Single Cell Modeling Professors and Supervisors | ApplySharif

جست‌وجوی استاد و پژوهشگر

استاد راهنما، استاد دانشگاه یا پژوهشگر مناسب برای مسیر پژوهشی‌تان را پیدا کنید و با او ارتباط بگیرید.

نمایش ۷۰۶-۷۲۰ از ۷٬۸۱۹ استاد راهنما

اطلاعات این پروفایل‌ها از صفحات عمومی دانشگاه‌ها جمع‌آوری شده و ممکن است به‌روز نباشد.

Kevin Huangمشاهده پروفایل

استاد

University of South CarolinaUnited States

SmartState Endowed Chair Professor at University of South Carolina directing Solid Oxide Fuel Cell Center. Associate Editor for Journal of Electrochemical Energy Conversion and Storage. Education: PhD Physical Chemistry (1992), University of Science and Technology Beijing; MS Physical Chemistry (1989), Northeastern University. Research focuses on solid oxide electrochemical systems including fuel cells, batteries, and gas separation membranes. Current projects include DOE-funded development of dynamic solid oxide fuel cells, stable critical materials for hydrogen production, and zinc-ion battery fundamentals. Author of foundational textbook on solid oxide fuel cell technology. Awards include Breakthrough Leadership in Research (2018) and multiple Siemens awards for industrial contributions. Supervised 30+ PhD students. Secured $15M+ research funding from NSF, DOE, DoD. Holds affiliate appointment in Chemical Engineering. Teaches mechanical engineering laboratories and advanced energy storage courses.

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Tian Hongمشاهده پروفایل

دانشیار

University of Texas at DallasUnited States

Tian Hong is an Associate Professor at the Department of Biological Sciences, The University of Texas at Dallas , with a joint appointment as Research Associate Professor at the Department of Biochemistry & Cellular and Molecular Biology, The University of Tennessee, Knoxville . His research focuses on systems biology, bioinformatics, cancer biology, and mathematical biology , particularly the plasticity and heterogeneity of epithelial and immune cells during development and cancer progression . He develops computational and mathematical models for gene regulatory networks, pattern formation, and dynamical systems. Education : PhD in Genetics, Bioinformatics and Computational Biology from Virginia Tech; MS in Bioinformatics from Nanyang Technological University, Singapore; BS in Biological Sciences from Nanyang Technological University, Singapore. Previous Appointments : Associate Professor (2023–2024) and Assistant Professor (2017–2023) at The University of Tennessee, Knoxville. His recent publications explore noncoding RNA-driven oscillations , Turing pattern formation without feedback , and epithelial-mesenchymal transitions using single-cell transcriptomics and mathematical modeling . He has received the Professional Promise in Research and Creative Achievement Award (2024) and the Transdisciplinary Team Science Fellow (2009). His work is funded by NIH grants including R35GM149531 (PI) and R01GM140462 (completed PI). Dr. Hong advises PhD and Master’s students and collaborates with researchers at the Center for Systems Biology, UT Dallas . His lab ( link ) actively seeks undergraduate, graduate, and postdoctoral researchers interested in interdisciplinary computational biology .

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Mark R. Martenمشاهده پروفایل

استاد

University of Maryland, Baltimore CountyUnited States

Mark R. Marten is a Professor and Department Chair at the University of Maryland, Baltimore County, in the College of Engineering and Information Technology, Department of Chemical, Biochemical and Environmental Engineering. He leads the Marten Lab, which focuses on understanding and manipulating microbial expression systems. Education: Ph.D. Chemical Engineering – Purdue University, 1991 M.S. Chemical Engineering – Purdue University, 1988 B.S. Chemical Engineering – State University of New York at Buffalo, 1986 Research interests center on fungal biology and biotechnology, particularly using advanced analytical tools like electron microscopy, atomic force microscopy, and proteomic analysis to study fungal cell walls and stress responses. Current projects investigate autophagy mechanisms and morphology-secretion relationships in filamentous fungi. His recent publications demonstrate a strong focus on Aspergillus nidulans cell wall stress responses, kinase signaling pathways, and mycelial material properties. Research integrates molecular biology with materials science, showing consistent themes in fungal adaptation mechanisms and biotechnological applications. Lab activities include collaborative projects on plant phenotype design, antifungal mechanisms, and nano-mechanical characterization of fungal structures.

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Ioannis Sgouralisمشاهده پروفایل

استادیار

University of TennesseeUnited States

Ioannis Sgouralis is an Assistant Professor in the Department of Mathematics at The University of Tennessee, Knoxville. He holds a Ph.D. from Duke University and specializes in Data Science and Applied Mathematics with a focus on interdisciplinary research in Physics, Chemistry, Biology, and Medicine. His research group integrates computational methods with experimental data to address challenges in biophysics, biochemistry, and biomedicine. His educational background includes a Ph.D. in a relevant field (specific details not provided). His research interests span Bayesian nonparametrics, computational statistics, mathematical biology, and multiscale modeling. He leads projects on single-molecule biophysics, image analysis, and cardiovascular system modeling. Key research areas include: Single molecule experiments and analysis Super-resolution microscopy and multi-particle tracking Biomedical systems modeling (cardiovascular/urinary) Statistical methods for noisy experimental data Ioannis actively recruits graduate/undergraduate students for projects in Data Science, Mathematical Biology, and Applied Mathematics. His group uses advanced computing techniques such as Hamiltonian Monte Carlo and topological data analysis. Labs/Teams: Sgouralis Research Group focuses on cross-disciplinary computational methods for biological systems. Their work involves collaborations across departments and institutions.

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Jeremy Gaskinsمشاهده پروفایل

دانشیار

University of LouisvilleUnited States

Jeremy Gaskins, PhD, is an Associate Professor in the Department of Bioinformatics & Biostatistics at the University of Louisville's School of Public Health and Information Sciences (SPHIS). He joined UofL in 2013 as an Assistant Professor, earning tenure and promotion to his current rank in 2019. His expertise lies in Bayesian statistical methods for complex data structures, including longitudinal analysis, missing data imputation, and joint modeling of mixed data types. He collaborates with researchers across multiple departments, including OB/GYN, Radiation Oncology, and Surgery. Education: Ph.D. (2013) in Statistics, University of Florida B.S. (2007) in Mathematics and Applied Mathematics, Auburn University Research Interests: Development of Bayesian methods for longitudinal and clustered data Computational strategies for complex model inference Applications in medical and public health research Missing data mechanisms and imputation techniques Teaching: PHST 661: Probability PHST 662: Mathematical Statistics Collaborations & Labs: Active collaborations with UofL medical departments on applied health research Focus on translational statistics for biomedical and public health problems

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Lior Sepunaruمشاهده پروفایل

دانشیار

University of California , Santa Barbara (UCSB)United States

Lior Sepunaru is an Associate Professor in the Department of Chemistry and Biochemistry at the University of California, Santa Barbara (UCSB), and a member of the Center of Polymers and Organic Solids. His research focuses on bioelectronics, single-entity electrochemistry, and nano-electrochemistry, with applications in enzyme catalysis, biosensor development, and liquid-liquid phase separation phenomena. He completed his postdoctoral studies at the University of Oxford and earned his Ph.D. from the Weizmann Institute of Science. Education: Ph.D., Weizmann Institute of Science (2014) Marie Curie Research Fellowship, University of Oxford Research interests include: Electrochemical analysis of biological droplets (coacervates) and their role in protocell-like systems Single-molecule electrocatalysis and nano-scale energy conversion mechanisms Biomolecule-mediated nanoparticle synthesis and characterization Electrochemical correlative microscopy for reaction dynamics Publications highlight advancements in electrochemical sensing, catalysis, and bio-inspired materials. His lab employs cutting-edge techniques such as liquid-cell TEM and impedimetric spectroscopy to bridge macroscopic and single-entity electrochemical phenomena. Advising and grants: Guides over 20 graduate students and postdocs in interdisciplinary projects Collaborates with groups in materials science, chemical engineering, and biomolecular science Labs/Teams: Director of the Electrochemistry Lab at UCSB, specializing in bioelectrochemistry and nanoelectrochemical systems.

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Jing Liمشاهده پروفایل

دانشیار

Washington University in St. LouisUnited States

Dr. Jing Li is Associate Professor of Medicine and Director of Administrative Data Core Services at Washington University School of Medicine's Division of General Medical Sciences. Her work focuses on health data science applications in medical research. Research examines cancer genomics, tumor evolution, and microenvironment interactions using spatial transcriptomics and computational approaches. Key investigations include allele-specific aberrations in tumors, CRISPR gene drive systems, and glioblastoma signaling pathways.

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Andrea Giomettoمشاهده پروفایل

استادیار

Cornell UniversityUnited States

Andrea Giometto is an Assistant Professor in the School of Civil and Environmental Engineering at Cornell University, part of the College of Engineering. He holds affiliations with Applied Mathematics, Biological and Environmental Engineering, and Microbiology. His research focuses on spatial growth of microbial communities, integrating statistical physics, experiments, and genetic engineering to study ecological and evolutionary processes in spatially extended systems. Education: Ph.D. in Environmental Engineering (EPFL, 2015), M.Sc. and B.Sc. in Physics (University of Padova). Postdoctoral research at Harvard University, supported by Swiss National Science Foundation fellowships. Visiting scientist at Imperial College London and Eawag, Switzerland. Research interests include microbial population dynamics, resource competition, and scaling patterns in ecosystems. Key methods involve microfluidics, evolutionary experiments, and mathematical modeling. Recent work explores how physical interactions and environmental factors shape microbial behavior and community structure. Awards: Earth Science Award (EPFL, 2016), Swiss National Science Foundation Postdoc Fellowships (2016–2018). Grants: Funded by Swiss National Science Foundation and Cornell Engineering. Labs/Teams: Leads research in microbial systems biology and evolutionary ecology at Cornell, collaborating with interdisciplinary teams in biophysics and environmental engineering.

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Hanqing Jinمشاهده پروفایل

دانشیار

University of OxfordUnited Kingdom

Dr. Hanqing Jin is an Associate Professor in the Mathematical Institute at the University of Oxford, specializing in Mathematical Finance and Applied Stochastic Analysis. His research focuses on investor decision-making under non-utility behavior, dynamic portfolio optimization, and stochastic control problems in financial markets. Prior to Oxford, he held an Assistant Professorship at the National University of Singapore. He has contributed to foundational work on mean-variance portfolio selection, behavioral finance, and the application of stochastic processes to financial modeling. His academic background includes extensive work on continuous-time portfolio optimization, risk management frameworks, and the intersection of behavioral economics with quantitative finance. Notable contributions include studies on optimal lockdown policies during pandemics, blockchain consensus algorithms analysis, and the development of novel biomarker detection techniques using Raman spectroscopy. Dr. Jin's research integrates advanced mathematical techniques with real-world financial challenges, bridging theoretical models with practical applications in areas such as robo-advising systems and cryptocurrency frameworks. His work on transaction cost analysis and dynamic mean-variance strategies has been widely cited in both academic and industry contexts.

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Andreas Janshoffمشاهده پروفایل

استاد

University of GöttingenGermany

Prof. Dr. Andreas Janshoff is a Full Professor (W3) of Biophysical Chemistry at the Institute of Physical Chemistry, Georg-August-University Göttingen (since 2008). He previously served as Dean of the Faculty of Chemistry in Göttingen (2013–2015). His research focuses on membrane biophysics, cell mechanics, sensor design, and single-molecule force spectroscopy. Key contributions include studies on actin cortices, cellular adhesion dynamics, and viscoelastic properties of biological systems. Education: 1987–1989: Biology studies at University of Münster 1989–1994: Chemistry studies at University of Münster (with honors) 1994–1997: PhD in Biochemistry under Prof. H.-J. Galla 1999–2001: Habilitation in Biochemistry at University of Münster Research Interests: His work integrates experimental biophysics with advanced microscopy and nanoindentation techniques to explore cell mechanics, membrane dynamics, and biomaterial interactions. Recent studies emphasize actin network rheology, collective cell migration, and protein-mediated tissue fluidity. Publications: Recent work highlights include studies on cytosolic actin isoforms’ mechanical roles (Nature Communications 2023), differential adhesion in cocultures (PNAS 2023), and vimentin viscoelasticity (Science Advances 2018). Themes emphasize quantitative biophysical analysis of cellular and molecular systems. Labs/Teams: Affiliated with the Göttingen Graduate Center for Neuroscience (GGNB), focusing on Physics of Biological and Complex Systems and Biomolecules: Structure-Function-Dynamics .

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Andreas Milias Argeitisمشاهده پروفایل

دانشیار

University of GroningenNetherlands

Systems Biology
Biochemistry & Molecular Biology
Mathematics, Interdisciplinary Applications
+۶ مورد دیگر

Andreas Milias Argeitis is an Associate Professor in the Faculty of Science and Engineering at the University of Groningen. He leads the Milias-Argeitis Lab within the Molecular Systems Biology research unit at the Groningen Biomolecular Sciences & Biotechnology Institute (GBB). His research focuses on integrating experimental and computational approaches to understand cellular processes, particularly the coordination between cell growth, division, and metabolic dynamics in budding yeast. Key research areas include systems biology, TOR signaling pathways, cell cycle regulation, and the application of machine learning and mathematical modeling. His lab develops advanced tools like optogenetic control systems and deep learning algorithms for cell segmentation and tracking. Recent work has revealed metabolic oscillations linked to the cell cycle and explored the role of proteins like Sch9 in TORC1-dependent signaling. Notable achievements include an NWO Vidi Grant (2018) and an ENW Science-M Grant (2023) for studying how growth drives the cell division cycle. He has over 40 peer-reviewed publications, including work in Nature Communications , Nature Metabolism , and Journal of Cell Science . His research bridges fundamental biology with technological innovations in single-cell analysis and synthetic biology. Lab activities include CRISPR/Cas9 genome editing protocols, fluorescent protein maturation studies, and the development of photo-switchable enzymes. Collaborations span biochemistry, mathematics, and engineering, reflecting his interdisciplinary approach to unraveling cellular mechanisms.

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Masako Suzukiمشاهده پروفایل

استادیار

Texas A&M UniversityUnited States

Masako Suzuki is an Assistant Professor in the Department of Nutrition at Texas A&M University, affiliated with the College of Agriculture & Life Sciences. She is a member of Texas A&M AgriLife Research and leads the Suzuki Lab (website: suzukilab.org ). Her work focuses on epigenetic mechanisms in developmental biology, nutrition, and disease, with a strong emphasis on DNA methylation dynamics, gene regulation, and cellular differentiation. Research interests include: Epigenetic impacts of vitamins (A/D) on developmental pathways Cancer epigenetics and tumor progression Kidney development and fibrosis Sex-specific neuroepigenetic responses to stress Genetic/epigenetic interactions in health disparities Recent work highlights novel findings in: Long-term epigenetic effects of prenatal vitamin deficiency Smoking-induced methylome changes in lung progenitor cells Role of MLL3 tumor suppressor in breast cancer Liver cell composition alterations from vitamin A deficiency Her research employs integrative approaches combining: Epigenomic profiling (DNA methylation, chromatin accessibility) Single-cell sequencing technologies Animal models for developmental studies Clinical samples from patient cohorts Current projects investigate: Mechanisms of epigenetic reprogramming in stem cells Epigenetic biomarkers for kidney diseases Intergenerational effects of maternal nutrition

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Didong Liمشاهده پروفایل

استادیار

University of North Carolina at Chapel HillUnited States

Dr. Didong Li is an Assistant Professor in the Department of Biostatistics at the Gillings School of Global Public Health , University of North Carolina at Chapel Hill. His research focuses on statistical methods for complex data, including manifold learning, nonparametric Bayesian inference, and spatial statistics, with applications in healthcare, genomics, and environmental health. He holds a PhD in Mathematics from Duke University (2020), and degrees from Beijing Institute of Technology (BS 2012, MS 2015). His research interests include dimension reduction, Gaussian processes, geometric data analysis, and network analysis. He has developed novel methods for analyzing electronic health records, single-cell RNA sequencing data, and spatial omics datasets. Notable contributions include the spherelet manifold approximation framework and the STimage-1K4M dataset. Dr. Li has received the Inaugural IMS Lawrence D. Brown PhD Student Award (2019) . He serves on the editorial board of the Journal of Machine Learning Research and organized sessions at JSM 2021-2022 on geometric Bayesian methods. His lab supports interdisciplinary collaborations, with ongoing projects in spatial transcriptomics, healthcare access analysis, and AI-driven medical informatics. Advising highlights include guiding students through successful PhD defenses (e.g., Dr. Sam Hawke, Dr. Jiawen Chen) and postdoctoral placements. His team secured a NVIDIA Academic Grant (2024) for multi-modal AI in spatial transcriptomics. The Li Lab maintains active partnerships with institutions worldwide, advancing both statistical theory and real-world applications.

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Fabian Theisمشاهده پروفایل

استاد

University of Southern DenmarkDenmark

Prof. Dr. Dr. Fabian Theis is a Professor of Mathematics in Systems Biology at the Technical University of Munich (TU Munich) and Director of both the Computational Health Center and the Institute for Computational Biology (ICB) at Helmholtz Munich. He specializes in applying artificial intelligence and machine learning to analyze single-cell sequencing data, modeling cellular behavior, and predicting biological outcomes in health and disease. His work bridges computational methods with clinical applications to advance personalized medicine. Education: Master's Degrees in Mathematics and Physics Doctorates in Physics and Computer Science Research interests revolve around AI-driven biological systems analysis, including cellular interaction modeling, disease mechanism prediction, and translational biomedical research. He coordinates initiatives such as the Human Cell Atlas Analysis Working Group, Single Cell Omics Germany (SCOG), and the Munich School for Data Science (MUDS). His leadership spans roles at HelmholtzAI as Science Director and as a guest scientist at global institutions like RIKEN Brain Science Institute (Japan) and FAMU/FSU (USA). Labs/Teams: Leads the Computational Health Center and chairs the Mathematical Modeling of Biological Systems program at TU Munich. Collaborates with interdisciplinary teams in AI and health research.

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Robert Nobleمشاهده پروفایل

مدرس ارشد

City, University of LondonUnited Kingdom

Dr. Robert Noble is a Senior Lecturer in Mathematics at City St George's, University of London, specializing in the evolution and ecology of cancer . His research integrates mathematical modeling with computational biology to address four key themes: systematic understanding of somatic evolution, patient-specific tumor forecasting, treatment strategies leveraging evolutionary dynamics, and disentangling ecological contributions to cancer risk. Education: DPhil in Zoology (University of Oxford, 2014) Previous Appointments: Postdoctoral roles at ETH Zurich (2017-2020) and University of Zurich (2018-2020) His methodological toolkit spans agent-based models , stochastic processes , and Bayesian data analysis , with software contributions like the ggmuller and demon packages. Recent work focuses on universal tree balance indices for biological data comparison and adaptive therapy frameworks for clinical translation. Collaborations with experimental biologists and clinicians inform his research, which has produced key insights into spatial tumor evolution , drug resistance dynamics , and clonal diversity-survival relationships . He maintains active peer-review roles for journals like PLOS Computational Biology and Nature Ecology & Evolution.

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