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Shaoqing HuView profile

Lecturer

• Millimeter Wave/THz Imaging
• Sparse Imaging
• Antennas and Arrays
• +3 more

Dr. Shaoqing Hu is a Lecturer in Electronic and Electrical Engineering at Brunel University of London's College of Engineering, Design and Physical Sciences, and serves as an Adjunct Professor at Hangzhou Dianzi University. He holds a PhD from Queen Mary University of London (2020), with prior degrees from University of Electronic Science and Technology of China. His academic roles include Departmental Level 4 Coordinator, Brunel University London Pathway College liaison tutor, and external reviewer. His educational background includes: B.Eng in Vacuum Electronics (UESTC, 2013) M.Eng in Physical Electronics (UESTC, 2016) PhD in Electronic Engineering (QMUL, 2020) Dr. Hu's research centers on millimeter wave/THz security detection, sparse imaging, antennas, and wireless communication. His work bridges theoretical signal processing with practical security applications, particularly in personnel screening and target detection systems. Current projects focus on MIMO mmWave 3D imaging for future screening systems and advanced millimeter-wave imaging with sparse arrays. His group develops specialized hardware including 220 GHz imaging systems and multi-band antennas. Analysis of his recent publications reveals strong emphasis on sparse array configurations (MIMO planar/sparse arrays), algorithmic innovations (back-projection, low-rank matrix recovery), and emerging applications in security screening (2025 Signal Processing Magazine overview). The research spans THz to microwave frequencies with increasing integration of deep learning techniques since 2022. His scientific recognition includes: Fellow of the Higher Education Academy (2024) First Prize Best Student Paper Award (UCMMT 2020) Student Paper Award (IEEE AP CAP 2015) VinFuture Prize Official Nominator (2023) Dr. Hu actively supports early-career researchers through supervision of PhD students (including Shiwei Hu and Wenyi Yan) and MSc candidates. He has secured multiple research grants including Brunel Research Initiative and Enterprise Fund (BRIEF 2022-23), Brunel Research Development Fund, and NSFC collaboration grants. He serves as referee for major awards like UK Doctoral Researcher Award and supports fellowship applications including RAEng and Marie Curie schemes. His laboratory specializes in mm-wave/THz sparse imaging systems, featuring a 1.5m x 1.5m planar scanning setup and 220 GHz MIMO imaging capabilities. The team develops specialized antennas including GNSS arrays, dual-polarized horns, and quasi-Yagi structures for applications ranging from security screening to health sensing.

Volker KühnView profile

Professor

• Wireless Communication Systems
• Information Theory
• Error-Correcting Coding
• +4 more

Prof. Dr.-Ing. habil. Volker Kühn serves as Professor and Head of the Institute of Communications Engineering at the University of Rostock within the Faculty of Computer Science and Electrical Engineering. He concurrently holds the position of Vice Dean of the Faculty while directing academic operations as Chairman of both the Electrical Engineering and Medical Information Technology Examination Boards. His research integrates wireless communications theory with biomedical applications, specializing in spatial modulation techniques, information bottleneck optimization, and electrical impedance tomography signal processing. Current work focuses on machine learning-enhanced physiological monitoring systems and distributed compression algorithms for sensor networks, with significant contributions to bias-free spectral estimation from irregularly sampled data. Analysis of his 2021-2025 publications reveals three dominant research thrusts: (1) neural network applications for medical signal processing, (2) information-theoretic optimization of communication protocols, and (3) advanced spectral estimation methods for biomedical and sensor data. This interdisciplinary approach bridges communications engineering with healthcare technology development. Prof. Kühn actively mentors students as Study Advisor for Electrical Engineering and Academic Advisor for Medical Information Technology. His leadership extends to the ITG Technical Committee 5.1 on Information and Systems Theory and IEEE societies. The Institute of Communications Engineering under his direction maintains specialized laboratories for wireless communications testing, medical signal acquisition, and MIMO system prototyping, supporting both fundamental research and industry collaboration projects in 5G/6G technologies and healthcare IoT.

• Recommender Systems
• Multimodal Learning
• Music Emotion Recognition
• +3 more

Marta Moscati works at the Institute of Computational Perception at Johannes Kepler University Linz , focusing on advanced recommendation systems and multimodal learning. Her research spans emotion-based music recommendation, privacy-preserving machine learning, and graph neural networks. Recent work includes: Developing multimodal single-branch architectures for cold-start scenarios Creating preference obfuscation techniques in implicit feedback systems Advancing music emotion recognition with semi-supervised graph networks Contributing to the FAME Challenge for multilingual face-voice association She has published extensively in top AI venues while maintaining technical expertise in both deep learning and theoretical physics , with early work on lepton universality violation. At JKU, she contributes to: Recommendation algorithms development Multimodal representation learning research Musical affective computing applications Privacy-preserving AI frameworks

• Regional Economics
• Input-Output Analysis
• Environmental Economics
• +1 more

Luis Cruz is a Professor at the Faculty of Economics of the University of Coimbra (FEUC), actively affiliated with the Centre for Business and Economics Research (CeBER). In April 2025, he conducted research under a Fulbright Scholarship at the University of Virginia (UVA), co-organizing the Regional Economics and Input-Output Modeling Workshop with the Weldon Cooper Center for Public Service. This event fostered collaboration among PhD students and prominent economists like Michael Lahr (Rutgers University) and Geoffrey Hewings (University of Illinois Urbana-Champaign), emphasizing cross-generational dialogue in regional economics. His core research examines Environmental and Socio-Economic Interactions, with specialization in Regional Economics and Input-Output Analysis. The UVA workshop highlighted his focus on community-building in economic modeling, where he described the atmosphere as having "great energy, great conversations, and a strong sense of community across generations of regional economists" . His Fulbright experience facilitated cultural exchange and connections in socio-economic policy research. Analysis of his 15 most recent publications reveals an unexpectedly broad interdisciplinary scope spanning computer vision (LiDAR compression, 3D modeling), electrical engineering (grid inspection), and occupational medicine (dermatitis, radiation exposure). This suggests collaborative work beyond traditional economics, possibly through CeBER's cross-departmental initiatives at the University of Coimbra. Professor Cruz has secured competitive international funding through the Fulbright Program, enabling workshops that mentor emerging economists. His leadership in organizing the UVA workshop demonstrates active engagement in academic community development, though specific grant details beyond the Fulbright are not documented in the provided sources. He operates within CeBER (Centre for Business and Economics Research), a key research unit under FEUC that drives economic policy studies. The center's focus on empirical modeling aligns with his workshop on input-output analysis, though no dedicated lab or specialized team is mentioned in the current materials.

Wenchao GuView profile

Research Fellow

• Artificial Intelligence
• Software Engineering
• Computational Linguistics
• +5 more

Dr. Wenchao GU is a Postdoctoral Researcher at the Chair of Software Engineering & AI at the Technical University of Munich (TUM) , working under the supervision of Prof. Chunyang Chen. His academic journey includes a PhD in Computer Science and Engineering from The Chinese University of Hong Kong (CUHK) (2024), an MSc in Information Science from Tohoku University (2017), and a B.Eng in Mechanical and Aerospace Engineering from Tohoku University (2015). Research Interests: Dr. GU's work focuses on Artificial Intelligence for Software Engineering (AI4SE) , leveraging Large Language Models (LLMs) to advance source code understanding, analysis, and generation. Key areas include code optimization , vulnerability detection , and UI code generation . His research bridges theoretical innovations with practical tools for automated code efficiency improvement, semantic code retrieval, and natural language generation in software development workflows. Publication Trends : His recent work emphasizes iterative LLM refinement via evolutionary search (2025), segmented deep hashing for scalable code retrieval (2025), and contrastive learning in code search (2023). Earlier studies explored AST-guided transformers for code summarization (2022) and semantic dependency learning in code retrieval (2021). Scientific Awards: Distinguished Paper Award, ICSE 2025 The Aoba Foundation Scholarship (2015) JASSO Scholarship (2012) Tohoku University President Fellowship (2012) Mentorship: Dr. GU supervises 11 current students (including 1 PhD) and has advised 5 graduated Master's students. He actively seeks candidates with web/Android development expertise for UI code generation projects.

• Machine Learning
• Object-Centric Learning
• Deep Learning
• +5 more

Rongzhen Zhao is a Doctoral Researcher at Aalto University's Department of Electrical Engineering and Automation, affiliated with the Robot Learning research group. Their work focuses on machine learning and object-centric learning, with recent publications in top-tier conferences like ECML and ICLR. Research interests include: Object-Centric Learning Slot Attention Mechanisms Spatiotemporal Processing Neural Network Architecture Design Brain-inspired AI IoT Middleware Development Recent trends in their publications highlight advancements in attention mechanisms, discrete representations for object recognition, and multi-scale fusion techniques. They also explore bio-inspired approaches for temporal data analysis and IoT systems. Research groups: Robot Learning Contact: Email: rongzhen.zhao@aalto.fi

Thomas Jean-HughView profile

Professor

• Acoustic Imaging
• Signal Processing
• UAV Acoustic Localization
• +7 more

Thomas Jean-Hugh is a Professor and Teacher-Researcher at Le Mans Université , affiliated with the Laboratoire d’Acoustique de l’Université du Mans (LAUM) , a leading research institute in acoustics in France. His role involves both teaching and active research in acoustic imaging, signal processing, and structural acoustics. Education PhD in Acoustics and Signal Processing (exact institution and year not specified in text) Research Interests Thomas Jean-Hugh’s research focuses on acoustic source localization and imaging , particularly through the use of microphone arrays , beamforming , and inverse problem solving . His work spans: Acoustic holography for non-stationary and confined sources UAV acoustic detection and tracking using sparse sensor arrays Vibro-acoustic diagnostics for automotive and naval structures Speech processing in meeting environments using distant microphone arrays Non-destructive testing using acoustic emission and imaging techniques Research Trends His recent publications (2022–2025) reveal a strong focus on machine learning-enhanced acoustic imaging , real-time UAV detection , and robust speech processing . He integrates genetic algorithms , Bayesian regularization , and circular harmonics into acoustic array processing, with applications in autonomous systems , marine biology , and automotive acoustics . Scientific Contributions Over 70 peer-reviewed publications in journals and conferences Active supervision of PhD students and postdocs in acoustics and signal processing Participation in international conferences such as Interspeech , ICSV , GRETSI , and ICA Collaborations & Labs He collaborates extensively within LAUM and with external partners including CNRS , ISCA , and international research groups. His lab work involves experimental acoustics , sensor array design , and real-time signal processing .

• Medical Image Processing
• Bioengineering
• Artificial Intelligence in Healthcare
• +3 more

Kristen Mariko Meiburger is an Associate Professor at the Department of Electronics and Telecommunications (DET) of Politecnico di Torino. She is a member of the PolitoBIOMed Lab , focusing on biomedical engineering research. Research Interests: Medical image processing, photoacoustic imaging, ultrasound technologies, and AI-driven diagnostic systems. Her work bridges industrial and information engineering with clinical applications. Recent Publications highlight advancements in photoacoustic uncertainty quantification, high-frequency vascular imaging, machine learning for muscular dystrophy analysis, and optoacoustic/ultrasound reconstruction techniques. Scientific Awards: Premio di Laurea GNB (2011) Premio di Qualità (2014) Key Projects: ImPACT-AI (2023-2025) AI-VASCUES (2023-2025) REAP (2021-2024) H2020-ICT (EU-funded) PhD Students: Bruna Cotrufo, Alen Shahini, Federico Sinnona, Matteo Salvi, Giulia Rotunno, Francesco Branciforti, Francesco Marzola.

• Control Theory
• Game Theory
• Machine Learning
• +4 more

Muhammad Umar B Niazi is a Marie-Curie Postdoctoral Fellow at both KTH Royal Institute of Technology and Massachusetts Institute of Technology , working under the guidance of Karl H. Johansson and Munther Dahleh respectively. Holding a Ph.D. in Automatic Control Engineering from Université Grenoble Alpes, Niazi's research spans secure monitoring of cyber-physical systems and dynamic incentive design for sociotechnical systems . Education : Ph.D. (Grenoble INP, 2021); M.Sc. & B.Sc. (Bilkent University, COMSATS) Research Directions : Secure estimation against cyberattacks in transportation networks and epidemic models Physics-informed learning for observer design in nonlinear systems Eco-driving incentives using Stackelberg game theory Aggregated monitoring of large-scale systems Scientific Contributions : 2025 publications on distributed observers and reachability analysis 2024 work on incentive mechanisms and sensor fault detection 2023 developments in physics-informed epidemic control 2022-2019 foundational work on network observability and opinion dynamics Awards : Marie-Curie Postdoctoral Fellowship (2022-2025) Best Student Paper Finalist, European Control Conference 2019 Niazi's interdisciplinary work combines control theory , game theory , and machine learning to address resilience in transportation, epidemic monitoring, and social network dynamics. His methods integrate theoretical rigor with practical implementations through tools like SUMO simulations and physics-informed neural networks.

Julien FageotView profile

Researcher

• Sparsity
• Random Processes
• Approximation Theory
• +7 more

Julien Fageot is a Researcher at the École Polytechnique Fédérale de Lausanne (EPFL) in the AudioVisual Communications Laboratory within the School of Computer and Communication Sciences. He previously held postdoctoral positions at Harvard University, McGill University, and EPFL. His educational background includes: Ph.D. in Electrical Engineering at EPFL (2012-2017) M.Sc. Mathematics, Vision, and Learning in ENS Paris-Saclay, France (2011) M.Sc. in Probability and Statistics at Université Paris Orsay, France (2009) École Normale Supérieure, Section Mathématiques, Paris, France (2007-2012) Dr. Fageot's research lies at the intersection of high-level mathematics and data sciences, focusing on mathematical properties of advanced processing tools for sparse signal reconstruction and synthesis. His expertise spans sparsity, random processes, approximation theory, splines, convex optimization, functional analysis, and signal/image processing. He explores probability theory (sparse stochastic processes), optimization theory (sparsity-promoting spline reconstruction), and applications in signal processing (inverse problems, segmentation, detection, CNNs). His publication record demonstrates a clear progression from theoretical foundations of stochastic processes to practical applications in biomedical imaging. Recent work shows increasing focus on machine learning applications while maintaining strong mathematical rigor, particularly in developing sparse representations for medical image analysis. His scientific achievements have been recognized with: Best Paper Award at the MIDL Conference (2019) EPFL Best Doctorate Award (2018) Outstanding PhD Thesis Distinction in Electrical Engineering, EPFL (2017) Education Award from the Life Science Department, EPFL (2013) Dr. Fageot actively mentors students, currently supervising PhD candidate Adrian Jarret and having previously guided Thomas Debarre and Shayan Aziznejad to completion. He has supervised numerous master's theses on spline-based reconstruction and biomedical image analysis. His research is supported by Swiss National Science Foundation grants including the Postdoc.Mobility fellowship for 'Mathematical Models for Analog Data Sciences: the Continuous Way' (2020) and the Early Postdoc.Mobility fellowship for 'Probabilistic and Variational Methods for Sparse Signals' (2018). As a key member of EPFL's AudioVisual Communications Laboratory, he collaborates with Prof. Martin Vetterli, Prof. Michael Unser, and Prof. Christian Genest, bridging theoretical mathematics with practical applications in signal processing and data science.

• 3D Computer Vision
• Geometric Deep Learning
• Topological Deep Learning
• +10 more

Tolga Birdal is an Assistant Professor (Lecturer) and UKRI Future Leaders Fellow in the Department of Computing at Imperial College London. As the Principal Investigator (PI) of the CIRCLE group , his research focuses on topological deep learning, geometric machine learning, and 3D computer vision, with theoretical interests in non-Euclidean inference and deep learning principles. Education: PhD and MSc in Computer Vision from Technical University of Munich (2018), BSc in Computer Science from Sabancı University (2008). Projects: PI for UKRI-EPSRC's UNTOLD (Topological Deep Learning), Royal Society's drug discovery initiative, and EPSRC's GNOMON (Generative Models in non-Euclidean Spaces). Leadership: Area Chair for CVPR, ICCV, and 3DV 2025 Publication Chair. His work bridges differential geometry, algebraic topology, and deep neural networks, with applications in quantum computer vision, 3D/4D generative priors, and medical imaging. Key contributions include novel frameworks for rotation forecasting, graph generation, and topological generalization bounds. Scientific Awards: UKRI Future Leaders Fellowship EMVA Young Professional Award

Lassi RoininenView profile

Professor

• Statistics and Probability
• Geophysics
• Applied Mathematics
• +5 more

Lassi Roininen is a tenured Professor of Applied Mathematics at LUT University's School of Engineering Sciences, holding this position since September 2022 after serving as Assistant Professor there from 2018 to 2022. He maintains significant adjunct appointments as Associate Professor at University of Oulu, Assistant Professor at Bahir Dar University (Ethiopia), and faculty member at AIMS Rwanda, demonstrating strong international academic engagement. Education: Master of Science (Engineering), Tampere University of Technology Doctorate in Applied Mathematics, University of Oulu (2015) - conducted at Sodankylä Geophysical Observatory His research integrates Statistics, Geophysics, and Applied Mathematics with core expertise in Bayesian inference, uncertainty quantification, and inversion problems. He develops computational frameworks for geophysical imaging, climate modeling, and industrial applications, emphasizing robust statistical methodologies for real-world data challenges. Recent work shows increasing focus on African climate adaptation and medical/industrial tomography. Analysis of his 15 most recent publications reveals dominant trends in Bayesian approaches to climate science (particularly East African adaptation studies), medical/industrial imaging (tomography and fault detection), and geophysical modeling. His work consistently bridges mathematical innovation with practical applications across environmental science, healthcare, and manufacturing sectors. Research support includes Academy of Finland postdoctoral funding. Through AIMS Rwanda, he actively mentors African mathematicians and contributes to capacity building in computational sciences across the continent. His collaborative projects demonstrate commitment to solving region-specific challenges through advanced statistical methods. His work is closely tied to geophysical research networks including Sodankylä Geophysical Observatory, with recent expansions into East African climate resilience initiatives. Current projects integrate multi-instrument atmospheric data with Bayesian frameworks to address pressing environmental challenges in developing regions.

Heikki HaarioView profile

Professor

• Bayesian Statistics
• Parameter Estimation
• Chaotic Dynamical Systems
• +7 more

Heikki Haario is a Professor in Computational Engineering at the LUT School of Engineering Sciences, LUT University, Lappeenranta. His research focuses on robust Bayesian inference, parameter estimation, and uncertainty quantification in chaotic and stochastic systems. Broad research areas: Bayesian Statistics, Chaotic Dynamical Systems, Gaussian Processes, Machine Learning The 15 most recent publications (2025-2023) demonstrate expertise in computational modeling, data-driven methods, and interdisciplinary applications spanning finance, biology, and engineering. Articles emphasize Bayesian techniques, kernel flows, and neural network integration for solving inverse problems and optimizing predictions in uncertain environments.

Martin EigelView profile

Researcher

• Numerical Mathematics
• Uncertainty Quantification
• Stochastic Partial Differential Equations
• +5 more

Martin Eigel is a Researcher at the Weierstrass Institute for Applied Analysis and Stochastics (WIAS) , specializing in numerical methods for stochastic partial differential equations, uncertainty quantification, and machine learning applications in computational mathematics. His work bridges tensor networks, Bayesian inversion, and quantum simulations. Research Interests : Adaptive stochastic Galerkin finite element methods Low-rank tensor approximations for high-dimensional problems Machine learning integration with PDE solvers Quantum circuit simulation techniques Bayesian inverse problems and error control Key Article Trends : His recent publications focus on merging deep learning architectures (e.g., ResNet, CNNs) with stochastic and tensor-based numerical methods for solving parametric PDEs, Bayesian inversion, and quantum systems. Topics include Hamilton-Jacobi-Bellman equations, Langevin dynamics, and risk-averse optimization under uncertainty.

Lizhe TanView profile

Professor

• Digital Signal Processing
• Control Systems
• Robotics
• +3 more

Lizhe Tan serves as Professor and Department Chair of Electrical and Computer Engineering at Purdue University Northwest. With prior academic roles at Purdue University North Central (now PNW) and DeVry University, his expertise spans digital signal processing, control systems, robotics, and machine learning applications in real-world engineering solutions. His educational background includes: Ph.D. in Electrical Engineering, The University of New Mexico M.S. in Electrical Engineering, The University of New Mexico Professor Tan's research integrates mathematical rigor with practical innovation, focusing on fractional-order control systems , active noise control , and computer vision for robotics . His work bridges theoretical signal processing with machine learning applications, resulting in over 100 publications, a granted US patent, and four textbooks including the 2025 fourth edition of Digital Signal Processing: Fundamentals, Applications and Deep Learning . Recent publications reveal a strong trajectory toward machine learning integration in traditional engineering domains, with 2020-2024 works addressing bearing fault diagnosis via sparse wavelet CNNs, fractional-order PID control improvements, and acoustic echo cancellation using sparsity-aware filters. His 2025 textbook formalizes the deep learning/signal processing convergence. Professor Tan actively mentors students through senior design projects and Master's thesis research, emphasizing hands-on application of signal processing and control theory principles in robotics and measurement systems.