جست‌وجوی استاد و پژوهشگر

• Network Science
• Nonlinear Dynamics
• Machine Learning
• +۴ مورد دیگر

Michelle Girvan is a Professor in the Department of Physics at the University of Maryland, College Park (UMD), with affiliations in the Institute for Physical Science and Technology and the Institute for Research in Electronics and Applied Physics. Her research focuses on network science applications in biological, social, and technological systems, integrating machine learning and nonlinear dynamics. She holds a B.S. from MIT (1999) and a Ph.D. from Cornell University (2003), followed by a postdoctoral fellowship at the Santa Fe Institute. Notable awards include the UMD Distinguished Scholar-Teacher (2022) and Fellow of the American Physical Society. Her work spans interdisciplinary topics such as information cascades, epidemiology, and genetic regulatory networks. Recent projects emphasize hybrid forecasting models combining machine learning with knowledge-based systems. Girvan leads the Girvan Networks Lab, advising over 20 graduate students and postdocs. She teaches courses like Physics 165 (Programming for Physical Sciences) and Physics 615 (Nonlinear Dynamics). Her $3M NSF grant supports training in network biology, leveraging computational methods to address complex systems challenges. Scientific contributions include studies on reservoir computing for dynamical systems prediction, jet lag asymmetry modeling, and network robustness in neuronal systems. She serves on the Santa Fe Institute's External Faculty and Science Steering Committee, promoting interdisciplinary collaboration in network science.

• Mathematical Biology
• Systems Biology
• Biochemical Networks
• +۱۱ مورد دیگر

Robyn Araujo is a prominent researcher in mathematical and systems biology, with a focus on the design principles of biochemical networks, particularly those enabling robust perfect adaptation and homeostasis. Her interdisciplinary work bridges mathematics, biology, and medicine, with applications in cancer, immunology, and metabolic regulation. Her research interests center on the topological and dynamical properties of biological networks. She investigates how network structure enables robustness and adaptation in systems such as cholesterol regulation, T-cell dynamics, and protein-protein interactions. Her work often employs control theory, algebraic methods, and computational modeling to uncover universal design principles in complex biological systems. The analysis of her recent publications reveals a strong trend toward understanding adaptation mechanisms in biochemical networks using topological and control-theoretic approaches. She frequently collaborates with Lance A. Liotta, focusing on the application of mathematical models to cancer biology and biomarker discovery. Her work spans theoretical foundations and practical biomedical applications, including immunovirotherapy and ecological resilience. Robyn Araujo has made significant contributions to the field through high-impact publications in journals such as Nature Communications , PLoS Computational Biology , and Bulletin of Mathematical Biology . Her research has been widely cited, indicating strong influence in mathematical biology. She has advised or collaborated with numerous researchers across disciplines, contributing to projects in cancer modeling, tuberculosis biomarkers, and opinion dynamics. While specific grant information is not provided, her sustained publication output suggests successful funding and collaborative research programs. Her work often involves interdisciplinary teams and computational modeling environments, though specific lab or team names are not mentioned in the provided text.

• Neurotechnology
• Neurosciences
• Cognitive Sciences
• +۵ مورد دیگر

Professor Simon Schultz is a Professor of Neurotechnology and Director of the Centre for Neurotechnology at Imperial College London’s Faculty of Engineering. He holds affiliate faculty status at the Sainsbury-Wellcome Centre for Neural Circuits and Behaviour at UCL. His research focuses on understanding memory mechanisms in the brain and developing neurotechnologies to treat memory disorders. He leads a lab specializing in neurotechnology development, experimental neuroscience, and computational modeling. Education includes a BSc/B.E. (Hons) from Monash University, ME(Res) from the University of Sydney, and a DPhil in computational neuroscience from Oxford University. Postdoctoral work included experimental neuroscience at NYU and UCL before joining Imperial in 2004. Research interests span neural coding, Alzheimer’s Disease modeling, and neurotechnology innovation. His lab develops advanced imaging techniques (e.g., multi-photon microscopy) and robotic electrophysiology systems. Recent work explores how amyloid pathology disrupts neural coding in Alzheimer’s models and how spatial memory circuits malfunction during disease progression. Awards and recognitions are not explicitly listed in the provided texts, though his leadership roles and prolific publication record suggest significant contributions to the field. Labs/Teams: Neural Coding and Neurodegenerative Disease Laboratory Centre for Neurotechnology (Director) Physics of Life research group Imperial College London’s Artificial Intelligence Network Future work emphasizes co-creating next-generation neuromodulation therapies (e.g., NEUROMOD+ initiative) and advancing robotic automation in neuroscience experiments to improve data acquisition speed and precision.

• Applied Mathematics
• Mathematical Physics
• Neurosciences
• +۸ مورد دیگر

Professor Paul C. Bressloff holds the Chair in Applied Mathematics and Stochastic Processes at the Department of Mathematics, Faculty of Natural Sciences, Imperial College London. His research focuses on stochastic and non-equilibrium processes in molecular/cell biology, leveraging tools like stochastic calculus, statistical physics, and dynamical systems theory. He authored the seminal graduate textbook Stochastic Processes in Cell Biology (Springer, 2022, 2nd ed.). Affiliations: Applied Mathematics and Mathematical Physics, Biomathematics Group, Mathematical Physics Group Former senior faculty in Mathematical Biology at the University of Utah (2001–2023) Advises graduate students in applied mathematics and mathematical biology Research interests include stochastic interacting particle systems, active phase separation, encounter-based diffusion models, and cytoneme-based morphogenesis. His recent work explores stochastic resetting mechanisms, semi-permeable interfaces, and nanoscale target search processes in biological systems. Notable contributions include modeling diffusion in singularly perturbed domains, analyzing stochastic thermodynamics, and developing mathematical frameworks for cytoskeletal dynamics and synaptic receptor trafficking. Over 150 peer-reviewed articles and two monographs highlight his interdisciplinary impact.

Md Taher Abu Saifمشاهده پروفایل

استاد

• Biohybrid robots
• Engineered living systems
• Biomechanics
• +۴ مورد دیگر

Md Taher Abu Saif is a Professor at the University of Illinois, holding concurrent appointments in Mechanical Science and Engineering, Bioengineering, Biomedical and Translational Sciences, Neuroscience Program, Beckman Institute for Advanced Science and Technology, and Carl R. Woese Institute for Genomic Biology. His research bridges mechanics and biology, focusing on nanoscale material behavior and cellular biomechanics. He pioneered studies on reversible plasticity in nanocrystalline metals and explored mechanical forces' role in cellular functions like cancer metastasis and neural activity. Education includes a Ph.D. in Theoretical & Applied Mechanics from Cornell University (1993), M.S. in Civil Engineering from Washington State University (1987), and B.S. in Civil Engineering from Bangladesh University of Engineering and Technology (1984, with Honors). His research interests span biohybrid robotics, engineered living systems, and deformation mechanisms in nanomaterials. Recent work addresses neural network ontogeny, tumor solid stresses, and ethics in biohybrid robotics. Key contributions include the Elastic Modulus Pen (EPen) and neural activity scoring frameworks. Advising and grants are central to his profile, though specific grant details are not provided. His labs and teams collaborate across disciplines, leveraging the Beckman Institute and Genomic Biology Institute's resources. Notable achievements include Science journal publications on self-healing metals and PNAS contributions on biohybrid systems.

• Mechanobiology
• Cell Dynamics
• Tissue Engineering
• +۳ مورد دیگر

Tom Golde is a Researcher leading the Integrative Cell and Tissue Dynamics group at the Institute for Bioengineering of Catalonia (IBEC), where he investigates the mechanical principles governing cellular and tissue behavior. His work bridges biophysics, bioengineering, and cancer biology through innovative experimental and theoretical approaches. His research focuses on mechanobiology and tissue dynamics , particularly examining how mechanical forces regulate epithelial morphogenesis, tumor-stroma interactions, and biopolymer network mechanics. Key interests include stress mapping in curved tissues, microfluidic immunotherapy models, and the physics of semiflexible polymer networks. His group employs advanced techniques like microfabrication, live-cell imaging, and computational modeling to unravel how physical cues influence cellular decision-making in development and disease. Recent publications (2023-2025) reveal trends in tumor microenvironment engineering and curved tissue mechanics , with breakthroughs in microfluidic immunotherapy testing platforms and epithelial stress mapping. Earlier work (2015-2019) established foundational principles in motor-free contractility and semiflexible polymer rheology , demonstrating how molecular crowding and filament entanglement generate biological forces without molecular motors. Golde directs the Integrative Cell and Tissue Dynamics laboratory, which integrates biophysical experimentation with computational modeling to study cellular mechanics across scales. The group collaborates extensively with cancer biologists and clinicians to translate mechanobiological insights into therapeutic applications, particularly in immunotherapy development and tissue engineering.

• mathematical biology
• stochastic modelling
• tumour growth
• +۱۱ مورد دیگر

Tomás Alarcón is an ICREA Research Professor at the Centre de Recerca Matemàtica (CRM) in Bellaterra, Barcelona, where he leads the Cancer Modelling Group. He has been affiliated with CRM since November 2010 and was appointed to an ICREA Research Professorship in October 2015. He also serves as deputy director of CRM since March 2016 and is an Affiliated Professor at the Department of Mathematics, Universitat Autònoma de Barcelona since September 2013. Dr. Alarcón earned his PhD in Theoretical Physics from the University of Barcelona in 2000. Following his PhD, he held postdoctoral positions at the University of Oxford (2001-2003), University College London (2003-2006), and Imperial College London (2006-2009). He briefly served as a senior researcher and group leader at BCAM in Bilbao, Spain (2009-2010) before joining CRM. His research focuses on mathematical modeling of complex biological systems, with particular emphasis on multiscale modeling of tumor growth, hybrid methods for multiscale models, stochastic modeling of somatic cell reprogramming, robustness and evolvability in relation to drug resistance, stochastic models in population dynamics, and membrane biophysics and microfluidics of biofluids. His work integrates phenomena across different biological scales, from angiogenesis to cell-cycle progression under oxygen starvation. Analysis of his recent publications reveals a strong focus on computational oncology, with particular attention to epigenetic regulation, tumor microenvironment mechanics, and cancer metabolism. His work combines mathematical rigor with biological relevance, often collaborating with experimentalists to validate theoretical predictions. Key themes in his recent work include chromatin dynamics, metabolic regulation in cancer, multiscale modeling approaches, and the application of dynamical systems theory to biological problems. ICREA Research Professorship (2015) Dr. Alarcón has mentored numerous PhD students and postdoctoral researchers, many of whom have gone on to successful academic careers. His group collaborates extensively with researchers at institutions including University of Oxford, University College London, Universitat Autònoma de Barcelona, and various international centers. He has received research funding for projects related to cancer modeling and mathematical biology, though specific grants are not detailed in the provided information. His laboratory, the Cancer Modelling Group at CRM, consists of research fellows, postdocs, PhD students, and collaborators working on various aspects of mathematical oncology and computational biology. The group is also part of the DysCoVir I2SysBio-CRM Associated Unit, indicating a broader collaborative network in systems biology.

• Inverse dynamics
• Structural health monitoring
• Linear and nonlinear vibrations
• +۵ مورد دیگر

Dr. Hamed Kalhori is a Lecturer in the School of Mechanical and Mechatronic Engineering at the University of Technology Sydney (UTS), Faculty of Engineering & Information Technology. As a Casual Academic, he contributes to teaching and research in mechanical engineering with a focus on dynamics, vibrations, and structural health monitoring. His work bridges theoretical and practical applications in civil infrastructure, aerospace systems, and composite materials. Dr. Kalhori earned his PhD in Mechanical Engineering from the University of Sydney between March 2013 and July 2017. Dr. Kalhori's research spans multiple domains within mechanical and structural engineering. His primary expertise lies in inverse dynamics problems , particularly impact force identification, where he develops advanced methodologies combining model-based approaches with machine learning techniques. He extensively investigates linear and nonlinear vibrations in various structures, from micro-plates to large-scale bridges. His work in structural health monitoring focuses on innovative approaches for bridge inspection, including drive-by assessment techniques and sensor network optimization. Additionally, he explores smart materials and structures , particularly carbon nanotube-reinforced composites and their mechanical behavior under dynamic loading conditions. Dr. Kalhori's publication record demonstrates a strong trajectory in mechanical engineering research with increasing focus on interdisciplinary approaches. His recent work shows growing integration of machine learning techniques with traditional mechanical engineering methods, particularly for solving inverse problems in structural dynamics. There's a clear progression from fundamental vibration analysis toward practical applications in infrastructure monitoring and composite material characterization. His research maintains strong connections between theoretical modeling and experimental validation across diverse structural systems. Dr. Kalhori has received notable recognition for his academic contributions: 2019 Dean's Award for Excellence in Teaching and Tutoring Multiple research grants in vibration-related fields Dr. Kalhori has secured research funding for projects related to vibration analysis and structural monitoring, including industry collaborations such as the Alstom DMU traincar crash test project (2025). He has led international research collaborations and conducted numerous workshops and short courses both locally and internationally. While the specific details of his advising relationships aren't provided in the available information, his extensive publication record and research leadership suggest active engagement with graduate students and early-career researchers. His research involves collaboration with the Centre for Autonomous Systems at UTS and Data61-CSIRO, where he previously worked as a research associate and research assistant respectively. These collaborations focus on structural health monitoring projects across New South Wales, leveraging advanced sensing technologies and computational methods for infrastructure assessment.

Steven Chuمشاهده پروفایل

استاد

• Biophysics
• Molecular and Cellular Physiology
• Medical Imaging
• +۴ مورد دیگر

Steven Chu is the William R. Kenan, Jr. Professor at Stanford University, holding appointments in the Department of Physics , Department of Molecular and Cellular Physiology , and Doerr School of Sustainability . He serves as a member of Bio-X and the Wu Tsai Neurosciences Institute . Ph.D. in Physics (UC Berkeley, 1976) B.S. in Physics and A.B. in Mathematics (University of Rochester, 1970) His research spans biophysics , medical imaging , nanoparticle synthesis , and energy storage . Recent work includes noninvasive virtual biopsy techniques , extreme weather climate modeling , and nanoscale mechano-sensors . Publications reflect interdisciplinary focus on optical coherence tomography , CO2 reduction , and SARS-CoV-2 decontamination . Dr. Chu has received 40+ honors, including the 1997 Nobel Prize in Physics , Gordon Moore Lifetime Innovation Award , and Foreign Member of 7 international academies . He has advised 17 doctoral and master's students, with research programs in bio-X and Stanford Advisees . Current initiatives include lithium extraction from seawater and deep learning analysis of N95 respirator structures .

Heman Shakeriمشاهده پروفایل

استادیار

• Machine Learning
• Learning-Based Control
• Network Science
• +۱۵ مورد دیگر

Heman Shakeri is an Assistant Professor at the School of Data Science, University of Virginia (UVA), where he leads the DYNAMO Lab. He is also a member of the UVA Center of Diabetes Technology and the UVA Cancer Center, reflecting his interdisciplinary research in biomedical applications of machine learning and control systems. Ph.D. in Electrical and Computer Engineering, Kansas State University M.S. in Mechanical Engineering, Amirkabir University of Technology – Tehran Polytechnic B.S. in Mechanical Engineering, Amirkabir University of Technology Dr. Shakeri's research focuses on algorithm development in machine learning , learning-based control , and network science , with applications in diabetes management, single-cell signaling, glaucoma risk assessment, and complex dynamical systems. His work bridges artificial intelligence, control theory, and data science to solve real-world biomedical challenges. The 15 most recent publications highlight a strong trend in dynamical systems modeling , stochastic flow matching , Koopman operator theory , and reinforcement learning for biomedical control . His papers frequently appear in top-tier venues such as ICML, NeurIPS, ICLR, and IFAC, demonstrating consistent innovation in both methodological and applied domains. Dr. Shakeri has received numerous scientific awards and grants, including: NIH R01 Grant ($1.6M) for optimizing renal cancer treatment Capital One Data Design Fellowship ($55k) LaunchPad Grant ($200k) for artificial pancreas research UVA Research Communications Fellow (2024) Multiple pilot awards from UVA Cancer Center, Brain Institute, and Ivy Foundation He actively mentors graduate students and postdoctoral researchers in the DYNAMO Lab, supporting translational research from algorithm design to clinical applications. His lab has secured funding from NIH, NCI, Oracle, and industry partners, indicating strong external validation of his research vision. The lab collaborates extensively with clinical and computational teams across UVA, particularly in diabetes and cancer research. The DYNAMO Lab is a hub for advancing machine intelligence for dynamical systems , with current projects including smart artificial pancreas systems, explainable AI for glaucoma, operator-theoretic methods, and complex network analysis. The lab emphasizes interdisciplinary collaboration, real-world impact, and methodological rigor.

Lingyan Shiمشاهده پروفایل

دانشیار

• Multimodal Super Resolution Microscopy
• Stimulated Raman Scattering (SRS)
• Metabolic Dynamics in Aging and Diseases
• +۵ مورد دیگر

Lingyan Shi is an Associate Professor in the Shu Chien-Gene Lay Department of Bioengineering at UC San Diego, with a faculty affiliation in Electrical and Computer Engineering. She leads the Shi Lab, pioneering multimodal super-resolution microscopy integrating stimulated Raman scattering (SRS), multiphoton fluorescence (MPF), and second harmonic generation (SHG) to study metabolic dynamics in aging, diseases, and immunology. Her research highlights include discovering the 'Golden Window' for deep-tissue imaging (1550–1870 nm), developing bioorthogonal metabolic imaging platforms (DO-SRS and STRIDE), and creating the Adam optimization-based Pointillism Deconvolution (A-PoD) for super-resolution SRS microscopy. She holds six patents and has received prestigious awards including the Blavatnik Regional Award (2018), Sloan Research Fellowship (2023), and Advancing Bioimaging Scialog Fellowships (2021–2023). Research Focus : Shi’s lab focuses on visualizing in situ metabolic activities in tissues, particularly lipid, protein, and nucleic acid synthesis. Applications target neurodegenerative diseases (e.g., Alzheimer’s), diabetes, cancer, and aging processes. Techniques emphasize sub-cellular resolution and real-time metabolic tracking. Publications & Impact : Over 50 peer-reviewed articles (2023–2025) explore topics like anti-aging effects of metformin, metabolic imaging in Drosophila brains, and exosome-based cancer classification. Her work bridges biosensor engineering , algorithm development , and clinical diagnostics . Awards & Mentorship : Recognized for mentoring underrepresented students and developing curricula in bioengineering. Teaches core courses in bioengineering at UCSD and actively promotes diversity in STEM.

Paul Françoisمشاهده پروفایل

استاد

• Biochemistry
• Bioinformatics
• Immunology
• +۳ مورد دیگر

Paul François is a Full Professor at the University of Montreal , affiliated with the Department of Biochemistry and Molecular Medicine and the MILA - Quebec Institute of Artificial Intelligence . His research integrates theoretical physics, computational methods, and machine learning to study biological systems, particularly immune cell signaling and embryonic development. He holds a PhD in Theoretical Physics from the École Normale Supérieure (Paris) and conducted postdoctoral research at Rockefeller University. Research Interests : Immunology: Mathematical modeling of T-cell activation and CAR-T cell therapy specificity Embryology: Segmentation clock dynamics and temperature compensation in vertebrate development Systems Biology: Epigenetic landscapes and cell fate decisions via generalized Hopfield networks Awards & Affiliations : Rutherford Memorial Medal in Physics (2019) Simons Investigator in Mathematical Modeling of Living Systems (2014) Affiliated with the Centre for Mathematical Research (CRM) and the Centre for Biomedical Innovation at UdeM Grants & Labs : Collaborations with Grégoire Altan-Bonnet (NCI) on immunodynamics Laboratory focuses on robotics-driven cytokine mapping and machine learning applications Labs/Teams : Hosts a multidisciplinary lab at UdeM’s Faculty of Medicine, combining biophysics, bioinformatics, and AI to address biological complexity.

Aseema Mohantyمشاهده پروفایل

استادیار

• nanophotonics
• optical beam shaping
• neuroengineering
• +۵ مورد دیگر

Aseema Mohanty is an Assistant Professor in the Department of Electrical and Computer Engineering at Tufts University, holding the Clare Boothe Luce Professorship. She earned her B.Sc. from MIT and Ph.D. from Cornell University, followed by postdoctoral research at Columbia University under Michal Lipson. Her research focuses on nanophotonics, optical beam shaping, and neuroengineering, with applications in quantum information systems, biomedical sensors, and chip-scale imaging. She leads the Mohanty Nanophotonics Lab, developing miniaturized optical circuits for neural interfaces, LiDAR, and quantum computing. Education: Bachelor of Science, Electrical Science & Engineering, MIT (2011) Ph.D., Electrical and Computer Engineering, Cornell University (2017) Research Interests: Chip-scale optical systems for neuroscience and quantum applications 3D optical beam shaping and sensing Biomedical optical interfaces and implantable devices Her work bridges nanophotonics and biomedicine, with recent breakthroughs including reconfigurable neural stimulation probes and chip-scale lasers spanning ultraviolet to near-infrared wavelengths. Over 30 peer-reviewed articles highlight her contributions to optical phased arrays, quantum photonics, and miniaturized imaging systems. Awards: Clare Boothe Luce Professorship (2020-present) Lab Activities: Mentoring 10+ students across ECE and Physics, advancing projects in quantum communication, biomedical optics, and LiDAR systems.

Jörn Dunkelمشاهده پروفایل

استاد

• Biological Physics
• Active Matter
• Soft Matter
• +۴ مورد دیگر

Jörn Dunkel is the MathWorks Professor of Mathematics and Professor of Applied Mathematics at the Massachusetts Institute of Technology (MIT), where he has been faculty since 2013. He holds appointments in the Department of Mathematics, with research spanning biological physics, active matter, and nonlinear dynamics. His work integrates theoretical modeling, computational approaches, and collaborative experiments to study complex systems across scales. Education: Dr. rer. nat. in Physics, Universität Augsburg (2008) Dipl. Math & Dipl. Phys, Humboldt-Universität zu Berlin (2004-2005) Visiting Student, University of Oxford (2000-2001) Research Focus: Dunkel's group develops mathematical frameworks for biological and soft matter systems, with emphasis on: emergent order in active materials; topological methods in biophysics; nonequilibrium statistical mechanics; and hydrodynamic theories of living systems. Recent breakthroughs include programmable active matter, topological control of biological networks, and entropy dynamics in living cells. Publication Trends: His 15 most recent works (2021-2024) reveal strong focus on: 1) Mechanobiology of cellular structures (membranes, nuclei), 2) Collective dynamics in active matter and biofilms, 3) Topological analysis of biological organization, and 4) Machine learning approaches for complex systems modeling. These consistently bridge mathematical theory with experimental biophysics. Awards & Honors: Schmidt Science Polymath Award (2023) Complex Systems Scholar Award, McDonnell Foundation (2016-2020) Alfred P. Sloan Research Fellowship (2015-2017) Gustav Hertz Prize, German Physical Society (2011) Multiple MIT-specific research and teaching awards Research Infrastructure: Leads the Physical Mathematics Group at MIT, collaborating with experimental labs worldwide. Regular group meetings (Thursdays) and seminar series (Physical Mathematics Seminar Tuesdays) anchor interdisciplinary research in mathematical biophysics.

M. Cristina Marchettiمشاهده پروفایل

استاد

• Active Matter
• Condensed Matter Physics
• Biological Physics
• +۳ مورد دیگر

M. Cristina Marchetti is a Professor of Physics and Director of the Biomolecular Science and Engineering Program at the University of California, Santa Barbara (UCSB). She holds a PhD from the University of Florida and has been at UCSB since 2018, following a 30-year tenure at Syracuse University where she served as Department Chair and Associate Director of the Syracuse Biomaterials Institute. Her research focuses on the emergent behavior of active matter, including biological tissues, collectively migrating cells, and nonequilibrium systems. She employs theoretical and computational approaches to study self-organization in complex systems, particularly in active nematics and epithelial mechanics. Marchetti is a co-Lead Editor of *Physical Review X* (2016–2021) and *Annual Reviews of Condensed Matter Physics*, and serves on the Leadership Board of the Boulder School for Condensed Matter and Materials Physics since 2005. Education: PhD in Physics, University of Florida, USA Bachelor's/Master's at University of Pavia, Italy Marchetti has received prestigious honors including Fellowships from the American Physical Society and AAAS, membership in the American Academy of Arts and Sciences and US National Academy of Sciences, and the 2019 Leo P. Kadanoff Prize. Her work bridges statistical physics, soft matter, and biological systems, with applications to tissue mechanics, collective dynamics, and nonequilibrium phenomena. Her research group investigates active matter systems using bottom-up modeling and top-down phenomenology, exploring topics like defect dynamics, phase separation, and fluid-structure interactions in biological contexts. Current projects include studying epithelial rigidity, active nematic flows, and the interplay between mechanics and biological processes.