جست‌وجوی استاد و پژوهشگر

Georgios Exarchakisمشاهده پروفایل

مدرس

• Machine Learning
• Theoretical Neuroscience
• Data Science
• +۱۶ مورد دیگر

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 Moldwinمشاهده پروفایل

استاد

• Space Physics
• Magnetospheric Physics
• Ionospheric Physics
• +۵ مورد دیگر

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.

Özlem TÜRKER BAYRAKمشاهده پروفایل

دانشیار

• Statistics
• Time Series Analysis
• Quality Control
• +۵ مورد دیگر

Dr. Özlem TÜRKER BAYRAK is an Associate Professor at Çankaya Üniversitesi, primarily affiliated with the Department of Statistics under the Inter-Curricular Courses unit. She has held academic roles since 1996, including Assistant Professor at the Faculty of Engineering and Visiting Position at University of Waterloo. Her research focuses on statistical methodologies, time series analysis, quality control, environmental acoustics, and energy economics. She has advised 11 graduate students and contributed to projects like the Turkish soundscape evaluation and robust estimation techniques. She teaches courses such as Engineering Statistics and Systems Analysis. Key research areas include non-normal distribution modeling, soundscape perception, and energy market dynamics. Recent work includes studies on urban nightlife impacts and adaptive statistical inference. She has over 40 publications in journals like Applied Acoustics and Quality and Reliability Engineering International.

Bo Zhangمشاهده پروفایل

دانشیار

• Seismic data processing
• Seismic interpretation
• Machine learning
• +۳ مورد دیگر

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.

Michael Engelمشاهده پروفایل

پژوهشگر

• Bayesian Inverse Problems
• Machine Learning
• Optimization Methods
• +۳ مورد دیگر

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 Aminمشاهده پروفایل

مدرس ارشد

• Neuroimaging
• Biomedical Signal Processing
• EEG Signal Processing with Machine Learning
• +۳ مورد دیگر

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.

• Population biology and ecology
• Quantitative Ecology
• Mathematical modeling
• +۵ مورد دیگر

Christelle Suppo is a Professor in the Department of Animal Biology and Genetics at the University of Tours, affiliated with the IRBI (Research Institute on Insect Biology) and the UFR of Sciences and Techniques. She serves as Deputy Director of IRBI and holds a HDR (Habilitation à Diriger des Recherches) in Applied Mathematics. Educational background includes a PhD in Applied Mathematics (1996), a Magistère in Mathematics and Mechanics (1993), and a DEUG in Mathematics and Computer Science (1989). She has been a Professor since 2009 and previously served as Maître de Conférences (1997–2009). Her research focuses on mathematical modeling of invasive species, including spatiotemporal dynamics of pests like the horse-chestnut leaf miner ( Cameraria ohridella ), Asian hornet, and the North American termite. Key themes include population dynamics, control strategies, and climate change impacts. She uses reaction-diffusion models, wavelet analysis, and finite element methods to study invasive species spread and ecological interactions. Recent work highlights the role of human-mediated dispersal in invasive species expansion and demonstrates the importance of spatial considerations in pest control. Collaborations include projects FRELON, TERMICENTRE, and INCA, addressing ecological modeling for real-world management. Supervision includes doctoral theses on topics such as varroosis treatment in bees and modeling the pine processionary moth’s phenology. She teaches quantitative ecology, statistics, and mathematical modeling at undergraduate and graduate levels.

Hélène HALCONRUYمشاهده پروفایل

استادیار

• Probability Theory
• Mathematical Statistics
• Stochastic Analysis
• +۴ مورد دیگر

Hélène Halconruy is an Assistant Professor at Télécom SudParis since September 2023. She previously served as a Lecturer at ESILV (2022–2023), Postdoctoral Researcher at the University of Luxembourg (2020–2022), and held roles as Pedagogical Manager and Lecturer at ESME Sudria (2013–2020). Her doctoral work (PhD 2020) focused on probability under Prof. Laurent Decreusefond at Télécom Paris. Research Interests : Probability theory and mathematical statistics, including stochastic analysis, Malliavin calculus for discrete-time processes, Stein's method applications, financial market models (e.g., Greeks computation, portfolio management), and non-parametric statistical inference (e.g., density estimation under shape constraints, robust methods). Recent Work Trends : Her articles address advanced topics like wavelet leaders distribution, privacy-aware parameter estimation, stochastic process generators, and jump-diffusion models. These contributions bridge theoretical probability with practical applications in finance and data science. Grants & Teams : Affiliated with the SAMOVAR research group at Télécom SudParis. No explicit grants or formal advising roles listed.

Lev Kantorovichمشاهده پروفایل

استاد

• Theoretical Surface Science
• Atomic Force Microscopy (AFM)
• Scanning Tunnelling Microscopy (STM)
• +۷ مورد دیگر

Professor Lev Kantorovich is a distinguished academic at King’s College London’s Department of Physics, affiliated with the Thomas Young Centre and the Centre for Non-Equilibrium Science (CNES). He holds a Professorship since 2009, progressing from a Lecturer role (2002) and Reader (2005). His academic journey includes a PhD from Latvian University (1975), followed by postdoctoral work at Keele University and University College London. His research focuses on theoretical simulations of solid surfaces, atomic-scale imaging via AFM/STM, and self-assembly of molecular systems. Key areas include non-equilibrium statistical mechanics, quantum transport in nanojunctions, graphene growth kinetics, and embedded-cluster methods for electronic structure calculations. He develops advanced computational techniques like non-equilibrium Green’s functions (NEGF) and kinetic Monte Carlo simulations. Recent work explores photon-assisted phenomena in nanojunctions, photoinduced surface reconstructions, and catalytic applications of molybdenum subnanoclusters. His mathematical contributions span transforms (Laplace, Fourier), linear algebra, and partial differential equations, underpinning interdisciplinary modeling. Affiliated with CNES, he collaborates globally on non-equilibrium science. His research addresses energy, materials, and nanotechnology challenges, with applications in molecular electronics and surface engineering.

Wael Koraniمشاهده پروفایل

استادیار

• Artificial Intelligence
• Machine Learning
• Optimization Techniques
• +۳ مورد دیگر

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 Hultmanمشاهده پروفایل

استادیار

• Biomedical Optics
• Microcirculation
• Biomedical Engineering
• +۷ مورد دیگر

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.

• Machine Learning
• Artificial Intelligence
• Clinical Movement Analysis
• +۶ مورد دیگر

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.

Jérôme Henri Kämpfمشاهده پروفایل

مدرس

• Urban Physics
• Modelling, simulation and optimisation
• Building Energy Efficiency
• +۸ مورد دیگر

Jérôme Henri Kämpf is a Lecturer at École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the School of Engineering (STI), Institute of Electrical Engineering (IEM), and the Laboratory of Intelligent Systems (LIDIAP). He also serves as a Senior Research Scientist in Energy Informatics at the Idiap Research Institute. His academic career spans roles at EPFL’s LESO-PB laboratory, including PhD student, post-doc, and team leader in sustainable urban development and urban systems simulation, followed by a professorship in Building Energy Efficiency at HEIA-FR (HES-SO Fribourg) from 2016 to 2018. PhD in On the Modelling and Optimisation of Urban Energy Fluxes, EPFL, 2009 Master in Pedagogics, Haute Ecole Pédagogique Lausanne, 2005 Master in Computer Science, University of Lausanne, 2003 Master in Physics, University of Lausanne, 2001 His research focuses on urban physics, modeling, simulation, and optimization of urban energy systems, daylighting, thermal comfort, and sustainable urban design. He has made significant contributions to urban energy simulation tools such as CitySim, BTDF-based daylight modeling, and real-time photometric systems. His work bridges computer science, physics, and building engineering to develop data-driven solutions for urban sustainability. The 15 most recent publications highlight a consistent focus on urban energy modeling, daylight simulation, and thermal comfort. Key themes include the integration of HDR sky monitoring for daylight control, urban greening for cooling, and parametric interfaces for urban simulation. His research employs advanced computational methods, including evolutionary algorithms and wavelet transforms, applied to building and urban-scale energy performance. He has advised several PhD students, including Chantal Basurto Dávila, Silvia Coccolo, Diane Perez, and Yujie Wu. While no formal scientific awards are listed, his extensive publication record in high-impact journals and conferences reflects strong scholarly recognition. He has been involved in significant research grants and collaborative projects related to urban sustainability, energy informatics, and climate-responsive design. Kämpf is associated with key research teams including the LESO-PB laboratory at EPFL and the Idiap Research Institute. His work in the Urban Systems Simulation team and contributions to the CitySim platform demonstrate leadership in integrating simulation tools into architectural and urban design workflows.

Dimitri Van De Villeمشاهده پروفایل

استاد

• Signal Processing
• Image Processing
• Functional Neuroimaging
• +۹ مورد دیگر

Dimitri Van De Ville is a Full Professor of Bioengineering at École Polytechnique Fédérale de Lausanne (EPFL) and the University of Geneva (UniGE), affiliated with the School of Engineering at EPFL and the Faculty of Medicine at UniGE. He leads the Medical Image Processing Laboratory (MIP:Lab) at Campus Biotech in Geneva and is a key figure at the CIBM Center for Biomedical Imaging. His work bridges signal processing, computational neuroscience, and clinical neuroimaging. Education: M.S. and Ph.D. in Computer Science, Ghent University, Belgium (1998, 2002) Post-doctoral Fellow, Biomedical Imaging Group, EPFL (2002–2005) His research focuses on advancing non-invasive brain imaging through methodological innovations in signal and image processing. He investigates the dynamic and network aspects of brain function using fMRI and EEG, with a special emphasis on dynamic functional connectivity, graph signal processing, and real-time neurofeedback. His work has demonstrated that EEG microstate sequences exhibit scale-free dynamics, linking fast electrophysiological events to slow hemodynamic changes. He pioneered connectivity decoding and contributed to the development of sparsity-based deconvolution methods for fMRI. The recent articles reflect a strong trend toward modeling brain function as a dynamic network process. Key themes include graph signal processing on brain connectomes, decomposition of transient brain activity, and the use of machine learning to decode brain states. His work increasingly integrates structural and functional data to understand brain organization at multiple scales. Scientific Awards: Technical Achievement Award, IEEE EMBS (2024) Fellow, EURASIP (2023) Distinguished Lecturer, IEEE Signal Processing Society (2021–2022) Fellow, IEEE (2020) Leenaards Award (2016) NARSAD Independent Investigator Award (2014) NeuroImage Editors' Choice Award (2013) Pfizer Research Award (2012) Van De Ville has secured substantial research funding through grants such as the SNSF Professorship and has advised numerous researchers. He plays a major role in the scientific community as founding chair of the EURASIP BISA SAT and former chair of the IEEE BISP TC. He has held editorial roles in top journals including IEEE Transactions on Signal Processing , SIAM Journal on Imaging Sciences , and Imaging Neuroscience . He leads the Medical Image Processing Laboratory (MIP:Lab) at Campus Biotech, which specializes in developing advanced signal processing tools for neuroimaging. The lab is part of a broader collaborative ecosystem involving EPFL, UniGE, and the CIBM, fostering interdisciplinary research in biomedical imaging and brain science.

Pier Luigi Dragottiمشاهده پروفایل

استاد

• Signal Processing
• Wavelet Theory
• Sampling Theory
• +۷ مورد دیگر

Prof. Pier Luigi Dragotti is a Professor of Signal Processing in the Department of Electrical and Electronic Engineering at Imperial College London, Faculty of Engineering. He leads the Communications, Signal Processing, and Control (CSP) research group and is actively engaged in both theoretical and applied research in signal processing and computational imaging. His research focuses on wavelet theory, sampling theory, sparse signal processing, computational imaging, and data-driven image processing . He integrates classical signal processing frameworks with modern deep learning techniques, particularly in solving inverse problems and image restoration. His work bridges model-based algorithms with data-driven approaches, enabling breakthroughs in fields such as art conservation and neuroscience imaging. The recent publications highlight a strong trend towards invertible neural networks, physics-informed deep learning, and hybrid model-data methods for image restoration, microscopy, and cultural heritage analysis. His team develops algorithms that are not only accurate but also interpretable and grounded in physical models. Scientific Awards and Recognitions: Editor-in-Chief of IEEE Transactions on Signal Processing (2018–2020), Certificate of Merit Supervised students winning: Eurasip 3M Thesis Competition, IEEE MMSP Best Paper Award, Outstanding PhD Thesis Awards (2020–2022), Ivor Tupper Prize Student Adam won Gold at the Commonwealth Games Advising and Grants: Prof. Dragotti has successfully supervised numerous PhD and MSc students, many of whom have gone on to achieve significant recognition. His group has secured funding that supports interdisciplinary research in imaging science, and several of his former students have launched innovative projects, including a start-up company. He fosters a dynamic research environment with strong industry and cross-institutional collaborations. Labs and Research Groups: He is a key member of the CSP (Communications, Signal Processing, and Control) group at Imperial College London, where he leads a vibrant team working on cutting-edge signal processing challenges. The group is known for its contributions to sampling theory, wavelets, and the integration of deep learning in imaging systems.