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Christian KlämbtView profile

Professor

• Drosophila glial cell development and function
• receptor tyrosine kinase signaling and differentiation
• neuron-glial interaction
• +2 more

Prof. Dr. Christian Klämbt is a distinguished faculty member at the Institute of Neuro- and Behavioral Biology , University of Münster. His work bridges developmental biology , behavioral biology , and molecular neurobiology , focusing on Drosophila melanogaster as a model organism. Research profile: Glial cell development, neuron-glial interactions, receptor tyrosine kinase signaling, and blood-brain barrier dynamics. Key technologies: FIM (Frustrated Internal Reflection Microscopy) for locomotion analysis, advanced imaging tools, and Drosophila genetic manipulation. Research Interests: Klämbt’s group investigates glial cell roles in brain function, emphasizing their involvement in axonal wrapping , blood-brain barrier integrity , and behavioral regulation . His work highlights the conserved functions of glia across species. Publication Trends: Recent articles focus on glial differentiation , neuronal signaling , and imaging technologies . Themes include Drosophila glial development , metabolic regulation , and neuroimmune interactions . Additional Roles: Active member of the Cells in Motion cluster, CiM-IMPRS graduate program , and Imaging Network . Co-inventor of a patent for animal detection systems.

Mina Jamshidi IdajiView profile

Researcher

• AI for Healthcare
• Computational Pathology
• Ophthalmology
• +3 more

Mina Jamshidi Idaji is a Postdoctoral Researcher at the Machine Learning Group, BIFOLD – Berlin Institute for the Foundations of Learning and Data, Technical University of Berlin. Her research focuses on AI-driven healthcare applications, including computational pathology, ophthalmology, and biomedical sensing. She holds a Dr.-Ing. (PhD) in Machine Learning from TU Berlin and the Max Planck Institute CBS, Leipzig (2022), an M.Sc. in Biomedical Engineering from Sharif University of Technology (2016), and dual B.Sc. degrees in Electrical Engineering and Mathematics from Isfahan University of Technology (2014). Her research interests span AI for healthcare, computational pathology, and biomedical engineering. Recent work includes developing explainable AI frameworks for histopathology (xMIL), EEG/MEG simulation tools (MEEGsim), and methods for nonlinear interaction decomposition (NID). She has contributed to open-source projects like Harmoni and xMIL, addressing challenges in neural data analysis and clinical AI applications. Mina’s publications highlight advancements in brain-computer interfaces (BCI), motor imagery studies, and neuroimaging techniques. Her work bridges machine learning and clinical neuroscience, emphasizing translational research in healthcare AI.

• Biomedical Engineering
• Medical Imaging
• Optical Coherence Tomography
• +3 more

Andrea Curatolo is an Adjunct Research Fellow at the School of Engineering, University of Western Australia, specializing in Electrical, Electronic and Computer Engineering. He holds a PhD from the same university, focusing on improving optical coherence tomography (OCT) and elastography through optical beam shaping and simulations. His research emphasizes biomedical applications, particularly in ophthalmology and cancer diagnostics, contributing to UN Sustainable Development Goals in health and innovation. Key areas include corneal biomechanics, retinal imaging, and telehealth technologies like smartphone-based optical palpation for skin and breast tissue analysis. Collaborations span global institutions, advancing non-invasive diagnostic methods. Education: PhD in Optical Imaging Technologies (UWA, 2017); BSc and MSc from Polytechnic of Milan. Research interests integrate OCT, elastography, and machine learning to address challenges in ophthalmic surgery, cancer margin assessment, and telemedicine. His work bridges engineering and clinical needs, with over 46 publications and 1503 citations, reflecting contributions to high-resolution imaging and biomechanical analysis. Recent articles highlight advancements in spatio-temporal OCT (STOC-T) for retinal hemodynamics, chirped flicker optoretinography, and cross-meridian air-puff OCT for keratoconus detection. Innovations in low-cost imaging systems and robotic tool tracking aim to democratize medical diagnostics. His lab develops handheld elastography probes and smartphone-integrated palpation tools, advancing point-of-care solutions. Awards: While no specific honors are listed, his high citation count and h-index (21) underscore peer recognition. Supervised work includes mentoring students on biomechanical and imaging projects.

Beren SemizView profile

Assistant Professor

• Biomedical Signal Processing
• Wearable Device Design
• Physiological Monitoring Systems
• +3 more

Beren Semiz is an Assistant Professor of Electrical and Electronics Engineering at Koç University, where he also directs the Physiological Analysis and Wearable Systems Research Lab. He holds a Ph.D. and M.Sc. from Georgia Institute of Technology (2020 and 2018), and a B.Sc. from Koç University (2016), supported by a Fulbright Scholarship. His research focuses on biomedical signal processing, wearable device design, and machine learning applications in healthcare. Key projects include drowsiness detection systems for drivers, pediatric rehabilitation monitoring, and cardiovascular assessment frameworks. Research interests emphasize wearable sensor development for continuous health monitoring, with a focus on optimizing physiological signal analysis for clinical applications. His lab's work has been featured in major engineering journals (e.g., Medical & Biological Engineering & Computing) and medical conferences including EMBC 2025. Recent breakthroughs include cuff-less blood pressure estimation and novel image processing methods for intra-articular bleeding quantification. Media Coverage: CNN, Fox News (U.S.) Awards: Fulbright Scholarship Labs: Physiological Analysis and Wearable Systems Lab Contact: besemiz@ku.edu.tr | +90 212 338 0917 His work bridges engineering innovation with clinical needs, producing over 30 peer-reviewed publications since 2017. Current projects include developing biofeedback VR systems for pediatric patients and optimizing wearable patches for pediatric vital sign monitoring.

David J. FrancisView profile

Professor

• Reading Disabilities
• English Language Learners
• Quantitative Methods
• +8 more

Dr. David J. Francis holds the Hugh Roy and Lillie Cranz Cullen Distinguished Chair of Quantitative Methods in the Department of Psychology at the University of Houston. He serves as Director of the Texas Institute for Measurement, Evaluation, and Statistics (TIMES) and the Center for the Success of English Learners, a National Research and Development Center funded by the Institute of Education Sciences. His work focuses on statistical modeling, reading disabilities, and the education of English Language Learners. Ph.D. in Clinical-Neuropsychology from the University of Houston (1985), specializing in Quantitative Methods His research spans developmental disabilities , neuroscience (e.g., brain injury consequences), and educational equity for at-risk populations. He has pioneered methods for improving personnel selection using random effects models and explored interventions for seizure disorders via brain stimulation. Recent publications highlight his expertise in statistical modeling applied to education and neuroscience , including studies on academic vocabulary instruction, bilingual phonological awareness, and neuroimaging techniques for seizure localization. Collaborative grants from NICHD, NINDS, and the Office of Naval Research underscore his interdisciplinary impact. Scientific Awards : Hedges Lecture Award (2021) Albert J. Harris Award (2006) Esther Farfel Award (2008) Fellowships: APA Division 5, AERA, APS Dr. Francis has directed foundational centers like CREATE and the Texas Center for Learning Disabilities. His leadership roles include Chairman of the Psychology Department (2002-2014) and advisory positions on the National Board of Education Science and NRC committees. Key labs and teams include: Texas Institute for Measurement, Evaluation, and Statistics (TIMES) Center for the Success of English Learners Data Management and Statistics Core (PI, Texas Center for Learning Disabilities)

Ilkka NissiläView profile

Researcher

• Biomedical Optics
• Neuroimaging
• Optical Tomography
• +7 more

Ilkka Nissilä, Ph.D., is a Staff Scientist at the Department of Neuroscience and Biomedical Engineering , Aalto University. With a career spanning over two decades, his work focuses on diffuse optical tomography , near-infrared spectroscopy , and neonatal brain imaging . He served as a Principal Investigator for the Academy of Finland-funded project Multimodal neuroimaging of children (2016–2018) and has collaborated internationally through visiting researcher appointments. Education : Doctoral Degree in Engineering, Helsinki University of Technology (2004) Master's Degree in Engineering, Helsinki University of Technology (1998) Research Themes : Biomedical optics and optical tomography for brain imaging Neurovascular coupling and hemodynamic responses Developmental neuroscience in infants/children Emotional and sensory processing in early life Algorithm development for image reconstruction Integration of NIRS with TMS/MEG/EEG Article Trends : Recent work combines deep learning with diffuse optical tomography for improved brain imaging Studies maternal mental health impacts on child brain development Advances frequency-domain data utilization and 3D anatomical modeling in neonatal studies Activities : Visiting Researcher (2001–2008) at multiple foreign institutions Conference Presentations Media Impact : 2023: AI-Mind General Assembly participation 2022: Co-developed a skin-stroking device for alcohol cravings 2019: Maternal anxiety effects on infant brain covered in Finnish media Collaborations : International partnerships in biomedical optics Interdisciplinary work with pediatric and psychiatric researchers

David LehotzkyView profile

Research Fellow

• Neuroscience
• Computational Biology
• Dynamical Systems
• +3 more

David Lehotzky is a Postdoctoral Research Associate in the Department of Biology at Northeastern University in Boston, Massachusetts (134 Mugar Life Sciences Building). His research bridges interdisciplinary fields including neuroscience, computational biology, and mechanical engineering. Key research areas include: Neuro-behavioral assays for evaluating anesthesia effects on electric fish physiology Computational modeling of neural oscillators and tissue growth mechanisms Stability analysis in milling processes and delayed dynamical systems Application of supervised learning algorithms to animal communication signals His publications demonstrate expertise in cellular automata modeling, spectral element methods, and neurophysiological studies of the weakly electric fish Apteronotus leptorhynchus . Recent work focuses on integrating computational neuroscience with industrial engineering principles.

• Biomechanics
• Computational Biomechanics
• Finite Element Analysis
• +7 more

Karol Miller is a Winthrop Professor of Applied Mechanics at The University of Western Australia and a Visiting Professor of Radiology at Harvard Medical School. He holds a MSc in aerospace engineering, a PhD in robotics, and a DSc in biomechanics. His research focuses on computational biomechanics, particularly in the biomechanics of the brain and vascular systems, aiming to advance personalized medicine through technology integration. Key roles: Editor of Modelling in Biology and Medicine , Associate Editor of International Journal for Numerical Methods in Biomedical Engineering , and former Associate Editor of Annals of Biomedical Engineering . Education: MSc (Warsaw), PhD (Warsaw), DSc (Warsaw) Research interests include patient-specific engineering, finite element analysis, and biomechanical modeling of brain-skull interfaces and abdominal aortic aneurysms. His lab, the Intelligent Systems for Medicine Laboratory (ISML), develops technologies to improve clinical outcomes. Notable projects include real-time brain shift correction in neurosurgery and biomechanics-meets-phenomics studies for AAA disease progression. Awards: Humboldt Research Award, NVIDIA GPU Computing Champion, UWA Student Guild Choice Award, and multiple teaching recognitions. Grants: Over 50 projects funded by ARC, NHMRC, NIH, and international agencies. Active projects include real-time neural network-based brain shift correction and computational biophysics integration in 3D Slicer. Labs/Teams: ISML, collaborations with Harvard Medical School, and leadership in computational biomechanics software development (e.g., SlicerCBM framework).

• Biomechanics
• Gait Analysis
• Prosthetics
• +4 more

Dr. Sjoerd Bruijn is an Associate Professor at Vrije Universiteit Amsterdam, affiliated with the Faculty of Behavioural and Movement Sciences, specifically in the Biomechanics department. He holds multiple associate professor roles within the Institute for Brain and Behavior Amsterdam (IBBA) and the Amsterdam Movement Sciences (AMS) centers focusing on Ageing & Vitality and Musculoskeletal Health. His work contributes to UN Sustainable Development Goals related to health and well-being. His research interests revolve around biomechanics, with a focus on gait analysis, prosthetics, low back pain, and balance control in aging populations. He explores how biomechanical interventions and technologies can improve mobility and stability, particularly in clinical populations such as stroke survivors and individuals with prosthetic limbs. Recent studies include investigations into prosthetic alignment effects, gait stability in older adults, and machine learning applications for gait recognition. His work often integrates experimental methods with computational modeling to understand and enhance human movement control. Dr. Bruijn is actively involved in teaching, leading courses such as Advanced 3D Kinematics and Applied Biomechanics . He has contributed to numerous datasets and software tools, promoting open-access research in biomechanics. He has not reported any ancillary activities and has not been mentioned in relation to awards or grants in the provided information.

• Magnetoencephalography (MEG)
• Traumatic Brain Injury
• Concussion
• +5 more

Elizabeth Moody Davenport, Ph.D., is an Assistant Professor of Radiology at UT Southwestern Medical Center, where she serves as the Technical Director for the Magnetoencephalography Center of Excellence. She holds secondary appointments in the Department of Biomedical Engineering and the Advanced Imaging Research Center (AIRC), and a tertiary appointment in the Department of Neurology. Her educational background includes a bachelor's degree in biomedical engineering from North Carolina State University and a doctorate in biomedical engineering from Virginia Tech-Wake Forest University. She completed postdoctoral training in clinical Magnetoencephalography (MEG) at UT Southwestern in the Advanced NeuroScience Imaging Research (ANSIR) Lab under Joseph Maldjian. Dr. Davenport's research focuses on magnetoencephalography (MEG) and advanced imaging techniques to study traumatic brain injury, concussion, Alzheimer's disease, dementia, and epilepsy. Her work examines subconcussive head impacts in contact sports and their effects on brain function/structure, aiming to develop imaging biomarkers for clinical diagnosis and prognosis. Her recent publications reveal strong trends in MEG-based investigation of brain connectivity changes following head impacts in youth football, with expanding applications in epilepsy surgery planning, Alzheimer's disease progression, and novel tool development for neural signal analysis. Scientific recognition includes: Patent for developing a system that allows for rapid intraosseous delivery of fluids from a fluid reservoir via an intraosseous port having a tip disposed within a patient's bone marrow Dr. Davenport actively mentors Ph.D. candidates and postdoctoral researchers in the Davenport Lab, securing research funding and extending her scientific expertise to nonprofit canine rescue grants. Her leadership in the ANSIR Lab fosters multidisciplinary collaboration across radiology, neurology, and biomedical engineering. The Davenport Lab specializes in quantitative brain imaging methods, developing MEG spectral analysis algorithms and advanced MRI structural analytics for concussion biomarkers, early dementia detection, and epilepsy localization, directly supporting clinical applications at UT Southwestern.

Jan-Philipp MachtensView profile

Professor

• Neurophysiology
• Ion Channel Biology
• Membrane Proteins
• +3 more

Prof. Dr. Jan-Philipp Machtens leads the Computational Neurophysiology group at the Institute for Biological Information Processes (IBI-1), Research Center Jülich. His work combines all-atom molecular simulations, machine learning, and experimental techniques to study neuronal signal processing at atomic resolution. Key research areas include ion channel/transporter function, sound amplification in the inner ear, and optogenetic tool development. Research Focus: Structure-dynamics-function relationships of membrane proteins SLC1 glutamate transporters and SLC26A5 (prestin) transport mechanisms Light-gated ion channels for optogenetics Multiscale computational methods for voltage-dependent proteins Techniques: Patch-clamp electrophysiology, time-resolved fluorescence spectroscopy, molecular dynamics simulations, machine learning Labs/Teams: Collaborates with the Calcium Channel Biology group (Prof. Patricia Hidalgo) and the Computational Structure Prediction team Recent work includes groundbreaking studies on outer hair cell electromotility mechanisms (2023), sodium channel stabilization (2023), and glutamate transporter dynamics in neurological disorders (2023). His lab's computational tools like g_elpot (2021) advance biomolecular electrostatic analysis.

• Biomedical Signal Processing
• Embedded Systems
• FPGA Hardware Implementation
• +7 more

Jose Vicente Frances Villora is an Associate Professor in the Department of Electronic Engineering at the School of Engineering, University of Valencia, Spain. He is an active researcher in digital systems and biomedical signal processing, with a strong focus on real-time embedded systems and hardware acceleration. His research interests include: Biomedical Signal Processing, particularly ECG and microelectrode recordings FPGA-based hardware implementation of neural networks and real-time algorithms Machine learning applications in healthcare, including arrhythmia detection and Parkinson’s disease treatment Neuromorphic computing and spiking neural networks Digital signal processing in embedded and real-time systems The analysis of his recent publications (2017–2015) reveals a consistent focus on leveraging machine learning—especially KNN and neural networks—for clinical applications such as ventricular fibrillation detection and deep brain stimulation. A significant portion of his work emphasizes hardware implementation on FPGAs, aiming to achieve real-time performance and low resource consumption in medical and robotic systems. He has contributed to educational materials, including textbooks and OCW resources, and supervises academic theses. While no specific scientific awards are mentioned in the provided text, his sustained publication output and integration of theory with hardware systems underscore his technical expertise. He advises students in thesis work and has supervised doctoral research, though specific student names are not listed. His research is supported by practical applications in medical diagnostics and robotics, and he is affiliated with the GPDD (Group for Digital Design and Processing), which focuses on digital system design and processing. He is actively involved in laboratory development, including a remote laboratory for industrial robots, and his team works on co-design environments for neural network hardware, FPGA acceleration, and real-time biomedical systems.

David J. AndersonView profile

Professor

• Neuroscience
• Neurobiology
• Neural Circuits
• +5 more

David J. Anderson is the Seymour Benzer Professor of Biology at the California Institute of Technology (Caltech), serving as Director and Leadership Chair of the Tianqiao and Chrissy Chen Institute for Neuroscience. He is also a long-standing Investigator of the Howard Hughes Medical Institute (HHMI), appointed in 1989. His academic credentials include an A.B. in Biochemical Sciences from Harvard University (1978, summa cum laude) and a Ph.D. in Cell Biology from Rockefeller University (1983), where he trained with Nobel Laureate Günter Blobel. Postdoctoral training followed at Columbia University with Nobel Laureate Richard Axel. Anderson's research centers on the neurobiological basis of emotional behaviors, particularly fear, anxiety, and aggression. His lab employs mice, Drosophila, and the jellyfish Clytia hemisphaerica to dissect neural circuits using optogenetics, chemogenetics, single-cell RNA sequencing, and advanced behavioral analysis. Key discoveries include identifying aggression-controlling neurons in the hypothalamus and fear-processing microcircuits in the amygdala. Recent publications reveal a strong emphasis on decoding aggression circuits through computational modeling of neural dynamics, whole-brain imaging, and cross-species comparisons. His work integrates molecular, cellular, and systems approaches to understand how internal emotional states emerge from neural activity and influence behavior. Major scientific awards include: 2018 Edward M. Scolnick Prize in Neuroscience 2017 New York Academy of Medicine Salmon Award 2016 Perl-UNC Neuroscience Prize 2007 National Academy of Sciences election Multiple named professorships and fellowships As an HHMI Investigator since 1989 and Allen Distinguished Investigator (2010), Anderson has secured substantial research funding. He serves on the Scientific Advisory Board of the Allen Institute for Brain Science and has mentored numerous trainees. His lab develops cutting-edge tools like the Mouse Action Recognition System (MARS) for behavioral analysis. The David Anderson Lab operates as a multidisciplinary hub integrating genetics, neurophysiology, and computational methods. Current collaborations with engineering groups focus on machine vision for behavior quantification, while expanding the Clytia model to investigate primitive internal states in brainless organisms.

• Neuromodulation
• Neurostimulation
• Neuropsychiatric Disorders
• +2 more

Kristin Sellers is an Assistant Professor at the University of California, San Francisco (UCSF) in the Department of Neurological Surgery under the School of Medicine. Her work focuses on intracranial electrical stimulation for treating neuropsychiatric disorders such as major depressive disorder and chronic pain, with an emphasis on translating findings from clinical trials into neurotechnology development. Education: BS in Human Development from Cornell University (2011) PhD in Neurobiology from University of North Carolina (2016) Certificate in Translational Medicine from University of North Carolina (2016) Postdoctoral Scholar in Neurological Surgery at UCSF (2020) Dr. Sellers specializes in closed-loop neuromodulation systems, aiming to create adaptive neurotechnologies that respond to real-time brain activity. Her research spans human neurophysiology, biomedical engineering, and therapeutic applications of brain stimulation. Key publication trends include innovations in deep brain stimulation protocols, advanced neural recording techniques, and computational approaches to understanding cortical dynamics. Her work has been cited over 2000 times across platforms like Mendeley, X, and news outlets, with notable coverage of treatments for severe depression and chronic pain. She collaborates extensively with UCSF colleagues including Andrew Krystal, Edward Chang, and Philip Starr. Labs & Collaborations: Dr. Sellers operates within UCSF's Neurological Surgery department, contributing to clinical trials and neurotechnology projects. She works with teams developing tools like the DREDge algorithm and Medtronic Summit RC+S systems for brain stimulation.

John A. WhiteView profile

Professor

• Mechanisms of Episodic Memory
• Pathophysiology of Epilepsy
• Computational Neuroscience
• +2 more

John A. White serves as Professor and Deputy Chair in the Department of Biomedical Engineering at Boston University, with joint appointments in Pharmacology and Experimental Therapeutics and Neuroscience. He directs the Neuronal Dynamics Lab and maintains affiliations with the Center for Systems Neuroscience, Neurophotonics Center, and Photonics Center. Education: PhD, Biomedical Engineering, Johns Hopkins University B.S., Biomedical Engineering, Louisiana Tech University His research integrates engineering principles with neuroscience to decode brain information processing mechanisms. Key focus areas include episodic memory formation, epilepsy pathophysiology, computational modeling of neural networks, real-time instrumentation design, and advanced imaging of neuronal and astrocytic activity. Current work targets therapeutic interventions for memory disorders through precisely timed brain stimulation and develops computational frameworks for understanding cortical coherence in cognitive functions. Analysis of his 2022-2025 publications reveals dominant themes in hippocampal memory coding (time/distance representation, engram segregation), theta-gamma oscillation dynamics, and neurotechnology development. Recent work emphasizes ultrasound-based deep brain stimulation, calcium imaging analysis toolboxes, and computational models of inhibitory networks, demonstrating consistent innovation at the engineering-medicine interface. Scientific Awards: 2019: Elected Fellow of the International Academy of Medical and Biological Engineering 2019: Elected President of the Biomedical Engineering Society 2014: Meeting Chair, Biomedical Engineering Society Fall Meeting 2011: Distinguished Alumnus Award, Dept. of BME, La. Tech U. 2006: Elected Fellow, Biomedical Engineering Society 2005: Elected Fellow, American Institute for Medical and Biological Engineering 2003-2008: Co-Director, Methods in Computational Neuroscience, Marine Biological Lab Professor White actively collaborates across Boston University's interdisciplinary centers while advancing his lab's mission to translate neural dynamics research into clinical applications, particularly for memory restoration in neurological disorders. His leadership in the Biomedical Engineering Society and ongoing development of real-time electrophysiology tools underscore his commitment to bridging engineering innovation with neurological therapeutics.