Anders Christian Hansen is a Professor at the University of Oslo and a Lecturer at the University of Cambridge. He holds positions as Royal Society University Fellow and Marie Curie Fellow. His research focuses on Functional Analysis, Artificial Intelligence, and Computational Mathematics, with emphasis on numerical analysis, inverse problems, and medical imaging. Hansen has authored numerous high-impact publications in journals like PNAS and SIAM, addressing challenges in compressed sensing, spectral computations, and AI stability. Education Ph.D., Mathematics, University of Cambridge (2008) M.A., Mathematics, University of California, Berkeley (2005) Siv. Ing., Norwegian University of Science and Technology (2002) Research Interests Develops mathematical frameworks for stable signal recovery Advances compressed sensing theory for real-world applications Explores computational barriers in AI and deep learning Specializes in spectral computations and operator theory Awards Royal Society University Research Fellowship Marie Curie Fellowship Labs/Teams Engaged with computational mathematics and operator algebra research groups at the University of Oslo and University of Cambridge.
Katerina Schindlerova holds the academic rank of Research Professor in the Faculty of Computer Science , affiliated with a research group focused on Data Mining and Machine Learning . Her work emphasizes causal inference, stochastic complexity, and applications in climatology and neural networks. She has been actively involved in research since 1997, with notable contributions to time series analysis and optimization. Education: PhD in Theoretical Computer Science (CSc. title) from Czech Academy of Sciences (1989–1993). MSc cum laude in Mathematics and Theoretical Computer Science (RNDr. title) from Charles University Prague (1983–1988). Research Interests: Causal modeling and variable selection in complex systems. Stochastic complexity and information-theoretic methods. Time series analysis with applications to climatology and finance. Neural networks and optimization algorithms. Recent Projects: Co-led the Learning Synchronization Patterns in Neural Signal project (2021–2024), funded as research funding. This project explores neural signal synchronization using advanced machine learning techniques. Labs/Teams: Active member of the Data Mining and Machine Learning Research Group , collaborating on topics like Hawkes processes and meteorological time series clustering.
Zhikun Hou is a Professor in the Department of Mechanical & Materials Engineering at Worcester Polytechnic Institute (WPI). He holds a MS (1985) and PhD (1990) from the California Institute of Technology. His teaching spans undergraduate courses like Statics, Stress Analysis, and Dynamics, as well as graduate courses such as Mechanical Vibrations and Elasticity Theory. He emphasizes student engagement through challenging coursework, viewing teaching as a control process that values student feedback. Research interests include engineering mechanics, dynamics, structural health monitoring, random vibration, and numerical methods. Notable scholarly work includes studies on sensor/actuator placement in flexible structures (2006), stochastic modeling of disordered processes (1998), and wavelet-based structural health monitoring (2005). His methodologies integrate advanced analytical techniques with practical engineering applications. No professional awards are listed, though his contributions to vibration analysis and finite element methods are highlighted. Advising and grant details are not specified; his lab and team affiliations remain unmentioned.
Georgios Exarchakis is a Lecturer at the University of Bath, specializing in Machine Learning, Theoretical Neuroscience, and Data Science. His research emphasizes transparent and interpretable modeling, with applications in Neuromorphic Engineering, Health Science, and Quantum Chemistry. He has held research positions at IHU Strasbourg, Institut de la Vision, and École Normale Supérieure, and earned his PhD from the University of Oldenburg. Education: Dr. rer. nat. in Machine Learning, 2016, Carl von Ossietzky University of Oldenburg M.Sc. in Computational Science, 2012, Goethe University Frankfurt Diploma in Mathematics, 2008, Aristotle University of Thessaloniki His research interests span Interpretable Machine Learning, Invariant Representations, Sparse Coding, Wavelet Scattering, Probabilistic Models, and Deep Learning . He investigates how models can extract meaningful, stable features from complex data, drawing inspiration from biological systems and theoretical neuroscience. His work often bridges theory and application, particularly in quantum chemistry and neurotechnology. His recent publications highlight a strong focus on efficient and interpretable models , including wavelet scattering for molecular property prediction, discrete sparse coding, and clustering algorithms for event-based vision. The articles show a consistent theme of developing mathematically grounded, invariant, and scalable methods for data analysis. He is the developer or a key contributor to open-source libraries such as Kymatio (Scattering Transforms in Python) and ProSper (Probabilistic Sparse Coding), which facilitate the use of advanced signal processing and learning techniques in the research community. Georgios has taught Machine Learning courses at the University of Strasbourg, École Polytechnique, and Oldenburg. He has collaborated with leading researchers like Stéphane Mallat and Jörg Lücke. His work is published in top-tier venues including CVPR, NIPS, JMLR, and JCP.
Mark Moldwin is the Arthur F. Thurnau Professor of Climate and Space Sciences and Engineering at the University of Michigan’s College of Engineering. He holds affiliations with the Space Physics Research Laboratory, Engineering Education Research program, African Studies Center, Michigan Institute for Plasma Science and Engineering, and Robotics Institute. As Faculty Director of UM’s Office of Postdoctoral Affairs and M-STEM’s M-Engin program, he leads initiatives in postdoctoral development and engineering education. His research focuses on space physics, including magnetospheric dynamics, plasma physics, and magnetometer technology, with over 200 peer-reviewed articles and four patents. Moldwin is a recipient of the NSF CAREER Award, AGU Waldo E. Smith Award, and 2024 STAR Award. He actively promotes DEI in STEM through educational outreach and has advised over 50 Ph.D. students across multiple disciplines. His work extends to planetary science via NASA’s Geospace Dynamics Constellation (GDC) and the HERMES payload on the Lunar Gateway. Education Ph.D. in Astronomy/Space Physics, Boston University (1992) B.A. in Physics with Honors, University of Alaska-Fairbanks (1987) Research Interests Moldwin’s work explores energy transfer from the Sun to Earth’s space environment, including magnetic structures, ULF waves, and plasma distribution in the magnetosphere. He develops advanced magnetometers for space missions (e.g., NASA’s Artemis Lunar Gateway, GDC) and ground-based arrays. His educational initiatives address inclusive mentoring, teacher training, and DEI in STEM, supported by NASA’s Michigan Space Grant Consortium and UM’s Office of Postdoctoral Affairs. Recent efforts include mental health advocacy and reimagining postdoctoral training in Earth and space sciences. Grants & Awards National Science Foundation CAREER Award Research Corporation Cottrell Scholar AGU Waldo E. Smith Award (2016) UM CoE Trudy Huebner Service Excellence Award (2020) Advising & Labs As a mentor, Moldwin has guided over 50 Ph.D. students in Geophysics, Space Physics, and Plasma Physics. He co-founded A2 Motus LLC, a company developing educational tools for kinesthetic learning in complex systems. His research groups collaborate on missions like NASA’s GDC and HERMES, focusing on heliospheric measurements and lunar science. He also chairs UM’s M-STEM program, fostering multidisciplinary engineering education.
Bo Zhang is an Associate Professor in the Department of Geological Sciences at the University of Alabama. He holds a PhD in Geophysics from the University of Oklahoma (2014), an MS from the Institute of Geology and Geophysics, Chinese Academy of Sciences (2009), and a BS from China University of Petroleum (2006). His research focuses on seismic data processing, interpretation, and machine learning applications in geophysics. Key areas include fault attribute estimation, seismic noise reduction, and reservoir characterization through integrated multidiscipline data analysis. He is affiliated with SEG and AAPG. Recent work emphasizes machine learning-driven solutions for seismic challenges, such as horizon extraction, first-arrival picking, and fault detection. His publications span high-impact journals like Geophysics , Interpretation , and IEEE Transactions . Collaborations include projects on shale brittleness evaluation, microseismic event analysis, and 3D seismic attribute integration. Ongoing research explores deep learning applications in groundwater modeling and self-supervised denoising techniques. Bo Zhang’s contributions bridge traditional seismic methods with modern AI tools, advancing exploration geology and resource evaluation. His workflow innovations, such as semi-automated horizon interpretation and VMD-based decomposition, enhance data fidelity and interpretation efficiency.
Dr. Raimund Kirner is a Reader in Cyberphysical Systems at the University of Hertfordshire, UK. His research focuses on embedded computing, parallel computing, and real-time systems, with notable contributions to worst-case execution time (WCET) analysis, compiler design for predictability, and cybersecurity. He leads several projects, including the EU-funded CRAFTERS initiative and the FWF-supported SECCO and FORTAS-rt projects. Kirner holds a PhD from TU Vienna (2003) and a Habilitation (2010). He has authored over 100 publications and holds two patents. His work bridges many-core and embedded systems, emphasizing reliability and predictability. Education: PhD in Computer Science, TU Vienna, 2003 Habilitation, TU Vienna, 2010 Research Interests: Kirner's research spans parallel computing architectures, WCET analysis for real-time systems, compiler optimizations for predictability, and cybersecurity in embedded systems. He explores machine learning applications for medical diagnostics (e.g., EEG-based Alzheimer detection) and quantum cryptography frameworks. His work on mixed-criticality systems aims to enhance reliability in automotive and aerospace domains. Grants and Projects: Principal Investigator (CRAFTERS): EU Artemis-JU project addressing multi-core predictability (2012–2015) Co-Investigator (ADVANCE): FP7 project optimizing parallel programs (2012–2015) FWF-funded projects: SECCO (coverage preservation), FORTAS-rt (WCET measurement), and CoSTA (compiler timing predictability) Awards: Two patents granted for innovations in cybersecurity and embedded systems Labs and Teams: Kirner contributes to the Centre for AI and Robotics Research and the Cybersecurity and Computing Systems Group at the University of Hertfordshire. His collaborations include projects with TU Vienna and industry partners like the Technology Strategy Board. Advising: He has supervised one doctoral student, as documented in his Scopus profile, and oversees interdisciplinary research teams in cyber-physical systems.
Michael Engel is a Researcher at the Chair of Remote Sensing Technology, Technical University of Munich since March 2021. He holds a Master's in Materials Science and Engineering (2020) and a Bachelor's in Engineering Science (2018), both from TU Munich, with a focus on Uncertainty Quantification and Bayesian Inverse Problems. His research interests revolve around optimizing objective functions in inverse problems, leveraging methods like Curriculum Learning and Multi-Task Systems via Multiresolution Analysis. He combines classical numerical techniques with machine learning to address challenges in Bayesian problems and regression. Education: Bachelor's in Engineering Science, TU Munich (2018) Master's in Materials Science and Engineering, TU Munich (2020) Research interests include: - Bayesian Inverse Problems - Machine Learning Integration in Numerical Methods - Optimization of Multi-Task Systems - Uncertainty Quantification in Environmental Models Key projects include the Global Earth Monitor (GEM) , aiming to enhance Copernicus data utilization for large-scale environmental monitoring. His publications span remote sensing optimization, wavelet-based sensitivity analysis, and deforestation assessment using multi-sensor data. He contributes to the TU Munich's Engineering Risk Analysis Group and collaborates with the Chair of Numerical Mathematics and Hydrology. His work bridges physical modeling principles with computational efficiency in earth observation systems.
Hafeez Ullah Amin is a Senior Lecturer in the School of Computer Science at Edge Hill University, UK. Previously, he served as an Assistant Professor at the University of Nottingham Malaysia Campus (2020–2023) and held various research positions at Universiti Teknologi PETRONAS (UTP), including Postdoctoral Researcher and Research Scientist. He holds a PhD in Electrical and Electronic Engineering from UTP, specializing in EEG Signal Processing with Machine Learning. His expertise spans Neuroimaging, Biomedical Signal Processing, and Applied AI/ML in healthcare and education. Education: PhD (Electrical and Electronic Engineering), Universiti Teknologi PETRONAS, 2011–2015 MPhil (Computer Science with AI), Kohat University of Science and Technology, 2006–2009 BSc (Information Technology), Kohat University of Science and Technology, 2001–2005 Research Interests: Neuroimaging, EEG Signal Processing, AI in Mental Healthcare, Data Analytics, and Machine Learning applications in education and business. His work intersects neuroscience, biomedical engineering, and computational methods to address challenges in memory assessment, stress mitigation, and learning efficacy. Publications: Over 50 articles in high-impact journals and conferences, including work on EEG-based neurofeedback, machine learning for healthcare analytics, and 3D educational content efficacy. Recent trends focus on EEG functional connectivity, fraud detection via graph autoencoders, and energy forecasting. Awards: Senior Member, IEEE Fellow (FHEA), Advance HE, UK Advising & Grants: Supervises PhD students in AI/ML applications. Active in research grants involving EEG experiments, patent on LTM assessment via EEG, and collaborations in smart cities and energy systems. Teaching: Leads courses in Data Science, AI, Microprocessor Systems, and Knowledge Representation. Past roles include Module Leader for Programming Principles and Techniques. Labs/Tech Teams: Associated with Edge Hill's Data and Complex Systems Research Centre and the International Centre for Applied Research in Education.
Wael Korani is an Assistant Professor at the University of North Texas, specializing in interdisciplinary research at the intersection of artificial intelligence, neuroscience, and healthcare technology. His work focuses on applying machine learning and optimization algorithms to solve critical challenges in mental health diagnostics, neurology, and medical imaging. He is affiliated with Discovery Park and holds a Ph.D. from the University of Regina, Canada, alongside advanced degrees from Tuskegee University, Cairo University, and Ain Shams University. Research interests include EEG signal analysis for depression and brain disorder prediction, optimization of medical therapy outcomes, and enhancing healthcare IoT security. Notable contributions include developing the TOP-EEG software for therapy prediction and proposing novel algorithms like Lionized Remora Optimization for medical imaging analysis. Korani's recent work emphasizes AI-driven solutions for Alzheimer’s disease pathogenesis, brain tumor detection, and automated seizure activity recognition. His publication trends reflect a strong focus on translational research bridging computational methods with clinical applications. While no formal awards or grants are listed, his active research program involves collaborations across disciplines, with a particular emphasis on optimizing neural networks using nature-inspired algorithms and addressing cybersecurity challenges in healthcare systems. Korani’s work often integrates entropy-based feature extraction, deep learning architectures, and hybrid optimization strategies to tackle complex medical diagnostics problems, demonstrating a commitment to advancing both theoretical and applied aspects of artificial intelligence in healthcare.
Martin Hultman is an Assistant Professor at the Department of Biomedical Engineering (IMT), Division of Biomedical Engineering (MT), Linköping University. His work centers on biomedical optics and microcirculation, with a strong emphasis on developing high-speed, real-time imaging algorithms using FPGA and GPU platforms. Department: Division of Biomedical Engineering (MT) University: Linköping University Academic Rank: Assistant Professor Email: martin.o.hultman@liu.se His research focuses on non-invasive optical techniques for assessing microvascular blood flow, particularly through laser speckle contrast imaging. These methods are applied in clinical contexts such as burn wound assessment and critical limb ischemia. He actively contributes to advancing real-time perfusion imaging with hardware-accelerated processing. The recent publications highlight a consistent trend in developing robust, speed-resolved, and depth-sensitive imaging techniques using multi-exposure laser speckle contrast imaging, wavelet analysis, and machine learning. These studies span applications in skin perfusion, coagulation monitoring, and longitudinal variability in microcirculation, reflecting a strong interdisciplinary focus at the intersection of biomedical engineering, signal processing, and clinical physiology. Scientific recognition includes: Best Research Presentation Award at Medicinteknikdagarna, awarded by the International Federation for Medical and Biological Engineering (IFMBE) and Svensk förening för medicinsk teknik och fysik (MTF) Martin Hultman collaborates closely with senior researchers such as Professor Tomas Strömberg, Senior Associate Professor Marcus Larsson, and Adjunct Associate Professor Ingemar Fredriksson. He is involved in the Biomedical Optics research group, contributing to innovations in optical imaging for clinical diagnostics. While no formal grants are mentioned, his work implies involvement in funded research projects related to real-time imaging and microcirculatory assessment. There is no indication of student advising at this time. He is part of the Biomedical Optics research team within the Division of Biomedical Engineering, which develops technologies for clinical and research applications using light-tissue interactions. The group is actively involved in translating optical methods into practical tools for healthcare, including real-time imaging systems and diagnostic algorithms.
François Chaplais is a faculty member at Mines ParisTech, affiliated with the Centre Automatique et Systèmes (CAS), also known as the Control and Systems Department. He is actively involved in research and teaching, with a focus on wavelets, optimal control, and their applications in aerospace, building thermal systems, and hybrid vehicles. He teaches the course Introduction to Signal Processing at the Master's level. His research interests include: Wavelets and multiresolution analysis Optimal control and time scales Signal processing and filter banks z-transform and convolution operators Nonlinear approximation in 1D and 2D signals Applications in energy and aerospace systems Chaplais has developed extensive educational materials, most notably an online Wavelet Matlab Tutorial , which elaborates on concepts from Stéphane Mallat's seminal book. This tutorial covers signal structures, LTI filters, z-transform, convolution, subsampling, and 2D wavelet applications in image processing such as grayscale and color approximation. He also provides downloadable MATLAB software for wavelet and signal processing applications. His industrial collaborations include work with IFPEN (Institut Français du Pétrole Énergies Nouvelles), indicating applied research in energy systems and control. Although no recent publications or articles are listed in the provided text, his sustained academic output in tutorials and software suggests continued scholarly engagement. There is no mention of formal scientific awards or student advisement in the available information. He maintains an active institutional email and homepage, suggesting ongoing academic affiliation. He leads or contributes to software development efforts, particularly in wavelet-based MATLAB tools, and has contributed to numerical implementations of wavelet transforms, perfect reconstruction filter banks, and edge detection algorithms. His work bridges theoretical signal processing with practical engineering applications.
Espen Alexander Furst Ihlen is an Associate Professor at the Department of Neuromedicine and Human Movement Science (INB), Norwegian University of Science and Technology (NTNU). His research focuses on the application of machine learning and artificial intelligence in clinical movement analysis , with particular emphasis on non-linear time series analyses (Hilbert spectral analyses, wavelet decompositions, multifractal analyses) and fractional stability of movement data. He leads the DeepInMotion project (2021-2025) for explainable AI in infant movement biomarker discovery and contributes to the PROMISE project on early cerebral palsy diagnosis. Bachelor in Human Movement Science (1999-2003) Master in Human Movement Science (2003-2007) PhD in Clinical Medicine (2009-2014) His publications (2025-2009) demonstrate consistent work in infant movement tracking for cerebral palsy prediction, elderly activity classification using wearable sensors, and phase-dependent gait stability research. Recent articles (2025-2024) focus on explainable AI methods and neural architecture search for clinical applications. Earlier works include ski jumping performance analysis and hip fracture gait assessment . Teaching assignments include Biomechanics (BEV2005) , Measurement of Physical Activity (BEV2101) , and Signal Processing in Matlab (BEV3201) . Affiliated with the Bevegelsessenteret (Movement Analysis Center) on clinical movement analysis and software development.
Professor Ahmed Khaled is a leading academic in power electronics and energy systems at the University of Strathclyde, UK, where he serves as Professor and Head of the Power Electronics, Drives, and Energy Conversion (PEDEC) research group within the Department of Electronic and Electrical Engineering, Faculty of Engineering. He holds a PhD from the same university, earned in 2008, and has held academic positions at both the University of Aberdeen and Strathclyde. PhD in Power Electronics, University of Strathclyde (2008) Lecturer in Power Electronics, University of Aberdeen (2011) Promoted to Senior Lecturer (2015) Professor and Research Group Head, University of Strathclyde (current) His research focuses on advanced power electronics for sustainable energy systems. Key areas include HVDC transmission, grid-forming converters, microgrids, offshore wind integration, and electrification of oil and gas platforms. His work contributes significantly to renewable energy integration and smart grid technologies, aligning with UN Sustainable Development Goals in clean energy and industry innovation. The recent publications highlight a strong trend toward modern grid challenges: inertia estimation in low-inertia systems, high-power DC-DC conversion for hydrogen storage and offshore interconnections, and hybrid control strategies. These reflect a focus on stability, efficiency, and scalability in future power systems dominated by inverter-based resources. Scientific recognition includes: Senior Member, IEEE Industrial and Power Electronics Societies Member, Institution of Engineering and Technology (IET) Chartered Engineer, Engineering Council UK Senior Fellow, Higher Education Academy Associate Editor, IEEE Open Journal of Industrial Electronics Society and Elsevier Alexandria Engineering Journal Prof. Ahmed has secured over £6 million in research funding as Principal or Co-Investigator from EPSRC, EU, Royal Society, British Council, Scottish Funding Council, and industry partners including SSE, Rolls-Royce, Iberdrola, and Technip. He has supervised 20 PhD students (12 graduated, 8 ongoing) and leads major projects such as Multi-port DC-DC Converter for All-Electric Ships and Off-grid EV Charging Infrastructure. He also serves on organizing committees for key conferences like IEEE eGrid and IET ACDC. He leads the PEDEC research group and collaborates extensively with industry on real-world applications, including delivering CPD courses for SSE engineers and developing load-bearing cable terminations with Technip. His lab is actively engaged in hardware-in-the-loop testing and control algorithm development for black start and grid-forming operations.
Feng Zhou is an Assistant Professor in the Department of Industrial and Manufacturing Systems Engineering at the University of Michigan-Dearborn , affiliated with the College of Engineering and Computer Science . He leads the Design for Human-Machine Systems (DfHMS) Lab , focusing on integrating human cognition and emotion into engineering systems. Education: Ph.D., Mechanical Engineering, Georgia Institute of Technology Ph.D., Human Factors Engineering, Nanyang Technological University M.S., Computer Engineering, Zhejiang University B.S., Computer Engineering, Ningbo University Research Interests include Human Factors , Physiological Computing , Affective-Cognitive Modeling , and User Experience Design for Autonomous Driving Systems , Healthcare Robotics , and Product Ecosystems . His work bridges Engineering Design with Human Behavior Analysis through Machine Learning and Decision-Theoretic Modeling . Recent Publications highlight trends in Autonomous Vehicle UX , Emotion Recognition via Physiological Data , and Adaptive System Design using Quantum-Inspired Algorithms . Articles focus on Social Network Effects , Fuzzy Reasoning Petri Nets , and Trust Modeling in human-machine systems. Advising and Collaborations : Actively recruiting PhD students from Industrial Engineering , Psychology , and Computer Science to join the DfHMS Lab , which also includes current members Jackie Ayoub and Lilit Avetisyan . Research spans Automated Driving , Healthcare Technology , and Emotion-Driven Design .
