جستجوی استاد راهنمای دانشگاهی

Gabriel E. Hineمشاهده پروفایل

پژوهشگر

• Biometrics
• Signal Processing
• Information Security
• +۱۳ مورد دیگر

Gabriel E. Hine is a Postdoc researcher at the Biometric Systems and Multimedia Forensics Lab within the Department of Engineering at Roma Tre University, Italy. He is actively engaged in research at the intersection of signal processing, biometrics, and information security, and contributes to teaching in advanced signal processing and biometric systems. Bachelor’s Degree in Electronic Engineering, Roma Tre University (2013, cum laude) Master’s Degree in Information and Communication Technology Engineering, Roma Tre University (2015, cum laude) PhD in Applied Electronics, Roma Tre University (2018) Research collaboration at Telefonica I+D, Barcelona (2016) Research involvement in EU H2020 ENCASE and PRIN 2011/2012 projects His research focuses on biometric cryptosystems, template protection, and privacy-preserving biometric recognition, with significant contributions to fingerprint, palmprint, EEG, and signature-based systems. He has also conducted influential work on online social media dynamics, particularly analyzing 4chan’s /pol/ forum. The analysis of his recent publications reveals a strong trend in developing secure and unlinkable biometric templates using deep learning, sparse signal representations, and zero-leakage frameworks. His work spans both theoretical formulation and experimental validation on real-world data, particularly in finger-vein and palm-vein recognition. He also explores novel biometric modalities such as in-air 3D signatures and resting-state EEG. Best Paper Runner-Up, ICWSM-17 (2017) Selected as 'Best of Arxiv' paper Widely covered in Nature , MIT Technology Review , The Independent , Vice , and Italian media Hine has served as a lecturer and teaching assistant for courses including 'Biometric Systems' and 'Signal Theory' at Roma Tre University. He has led a didactic laboratory on biometric systems, guiding students in implementing and analyzing biometric recognition solutions. His research is supported by major grants such as EU H2020 ENCASE and PRIN 2011/2012. He collaborates extensively with Prof. Patrizio Campisi and other researchers in the BioMedia4n6 lab. He is a core member of the BioMedia4n6 research group, which specializes in biometrics, data hiding, and blind deconvolution, applying advanced signal processing techniques to multimedia forensics and security challenges.

• RNA Sequencing
• Genetics
• Enhancer Region
• +۵ مورد دیگر

Dr. Eskeatnaf Mulugeta is an Assistant Professor at Erasmus MC in the Internal Medicine department. His research focuses on Genetics , Molecular Biology , and Medicine , particularly in the context of liver disease, transcriptome analysis, and genomic regulatory elements. Research Interests : RNA Sequencing and Transcriptomics Enhancer Regions and Non-coding Genomics Endothelial and Hepatic Stellate Cell Biology Liver Fibrosis and Metabolic Dysfunction Publications : Recent work includes studies on ZEB2's role in liver steatosis (2025), forensic single-cell DNA sequencing (2024), and SPLiT-seq pipeline comparisons (2024). His research spans functional genomics, computational biology, and clinical applications of genomic data. Collaborations : Active in multi-institutional research networks with contributions to forensic genetics and metabolic liver disease studies. Supervised Work : Mentored 2 supervised research projects, though specific advisees are not named in the text. Scientific Awards : No explicit awards or honors are mentioned in the provided text.

Jean-Christophe Pesquetمشاهده پروفایل

استاد

• Optimization
• Signal Processing
• Image Processing
• +۴ مورد دیگر

Jean-Christophe Pesquet is a Professor affiliated with the Laboratory Digital Vision Center. His work spans optimization, inverse problems, artificial intelligence, signal and image processing . He actively collaborates on advanced algorithm design and computational methods for imaging and signal recovery. Key research areas: Optimization, Wavelet Analysis, Blind Source Separation, Deep Learning Recent publications focus on nonconvex optimization, primal-dual splitting, and deep unfolding techniques. His methodological contributions include proximal algorithms, stochastic subspace approaches, and Bregman divergences. Applications range from biomedical imaging (e.g., PET, CT scans) to seismic data analysis and aerospace defect detection. Notable collaborations include researchers like Emilie Chouzenoux , Audrey Repetti , and Caroline Chaux . Current projects integrate neural networks with classical signal processing frameworks (e.g., lifting schemes, MCMC algorithms).

Fred Parkمشاهده پروفایل

استادیار

• Mathematical Image Processing
• Computer Vision
• Numerical Partial Differential Equations
• +۳ مورد دیگر

Fred Park serves as Assistant Professor of Mathematics in the Department of Mathematics & Computer Science at Whittier College. His research bridges mathematical theory with practical applications in image processing and computer vision, maintaining active collaborations across computational disciplines. Education: Ph.D. in Mathematics, University of California, Los Angeles M.A. in Mathematics, University of California, Los Angeles Professor Park's research focuses on mathematical image processing with emphasis on total variation methods, blind deconvolution, and shape modeling. His work spans computer vision applications including 3D surface reconstruction, texture extraction, and medical image analysis. Current projects explore non-convex optimization for deep learning compression and scientific computing implementations for real-world problem solving. His teaching philosophy integrates real-world mathematical problems to inspire undergraduate learning. Recent publications demonstrate strong trends in image segmentation frameworks combining anisotropic-isotropic total variation, network slimming for CNN efficiency, and medical imaging applications such as COVID-19 lung scan clustering. His work consistently bridges theoretical mathematics with computational implementation. Professor Park actively seeks to incorporate research into undergraduate education, developing methodologies to motivate learning through practical applications. His teaching experience spans undergraduate lower and upper division courses at UCLA, UC Irvine, and University of Michigan during postdoctoral positions. Research facilities include computational resources at Whittier College's Science & Learning Center (SLC 218), supporting work in image processing, shape modeling, and 3D reconstruction from point cloud data.

Dianne P. O'Learyمشاهده پروفایل

استاد

• Scientific Computing
• Numerical Analysis
• Computational Science
• +۶ مورد دیگر

Dianne P. O'Leary is a Professor affiliated with the Department of Computer Science at the University of Maryland. Her work focuses on scientific computing, numerical analysis, and computational science, with a particular emphasis on matrix factorizations, differential equations, and Monte Carlo methods. She authored the textbook *Scientific Computing with Case Studies* (SIAM Press, 2009), which integrates theoretical concepts with practical case studies across physics, engineering, epidemiology, and biology. Her research explores topics such as image deblurring, blind deconvolution, elastoplastic torsion, and iterative methods for solving large linear systems. The book includes nineteen case studies that illustrate algorithm design, mathematical modeling, and software development principles. Notably, the text emphasizes stability, error analysis, and MATLAB implementation grounded in machine arithmetic and memory management. While no explicit scientific awards or student advisees are mentioned in the provided texts, her contributions to computational education and algorithm development are central to her work. Supplementary materials include solution manuals, MATLAB code repositories, and detailed notes on topics like sensitivity analysis, computer memory management, and finite element methods. Her case studies often bridge theory and application, such as modeling epidemics, robot control systems, and structural engineering problems. The book’s structure reflects a commitment to hands-on learning, with units dedicated to dense matrix computations, optimization, Monte Carlo simulations, and sparse matrix techniques for partial differential equations.

Alex Harkerمشاهده پروفایل

مدرس ارشد

• Audio Programming
• Spectral Domain Audio Processing
• Convolution and Deconvolution
• +۷ مورد دیگر

Alex Harker is a Senior Lecturer in Music and Music Technology at the University of Huddersfield, affiliated with the Department of Media, Humanities and the Arts and the School of Arts and Humanities. He serves as Director of the Creative Coding Lab and is part of the Centre for Research in New Music and Centre for Experimental Practices (CXP). His work bridges music composition, creative programming, and audio technology, with international recognition for software tools like the HISSTools Library and the Fluid Corpus Manipulation Toolbox. University: University of Huddersfield Academic Rank: Senior Lecturer Projects: FluCoMa (2017-2023), TAAT (2025) Harker’s research focuses on the intersection of computational methods and musical expression, including spectral processing, convolution techniques, and real-time audiovisual systems. His work aligns with UN Sustainable Development Goals related to innovation and cross-domain collaboration. He supervises PhD students and has contributed to conferences like the International Computer Music Conference and Sound and Music Computing Conference. Recent publications include software tools (HISSTools Library, Fluid Corpus Manipulation Toolbox), compositions (e.g., drift shadow ), and interactive installations (e.g., Tell It To The Trees ). His software solutions are used globally in academia and industry for applications ranging from psychoacoustics to audio DJing.

Cong Maمشاهده پروفایل

استادیار

• Mathematics of Data Science
• Reinforcement Learning
• Transfer Learning
• +۳ مورد دیگر

Cong Ma is an Assistant Professor in the Department of Statistics at the University of Chicago. He holds a PhD from Princeton University (2020), advised by Yuxin Chen and Jianqing Fan, and a BEng from Tsinghua University (2015). Previously, he was a postdoctoral researcher at UC Berkeley under Martin Wainwright. His research focuses on the mathematics of data science, emphasizing reinforcement learning, transfer learning, multi-modal learning, high-dimensional statistics, and nonconvex optimization. Key areas include developing computationally and statistically efficient methods for large-scale data problems, with recent work on contrastive learning, contextual bandits, and robust matrix completion. Education: PhD in Operations Research & Financial Engineering, Princeton University (2020) BEng in Electrical Engineering, Tsinghua University (2015) Awards: SIAM Activity Group on Imaging Science Best Paper Prize (2024) Teaching: STAT 253/317: Introduction to Probability Models (Winter 2025) STAT 28000: Optimization (Winter 2025) Machine Learning (Autumn 2022) His work bridges theoretical insights with practical applications, often addressing challenges in statistical guarantees for optimization algorithms and distributionally robust learning. Recent highlights include contributions to multi-modal contrastive learning and batched contextual bandits.

Tsuhan Chenمشاهده پروفایل

استاد

• Artificial Intelligence
• Computer Vision
• Pattern Recognition
• +۳ مورد دیگر

Tsuhan Chen is a Distinguished Professor and Deputy President (Research and Technology) at the National University of Singapore (NUS), serving as Chief Scientist of AI Singapore and Co-Chair of the SIA-NUS Digital Aviation Corporate Laboratory. Previously, he served as Dean of the College of Engineering at Nanyang Technological University and held professorships at Cornell University and Carnegie Mellon University. Education: Ph.D. in Electrical Engineering, California Institute of Technology M.S. in Electrical Engineering, California Institute of Technology B.Sc. in Electrical Engineering, National Taiwan University A globally recognized expert in pattern recognition, computer vision, and machine learning, Prof. Chen's research centers on media artificial intelligence including computer vision, pattern recognition, multimedia coding/retrieval, and biometric authentication. His work has produced over 300 technical publications and nearly 30 US patents. His recent publications reveal dominant themes in computer vision and multimedia processing, with significant contributions to image captioning, semantic segmentation, emotion recognition, and medical imaging through advanced techniques like deep learning, tensor regularization, and multimodal fusion. Scientific Awards: Charles Wilts Prize (1993) NSF CAREER Award (2000-2003) Benjamin Richard Teare Teaching Award (2006) Eta Kappa Nu Award (2007) Michael Tien Teaching Award (2014) Robert M Janowiak Award (2017) Fellow of IEEE Fellow of Institute of Engineers, Singapore Prof. Chen has secured substantial research funding including the NSF CAREER Award and directed the Advanced Multimedia Processing Laboratory at Cornell. He currently leads national AI initiatives through AI Singapore and has championed interdisciplinary research programs aligned with Singapore's RIE2020 strategy.

Chris Bleakleyمشاهده پروفایل

استاد

• Pervasive Computing
• Indoor Positioning Systems
• Networked Embedded Systems
• +۳ مورد دیگر

Chris Bleakley is a Professor at the School of Computer Science, University College Dublin (UCD). His research focuses on pervasive computing, indoor positioning systems, and networked embedded systems. He has secured over €2 million in research funding as PI, supervised 12 PhD and 4 MSc students, and authored 1 book and numerous journal/conference papers. Prior to UCD, he held roles as VP Engineering at Massana Ltd, Senior Researcher at Broadcom Eireann, and IT consultant with Accenture. He holds a PhD in Electronic Engineering from Dublin City University and a BSc in Computer Science from Queen's University Belfast. Professional Roles: Associate Editor of Electronics Letters, Senior IEEE Member, ACM Member. Former Head of School of Computer Science at UCD (2019–2022). Research Interests: Indoor positioning (ultrasonic/WiFi), embedded systems, sensor networks, and algorithmic history. Key applications include smart agriculture, biomedical devices, and environmental monitoring. Grants: Includes projects like WiPhyLoc8 (WiFi positioning), EEDSP (embedded health systems), and MobiFit (sports monitoring). Over 18 grants secured between 2004–2022. Education: PhD (1995, DCU), BSc (1st class, Queen's University Belfast). Notable Work: Developed high-accuracy indoor positioning systems using ultrasonic and WiFi technologies. Authored Poems That Solve Puzzles , a popular science book on algorithm history.

• Neural Networks
• Hardware-Accelerated Computing
• FPGA Systems
• +۴ مورد دیگر

Walter Stechele is a Professor at the Technical University of Munich (TUM), holding a position in the Department of Integrated Systems within the TUM School of Computation, Information and Technology. His research focuses on hardware-software co-design for neural networks, embedded systems optimization, and edge computing applications. Key areas include FPGA acceleration of convolutional neural networks (CNNs), quantization-aware training, and adversarial robustness in multi-bit networks. He also investigates sensor fusion, automotive imaging systems, and medical image registration techniques. His work bridges theoretical advancements in machine learning with practical deployment challenges on resource-constrained hardware. Notable contributions include methodologies like MATAR (multi-quantization-aware training) and HW-flow-fusion (inter-layer scheduling for CNN accelerators). His research addresses critical issues such as numerical stability in 8-bit Winograd convolutions and optimizing imaging through automotive windshields. Leveraging FPGA platforms, he explores energy-efficient implementations of binarized neural networks (BNNs) for tasks like driveable area detection and gesture recognition on edge devices. His publications frequently emphasize real-world applications in autonomous driving, robotics, and medical imaging, demonstrating a commitment to translating algorithmic innovations into deployable systems.

• Signal Processing
• Image Analysis
• Medical Imaging
• +۳ مورد دیگر

Jeffrey J. Rodriguez is a Professor in the Department of Electrical and Computer Engineering at The University of Arizona, where he has been a faculty member since 1990. He currently directs the Signal and Image Laboratory. His research focuses on signal/image/video processing, automated image analysis, and medical imaging applications. Rodriguez holds a B.S. and Ph.D. from The University of Texas at Austin (1984, 1990) and an M.S. from MIT (1986), all in electrical engineering. He has served in leadership roles, including Co-Director of the NSF-funded Connection One research center (2003–2008) and General Chair of major conferences like the 2016 IEEE Southwest Symposium on Image Analysis. His work spans algorithm development for medical imaging (e.g., MRI segmentation, cell nuclei detection), computer vision (vehicle tracking, aerial imagery analysis), and optics (super-resolution imaging). Awards include the 2016 Best Poster Award from the American Otological Society. Rodriguez teaches courses such as Digital Image Processing (ECE 533/OPTI 533), Digital Signal Processing (ECE 429/529), and Circuit Theory (ECE 320A). His research and grants emphasize automated image analysis tools with applications in healthcare, surveillance, and environmental monitoring.

John Wrightمشاهده پروفایل

دانشیار

• High-dimensional data analysis
• Signal processing
• Computer vision
• +۵ مورد دیگر

John Wright is an Associate Professor in the Department of Electrical Engineering at Columbia University's Fu Foundation School of Engineering and Applied Science, with additional affiliations in the Department of Applied Physics and Applied Mathematics and the Data Science Institute. He maintains his office at 408 Mudd Hall within the Data Science Institute and leads a research group focused on high-dimensional data analysis. Wright received his PhD in Electrical Engineering from the University of Illinois at Urbana-Champaign in October 2009, followed by a research position at Microsoft Research from 2009-2011 before joining Columbia University. His research focuses on developing algorithms for robustly recovering structured signal representations from incomplete and corrupted observations, with applications in imaging and vision. His research interests span high-dimensional data analysis, signal processing, computer vision, and optimization. Wright's work places particular emphasis on finding methods that perform well even when data is unreliable - noisy or corrupted - and which come with proofs of correctness. His research group develops tools for robustly analyzing high-dimensional data and applying them to practical problems such as image and video compression, face and object recognition. Wright's publication record shows a clear evolution from sparse representation and dictionary learning toward more complex deep learning architectures. His recent work focuses on geometric approaches to nonconvex optimization problems, blind deconvolution methods, and the theoretical foundations of deep networks. His publications consistently bridge theoretical guarantees with practical applications across computer vision and signal processing domains. Scientific Awards and Honors: 2009 Lemelson-Illinois Prize for Innovation for his work on face recognition 2009 UIUC Martin Award for Excellence in Graduate Research 2008-2010 Microsoft Research Fellowship 2012 COLT Best Paper Award (with Wang and Spielman) Wright has successfully mentored numerous PhD students who have gone on to prestigious positions at institutions including TTIC, Google, Rutgers University, and Boston University. He also co-authored the influential textbook 'High-Dimensional Data Analysis with Low-Dimensional Models: Principles, Computation, and Applications' published by Cambridge University Press in 2022, which has been used as course material at multiple leading universities. His research group maintains active collaborations across multiple institutions and regularly presents at top conferences including NeurIPS, ICML, CVPR, and COLT. The group holds weekly seminars (typically on Thursdays from 10-12 in 408 Mudd) where current research is discussed and developed.

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.

Ahmet Enis Cetinمشاهده پروفایل

استاد

• Computer Vision
• Artificial Intelligence
• Signal Processing
• +۷ مورد دیگر

Ahmet Enis Cetin is a Professor in the Department of Electrical and Computer Engineering at the University of Illinois at Chicago. His work spans computer vision, biomedical imaging, and energy-efficient signal processing for cyber-physical systems. Department of Electrical and Computer Engineering (University of Illinois at Chicago) Member, Turkish Academy of Sciences (2015–present) Fellow, IEEE Research Interests include: Computer vision for wildfire and VOC detection Biomedical image processing and microscopy analysis Energy-efficient deep learning and multiplication-free operators Speech and sound analysis for industrial and medical applications Cyber-physical systems for environmental monitoring Scientific Awards : Fellow of IEEE (2009) 2012 Best Paper Award at UNESCO-organized conference Member, Turkish Academy of Sciences (2015–present) Advising and Grants : Mentored students on wildfire detection, VOC monitoring, and biomedical imaging. Secured NSF and Intel grants for energy-efficient neural networks and cyber-physical systems.

Andreas Henriciمشاهده پروفایل

پژوهشگر

• NMR spectroscopy
• Dynamical systems
• Mathematical analysis
• +۴ مورد دیگر

Andreas Henrici is a research-focused academic at the Zurich University of Applied Sciences (ZHAW), School of Engineering, within the Applied Complex Systems Science research centre. His core activities revolve around NMR spectroscopy, dynamical systems theory, and the development of machine-learning methods for automated spectral analysis. Education & professional development: Certificate of Completion, ZHAW Life Sciences and Facility Management, 02/2019 Doctorate (Dr.) – exact discipline and institution not specified in the source Research interests: Henrici combines analytical mathematics with practical spectroscopy. Early work concentrated on stability and symmetry analyses of nonlinear lattices (Toda lattice). More recently he leverages deep-learning architectures (CNNs, DETR-style networks) to deconvolute and classify 1D/2D-NMR data, pushing automation in metabolomics and fragment-based drug discovery. Bayesian inference, image-processing techniques, and trustworthy AI are recurring themes. Recent publication trends (2022-2025): Over 15 peer-reviewed articles and conference posters illustrate a clear shift toward AI-driven NMR: deconvolution networks, automated multiplet segmentation, spin-system identification, and uncertainty-aware classification. Collaborative projects yield open data, open code, and cross-disciplinary authorship with chemists, computer scientists, and industry partners. Editorial & organisational service: Guest editor for Frontiers in Artificial Intelligence volumes summarising European COST conferences on AI in Industry & Finance (2022, 2023) Project leader for the European conference series AI in Industry and Finance (completed) Current projects & funding: Smart Acquisition for Ultra-High field NMR Spectroscopy – project leader (ongoing) NMR-based drug discovery – co-project leader (ongoing) Several completed COST and ZHAW-internal grants on machine-learning for spectroscopy and mathematics-for-industry Labs & teams: Henrici is embedded in the ZHAW Applied Complex Systems Science focus area, working closely with the NMR, metabolomics, and data-science groups. Shared facilities include high-field NMR spectrometers and GPU clusters for deep-learning experiments.