Find Academic Supervisors

Caroline FossatiView profile

Professor

• Signal Processing
• Hyperspectral Imaging
• Medical Imaging
• +2 more

Caroline Fossati is a Full Professor at Ecole Centrale de Marseille and a member of the Institut Fresnel - GSM Signal Processing lab. Her work bridges signal processing, hyperspectral imaging, and medical diagnostics, with a focus on Alzheimer's disease detection using PET imaging and tensor-based noise reduction techniques. Current Affiliations: Ecole Centrale de Marseille, Institut Fresnel Past Affiliations: Université Paul Cézanne (1996-1997) Her research spans: Medical Imaging: Developing advanced algorithms for Alzheimer's diagnosis through 18F-FDG PET scans and multi-level feature extraction. Hyperspectral Processing: Innovating tensor-based methods for noise reduction, dimensionality reduction, and target detection in complex imaging environments. Optical Engineering: Investigating CMOS sensor design, photolithography simulations, and defect characterization in optical materials. Publications demonstrate expertise in multidimensional signal processing, array modeling, and biomedical applications. While no specific awards are documented in the provided text, her extensive co-authorship network and technical contributions across disciplines underscore her academic impact.

• Machine Learning
• Image Processing
• Evolution Strategies
• +2 more

Oswin Krause is an Associate Professor at the Department of Computer Science , University of Copenhagen , specializing in Machine Learning . His research focuses on applying machine learning techniques to diverse domains including quantum computing, medical imaging, and astrophysics. Quantum dot array optimization and Coulomb diamond estimation Medical image analysis for clinical applications Evolutionary optimization algorithms and deep learning Neural networks for astronomical data interpretation Recent publications demonstrate a strong emphasis on quantum device calibration (2025), medical outcome prediction (2024), and algorithmic improvements in optimization (2022-2023). While no specific scientific awards are mentioned in the data, his work appears in journals like Physical Review Applied and Medical Image Analysis . Collaborations extend across physics, medicine, and computer science disciplines.

Vikram S. DograView profile

Professor

• Ultrasound
• Medical Imaging
• Genitourinary Radiology
• +3 more

Dr. Vikram S. Dogra is a Professor (Part-Time) of Radiology, Urology, and Biomedical Engineering at the University of Rochester Medical Center , where he serves as Associate Chair of Education and Research and Director of the Division of Ultrasound . He is also an Affiliate Professor at the Chester F. Carlson Center for Imaging Science, Rochester Institute of Technology . With expertise in ultrasound, MRI, CT, and genitourinary radiology, Dr. Dogra has established a global reputation through extensive research, teaching, and clinical innovation. Dr. Dogra’s research focuses on photoacoustic imaging , ultrasound elastography , and genitourinary diagnostics . His work has led to three patents for low-cost imaging devices and over 100 peer-reviewed publications . He is a prolific contributor to radiology literature, having authored eight books and 34 book chapters , and serves as Editor-in-Chief of the Journal of Clinical Imaging Science . His recent publications highlight advancements in multispectral imaging for thyroid and prostate cancer, pelvic venous disorders , and vascular elastography . These studies span ultrasound , photoacoustic technology , and diagnostic accuracy across diverse pathologies, reflecting his commitment to translational imaging research. Scientific Awards : Fellow, 2020 Honorary Membership of Indian Federation of Ultrasound in Medicine and Biology, 2017 Shu-Ren-Lin Award for Excellence in Teaching, 2008 RSNA Trainee Research Prize, 2006 Teacher of the Year Award, 2001-2002 Dr. Dogra leads the Medical Imaging Partnership , a nonprofit providing imaging expertise and equipment to developing countries. He has served on editorial boards, delivered over 200 invited lectures, and contributed to global radiology standards via organizations like the Radiological Society of North America and American Institute of Ultrasound in Medicine .

Alejandro AcevesView profile

Professor

• Nonlinear Wave Phenomena
• Dynamical Systems
• Nonlinear Optics
• +6 more

Alejandro Aceves is a Professor at Southern Methodist University specializing in nonlinear wave phenomena and dynamical systems with applications in nonlinear optics, photonics, and climate modeling. He holds a Ph.D. from the University of Arizona (1988) and maintains an affiliate position with the Theoretical Division at Los Alamos National Laboratory. His research integrates advanced applied mathematics techniques with numerical simulations to investigate complex physical phenomena across multiple scientific domains. Professor Aceves' research spans several interconnected fields with nonlinear optics as the central theme. His primary focus involves mathematical modeling of pulse dynamics in nonlinear optical media, including ultraviolet light filament formation in the atmosphere and optical pulse propagation in novel photonic structures. In recent years, he has expanded his research to include climate modeling related to the Atlantic Meridional Overturning Circulation (AMOC) and epidemiological studies of covid-19 and malaria spread in Africa through collaborations with African scientists. His methodological approach combines rigorous mathematical analysis with computational techniques to address both fundamental theoretical questions and practical applications. The publication record shows a consistent trajectory of research in nonlinear wave phenomena with increasing interdisciplinary applications. Recent work demonstrates strong connections between fundamental mathematical investigations of nonlinear wave equations and practical implementations in optical technologies, climate science, and epidemiology. The research output reveals a pattern of expanding from core optical phenomena to broader applications while maintaining mathematical rigor. Professor Aceves has earned significant recognition in his field: Fellow of the Society for Industrial and Applied Mathematics (SIAM) Fellow of the Optical Society of America (OSA) Principal Investigator of an NSF-funded Research Training Group (RTG) program supporting graduate education As an academic mentor, Professor Aceves supervises PhD students and postdoctoral fellows while maintaining extensive national and international collaborations. His research partnerships include the Air Force Research Laboratory, University of New Mexico's Department of Physics and Astronomy, Nokia Bell Labs, and research teams across Italy, the UK, and France. The NSF RTG program he leads provides structured training for graduate students in applied mathematics with connections to real-world problems. Professor Aceves' research ecosystem includes strong ties to the Theoretical Division at Los Alamos National Laboratory as an affiliate member. His work frequently involves interdisciplinary teams that bridge mathematics, physics, engineering, and environmental science to tackle complex problems at the intersection of theoretical foundations and practical applications in optics, climate dynamics, and public health.

• Ocean Acoustics
• Signal Processing
• Underwater Communications
• +2 more

Zoi-Heleni Michalopoulou is Professor in Mathematical Sciences at New Jersey Institute of Technology, specializing in ocean acoustics and signal processing. Her research develops advanced algorithms for underwater signal detection, geoacoustic inversion, and ocean parameter estimation. Current projects include Navy-sponsored investigations into shallow water inversion using machine learning and optimization methods. Research integrates Gaussian processes, genetic algorithms, and Bayesian methods for solving inverse problems in uncertain ocean environments. Recent publications focus on multichannel signal detection in distorted underwater channels, direction-of-arrival estimation, and particle filter optimization. Maintains active collaborations with Navy research divisions. Publications demonstrate consistent methodological innovation in statistical signal processing, with applications to underwater sensor networks and acoustic characterization. Currently leads NSF-funded nanotechnology education initiatives.

Dryver HustonView profile

Professor

• Smart Cities and Structures
• Structural Health Monitoring
• Subsurface Infrastructure Sensing
• +7 more

Dr. Dryver Huston is a Professor in the Department of Mechanical Engineering at the University of Vermont (UVM). He has been affiliated with UVM since 1987 and received the Kroepsch-Maurice Excellence in Teaching Award (2012). His academic background includes a Ph.D. (1986) and M.A. (1982) from Princeton University, and a B.S. (1980) from the University of Pennsylvania. Dr. Huston’s research focuses on Smart Cities and Structures , Structural Health Monitoring , Subsurface Infrastructure Sensing , and Electromechanical Systems . His work integrates advanced technologies like ground-penetrating radar (GPR) , robotics , augmented reality (AR) , and IoT sensors to address challenges in civil infrastructure resilience, environmental monitoring, and materials science. Notable projects include the Hydraulic Inspection Vehicle Explorer (HIVE) for culvert assessment and Chitosan-based materials for concrete durability enhancement. Selected Awards: Kroepsch-Maurice Excellence in Teaching Award (2012) Research & Technical Contributions: Developed cognitive GPR systems for subsurface object detection using deep reinforcement learning. Advanced orbital angular momentum (OAM) radar and phased array technologies for improved subterranean sensing. Pioneered mechanomagnetic telemetry for monitoring underground water infrastructure. His publications span IEEE Transactions , Journal of Materials in Civil Engineering , and Transportation Research Record , with a focus on sensor integration, infrastructure assessment, and environmental sustainability. Labs & Teams: His research involves collaborations with UVM’s College of Engineering and partnerships with agencies like the National Science Foundation (NSF), emphasizing interdisciplinary problem-solving in civil and mechanical engineering domains.

• Antenna Design
• Radio Astronomy
• Electromagnetics
• +3 more

David Prinsloo is an Assistant Professor in the Department of Electrical Engineering at Eindhoven University of Technology. His research focuses on antenna design, electromagnetic systems, and space-based instrumentation, particularly for lunar and radio astronomy applications. Key collaborations include projects like the Dark Ages Explorer (DEX) and the Netherlands-China Low-Frequency Explorer (NCLE). Research interests include phased array technologies, linear array synthesis, and sparse array configurations. He actively contributes to lunar-based detection systems for cosmic signals and low-frequency radio observations. His work bridges theoretical antenna design with practical implementation in extreme environments. Publications highlight advancements in array optimization, matrix pencil algorithms, and lunar instrument architecture. While no formal awards are listed, his contributions to lunar far side radio interferometry and collaborative international projects demonstrate significant impact. He teaches the course 'Phased array and smart antennas' since 2015, emphasizing practical engineering applications.

• Computer Architecture
• Memory Systems
• Reconfigurable Computing
• +6 more

Ronald Dreslinski is a Professor in the Department of Computer Science and Engineering at the University of Michigan, College of Engineering. His work focuses on computer architecture, memory systems, and reconfigurable computing, with applications in machine learning, wireless communication, and autonomous systems. Research spans hybrid memory systems, fault-tolerant networks, and low-power processors Key technologies include PIM (Processing-in-Memory), systolic arrays, and Galois field accelerators His publications highlight trends in adapting reconfigurable architectures for sparse data, optimizing wireless communication decoders, and integrating neural networks with traditional circuit design. He mentors PhD students through weekly 1-on-1 meetings, project-specific sessions, and group presentations. The department emphasizes broadening participation in computing and student support through community initiatives.

Liam PaninskiView profile

Professor

• Neuroscience
• Statistics
• Computational Neuroscience
• +3 more

Liam Paninski is a Professor of Statistics and Neuroscience at Columbia University, co-director of the Grossman Center for the Statistics of Mind, and a member of the Center for Theoretical Neuroscience, Kavli Institute for Brain Science, and Doctoral Program in Neurobiology and Behavior. His research focuses on statistical analysis of neural data, computational neuroscience, and developing advanced methods for analyzing large-scale neural recordings. His work spans topics such as spike sorting, calcium imaging analysis, behavioral video analysis, and decoding methods. He has received multiple grants and awards, including NSF/NIH funding, Simons Collaboration grants, and fellowships like the Alfred P. Sloan Research Fellowship. His lab collaborates on projects involving neural data science, aiming to bridge experimental neurotechnology with analytical methods. Paninski teaches advanced courses on statistical analysis of neural data and computational statistics, emphasizing applications in neuroscience. His research integrates statistical theory with practical challenges in neuroscience, addressing questions in neural coding, network connectivity, and high-dimensional data analysis.

Zhiyuan YanView profile

Professor

• Coding Theory
• Wireless Communications
• VLSI Design
• +4 more

Zhiyuan Yan is a Professor and Associate Chair in the Department of Electrical and Computer Engineering at Lehigh University. He holds a B.E. from Tsinghua University and M.S./Ph.D. degrees from the University of Illinois at Urbana-Champaign. His research focuses on coding theory, VLSI design, wireless communications, and embedded systems. He is a Senior Member of the IEEE and received the NSF CAREER Award in 2011. Educational Background: B.E., Electrical Engineering, Tsinghua University M.S., Electrical Engineering, University of Illinois at Urbana-Champaign Ph.D., Electrical Engineering, University of Illinois at Urbana-Champaign Research Interests: His work spans coding theory (including polar codes and LDPC codes), VLSI implementations for communication systems, and cybersecurity through physical unclonable functions (PUF). He explores neuromorphic computing, memristive neural networks, and hardware-efficient error correction techniques. His contributions include efficient decoder architectures and distributed storage solutions. Scientific Awards: 2011: NSF CAREER Award Advising & Grants: While specific student names aren't listed, his research has been supported by multiple grants. His lab focuses on collaborative projects between academia and industry, emphasizing practical implementations of theoretical advancements. Lab & Teams: His research team develops cutting-edge VLSI systems, neural network accelerators, and secure communication protocols. Collaborations include work on memristive hardware and neuromorphic computing architectures.

Charles SOUSSENView profile

Professor

• Inverse Problems
• Sparse Approximation
• Signal Processing
• +3 more

Charles SOUSSEN is a Professor at CentraleSupélec and L2S (Laboratoire des Signaux et Systèmes). He holds a PhD from Université Paris-Sud (2000) and a Habilitation from Université de Lorraine (2013). His research focuses on inverse problems, sparse approximation, signal processing, and biomedical applications. He leads the Signals and Statistics Pole at L2S since 2020 and has coordinated the French ANR project BECOSE (2016–2020). His work spans areas like hyperspectral imaging, optical spectroscopy, and algorithm development for sparse representations. Education : Ecole Nationale Supérieure en Informatique et Mathématiques Appliquées (ENSI-MAG), Grenoble (1996) PhD in Signal and Image Processing, L2S, Université Paris-Sud (2000) Habilitation à Diriger des Recherches, Université de Lorraine (2013) Research Interests : His primary research areas include inverse problems, sparse approximation, signal processing algorithms, biomedical imaging, and hyperspectral data analysis. He develops methods for signal deconvolution, source separation, and modal analysis with applications in astronomy, geology, and medical diagnostics. Grants & Projects : ANR BECOSE (2016–2020): Sparse approximation algorithms for ill-conditioned inverse problems Labs & Teams : He is affiliated with L2S and leads the Signals and Statistics research group. His work involves collaborations in biomedical optics, hyperspectral analysis, and algorithm development for engineering and medical applications.

• Wireless Communications
• Integrated Sensing and Communications (ISAC)
• Millimeter-wave Systems
• +7 more

Kai Wu is an Associate Professor at Xidian University's School of Electronic Engineering, working within the MOE Key Laboratory of Intelligent Perception and Image Understanding in Xi'an, China. His research spans wireless communications, integrated sensing and communications (ISAC), and signal processing, with a particular focus on millimeter-wave systems, radar signal processing, and MIMO technologies. He maintains strong collaborative ties with international researchers, particularly with J. Andrew Zhang, Y. Jay Guo, and Xiaojing Huang from institutions including University of Technology Sydney. Dr. Wu's research interests center on the integration of communication and sensing technologies, with significant contributions to frequency-hopping MIMO radar-based communications, angle-of-arrival estimation, and RIS-assisted ISAC systems. His work addresses critical challenges in wireless communications including spectrum efficiency, channel modeling in complex environments, and the development of energy-efficient communication systems. Recent research has expanded into transformer-based deep learning approaches for signal processing applications and the application of magnetic particle spectroscopy in biomedical contexts. Analysis of Dr. Wu's publication record reveals a strong focus on practical implementations of theoretical concepts, with numerous experimental validations of proposed communication and sensing techniques. His work demonstrates a clear progression from fundamental signal processing techniques to integrated communication-sensing systems, with increasing emphasis on RIS-assisted technologies and machine learning applications in recent years. The interdisciplinary nature of his research spans telecommunications engineering, signal processing, and biomedical applications. Dr. Wu has established himself as a leading researcher in the field of integrated sensing and communications, with over 40 publications in top-tier IEEE journals and conferences. His collaborative network includes prominent researchers in wireless communications, and he serves as a frequent reviewer for major IEEE publications in his field. While specific grant information isn't detailed in the available records, his extensive publication output suggests successful funding from national research programs in China.

• Wireless Communications
• MIMO Systems
• Signal Processing
• +5 more

Jihoon Choi is a Professor in the Department of Electrical Engineering at Korea University's College of Engineering. With an extensive publication record spanning over two decades from 2000 to 2025, he has established himself as a leading researcher in wireless communications and signal processing. His work primarily focuses on MIMO systems, space-time coding techniques, and channel estimation methodologies. Choi's research interests center around advanced wireless communication techniques, particularly in next-generation wireless systems. His work spans multiple specialized areas including Space-Time Line Coding, mmWave communications, physical layer security, and intelligent reflecting surface technologies. He has made significant contributions to the development of novel precoding and combining strategies for multiuser MIMO systems, with special emphasis on rate balancing and interference management techniques. Analysis of his recent publications (2021-2025) reveals a strong research trajectory focused on space-time line coding applications across diverse scenarios including UAV systems, SAR imaging, and physical layer security. His work demonstrates a consistent pattern of innovation in wireless communications, with increasing emphasis on practical implementations for 5G/6G systems. The publications show a clear evolution from fundamental signal processing techniques to more complex system-level implementations addressing real-world challenges in wireless communications. Throughout his career, Professor Choi has maintained a highly productive research output with over 70 publications in prestigious IEEE journals and conferences. His work demonstrates strong collaboration patterns, particularly with researchers like Jingon Joung, Wonjun Lee, and Yong Hoon Lee, suggesting active participation in research groups or laboratories focused on wireless communications. His recent publications indicate ongoing research activity with multiple 2025 publications currently in process.

Zoran CvetkovicView profile

Professor

• Digital Signal Processing
• Wavelets
• Time-Frequency Analysis
• +9 more

Professor Zoran Cvetkovic is currently based at the Faculty of Natural, Mathematical & Engineering Sciences at King's College London , where he serves as Professor of Digital Signal Processing and Deputy Head of Department (Research) . He is affiliated with the Centre for Telecommunications Research , Engineering Security Hub , and Music Computing Lab . Education: PhD (University of California, Berkeley, 1995), MPhil (Columbia University, 1998), MSc (University of Belgrade, 1992), BSc (University of Belgrade, 1989) Previous Positions: Principal Member of Technical Staff at AT&T Labs (1997-2022), Research Fellow at Harvard University (2002-2003), Research Associate at EPFL (1996-1997) and UCSF (1994), Research/Teaching Assistant at University of Belgrade (1989-1992) Professor Cvetkovic's research spans signal processing theory , wavelets , time-frequency analysis , and their applications in neurophysiology , audio/spoken language technology , and biomedical systems . His recent work focuses on immersive audio technology , real-time acoustic simulation , superdirective beamforming , and multimodal speech recognition . With over 146 research outputs, his publications demonstrate expertise in machine learning for speech processing , acoustic environments , and information theory . His academic activities include organizing IEEE conferences , editorial roles at IEEE Transactions on Signal Processing , and collaborations with institutions like Stanford University , University of California, Berkeley , and SRI International . He supervises 7 research projects totaling over £3.5 million in funding from EPSRC , Leverhulme Trust , and Royal Academy of Engineering . His work contributes to UN Sustainable Development Goals related to education and technological innovation .

Jemma BrownView profile

Researcher

• Medical Imaging
• Ultrasound Imaging
• Super-Resolution Techniques
• +3 more

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.