Jemma Brown is an academic affiliated with King's College London, where she contributes to research in medical imaging and biomedical engineering. She holds a Master of Research in Medical Imaging (King's College London, 2016) and a Master of Physics from the University of Oxford (2015). Her work focuses on super-resolution ultrasound imaging, microbubble/nanodroplet contrast agents, and 3D printing for medical device validation. She has co-authored numerous peer-reviewed articles on topics such as immune ageing, post-COVID-19 recovery, and ultrasound imaging innovations. Her research aligns with UN Sustainable Development Goals related to health and well-being. Education: Master of Research (Medical Imaging), King's College London (2016); Master of Physics, University of Oxford (2015) Research Interests: Super-resolution imaging techniques, medical ultrasound advancements, microbubble dynamics, and translational medical technologies. Her recent work includes developing cardiac phantoms for interventional simulations and studying immune responses in severe COVID-19 cases. She collaborates widely, contributing to large-scale studies like the PHOSP-COVID cohort analysis.
Justin Romberg is the Schlumberger Professor and Associate Chair for Research in the School of Electrical and Computer Engineering at Georgia Tech, and serves as Associate Director for the Center for Machine Learning. He holds a B.S.E.E., M.S., and Ph.D. from Rice University, followed by postdoctoral work at Caltech and visiting roles at UCLA and the Laboratoire Jacques-Louis Lions. His research bridges signal processing, machine learning, optimization, and applied probability, with notable contributions to imaging inverse problems, data compression, and broadband beamforming. Dr. Romberg has received prestigious awards including the ONR Young Investigator Award (2008), PECASE (2009), Packard Fellowship (2009), and IEEE Fellowship. His recent work emphasizes real-time RF emulation, neural adjoint methods for sound speed learning, and decentralized optimization frameworks. Current projects include radar signal processing, multi-agent reinforcement learning, and hardware-friendly sparse neural networks. His lab focuses on translating theoretical insights into practical systems, such as low-bit beamforming accelerators and medical monitoring devices using MEMS sensors.
Visvesh S. Sathe is an Associate Professor in the School of Electrical and Computer Engineering at Georgia Institute of Technology. Previously, he served as Associate Professor at the University of Washington (2013-2022) and held roles at AMD (2007-2013), where he contributed to energy-efficient microprocessor design. His research focuses on energy-efficient computing, implantable electronics, and closed-loop neural interfaces. He leads the Processing Systems Lab (PSyLab), exploring computational techniques for hardware optimization in digital and mixed-signal systems. Education: B.Tech from IIT Bombay; M.S. and Ph.D. from the University of Michigan, Ann Arbor. Research Interests: Energy-efficient IC design, power management architectures, bi-directional neural interfaces, adaptive baseband processing, and SoC optimization. His work emphasizes run-time control of clocking, voltage regulation, and thermal management. Key Awards: NSF CAREER Award (2019), Intel Outstanding Researcher Award (2021), and IEEE Distinguished Lecturer (2021-2022). Notable contributions include the first resonant-clocked production processor and adaptive clocking for supply droop mitigation. Advisees/Grants: While specific grant details aren't provided, his lab’s work is funded by NSF and industry partnerships. His research spans from circuit-level innovations to system-level integration, with applications in biomedical and high-performance computing. Labs/Teams: PSyLab at Georgia Tech drives interdisciplinary projects in energy-efficient systems and neural interfaces, collaborating with industry and academic partners globally.
Prof. Mathini Sellathurai is a Full Professor and Dean of Science and Engineering at Heriot-Watt University, Edinburgh. She leads the Signal Processing for Intelligent Systems and Communications Lab, with expertise in radar technology, MIMO systems, and wireless communications. Her research focuses on adaptive signal processing applications in radar, lidar, sonar, and RF networks. She holds adjunct roles at McMaster University and has served on IEEE editorial boards and technical committees. Education & Experience: PhD with NSERC doctoral award Industrial experience at Bell Laboratories (2000) and Canadian Communications Research Centre (2001-2004) Research Interests: Signal Processing innovations in parasitic antennas, MIMO radar, cognitive radio, and underwater communications. She explores network coding, iterative receiver design, and beamforming techniques to enhance communication systems efficiency and performance. Awards: IEEE Ellersick Best Paper Award (2005) Industry Canada Public Service Awards (2005) Technology Transfer Awards (2004) Teaching & Leadership: Teaches electrical engineering fundamentals, image processing, and advanced analysis. Oversees multi-institutional projects funded by EPSRC, DSTL, and EU Framework programs (e.g., Cognitive Radio Oriented Wireless Networks). Labs & Collaborations: Heads the Signal Processing Lab (EM 3.24), collaborating on EU-funded initiatives and industry partnerships with QinetiQ and Wireless Fiber Systems. Active in defense-related radar research through UDRC and EPSRC grants.
Di Xiao is a Sessional Lecturer at the University of Waterloo. Their work focuses on advancing medical imaging technologies, particularly in ultrasound and MRI, with an emphasis on real-time processing, deep learning integration, and algorithm optimization. They are affiliated with the Sessional lecturers group. Research interests include ultrasound imaging innovations such as speed-of-sound estimation, beamforming techniques, and sparse-array optimizations. Their work also spans MRI reconstruction, signal processing, and applications of machine learning in medical diagnostics. Notable contributions include developing frameworks for live tissue assessment and minimizing image quality loss in sparse data scenarios. Publications highlight trends in real-time ultrasound systems, neural network-driven imaging enhancements, and sparsification strategies for MRI. The articles collectively emphasize interdisciplinary approaches merging AI with traditional medical imaging challenges. No scientific awards or grants are explicitly mentioned. No lab affiliations or student advising records are provided.
P. Sadayappan is a Professor at the School of Computing, University of Utah, specializing in high performance computing, compiler optimization, and scalable machine learning. His research focuses on developing efficient computational methods for scientific applications, particularly in the areas of sparse/dense matrix and tensor computations. His research interests include: Compiler Optimization for High Performance Computing Optimization of Sparse/Dense Matrix/Tensor Computations Scalable Machine Learning Algorithm-Architecture Co-Design Optimization Sadayappan's recent publications demonstrate a strong focus on tensor computations, GPU acceleration, and compiler optimizations for machine learning workloads. His work spans from fundamental compiler theory to practical implementations that improve performance across various architectures. A significant trend in his recent work involves the development of frameworks for efficient tensor operations, sparse matrix computations, and domain-specific code generation, with particular emphasis on performance portability across heterogeneous computing platforms. His notable scientific achievement includes receiving the ACM SIGPLAN Most Influential PLDI Paper Award in 2018 for his work on polyhedral compilation. Sadayappan has been principal investigator or co-investigator on numerous significant research grants, including: NSF award #2217154 (2022-2027): A Comprehensive Framework for Efficient, Scalable, and Performance-Portable Tensor Applications NSF award #2112606 (2021-2026): AI Institute for Intelligent CyberInfrastructure with Computational Learning in the Environment (ICICLE) NIH SBIR-Phase 2 (2023-2025): Enabling next generation machine learning for large scale image analysis NSF award #2009007 (2020-2024): Data Locality Optimization for Sparse Matrix/Tensor Computations DARPA SBIR-Phase 2 (2017-2022): Performance Portable Framework for Developing Graph Applications He teaches CS 4230/6230 (Parallel and High-Performance Computing) at the University of Utah and collaborates extensively with researchers across multiple institutions on projects involving computational chemistry, physics simulations, graph analytics, and machine learning.
John O'Donnell is an Honorary Lecturer at the University of Glasgow's School of Computing Science. His research spans functional programming, hardware description languages, parallel computing, and computer science education, with notable applications in music technology. Key research areas: Functional approaches to hardware design and simulation Parallel data structures and algorithms for specialized architectures Programming misconception identification and pedagogical tools Computational modeling of musical performance techniques Publications demonstrate consistent innovation in applying functional programming paradigms to diverse domains, from circuit design to music pedagogy. Recent work focuses on educational aspects of computer systems and programming.
Dr. Gregory Duthé is a researcher at the ETH Zürich in the Structural Mechanics and Monitoring department. His work bridges computational modeling and structural diagnostics with applications in renewable energy systems. Research contributions include: Development of Graph Neural Networks for aerodynamic flow reconstruction Advancements in wake-induced load estimation for wind farms Innovations in unsupervised fault detection for offshore turbine systems Key publication trends show focus on physics-informed machine learning (2025), multi-agent infrastructure decision support (2025), and leading edge erosion modeling (2021). He combines graph-based architectures with structural health monitoring techniques across wind energy applications. Contact: duthe@ibk.baug.ethz.ch
Spencer T Brinker is an Adjunct Assistant Professor at the Yale School of Medicine, specializing in focused ultrasound technology for neurological applications. His research spans neuromodulation, epilepsy treatment, and anesthesiology, with a focus on developing clinical ultrasound systems and MRI-guided interventions. He holds a PhD in Mechanical Engineering from the University of Illinois at Chicago (2016) and leads research in transcranial ultrasound stimulation, device development, and multimodal imaging techniques. His work includes pioneering studies on schizophrenia and status epilepticus treatment, as well as advancements in neuronavigation and acoustic mapping technologies. Education: PhD in Mechanical Engineering, University of Illinois at Chicago (2016) Research Interests: Dr. Brinker’s work combines biomechanical engineering with clinical neuroscience, targeting non-invasive brain therapies. Key areas include: Development of focused ultrasound platforms for neuromodulation Multimodal imaging integration (MRI/elastography) Clinical translation of transcranial ultrasound systems His studies often emphasize device innovation and translational applications in neurological disorders. Publications Overview: His recent articles explore transcranial ultrasound efficacy in schizophrenia, global intracranial sonication, and MRI-guided systems. Themes include technical advancements in beam modeling, acoustic mapping, and clinical feasibility. Labs/Teams: His research is supported by collaborations within Yale’s biomedical engineering and neurology communities, leveraging advanced imaging and device fabrication facilities.
Wen-shin Lee is a Lecturer at the University of Stirling's Division of Computing Science and Mathematics, specializing in computational mathematics and signal processing. Her research focuses on exponential analysis, sparse interpolation, and symbolic-numeric computation. She holds a PhD from North Carolina State University and has held positions at institutions like the University of Antwerp and INRIA. Current affiliations include the Computational Mathematics and Optimisation Research Group (COMMON). Education: Bachelor’s in Mathematics, National Taiwan University PhD in Computational Mathematics, North Carolina State University Research Interests: Her work bridges computer algebra and signal processing, emphasizing applications like antenna positioning, radar imaging, and texture decomposition. Recent trends include sub-sampled exponential analysis, validated algorithms, and high-resolution signal reconstruction from sparse data. Labs/Groups: Active in the COMMON group at the University of Stirling and collaborates on the EXPOWER project (Exponential Analysis Empowering Innovation).
Professor Mario Dagenais is a Professor of Electrical and Computer Engineering at the University of Maryland, with affiliate roles in the Bioengineering department and Chemical Physics faculty. His research focuses on nanophotonic devices, quantum information systems, and astrophotonics. He holds a PhD from the University of Rochester (1978), and has held roles at Harvard University and GTE Laboratories before joining UMD in 1987. Research interests include Si3N4/SiO2 integrated photonics, Bragg grating technologies, GaN light sources, and high-power semiconductor lasers. He has authored over 400 publications and led significant projects like the NSF Industry-University Cooperative Research Center on Optoelectronics (1994-2000). Awards : IEEE Fellow (2009), Optical Society of America Fellow, Fellow of the Electromagnetic Society. Grants & Leadership : Led a $2M EFRI grant on quantum communication networks (2020s), co-chaired international photonics meetings, and developed the first photon antibunching observation with Jeff Kimble (1978). Labs : Director of the Laboratory for Nanophotonics and Quantum Integration, actively recruiting students for photonic solutions to multidisciplinary problems.
Michael Joham is a Senior Researcher at the Chair of Signal Processing Methods at the Technical University of Munich (TUM). He teaches courses in Circuit Theory, Systems Theory, MIMO Systems, Numerical Linear Algebra for Signal Processing, and Introduction to Signal Processing. Circuit Theory (Winter Term) Systems Theory (Summer Term) Signal Processing Systems (Winter Term) MIMO Systems (Winter Term) Numerical Linear Algebra for Signal Processing (Summer Term) Introduction to Signal Processing (Summer Term) His research spans Signal Processing for Communications, Machine Learning in Physical Layer Wireless Communications, Intelligent Reflecting Surface Systems, Multi-User MIMO Communications, Automotive Safety and Autonomous Driving, Quantum Radar, Generative Modeling for Biomedical Applications, and methodologies like Applied Information Theory, Array Processing, and Sparse Signal Processing. Contact: joham@tum.de , Phone: +49-89-289-28510, Fax: +49-89-289-28522, Postal Address: 80290 Munich, Germany, Visitor Address: Theresienstrasse 90, Room N1125A.
Peter Weinberg is a Professor of Cardiovascular Mechanics in the Department of Bioengineering at Imperial College London, Faculty of Engineering. He is based at the Royal School of Mines on the South Kensington Campus and can be contacted at p.weinberg@imperial.ac.uk. His research is centered on the biomechanics of cardiovascular diseases, particularly atherosclerosis and heart failure. He leads a research group focused on fluid dynamics, endothelial function, and advanced ultrasound imaging techniques. Education: Natural Sciences, University of Cambridge (Scholarship recipient) DIC, MSc, PhD in Physiological Flow Studies, Imperial College London Lady Davis Postdoctoral Fellowship, Technion – Israel Institute of Technology His research interests lie at the intersection of biomedical engineering, cardiology, and biomechanics . He investigates how hemodynamic forces such as wall shear stress influence endothelial permeability and atherosclerosis development. A major focus is on transcytosis of LDL , disturbed blood flow patterns , and non-invasive detection of heart failure using B-mode ultrasound and wave intensity analysis. His lab develops novel ultrasound imaging methods, including super-resolution techniques using nanodroplets and microbubbles, and coherence-based beamforming for 3D vascular mapping. His recent publications (2021–2025) demonstrate a strong trend toward advanced ultrasound diagnostics and molecular mechanobiology . The articles span from computational beamforming improvements to in vivo validation of endothelial activation pathways. Key themes include ultrasound velocimetry , macromolecule transport , shear stress modeling , and early disease detection . The work combines engineering innovation with deep biological inquiry, aiming to translate biomechanical insights into clinical tools. Scientific Awards and Honors: Fellow, Royal Microscopical Society Ordinary Member, The Physiological Society Member, British Atherosclerosis Society Committee Member, London Microcirculation Group Committee Member, British Society for Cardiovascular Research Committee Member, British Atherosclerosis Society Lady Davis Fellow Peter Weinberg has held key leadership roles in the Department of Bioengineering, including Director of Postgraduate Studies (Research) , Director of Research , and Academic Line Manager . He led the department’s efforts in the Research Assessment Exercise 2008 and Research Excellence Framework 2014. He founded and served as president of the Bioengineering Society (now BioMedEng), and was Associate Editor of the journal Atherosclerosis . He has organized major conferences such as the joint British Society for Cardiovascular Research and British Atherosclerosis Society meeting, and chaired BioMedEng18, attracting over 500 delegates. He has secured research grants, though specific details are not listed in the text. He leads a research laboratory in the Department of Bioengineering at Imperial College London, focusing on cardiovascular mechanics . The lab website details ongoing projects in ultrasound imaging, endothelial mechanobiology, and atherosclerosis modeling. The team uses a combination of computational modeling, in vitro bioreactors, and in vivo imaging to study vascular function and disease progression.
Euripedes Montagne is a Senior Lecturer in the Department of Computer Science at the University of Central Florida (UCF). He teaches courses in operating systems, system software, computer architecture, programming languages, and social networks. Ph.D. in Computer Science – Universidad Central de Venezuela His research focuses on program optimization for irregular problems, particularly sparse matrix operations, parallel processing, non-standard computer architectures, and computer science education. His work spans algorithm design for systolic arrays, granularity modeling in parallel systems, and performance analysis of block-structured programs. The trends in his publications highlight advancements in systolic array design, sparse matrix optimization, and parallel computing. His 2006 paper on quantum circuits demonstrates interdisciplinary applications, while older works (1986–1994) emphasize foundational contributions to parallel architectures and performance modeling. No scientific awards or honors are explicitly documented in the provided text. No students, labs, teams, or grants are mentioned in the given information.
Michael Muma is a Professor in the Department of Electrical Engineering and Information Technology at Technische Universität Darmstadt. His research focuses on robust data science theory and methods applied to signal processing and machine learning in biomedicine and engineering. He leads the ERC Starting Grant ScReeningData project, developing methods for reproducible information discovery in biomedical databases, and is a Principal Investigator in the LOEWE center emergenCITY and BMBF cluster curATime. Prior roles include Independent Junior Research Group Leader (Athene Young Investigator) and Lecturer at TU Darmstadt from 2017 to 2022, and Research Associate (Post-Doc since 2014) from 2009 to 2017. His research interests span robust statistical methods, high-dimensional data analysis, emergency response systems, and biomedical signal processing. Notable projects include FDR-controlled portfolio optimization, ECG delineation algorithms, and radar-based vital sign estimation. Muma has contributed to distributed sensor networks, robust clustering, and sparse regression techniques. His work addresses challenges in multi-source detection, financial data analysis, and genomics through interdisciplinary approaches combining signal processing, machine learning, and robust statistics. Recent publications emphasize scalable solutions for high-dimensional problems, including applications in robotics, cardiology, and financial index tracking.
