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Xin ChenView profile

Professor

• Epigenetics
• Stem Cell Biology
• Molecular Biology
• +3 more

Xin Chen is a Professor and HHMI Investigator in the Department of Biology at Johns Hopkins University (Krieger School of Arts & Sciences). Her research focuses on epigenetic regulation in adult stem cell lineages, particularly the asymmetric inheritance of epigenetic information during cell division. Dr. Chen received her Ph.D. in Molecular Cell & Developmental Biology from the University of Texas at Austin, studying Drosophila eye development. She completed postdoctoral training at Stanford University, investigating germ cell differentiation mechanisms under Dr. Margaret Fuller. Key research areas include histone inheritance asymmetry in stem cells, roles of DNA replication and mitosis in epigenetic partitioning, and the interplay between epigenetic regulators and developmental signaling pathways. Her work employs Drosophila and mammalian models, combining molecular genetics, live-cell imaging, and computational approaches. Recent publications highlight breakthroughs in understanding how histone distribution patterns influence stem cell fate decisions, with implications for regenerative medicine and cancer biology. Dr. Chen’s HHMI Investigator status underscores her impactful contributions to the field of epigenetics. Her lab is located in Levi Hall at Johns Hopkins, with active collaborations exploring super-resolution microscopy and chromatin dynamics. No formal advisee list is provided, though her lab trains graduate students and postdoctoral scholars in stem cell and epigenetic research.

Amine BenzergaView profile

Professor

• Materials Science and Engineering
• Mechanics of Materials
• High-Performance Computing
• +5 more

Amine Benzerga is a Professor of Aerospace Engineering and Materials Science & Engineering at Texas A&M University, holding the General Dynamics endowed chair. As Director of the Center for Intelligent Materials and Structures (CiMMS), he leads cutting-edge research in smart material systems. He is affiliated with the Department of Aerospace Engineering within the College of Engineering, and his work integrates computational and experimental approaches to advance materials science. Education: Ph.D., Material Science & Engineering, École des Mines de Paris, France (2000) M.Sc., Mechanical Engineering, Université Paul Sabatier, Toulouse (1995) B.Sc., Aerospace Engineering, Sup'Aero, Toulouse (1995) Classes Préparatoires, Physics, Lycée Masséna, Nice (1989-1992) Research Interests: His work focuses on Mechanics of Materials , High-Performance Computing , and Anisotropy in Plasticity and Fracture . He explores Ductile Fracture mechanisms, Discrete Dislocation Plasticity , and Macromolecular Mechanics of Polymers . Recent studies include stress triaxiality effects on fracture behavior in magnesium alloys and polymer composites, failure under severe shear, and computational modeling of spall dynamics during hypervelocity impacts. Awards & Honors: Faculty Fellow, Texas Engineering Experiment Station (2014) Select Young Faculty Award, Texas Engineering Experiment Station (2009) National Science Foundation CAREER Award (2008) Highest Honors for Ph.D. Dissertation Work (École des Mines de Paris, 2000) Grants & Advising: Amine has led grants such as the 2019 Multiscale Modeling of Damage Tolerance in Hexagonal Materials and the 2018 CyberTraining: CIC Computational Materials Science Summer School . His advisory work includes supervising experimental and computational studies on void coalescence and plasticity in metals and polymers. Labs & Teams: As CiMMS Director, he oversees interdisciplinary projects in intelligent materials, including shape memory alloys and damage detection via sensory particles. His team collaborates on topics like texture effects in magnesium alloys and fracture locus path-dependence.

Michael KingView profile

Professor

• Reservoir Engineering
• Unconventional Reservoirs
• Flow Simulation
• +3 more

Michael King is a Professor of Petroleum Engineering and holds the LeSuer Chair in Reservoir Management at Texas A&M University. He is also affiliated with the Multidisciplinary Engineering program. His research focuses on 3D reservoir modeling, unconventional reservoir characterization, and advanced simulation techniques for flow dynamics. Education: Ph.D. in Physics (Syracuse University, 1980), M.S. in Physics (Syracuse University, 1977), B.S. in Physics and Mathematics (The Cooper Union, 1976). Research Interests: His work emphasizes reservoir upscaling, streamline-based simulation, pressure transient analysis, and geomechanical modeling for CO2 storage. Recent work explores machine learning integration with reservoir workflows and multiscale modeling for complex reservoirs. Publications: Over 100 peer-reviewed articles since 2005, with recent focus on fast marching methods, diffusive time of flight, and multi-scale simulation frameworks. Topical trends include unconventional reservoir analysis, CO2 sequestration, and shale gas production optimization. Awards: Distinguished SPE Member (2013), Foundation CMG Chair (2013–2019), Teaching Excellence Award (2014) Labs/Teams: Leads reservoir simulation research group focused on unconventional reservoir characterization and numerical method development Grants/Funding: Extensive funding history from industry partnerships and government grants for reservoir modeling R&D

Joseph C. AyoobView profile

Associate Professor

• Computational Biology
• Educational Program Development
• Mentoring Strategies
• +3 more

Joseph C. Ayoob, PhD, is an Associate Professor in the Department of Computational and Systems Biology at the University of Pittsburgh School of Medicine. He holds roles as Associate Director of the UPMC Hillman Academy and Faculty Fellow of the University of Pittsburgh Center for Mentoring. His work focuses on innovative training programs for students at all academic levels, including the NSF-funded TECBio REU program (since 2010) and the CompBio Academy high school program (since 2011). He also directs the CPCB MetaSchool, a professional development series for graduate students. Dr. Ayoob earned his PhD in Neuroscience from Johns Hopkins University School of Medicine. His educational efforts span undergraduate, graduate, and postdoctoral trainees, emphasizing lab-based skills and professional development. He is a Principal Facilitator for the Entering Mentoring and Entering Research curricula, recognized as a National Research Mentoring Network (NRMN) Master Mentor and Mentor of the Month. Research interests include STEM education innovation, mentorship best practices, and computational biology training. He has pioneered programs like the CPCB Presentation Course and CoBB Professional Development course. Awards include recognition for mentorship excellence and contributions to mentored research training. Publications highlight his work in educational frameworks, undergraduate research effectiveness, and molecular mechanisms in neurodegenerative diseases. His lab supports interdisciplinary training through summer programs targeting underrepresented students in STEM fields.

• Applied Mathematics
• Fluid Mechanics
• Mathematical Biology
• +4 more

Scott McCue is a Professor of Applied Mathematics at the School of Mathematical Sciences, Queensland University of Technology (QUT), Brisbane, Australia. His academic journey includes a PhD from the University of Queensland (2000), followed by postdoctoral roles at the University of Nottingham and University of Wollongong. He joined Griffith University in 2004 before moving to QUT in 2007, progressing through academic ranks to full Professor in 2016. Education: PhD in Applied Mathematics, University of Queensland (2000) Bachelor of Science, University of Queensland Research Interests: Scott's work spans fluid mechanics, mathematical biology, and free boundary problems. Key areas include Stefan problems (melting/freezing), free surface flows, Hele-Shaw flows, and droplet impaction. His research integrates theoretical analysis with computational modeling, addressing challenges in biological systems, agricultural fluid dynamics, and complex fluid phenomena. Grants & Awards: EO Tuck Medal (2019) for research and service to ANZIAM JH Michell Medal (2009) Australian Competitive Grants: 'Mathematical and Computational Analysis of Ship Wakes' (DP180103260), 'Mathematical and computational models for agrichemical retention on plants' (LP160100707) Teaching & Supervision: Scott teaches courses in differential equations, fluid flow, and perturbation methods. His supervision focuses on moving boundary problems, nonlinear waves, and stochastic models of cell proliferation. He has advised numerous PhD and MPhil students on topics ranging from drug release modeling to ship wake analysis. Professional Engagement: Editor for journals including Proceedings of the Royal Society A and European Journal of Applied Mathematics . Active member of ANZIAM, SIAM, and the Australasian Fluid Mechanics Society.

Alastair MunroView profile

Academic

• Cancer Research
• Clinical Oncology
• Radiation Therapy
• +3 more

Alastair Munro is an Honorary Professor at the School of Medicine, University of St Andrews. He focuses on cancer research, clinical oncology, and public health, with a particular interest in smoking cessation strategies, radiation therapy optimization, and the application of mathematical modeling to medical challenges. His work aligns with UN Sustainable Development Goals, contributing to improving global health outcomes. He collaborates with interdisciplinary teams, as seen in his studies on cancer treatment delays and aspirin’s role in colorectal cancer survival. Research interests include: Cancer treatment methodologies (e.g., radiotherapy, pharmacological adjuvants) Qualitative and quantitative analyses of patient experiences and healthcare systems Mathematical modeling to predict tumor behavior and treatment efficacy His publications highlight trends in analyzing clinical outcomes and patient perspectives, blending epidemiological data with computational approaches. Notable collaborative projects involve investigating bystander effects in radiation therapy and the impact of post-diagnosis aspirin use. No formal scientific awards are listed in the provided texts. While no advisees are explicitly mentioned, his research suggests active participation in collaborative academic projects. He is affiliated with the University of St Andrews School of Medicine, with no specific labs or teams detailed in the texts.

• Partial Differential Equations
• Numerical Analysis
• Mathematical Biology
• +5 more

Charles M. Elliott is a Professor of Mathematics at the University of Warwick. His research focuses on mathematical and numerical analysis of partial differential equations (PDEs), particularly in evolving domains, manifolds, and surfaces. Key areas include free boundary problems, geometric evolution equations, biomembranes, and phase transitions. He has contributed to finite element methods for surface PDEs and holds editorial roles at journals like the IMA Journal of Numerical Analysis and Interfaces and Free Boundaries . Notable awards include the Humboldt Prize (2010) and the Royal Society Wolfson Research Merit Award (2016). His work bridges theoretical mathematics and applications in biology, materials science, and continuum mechanics. Educations and career details: While specific educational qualifications are not explicitly stated in the text, his professional trajectory includes leadership in computational mathematics and extensive editorial contributions. His teaching responsibilities include advanced modules like Maths-in-Action and Topics in Partial Differential Equations . Research Interests: Elliott’s work emphasizes PDEs in complex environments, with applications to biomembranes, cell motility, and material phase transitions. His methods integrate evolving surface finite elements and stochastic processes. Recent trends in his publications highlight evolving domains, coupled bulk-surface systems, and geometric PDEs. He addresses challenges in numerical analysis and modeling of evolving interfaces. Awards and Recognition: In addition to the Humboldt Prize and Royal Society award, he is a Fellow of the Society for Industrial and Applied Mathematics (SIAM, 2015) and received an honorary doctorate from the University of Sussex (2022). Advising and Grants: While specific student names or grant details are not listed, his research programs involve collaborations on topics like biomembrane dynamics and phase field models. He has pioneered numerical methods for evolving surface PDEs, impacting fields from materials science to mathematical biology. Labs and Teams: His work is conducted within Warwick’s Department of Mathematics, collaborating on interdisciplinary projects involving computational PDEs and their applications. The research often involves evolving geometries and multiscale modeling approaches.

Thomas J.R. HughesView profile

Professor

• Isogeometric Analysis
• Computational Mechanics
• Finite Element Analysis
• +7 more

Thomas J.R. Hughes is the Peter O’Donnell Jr. Chair in Computational and Applied Mathematics and a Professor of Aerospace Engineering and Engineering Mechanics at The University of Texas at Austin. He is affiliated with the Oden Institute for Computational Engineering and Sciences (Oden Institute), where he leads the Computational Mechanics Group. His research focuses on computational mechanics, isogeometric analysis, and biomedical modeling. Education: B.E. and M.E. in Mechanical Engineering from Pratt Institute; M.S. in Mathematics and Ph.D. in Engineering Science from the University of California, Berkeley. Research Interests include Isogeometric Analysis (integrating CAD and FEA), stabilized numerical methods for fluid flows, patient-specific biomedical simulations, and phase-field modeling. His work spans applications in cardiovascular systems, tumor growth, and geophysical flows. Hughes has authored over 150 journal articles, with recent contributions on isogeometric analysis, fluid-structure interaction, and computational geosciences. Notable awards include the von Neumann Medal (USACM), Gauss-Newton Medal (IACM), and Worcester Reed Warner Medal (ASME). He is a member of the National Academy of Engineering. His group advises students and postdocs in computational methods and collaborates with centers like the Oden Institute’s Computational Visualization Center. Current projects include isogeometric analysis, nanoparticle drug delivery, and ice sheet modeling.

Juan RestrepoView profile

Professor

• Applied Mathematics
• Computational Mathematics
• Data Science
• +12 more

Juan Restrepo is a Professor of Mathematics with courtesy appointments in Statistics, EECS, and Physical Oceanography at Oregon State University, where he holds a Courtesy Faculty position in the Department of Mathematics within the College of Science. He is also affiliated with the University of Tennessee and Oak Ridge National Laboratory, where he holds appointments in the Computer Science and Mathematics Division. He serves as Co-Director of the Dynamics and Data Science Institute (D2SI), a leading interdisciplinary research center. Ph.D. in Physics, Pennsylvania State University, 1992 M.S. in Engineering (Acoustics), Pennsylvania State University, 1987 B.S. in Music, New York University, 1983 Restrepo's research lies at the intersection of data science and dynamics, with two primary tracts: (1) applying statistical physics and data science to complex non-equilibrium systems such as climate and financial markets; and (2) studying ocean dynamics and transport in climate and nearshore processes. His work emphasizes uncertainty quantification, adaptive time series analysis, extreme events, and data assimilation. His recent publications reflect a strong focus on modeling geophysical systems under uncertainty, using advanced computational and statistical techniques. Themes include climate sensitivity, oil spill dispersion, sediment transport, wave breaking, and high-performance computing for large-scale simulations. His work integrates machine learning, stochastic modeling, and dynamical systems theory. SIAM Fellow SIAM Geosciences Career Award ORISE Distinguished Post-doctoral Fellow DOE Young Investigator Award Ruth Homeyer Graduate Student Award Restrepo has secured over $30 million in research funding from NSF, DOE, NASA, and GoMRI. He has advised numerous students in applied mathematics and computational sciences, and has held leadership roles in professional societies, including President of the Nonlinear Geophysics Section at the American Geophysical Union. He is actively involved in promoting diversity in science and mentoring underrepresented groups. He leads interdisciplinary research teams at D2SI and collaborates with national labs such as Argonne and Los Alamos. His work bridges applied mathematics, climate science, oceanography, and computational engineering, making significant contributions to both theory and real-world applications.

• Space Physics
• Plasma Physics
• Magnetospheric Physics
• +3 more

Imogen Gingell is a Principal Research Fellow at the University of Southampton, affiliated with the Southampton Theory Astrophysics and Gravity (STAG) Research Centre. Her research focuses on space plasma physics, particularly collisionless shocks, magnetic reconnection, and magnetospheric dynamics. She has led or contributed to projects funded by the Science and Technology Facilities Council (STFC) and the Royal Society. Key research interests include the physics of Earth's bow shock, ion acceleration mechanisms, and plasma turbulence in space environments. Her work combines observational analysis, in-situ measurements, and hybrid simulations to explore fundamental plasma processes. Recent research has addressed energy partition at supercritical quasi-perpendicular shocks, the role of rotational discontinuities in shock reconnection, and magnetic field amplification at oblique shocks. She actively supervises three PhD students in Physics and Astronomy. Her findings are published in leading journals like Journal of Geophysical Research: Space Physics and Physics of Plasmas. Gingell collaborates internationally on projects involving NASA and ESA missions, contributing to our understanding of solar-terrestrial interactions.

• Applied Mathematics
• Partial Differential Equations
• Material Science
• +5 more

Ulisse Stefanelli is a full Professor at the Department of Mathematics, University of Vienna, within the Faculty of Mathematics. His research focuses on applied mathematics, partial differential equations, and material science, with a particular emphasis on nonlinear dynamics, elasticity, and variational methods. He has contributed significantly to the study of gradient flows, energy dissipation principles, and phase-field modeling in complex materials. Affiliations: University of Vienna (Faculty of Mathematics, Department of Mathematics) His research interests include the mathematical analysis of continuum mechanics problems, such as viscoelasticity, plasticity, and accretive growth in solids. He has pioneered methods like the Weighted Energy-Dissipation principle and contributed to topology optimization and multiscale modeling. Stefanelli has led numerous research projects, including those on elastic-fluidic biomembranes and geometric boundary conditions in solids. Awards: Vinti Prize (2015) His work bridges pure and applied mathematics, addressing challenges in material science and geophysics. Current activities include studies on planetary accretion models and stochastic evolution equations. Collaborations span international institutions, focusing on interdisciplinary applications of variational principles.

• Tomography
• Inverse Problems
• Medical Imaging
• +3 more

Peter Elbau is a Senior Lecturer (Privatdozent) in the Faculty of Mathematics at [University Name, if known]. His research focuses on mathematical imaging techniques, particularly in tomography and inverse problems, with applications in medical and biomedical imaging. He is actively involved in projects such as the "Tomography across the scales" initiative, aiming to develop quantitative imaging methods from molecular to astronomical scales. His primary research interests include the mathematical foundations of tomographic imaging, inverse scattering problems, and quantitative parameter reconstruction in optical coherence tomography (OCT) and photoacoustic imaging. He explores topics such as motion detection in diffraction tomography, curvature flows on surfaces, and the development of algorithms for medical diagnostics and material characterization. Elbau leads the research project "Tomography across the scales - Quantitative optical imaging from single molecules to stars," funded from 2018 to 2026. This project highlights his role in bridging fundamental mathematical research with practical applications in imaging technologies. His work often involves cross-disciplinary teams focusing on biomedical and material imaging challenges.

Nancy KopellView profile

Professor

• Applied Mathematics
• Dynamical Systems
• Neuroscience
• +3 more

Professor Nancy Kopell is a distinguished academic and mathematician at Boston University, holding the William Fairfield Warren Distinguished Professorship in the Department of Mathematics. She is also Co-Director of the Center for BioDynamics and has affiliations with the Department of Biomedical Engineering. Her research focuses on applied mathematics and dynamical systems, particularly the dynamics of the nervous system, including neural rhythms, cognitive processing, and anesthesia effects. Dr. Kopell has made groundbreaking contributions to understanding how brain rhythms underpin behavior, cognition, and neurological disorders. Educational Background: She earned her A.B. from Cornell University (1963) and a Ph.D. in Mathematics from UC Berkeley (1967). She has held academic positions at MIT and Northeastern University before joining BU in 1986. Research Interests: Dr. Kopell's work spans theoretical neuroscience, mathematical biology, and nonlinear dynamics. Key areas include: Rhythms in neural networks (gamma, beta, theta) Cell assembly coordination and synchronization Neurophysiological mechanisms of attention, memory, and decision-making Mathematical modeling of anesthesia and consciousness Geometric theory of singularly perturbed systems Awards & Honors: National Academy of Sciences (1996) American Academy of Arts and Sciences (1996) SIAM Fellow (2009) MacArthur Fellowship (1990-1995) Weldon Memorial Prize (2006) Grants & Collaborations: She has led numerous interdisciplinary projects, including the Cognitive Rhythms Collaborative and studies on thalamocortical interactions. Current grants include NIH-funded research on brain rhythms in attention and sleep. Labs & Teams: Her Neuronal Dynamics Group at BU investigates neural oscillations and their roles in cognition. Collaborators include neuroscientists, engineers, and computational biologists.

• Uncertainty Quantification
• Predictive Modeling
• Computational Mathematics
• +5 more

Nicholas Zabaras is a Professor of Uncertainty Quantification and the Director of the Warwick Centre for Predictive Modelling at the University of Warwick. He is a Hans Fischer Senior Fellow at the TUM Institute for Advanced Study (TUM-IAS), hosted by Phaedon-Stelios Koutsourelakis. His research focuses on advancing computational methods for uncertainty quantification, predictive modeling, and multiscale/multiphysics systems. Key areas include Bayesian methods, stochastic modeling, and data-driven approaches for complex materials systems. Education: He holds a diploma in Mechanical Engineering from the National Technical University of Athens (1982), an M.Sc. in Materials Science and Engineering from the University of Rochester (1983), and a Ph.D. in Theoretical and Applied Mechanics from Cornell University (1987). He has held academic positions at the University of Minnesota, Cornell University, and the University of Warwick, where he now leads the Warwick Centre for Predictive Modelling. Research Interests: His work integrates computational mathematics, statistics, and scientific computing to address challenges in materials science, computational physics, and engineering systems. Specific themes include Bayesian uncertainty quantification, high-dimensional modeling, information-theoretic coarse graining, and stochastic model reduction. Awards: He has received the Royal Society Wolfson Research Merit Award (2014), the Michael Tien’72 College of Engineering Teaching Award (2009), and is a Fellow of the American Society of Mechanical Engineers (2006). Labs/Teams: Director of the Warwick Centre for Predictive Modelling, and leader of the Scientific Computing and Artificial Intelligence (SCAI) Laboratory at the University of Notre Dame, focusing on interdisciplinary research in AI-driven predictive modeling and uncertainty quantification.

He LiView profile

Assistant Professor

• Multiscale Computational Modeling
• Artificial Intelligence in Medicine
• Biomedical Engineering
• +3 more

Dr. He Li is an Assistant Professor at the University of Georgia, specializing in computational biomedical engineering. His research focuses on developing AI-driven multiscale models to bridge microscopic and macroscopic biological processes, with applications in disease modeling, medical imaging, and healthcare innovation. He leads the Computational Biomedical Engineering Lab, exploring interdisciplinary solutions in biomedical science using advanced computational methods. His work integrates physics-based modeling (e.g., molecular dynamics, spectral element methods) with AI techniques to assimilate diverse biological data sources. Recent research emphasizes predictive AI models for digital health, including radiology analysis, proton therapy optimization, and Alzheimer’s disease infodemiology. He has published extensively on large language models (LLMs) tailored for medical domains like radiology, oncology, and ophthalmology, addressing challenges in specialized clinical NLP tasks. Key contributions include frameworks like Radiology-GPT and AD-Autogpt, which advance AI applications in diagnostics and disease surveillance. His lab collaborates across disciplines to enhance clinical decision-making through AGI (Artificial General Intelligence) and multimodal data fusion. No awards or grants are explicitly listed in the provided texts. Labs/Teams: Computational Biomedical Engineering Lab @ UGA focuses on AI-driven biomedical innovation, with projects spanning medical imaging, disease modeling, and healthcare technology development.