Quantitative Biology
• Evolutionary Biology
• Genotype-Phenotype Mapping
• +۳ مورد دیگر

Juannan Zhou is a Professor in the Department of Biology at the University of Florida , where they recently established their research lab. Their work focuses on genotype-phenotype mapping using machine learning , mathematical modeling , and wet lab experiments to understand evolutionary dynamics. Research Lab : Department of Biology, University of Florida Contact : juannanzhou@ufl.edu Key research themes include: Mapping genotype-phenotype relationships in diploid organisms Investigating protein fitness landscapes through unsupervised language models Developing machine-learning assisted directed evolution techniques Studying complex probability distributions over biological sequence spaces Exploring interactions between biochemical constraints and evolutionary outcomes Recent publications demonstrate technical innovations in: Gaussian process priors for sequence-function modeling Field-theoretic density estimation methods Higher-order epistasis analysis Nonparametric Bayesian approaches to biological sequence space Differentiable Smith-Waterman algorithms for sequence alignment The lab actively seeks postdocs, lab technicians, and graduate students interested in machine learning applications in population genetics and evolutionary modeling . Current projects include developing gpmap-tools for genotype-phenotype map visualization and implementing empirical variance component regression frameworks.

Muneer Ahmadمشاهده پروفایل

مدرس ارشد

• Artificial Intelligence
• Data Science
• Healthcare Informatics
• +۳ مورد دیگر

Dr. Muneer Ahmad is an experienced academic with over 22 years of international experience in teaching, research, and academic leadership within Computer Science, specializing in Artificial Intelligence, Data Science, and Healthcare Informatics. He currently serves as Senior Lecturer and Program Lead for the MSc Data Science at the University of Roehampton , UK, having previously held teaching and leadership roles in South Korea, Malaysia, Saudi Arabia, and Pakistan. Education: PhD in AI & Data Science His research is multidisciplinary and application-driven, focusing on AI for healthcare, precision agriculture, and industrial IoT. He leads large, diverse student cohorts and collaborates with academic teams to deliver inclusive education. Recognized as a Senior Fellow of Advance HE (SFHEA) , he emphasizes curriculum innovation and pedagogy. With over 100 peer-reviewed publications and £200,000+ secured funding, his work spans AI in healthcare diagnostics Smart urbanization Genomic data processing Cybersecurity Educational technology Industrial 4.0 He has supervised over 50 PhD/Master's students and serves on editorial boards of journals like Information and Bioengineering . Recent projects include AI-driven crop yield estimation, cervical cancer diagnosis, and pandemic resilience technologies.

Mehdi Mobliمشاهده پروفایل

استاد

• NMR Spectroscopy
• Structural Biology
• Biomolecular Modeling
• +۲ مورد دیگر

Mehdi Mobli is Professor and Group Leader at the Australian Institute for Bioengineering and Nanotechnology, University of Queensland. His research focuses on biomolecular structure and dynamics using NMR spectroscopy, with applications in peptide engineering and ion channel modulation. Key research areas: Advanced NMR techniques for protein structure determination Peptide toxin engineering for ion channel modulation Computational methods for biomolecular design Structural basis of venom peptide function Development of cyclic peptide therapeutics Honors include the ASBMB Merck Medal, Tregear Award, Sir Paul Callaghan Medal, and ARC Future Fellowship. Research has yielded novel peptide therapeutics and computational tools for structural biology.

Michael Cianfroccoمشاهده پروفایل

دانشیار

• Structural Biology
• Cryo-Electron Microscopy (Cryo-EM)
• Microtubule-Based Intracellular Transport
• +۴ مورد دیگر

Michael Cianfrocco is an Associate Professor at the University of Michigan, holding dual roles in the Department of Biological Chemistry (Medical School) and the Life Sciences Institute. His research focuses on structural biology and microtubule-based transport, leveraging cryo-electron microscopy (Cryo-EM), single-molecule methods, and computational tools to study motor proteins like kinesin and dynein. Education: B.S. in Biochemistry, Providence College (2007) Ph.D. in Biophysics, University of California, Berkeley (2012) Postdoctoral Fellowships at Harvard Medical School and UC San Diego (2013–2017) Research Interests: His lab investigates how motor proteins transport cargo in cells, with emphasis on specificity, regulation, and pathogen hijacking. They also develop computational tools like cryoEDU and CryoRL to streamline Cryo-EM workflows. Key areas include viral transport mechanisms, microtubule dynamics, and AI-driven microscopy automation. Awards: 2013 Damon Runyon Cancer Research Foundation Award 2017 University of Michigan Biological Sciences Scholar Advising & Leadership: Director of the Perrigo Undergraduate Summer Fellowship Program. Mentors students in the Life Sciences Institute and collaborates with teams in cellular and molecular biology. Active in graduate programs in Biomedical Sciences, Chemical Biology, and Biophysics. Labs & Teams: Leads the Cianfrocco Lab, which includes researchers, postdocs (e.g., Andrew Hensley, Lai Wei), and a vibrant graduate cohort. Collaborates with experts in structural biology, computational science, and virology.

Narges Baniasadiمشاهده پروفایل

استاد مدعو

• Health Entrepreneurship
• Health Equity
• Bioinformatics
• +۳ مورد دیگر

Narges Baniasadi serves as an Adjunct Professor in the Department of Medicine at Stanford University School of Medicine, where she teaches impact entrepreneurship focused on Prevention and Health Equity. She is the founder and executive director of Stanford's Emergence program, developing educational and translational initiatives to improve societal health through entrepreneurial solutions. Her research spans critical interdisciplinary domains: Health Entrepreneurship Health Equity Bioinformatics Artificial Intelligence in Healthcare Genomics Cancer Research With over a decade of leadership at the technology-life sciences intersection, she founded Bina—a bioinformatics company that pioneered high-performance computing and AI platforms for cancer genomics analysis—based on research conducted at Stanford and UC Berkeley. Following Roche's acquisition of Bina, she served as VP of Informatics at Roche Sequencing until 2018, directing clinical software development and AI research. No scientific awards, student advising records, or research grants are documented in the source material. Her current work centers on the Emergence program, which cultivates innovation in health entrepreneurship and societal health improvement through translational educational frameworks.

David Spielerمشاهده پروفایل

استاد

• Machine Learning
• Stochastic Systems
• Biochemical Systems Modeling
• +۳ مورد دیگر

David Spieler is a Professor of Machine Learning at the University of Applied Sciences Munich since September 2018. Previously, he served as a product owner for big data topics at Audi AG (2015–2018), a system developer at Bosch SoftTec (2014–2015), and completed his PhD in Modeling and Simulation at Saarland University (2009–2014). His research focuses on stochastic systems, biochemical modeling, parameter estimation, and oscillatory behavior analysis in Markovian systems. He has contributed to numerical methods for steady-state analysis, formal verification techniques, and applications in systems biology. Research interests include stochastic hybrid systems, sensitivity analysis of biochemical networks, geometric bounds for CTMC steady-state distributions, and oscillatory dynamics in chemical reaction networks. He has published extensively on topics like model checking, parameter estimation, and algorithmic solutions for stochastic processes. His work bridges theoretical computer science with practical applications in bioinformatics and engineering. Spieler has taught courses on data networks, quantitative model checking, and stochastic simulation techniques. He has reviewed manuscripts for over 20 conferences including CAV, CMSB, HSCC, and QEST, contributing to the advancement of formal methods and computational systems biology.

• Bioinformatics
• Genomics
• Sequence Analysis
• +۳ مورد دیگر

Antoine Limasset is a Researcher (Chargé de recherche) at CNRS, affiliated with the CRIStAL research center and the BONSAI team at Université de Lille, France. His work focuses on computational methods and data structures in sequence bioinformatics, particularly addressing challenges in genome assembly, metagenomics, and third-generation sequencing data analysis. He holds a Ph.D. in Computer Science from Université de Rennes 1 (2014-2017), supervised by Pierre Peterlongo and Dominique Lavenier, followed by a postdoctoral position at Université Libre de Bruxelles (2017-2018). Key research areas include de Bruijn graph algorithms, k-mer indexing, and scalable genomic data processing. Limasset has developed tools like BLight for efficient k-mer management, STRONG for metagenomic strain resolution, and ELECTOR for evaluating long-read correction methods. He actively contributes to conference committees, including SPIRE, RECOMB, and ACM-BCB, and leads the ANR JCJC grant on graph structures for third-generation sequencing exploration. He advises two Ph.D. students: Coralie Rohmer (on multiple alignment algorithms for third-gen sequencing) and Léa Vandamme (on indexing third-gen sequencing datasets). His publications span high-impact venues like ISMB, WABI, and Genome Biology, with a focus on optimizing algorithms for genomic data scalability and accuracy.

• Analytical Chemistry
• Metabolomics
• NMR Spectroscopy
• +۳ مورد دیگر

Panteleimon G. Takis is an Assistant Professor at the Department of Chemistry, University of Ioannina, and an Honorary Research Associate at Imperial College London's Faculty of Medicine. His research focuses on advancing NMR spectroscopy for clinical and translational metabolomics, integrating AI/ML for biomarker discovery and disease diagnostics. He specializes in developing computational tools and methodologies for analyzing complex biofluids, with applications in gastrointestinal and cancer-related disorders. His work bridges fundamental chemistry with clinical applications, emphasizing reproducibility and interdisciplinary collaboration. Research interests include NMR-based metabolomics, AI-driven data analysis, and clinical translation of biomarkers. He has contributed to protocols for standardized metabolomics workflows and developed software like NMRpQuant for protein quantification. His recent work addresses long-term consequences of COVID-19 and gut-microbiome interactions in metabolic diseases. Notable contributions include over 20 peer-reviewed articles in high-impact journals, patents on NMR methodologies, and editorship roles in Frontiers in Molecular Biosciences and Metabolites . He actively engages in international conferences, leading sessions on computational metabolomics and NMR applications.

Richard Klimanمشاهده پروفایل

استاد

• Evolutionary Genetics
• Ecological Genetics
• Population Genomics
• +۳ مورد دیگر

Dr. Richard Kliman is a Professor in the Department of Biological Sciences at Cedar Crest College, joined in 2002 after teaching at large public universities. He holds a Ph.D. from Wesleyan University and postdoctoral fellowships at Rutgers University and Harvard University. His research focuses on evolutionary and ecological genetics, particularly population genomics of invasive species and marine ecology. He collaborates on studies like the Belize marine reserve analysis and has been funded by NIH and Conservation International. Education: A.B. in Biology and Music (Colby College), Ph.D. in Biology (Wesleyan University) Research interests include molecular evolution, speciation mechanisms, and evolution education advocacy. He serves as chair of the Education and Outreach Committee for the Society for the Study of Evolution and previously held roles at the NSF and as editor of the Encyclopedia of Evolutionary Biology. He developed the EvolGenius simulation tool for teaching population genetics. His publications span evolutionary biology, genetics, and computational methods. He has held administrative roles including Department Chair (2017-2022) and Faculty Council President at Cedar Crest.

• Molecular Biophysics
• Supramolecular Chemistry
• Nanobiomimetics
• +۴ مورد دیگر

Angel Piñeiro Guillén is a Professor at the University of Santiago de Compostela, affiliated with the Department of Applied Physics within the Higher Polytechnic School of Engineering. His research focuses on supramolecular systems, nanobiomimetics, and molecular biophysics, with active involvement in the SupraNanoBioMol research group. He earned his doctorate from the University of Santiago de Compostela in 2001 with a thesis on thermophysical models in liquid mixtures. His research interests span cyclodextrin chemistry, membrane dynamics, molecular simulations, and drug design. Key contributions include the development of computational tools like the AFFINImeter software for molecular recognition analysis, and the CYCLOPEp Builder platform for cyclic peptide research. He has also explored post-COVID-19 pathophysiology, lipidomics, and the application of quantum computing to biophysical simulations. His articles emphasize interdisciplinary approaches, combining experimental biophysics with computational modeling. Recent work addresses drug delivery systems, peptide-membrane interactions, and therapeutic strategies against atherosclerosis using cyclodextrin dimers. His publications also highlight innovations in AI-driven drug design and data-driven biomolecular analysis through platforms like the Overlay databank. Prof. Piñeiro Guillén collaborates with multiple research groups, including the Group of Soft Matter and Molecular Biophysics (MBBM) and the Group of Biophysics and Interfaces (GBI). His lab's work bridges fundamental science and translational applications, aiming to advance targeted therapies and diagnostic tools.

Jason Hodgsonمشاهده پروفایل

مدرس ارشد

• Human evolution
• Primate evolution
• The role of culture in human evolution
• +۳ مورد دیگر

Dr. Jason Hodgson is a Senior Lecturer in Bioinformatics and Big Data at Anglia Ruskin University, within the Faculty of Science and Engineering's Life Sciences department. His research focuses on human and primate evolutionary genetics, employing bioinformatics and population genomics to explore how cultural practices influence biological evolution, particularly in Madagascar. He joined Anglia Ruskin in 2022 and has held roles at institutions including Imperial College London and Cambridge University. Education: PhD in Biological Anthropology (New York University, 2012), MPhil from Cambridge University, and a BS in Evolutionary Anthropology from Rutgers University. Research interests include human and primate evolution, the interplay of culture and biology, and population genetics. He leads projects on Madagascar’s genetic diversity and Neolithic migration patterns in Europe. He is a member of the Behavioural Ecology and Biomedical Research Groups at Anglia Ruskin. Teaching expertise spans bioinformatics, statistics, and software engineering. His work bridges anthropology, genetics, and computational methods, with recent presentations on topics like cultural influences on genetic diversity and Neolithic human migration.

Paulo Jorge Azevedoمشاهده پروفایل

مدرس

• Machine Learning
• Data Mining
• Bioinformatics
• +۸ مورد دیگر

Paulo Jorge Azevedo is a Lecturer at the Department of Informatics, University of Minho, and a Senior Researcher at HASLab/INESC TEC. His research focuses on machine learning, data mining, and bioinformatics, with contributions to explainable AI, subgroup mining, and social network analysis. Education: PhD in Computing from Imperial College (University of London), specializing in logic programming and association rule mining. Research Interests: Machine learning methodologies and their interpretability Data mining techniques for pattern discovery Bioinformatics applications in protein dynamics Social network analytics and graph mining Key Research Contributions: Development of Error Distribution Rules (EDRs) for regression model analysis, dynamic influence analysis frameworks for micro-blogs, and sequence mining for software test generation. Advising & Supervision: Supervised 4 theses including work on explainable regression models, deep learning formalization, and fraud detection systems. Active in guiding students on both technical and theoretical aspects of machine learning and data engineering. Labs & Teams: Member of HASLab/INESC TEC, collaborating on interdisciplinary projects combining AI with bioinformatics and software engineering challenges.