جست‌وجوی استاد و پژوهشگر

Solomon G. Diamondمشاهده پروفایل

دانشیار

• Biomedical Imaging
• Functional Neuroimaging
• Magnetic Nanoparticle Imaging
• +۳ مورد دیگر

Solomon G. Diamond is an Associate Professor of Engineering at Dartmouth College's Thayer School of Engineering, serving as Co-Director of the Design Initiative at Dartmouth. He holds degrees from Dartmouth (AB 1997, BE 1998) and Harvard (SM 2001, PhD 2004). His research focuses on biomedical imaging, functional neuroimaging, and magnetic nanoparticle imaging, with emphasis on diagnostic technologies and medical device development. He has received awards including the 2023 Outstanding Service Award and the 2002 Derek Bok Teaching Award. Research projects include neurovascular coupling studies, clinical optical-electric probes, and magnetic nanoparticle imaging. He co-founded Lodestone Biomedical, a medtech company advancing nanoparticle-based biosensors. His work bridges engineering and medicine, with notable contributions to magnetic nanoparticle characterization and imaging array systems. He teaches courses like ENGS 90 (Engineering Design Methodology) and ENGS 29 (Computer-Aided Design & Kinematics). Recent work includes a 2024 study on salt concentration effects in magnetic nanoparticle biosensors with PhD candidate Gabby Moss. He holds patents for technologies like magnetic susceptibility tomography and in-bed exercise machines. His interdisciplinary collaborations span biomedical engineering, materials science, and clinical diagnostics.

Thanasis Fokasمشاهده پروفایل

استاد

• Nonlinear Mathematical Science
• Boundary Value Problems
• Integrable Systems
• +۷ مورد دیگر

Thanasis Fokas is a Professor of Nonlinear Mathematical Science at the University of Cambridge's Faculty of Mathematics, holding the Chair in Nonlinear Mathematical Science since 2002. Previously, he held chairs at Imperial College London (1996) and Clarkson University (1986). His research focuses on integrable systems, boundary value problems for PDEs, inverse problems in medical imaging, and novel complex analysis techniques. He is affiliated with the Cantab Capital Institute for the Mathematics of Information and the Centre for Mathematical Imaging in Healthcare. Education: PhD in Applied Mathematics (Clarkson University) Doctorate in Physics (Caltech, assumed based on career timeline) Research Interests: Prof. Fokas develops analytical methods for solving linear and nonlinear PDEs, with applications to water waves, medical imaging (EEG/MEG/PET), and mathematical biology. His work bridges pure and applied mathematics, contributing to fields like quantum gravity and cognitive neuroscience through innovative inverse problem solutions and imaging algorithms. Key Contributions: Unified Transform Method (Fokas Method) for PDEs Mathematical models for SARS-CoV-2 transmission dynamics Neuronal current reconstruction via EEG/MEG Labs/Teams: He leads research groups in the Centre for Mathematical Imaging in Healthcare and collaborates with the DAMTP Applied Mathematics and Theoretical Physics division.

Benjamin E. Barrowesمشاهده پروفایل

دانشیار مدعو

• Electromagnetic Wave Theory
• Electromagnetic Induction (EMI)
• Subsurface Detection
• +۵ مورد دیگر

Benjamin E. Barrowes is an Adjunct Associate Professor of Engineering at Dartmouth College and a Research Physicist at the U.S. Army ERDC Cold Regions Research and Engineering Laboratory (CRREL). He holds a PhD in Electrical Engineering from MIT (2004), preceded by BS and MS degrees from Brigham Young University (1999). His research focuses on electromagnetic wave theory and modeling, with applications in subsurface detection (UXO/IEDs), fusion energy, and interdisciplinary studies linking technology to political science. Key areas include electromagnetic induction methods, fast solver algorithms for 3D simulations, and neuronal modeling techniques. Barrowes has authored/co-authored over 150 publications, including foundational work on spheroidal wave functions, sparse matrix methods, and dense medium scattering. His recent work explores secure digital information systems ('Information Banks') and landmine detection technologies. Awards: NSF Graduate Fellowship, Director's Postdoc (Los Alamos), Army Corps of Engineers Innovator of the Year Patents: 'High Frequency Electromagnetic Induction' (20170097439) His interdisciplinary approach bridges engineering, physics, and policy, with practical applications in defense, energy, and global security.

Adrien MERLINIمشاهده پروفایل

پژوهشگر

• Computational Electromagnetics
• Integral Equations
• Neuroimaging
• +۳ مورد دیگر

Adrien MERLINI is a Researcher at IMT Atlantique's Microwave Department in Brest, France. His work focuses on computational electromagnetics, integral equation methods, and neuroimaging applications. He specializes in developing numerical techniques for low-to-high-frequency electromagnetic modeling, preconditioning strategies for integral equations, and machine learning-enhanced solutions for inverse problems. His research bridges fundamental theory and applied engineering, addressing challenges in medical imaging, material characterization, and high-performance computing. Key areas of expertise include: High-frequency spectral analysis of boundary integral operators Stabilized formulations for low-frequency electromagnetic simulations Supervised learning approaches for electrical source imaging Quasi-Helmholtz projector-based preconditioning techniques Fast direct solvers for integral equations His innovations in numerical methods have advanced applications in brain-computer interfaces, microwave-based medical imaging, and terahertz dosimetry. Collaborative projects include developing the simBCI framework for EEG simulation and the Pythran compiler for accelerating scientific Python codes. Research contributions span over 30 peer-reviewed articles since 2015, with recent emphases on conditioning analysis of electromagnetic integral equations and regularization strategies for neuroimaging inverse problems.

• Neuroelectromagnetics
• Inverse Problems
• Electroencephalography (EEG)
• +۱۲ مورد دیگر

Joonas Lahtinen is a University Instructor in Computing Sciences and a Doctoral Researcher in the Doctoral Programme in Engineering and Natural Sciences at Tampere University, Finland. His research is conducted within the Mathematics Research Centre, focusing on advanced computational techniques for brain imaging. He recently completed his doctoral thesis in 2025 on mathematical methods for neuroelectromagnetic inverse problems. His research interests lie at the intersection of applied mathematics, neuroscience, and biomedical engineering. Key areas include electroencephalography (EEG), magnetoencephalography (MEG), source localization, inverse problems, and noise-robust signal reconstruction. He specializes in developing standardized, adaptive, and dynamic algorithms—such as Kalman filtering and Lp regression—for improved accuracy in focal brain activity imaging, particularly in epilepsy studies and somatosensory evoked potentials. The trend in his recent publications (2023–2025) shows a strong focus on mathematical rigor and clinical applicability. His work addresses forward and inverse errors, bias reduction via standardization, and dynamic modeling of brain activity. These contributions advance the precision of non-invasive brain imaging, supporting both fundamental neuroscience and clinical diagnostics. Scientific Contributions: Developed standardized Kalman filtering for concurrent subcortical and cortical source localization. Introduced adaptive Lp regression methods for noise-robust epilepsy source reconstruction. Quantified effects of source space resolution and randomization on localization accuracy. Proposed bias reduction techniques in discretized electromagnetic source models. Joonas Lahtinen contributes to teaching in computing sciences and conducts independent and collaborative research within a multidisciplinary team. His doctoral thesis represents a significant contribution to the field of computational neuroimaging. While no formal advising roles or grants are mentioned, his active publication record and role as a researcher and instructor indicate ongoing academic engagement. His work is associated with the Mathematics Research Centre at Tampere University, where he collaborates with experts in computational neuroscience and biomedical engineering. His research network includes national and international collaborators, particularly in Germany and Finland, as evidenced by co-authorship with researchers such as C. Wolters and S. Pursiainen.

David M. Sensemanمشاهده پروفایل

دانشیار

• Neuroscience
• Neurophysiology
• Cortical Network Modeling
• +۳ مورد دیگر

David M. Senseman is an Associate Professor in the Department of Biology, Health, and the Environment at the University of Texas at San Antonio College of Sciences. His research focuses on cortical network activity, voltage-sensitive dye imaging, and neural signal processing across diverse biological systems. Ph.D., Princeton University M.S., Princeton University B.S., Kent State University Senseman's research spans neurophysiological mechanisms in cortical networks, synaptic activity mapping, and neuroimaging techniques. His work includes high-speed optical imaging, wave decomposition in neural systems, and comparative studies across species from salamanders to humans. Key publications highlight advancements in voltage-sensitive dye applications, retinal input processing, and neuroglandular activity imaging. His academic contributions intersect neuroscience, computational biology, and biomedical imaging.

• Neuroscience
• Neural Stem Cell Research
• Electromagnetic Field Applications in Medicine
• +۳ مورد دیگر

Jasmina Isakovic is a Lecturer in Histology at the European University Cyprus, School of Medicine, Frankfurt Branch. She actively contributes to academic leadership as Acting Vice-Chairperson while maintaining a research focus on neuroregeneration and electromagnetic field applications. Academic Appointment: Lecturer in Histology Institutional Affiliation: European University Cyprus Academic Role: Acting Vice-Chairperson Her research spans multiple neuroscience domains, particularly examining: Neural stem cell differentiation and regeneration Therapeutic applications of electromagnetic fields Mitochondrial dynamics in neurological disorders Development of automated imaging analysis tools (LUSCA) Neurodegenerative disease mechanisms Translational regenerative medicine approaches Recent publications highlight her work on electromagnetic field therapies for stroke rehabilitation, glial interactions, and computational modeling. She has developed open-source imaging tools to analyze cellular structures and investigates cross-organ regenerative mechanisms. While the text mentions Academic Advisory Services and Awards sections, specific details about scientific honors or advisory roles remain unlisted in the provided content.

• Multi-compartment modeling
• Cerebral arterial circulation
• Electrical conductivity
• +۵ مورد دیگر

Maryamolsadat Samavaki is a Researcher affiliated with the Computing Sciences Mathematics Research Centre , focusing on computational modeling of cerebrovascular dynamics and neuroelectromagnetism. Her work bridges biomedical engineering and applied mathematics through advanced numerical methods. Key research areas include EEG source localization , transcranial stimulation , and multi-compartment head modeling . Develops spatiotemporal hemodynamic models to analyze microcirculation and blood volume fractions. Applies L1-norm optimization and metaheuristic algorithms for neurostimulation montage design. Recent publications explore cerebral circulation's impact on electrical conductivity and in silico imaging techniques using anatomical atlases . Collaborations emphasize boundary-fitted meshing for subcortical structures in EEG modeling.