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Brett WickView profile

Professor

• Complex Analysis
• Harmonic Analysis
• Operator Theory
• +3 more

Brett D. Wick is a Professor of Mathematics at Washington University in St. Louis, specializing in analysis with a focus on complex analysis, harmonic analysis, and operator theory. He holds a PhD from Brown University and has been recognized with prestigious awards including the NSF CAREER Award and the Alexander von Humboldt Fellowship. His research explores interactions between these fields, particularly extending results from complex and harmonic analysis to higher dimensions and addressing the Corona Problem in multiple variables. Education: PhD in Mathematics from Brown University. Research interests include several complex variables, operator theory, and multi-parameter harmonic analysis. He has contributed to studies on Riesz transforms, paraproducts, and weighted inequalities, with applications to quasiregular maps and functional spaces. Wick organizes conferences and collaborates internationally, appearing at events like the International Workshop on Operator Theory (IWOTA) and the Banff International Research Station. Scientific Awards: Fellow of the American Mathematical Society, Alexander von Humboldt Fellow, NSF CAREER Award, Jerrold E. Marsden Postdoctoral Fellow. Grants and Funding: His work is supported by the National Science Foundation. Collaborations involve researchers globally, focusing on topics like Schatten classes, commutators, and function spaces. Labs/Teams: Active in the Washington University Analysis group and collaborates with international teams on operator theory and harmonic analysis projects.

Mladen Victor WickerhauserView profile

Professor

• Harmonic Analysis
• Wavelets
• Numerical Algorithms
• +4 more

Mladen Victor Wickerhauser is a Professor of Mathematics and Biomedical Engineering at Washington University in St. Louis. He holds a B.S. from the California Institute of Technology and M.S./Ph.D. from Yale University. His research focuses on Harmonic Analysis, Wavelets, Numerical Algorithms, and their applications in Biomedical Engineering, Medical Imaging, and Financial Mathematics. He has over 1,000 publications, six U.S. patents—including a FBI fingerprint compression algorithm—and received the 2002 Wavelet Pioneer Award from SPIE. Education: B.S. with Honor in Mathematics, California Institute of Technology (1980) M.S. and Ph.D. in Mathematics, Yale University (1982, 1985) Research Highlights: Dr. Wickerhauser’s work bridges mathematical theory and practical applications. His contributions include entropy-based signal processing for medical imaging, wavelet algorithms for data compression, and financial mathematics textbooks. His patents cover medical treatment planning and signal enhancement techniques. Awards & Grants: Wavelet Pioneer Award (SPIE, 2002) NSF grants for computational harmonic analysis and wavelet algorithms Students & Collaborations: He has advised PhD students like David Meyer and Wojciech Czaja. Collaborators include experts in biomedical engineering, signal processing, and medical physics. His lab focuses on molecular imaging and entropy-based diagnostics.

• Radio Astronomy
• Medical Imaging
• Compressed Sensing
• +3 more

Yves Wiaux is a Professor at Heriot-Watt University's School of Engineering & Physical Sciences, affiliated with the Institute of Sensors, Signals & Systems. He specializes in advanced signal processing techniques with applications in radio astronomy and medical imaging. His research focuses on compressed sensing, optimization algorithms, and imaging methods for radio interferometry and MRI. His work integrates mathematical frameworks like wavelet transforms and sparse regularization to address challenges in high-dimensional data reconstruction. Notable contributions include the development of algorithms like SARA (Sparsity Averaging Reweighted Analysis) and PURIFY for radio interferometric imaging, as well as applications in medical imaging such as magnetic resonance fingerprinting. Wiaux collaborates extensively in international projects, including the LOFAR Two Meter Sky Survey. He advises PhD students through the BASP (Biomedical and Astronomical Signal Processing) group and leads research initiatives in scalable imaging techniques for large datasets.

Prashan PremaratneView profile

Senior Lecturer

• Computer Vision
• Pattern Recognition
• Image Processing
• +4 more

Prashan Premaratne is a Senior Lecturer at the University of Wollongong's School of Electrical, Computer and Telecommunications Engineering, where he has been affiliated since 2003. His academic qualifications include a Doctor of Philosophy in Image/Signal Processing from the National University of Singapore (1999-2001), a Bachelor of Engineering in Optical Communications and Signal Processing from the University of Melbourne (1994-1997), and a Graduate Certificate in Higher Education from the University of Wollongong (2008-2009). His research focuses on: Computer vision systems for gesture recognition and object detection Advanced image/video processing techniques including steganography Artificial intelligence applications in speech processing and sensor networks Energy system optimization through electromagnetic modeling His publications demonstrate strong focus on computer vision (45%), signal processing (30%), and AI applications (25%), with recent work emphasizing real-time systems optimization and deep learning architectures. Award recognition includes: Vice Chancellor's Award for Teaching Excellence (2020) IEEE Senior Member recognition (2008) Singapore Science Award (1999) He actively supervises graduate researchers in computer vision and AI, with 11 completed PhD/MPhil students. Grant funding includes internal university awards for robotics education and face detection technology. He serves as program chair for major AI conferences and reviews for national research councils.

• Financial Risk Management
• Climate Science and Modeling
• Actuarial Science
• +5 more

Samuel Gyamerah is a Postdoctoral Fellow at the University of Waterloo under the supervision of Ruodu Wang and Alex Schied, active from September 2023 to 2025. His research focuses on interdisciplinary applications of quantitative methods in finance, climate science, and actuarial science. Key areas include stochastic modeling of weather derivatives, systemic risk analysis in banking networks, and machine learning-driven predictive analytics in healthcare and agriculture. Education: Samuel holds prior academic affiliations with Kwame Nkrumah University of Science and Technology, as indicated by his 2014 research contributions. His work bridges theoretical advancements with practical solutions, such as optimizing crop yield forecasts and mitigating financial risks through innovative modeling techniques. Research Trends: His publications highlight a strong emphasis on climate-related financial instruments, geopolitical risk transmission, and the integration of advanced statistical methods (e.g., wavelet coherence, hybrid machine learning models) into diverse fields. Notable contributions include frameworks for longevity bond pricing under geopolitical volatility and state-space models for long-term bond valuation. Advising & Grants: While no formal advisees are listed, his postdoctoral work suggests active collaboration in grant-funded projects exploring systemic risk, green bonds, and agricultural resilience. His research has been applied to real-world challenges like Ghana’s National Health Insurance Scheme and SME digital transformation strategies. Labs/Teams: Engaged in collaborative projects at the intersection of finance, climate science, and data science, likely contributing to interdisciplinary research groups at the University of Waterloo.

• Mathematical Analysis
• Complex Functions
• Signal Processing
• +3 more

Dr. Abdelbaset Zeyani is an Assistant Teaching Professor at Wichita State University's Fairmount College of Liberal Arts and Sciences, affiliated with the Department of Mathematics and Statistics. He specializes in mathematical analysis and signal processing, with a focus on polynomial-based function theory and advanced signal separation techniques. His research interests span: Mathematical Analysis Complex Functions Signal Processing Polynomials Functional Analysis Applied Mathematics Dr. Zeyani's publications examine bi-univalent functions through Gegenbauer and Lucas-balancing polynomials, addressing coefficient estimation and Fekete-Szegö functionals. His work also explores signal processing innovations like synchrosqueezing transforms and wavelet analysis for instantaneous frequency estimation and signal separation.

Sugata BanerjiView profile

Associate Professor

• Computer Vision
• Scene Understanding
• Machine Learning
• +3 more

Sugata Banerji is an Associate Professor of Computer Science at Lake Forest College and Director of the Applied Data Center. His research focuses on computer vision, scene understanding, and machine learning, with applications in medical imaging and image retrieval. He holds a PhD in Computer Science from New Jersey Institute of Technology (2013) and a Postdoctoral fellowship in Computer Vision at George Mason University (2013–present). His educational background also includes a BE in Information Technology from West Bengal University of Technology. Research Interests include image processing, pattern recognition, and novel descriptors for object and scene classification. His work spans deep learning for medical diagnostics, efficient feature extraction methods like EFM-HOG, and geo-localization of buildings using computer vision. He has received the Department of Computer Science Travel Award (2012) and served as a Teaching Assistant (2008–2013). Key contributions include advancements in HOG descriptor variants (e.g., HaarHOG), color-based feature integration, and solar energy growth modeling. He teaches courses such as Roadmap to Computing with Python and Introduction to Computer Science, emphasizing practical programming and graphics problem-solving.

Sixin ZhangView profile

Associate Professor

• Machine Learning
• Optimization
• Signal Processing
• +7 more

Sixin Zhang is an Associate Professor at INP-Toulouse, specifically affiliated with the ENSEEIHT school. He holds a Ph.D. from the Courant Institute of Mathematical Sciences, New York University (2012), following undergraduate studies. His postdoctoral research spanned institutions including ENS Paris, Peking University, and IRIT in France. His research focuses on machine learning, optimization, signal/image processing, statistics, dynamical systems, and high-dimensional data analysis. Key research areas include representation learning for recognition and inverse problems, optimization algorithms, and applications in industrial systems and astrophysics. He contributed to the development of the Kymatio library for scattering transforms and co-authored foundational work on transform learning in non-negative matrix factorization. His work bridges theoretical mathematics with applied machine learning, including contributions to generative models, data assimilation networks, and optimization techniques for neural networks. Notable projects include the MeteoNet AI challenge and the RWST statistical framework for interstellar medium analysis.

• Statistical and Machine Learning
• Applied and Computational Harmonic Analysis
• Financial Mathematics
• +3 more

Frederick Warner, PhD, is an Associate Research Scientist at Yale School of Medicine and a member of the Center for Outcomes Research and Evaluation (CORE). His research focuses on statistical and machine learning, computational harmonic analysis, and financial mathematics. He holds a PhD in Mathematics from Yale University (1992). Previously, he worked at Plain Sight Systems and served as a Senior Research Scientist and Lecturer in Yale’s Department of Mathematics. Dr. Warner’s research interests span interdisciplinary domains, including applications of advanced mathematical techniques to healthcare challenges. Notable collaborations include work with Professors Ronald Coifman (Mathematics), Akiko Iwasaki (Immunobiology), and Harlan Krumholz (Cardiology). His recent studies address Long COVID’s clinical manifestations, cardiovascular health disparities, and vaccine efficacy against SARS-CoV-2 variants. He also contributes to methodological advancements in hyperspectral data analysis and health informatics. Key affiliations include the Cardiovascular Medicine Department and the Yale LISTEN Research Cohort. His work bridges mathematical rigor with clinical and public health applications, emphasizing translational outcomes and data-driven solutions.

Katrina NevilleView profile

Senior Lecturer

• Electrical and Electronic Engineering
• Communications Technologies
• Artificial Intelligence
• +3 more

Dr. Katrina Neville is a Senior Lecturer in the School of Engineering at RMIT University, Australia. Her research focuses on interdisciplinary areas including electrical engineering, communications, artificial intelligence, image processing, and educational technology. She actively supervises research projects in robotics, stroke rehabilitation, and signal processing. Her work bridges theoretical advancements with practical applications in healthcare technology and manufacturing systems. Research Interests: Electrical/Electronic Engineering, Communications Technologies, AI & Image Processing, and Education Technology. Teaching expertise spans signal processing, speech processing, and communication systems. She has published extensively in journals like IEEE Transactions and conferences such as SigTelCom and ICCE. Supervision Projects: Includes topics like collaborative robotics programming, post-stroke motor function recovery via VR/motor imagery, and wind noise reduction mechanisms. She has advised projects ranging from neural network applications in image recognition (2015) to advanced OFDM systems (2008). Labs/Teams: While specific lab affiliations aren’t listed, her research collaborations involve interdisciplinary teams working on biomedical informatics, robotics, and educational technology innovations.

Golnoush AbaeiView profile

Lecturer

• Software Engineering
• Machine Learning
• Artificial Intelligence
• +5 more

Dr. Golnoush Abaei is a Lecturer in Industry Practice at RMIT University's School of Computing Technologies. She holds dual roles as Professional Practice Lead (since 2022) and Program Manager for the Bachelor of Software Engineering (since 2023). Her expertise bridges academia and industry, focusing on software engineering, machine learning, and data analytics. She coordinates the Software Testing Course and supervises capstone projects for software engineering undergraduates and IT postgraduates. Research interests include software quality, INS/GPS navigation systems, health-related recognition applications, and blockchain. Notable projects involve AI-powered digital lecturer twins for education and machine learning-driven visual defect detection in software interfaces. Teaching methodologies emphasize collaborative active learning (CAL), problem-based learning (PBL), and curriculum design innovation. She actively supports student internships and graduate job placements through industry partnerships. Over 25 peer-reviewed articles span topics like anomaly detection in IoT networks, medical image processing, and hybrid optimization algorithms. Key Projects: Machine Learning for Testing, AI in Education, BDD Test Automation Grants/Industry Collaboration: Multiple industry-sourced capstone projects Curriculum Development: PBL integration, CAL frameworks, and active learning strategies

Nicolas MenicucciView profile

Professor

• Quantum Information Theory
• Quantum Computation
• Quantum Optics
• +3 more

Professor Nicolas Menicucci is a Professor of Theoretical Physics at RMIT University's School of Science, located at the City Campus in Australia. His research focuses on quantum information theory, quantum computation, quantum optics, and relativistic quantum information, with affiliations to the QuRMIT Theory Lab and as a Chief Investigator in the Centre for Quantum Computation and Communication Technology (CQC2T). Education: PhD in Theoretical Physics from Princeton University (2008), BS in Physics from the University of New Mexico (2002). Positions: Held roles at Perimeter Institute for Theoretical Physics (2008–2011), University of Sydney (2011–2015), and RMIT since 2015, where he became a Professor in 2022. His research interests span quantum computing architectures, quantum error correction, and foundational aspects of quantum mechanics. Notable contributions include pioneering work on continuous-variable cluster states for optical quantum computing and exploring relativistic effects in quantum information. He has published over 150 articles and secured prestigious awards like the 2012 ARC DECRA and the 2015 RMIT Vice-Chancellor’s Senior Research Fellowship. Recent projects include developing fault-tolerant quantum computing protocols, studying entanglement harvesting in relativistic settings, and advancing quantum sensing techniques. His work bridges theoretical insights with experimental feasibility, emphasizing scalable quantum technologies. Key Awards: 2015 Vice-Chancellor Senior Research Fellowship 2012 Australian Research Council DECRA Advising and Grants: Supervises research on topics like machine learning-based quantum control and photonic quantum computation. Manages projects on quantum systems identification, error correction, and hybrid quantum processors. He leads the QuRMIT Theory Lab and collaborates internationally on quantum technologies. His work aligns with UN Sustainable Development Goals 4 (Quality Education) and 9 (Industry, Innovation, and Infrastructure).

Julio Enrique Castrillon-CandasView profile

Academic

• Uncertainty Quantification
• Partial Differential Equations
• Integral Equations
• +5 more

Julio Enrique Castrillon-Candas is a Research Assistant Professor at Boston University's Department of Mathematics and Statistics within the College of Arts & Sciences. He is a prominent member of the Probability and Statistics research group, focusing on computational mathematics and uncertainty quantification. His academic journey includes a Ph.D. in electrical engineering and computer science from MIT. His research spans several interconnected fields including Uncertainty Quantification, Partial Differential Equations, Integral Equations, Fast Multi-Level Kriging for large spatial datasets, Fast Radial Basis Function Interpolation, and Machine Learning applications. His work bridges theoretical mathematics with practical computational approaches for solving complex problems in science and engineering. Analysis of his recent publications reveals a strong focus on developing efficient computational methods for uncertainty quantification, particularly through multi-level approaches to spatial statistics and stochastic collocation methods for PDEs with random domains. His research demonstrates a consistent progression from foundational mathematical theory to practical implementations for large-scale problems. Dr. Castrillon-Candas has secured significant funding including an NIH grant titled 'Stochastic Dynamic Modeling of Cellular Protein Interactions' (Award Number: 1R01GM131409-01, $323,280) and an NSF/DOE AMPS grant on 'Uncertainty Quantification for Stochastic Analysis of Electrical Power Networks' (Award Number: 1736392, $229,279). He maintains an active research collaboration network with prominent academics including Raul Tempone (KAUST), Fabio Nobile (EPFL), Marc G. Genton (KAUST), and Rio Yokota (Tokyo Institute of Technology). His work has been presented at numerous invited talks at prestigious institutions including Harvard University, Tufts University, and MIT.

Ziyu ChenView profile

Visiting Assistant Professor

• Mathematical Statistics
• Machine Learning
• Time-Frequency Analysis
• +3 more

Ziyu Chen is a Marshall H. Stone Visiting Assistant Professor in the Department of Mathematics and Statistics at the University of Massachusetts Amherst. His research focuses on intersections of machine learning, mathematical statistics, and signal processing, with emphasis on equivariant models, time-frequency analysis, and generative frameworks. He has contributed to theoretical guarantees for group-invariant algorithms and applications in biomedical signal analysis. Education background not explicitly listed, but his research trajectory suggests advanced training in applied mathematics and computational statistics. Research interests include: Generative Models with Symmetry Exploitation Statistical Guarantees for Machine Learning Algorithms Synchrosqueezed Transforms for Signal Analysis Graph Neural Networks and Spectral Embeddings Recent publications emphasize methodologies for handling symmetric data structures, improving time-frequency localization, and clinical applications of machine learning. Notable work includes equivariant score-based models, group-invariant GANs, and disentangling frequency modes in complex time series. Recipient of teaching awards at UMass Amherst, though specific prizes not listed. No documented grants or advising roles found in provided text. Active in interdisciplinary research spanning mathematics, computer science, and biomedical engineering.

Alexis LechervyView profile

Assistant Professor

• Multimodal Learning
• Frugal AI
• Metric Learning
• +3 more

Alexis Lechervy is an Assistant Professor at the University of Caen within the GREYC Laboratory's Image team. His research develops machine learning methods for efficient multimedia analysis and retrieval systems. Core research themes include: Multi-exit neural architectures for adaptive computation Multimodal fusion strategies for visual and textual data Metric learning for content-based retrieval Interactive learning systems for human-AI collaboration Resource-efficient deep learning models Dr. Lechervy's work bridges fundamental machine learning research with applications in medical imaging, biometrics, and multimedia retrieval. His algorithms optimize the balance between computational efficiency and predictive accuracy in resource-constrained environments.