Federica Porta is an Associate Professor at the Department of Physical, Computer and Mathematical Sciences, University of Modena and Reggio Emilia. Her academic career focuses on numerical analysis, optimization, and stochastic gradient methods, particularly in machine learning and image restoration. Her research interests include: Numerical Analysis and Statistics for Computer Engineering Numerical Optimization for Artificial Intelligence Stochastic Gradient Descent with Variance Control Deep Image Prior Frameworks Regularization Techniques for Biomedical Imaging Federica's recent publications (2021–2025) demonstrate expertise in hybrid gradient projection methods, adaptive learning rate selection, and deep learning applications for image segmentation and classification. She collaborates extensively with researchers like Giorgia Franchini, Valeria Ruggiero, and Luca Zanni, applying these methods to both convex and non-convex optimization problems. She teaches courses such as Numerical Analysis and Statistics for Computer Engineering, Numerical Analysis for Mathematics, and Numerical Optimization for Artificial Intelligence. Her teaching emphasizes MATLAB/Python implementation of numerical methods, convergence properties, and computational complexity analysis. Contact: federica.porta@unimore.it | Office: Mathematics Building, Via Campi 213/b
Philippe Pasquier is a Full Professor in the Department of Civil, Geological and Mining Engineering at Polytechnique Montréal. He holds the NSERC Industrial Chair in Geothermal Energy and leads the Geothermal and Hydrogeology Research Group. His research integrates spectral methods, artificial intelligence, and stochastic modeling to advance geothermal system design, with experimental validation at the Geothermal Research Unit (URG). Education & Teaching: Teaches Applied Hydrogeology (GLQ2601) and Low-Temperature Geothermal Energy (GML6113). Recognized with multiple teaching awards, including Best Professor in Civil/Geological/Mining Engineering (2011-2015). Research Focus: Specializes in low-temperature geothermal systems (open/closed loops, standing column wells), thermal response testing, inverse problems in hydrogeology, and energy-efficient building integration. His work aims to reduce costs and promote geothermal adoption through advanced computational tools. Publications: Recent articles (2022-2025) emphasize machine learning applications for geothermal modeling, thermal plume dynamics, and system optimization, reflecting a trend toward AI-driven sustainable energy solutions. Awards & Leadership: NSERC Industrial Chair in Geothermal Energy Best Professor Awards (2010-2015) Head, Geothermal Research Unit (URG) Students & Grants: Supervised 7 doctoral and 11 master's students. Secured NSERC Alliance grants for large-scale geothermal deployment in Quebec institutions. Tools & Outreach: Developed open-source software TRT-SInterp for thermal response test analysis. Frequently featured in media (Le Devoir, La Presse) advocating geothermal solutions.
Øyvind Ryan is a Senior Lecturer at the Department of Differential Equations and Computational Mathematics at the University of Oslo. He specializes in computational mathematics, signal processing, and random matrix theory. His research integrates theoretical mathematics with practical applications in signal processing, image compression, and wireless communication systems. Research interests include wavelet analysis, numerical methods, statistical inference, and the asymptotic behavior of random matrices. His work often bridges pure mathematics and engineering challenges, such as optimizing channel capacity estimation and developing efficient compression techniques for low-bit-depth images. Key contributions span topics like Vandermonde matrices in cognitive radio systems, free probability theory applications, and runlength-based processing for map images. His articles frequently appear in top journals such as IEEE Transactions on Signal Processing and Acta Applicandae Mathematicae. Affiliated with the Computational Mathematics research group at the University of Oslo, Ryan collaborates on projects involving advanced signal processing methodologies. No specific advising roles or grants are explicitly listed in the provided materials, though his extensive publication record reflects sustained scholarly activity.
Wanwen Zeng is a Postdoctoral Scholar in the Department of Statistics at Stanford University, advised by Wing H Wong. Their research focuses on computational biology, integrating genomics, epigenomics, and machine learning to address problems in genetics and drug discovery. Key research interests include gene regulatory networks, epigenetic prediction using deep learning (e.g., transformers), and developing bioinformatics tools for analyzing single-cell data. Their work spans databases like HiChIPdb (regulatory interactions) and SilencerDB, alongside methodological advancements such as CREATE and scGraph. Recent publications highlight applications of graph neural networks in drug discovery (DeepDrug), causal modeling of genotype-phenotype relationships, and improving polygenic predictions through epigenomic features. This reflects a strong emphasis on bridging computational methods with biological systems. No scientific awards are listed. Advising and grants sections remain unspecified in available data. Their contributions are primarily technical, centered on advancing genomic data analysis frameworks and databases.
Dr Ryan Cunningham serves as a Lecturer in Data Science within the Department of Computing and Mathematics at Manchester Metropolitan University. His academic profile centers on developing advanced deep learning systems for medical image analysis, with particular focus on real-time skeletal muscle assessment via ultrasound and facial expression recognition in video sequences. His work bridges computer science and healthcare to address neurological disorders including motor neuron disease and dystonia. Education: Ph.D in Computing, Manchester Metropolitan University (2012-2015) BSc in Artificial Intelligence, Manchester Metropolitan University (2009-2011) HND in Computing, The Manchester College (2007-2009) Dr Cunningham's research spans machine learning, deep learning, computer vision, and medical image analysis with consistent application to healthcare challenges. His primary focus involves developing convolutional neural networks for ultrasound-based muscle segmentation and real-time analysis to enable early disease diagnosis. Recent work extends to facial expression recognition using 3D-CNNs and generative models for both macro and micro-expressions in long-duration videos, demonstrating interdisciplinary innovation at the intersection of AI and clinical medicine. His technical expertise includes MATLAB, Python, Java, and C/C++ for implementing complex algorithms into practical software solutions. Analysis of his 15 most recent publications reveals a strong trajectory in medical AI applications, with 60% focused on ultrasound-based muscle analysis for neurological disorders and 40% on facial expression recognition systems. Key technical trends include progression from foundational segmentation models to efficient lightweight architectures (2021), integration of temporal modeling for video sequences (2021), and recent exploration of generative approaches for expression synthesis (2023). The research consistently targets real-world clinical utility through real-time processing capabilities and automated diagnostic tools. Scientific Awards: No scientific awards or fellowships are documented in the provided materials. Dr Cunningham currently supervises a PhD candidate investigating deep learning applications for macro and micro facial expressions in high spatiotemporal resolution videos. His teaching responsibilities include postgraduate instruction in High Performance Computing and Big Data, specifically covering TensorFlow as a deep learning framework. Previous teaching experience includes advanced undergraduate programming courses, demonstrating commitment to both research and pedagogy. While no specific grants are mentioned, his research output suggests active engagement with medical imaging and AI funding streams. His work is conducted within Manchester Metropolitan University's Department of Computing and Mathematics, leveraging institutional resources including the Dalton Building facilities. The research direction indicates collaboration with medical professionals and neurology specialists, though specific lab affiliations or research teams are not explicitly documented. Current projects focus on extending deep learning capabilities for real-time ultrasound analysis and advancing facial expression recognition systems for clinical applications in neurological assessment.
Russell C. Hardie is a full-time Professor at the University of Dayton , holding positions in the Department of Electrical and Computer Engineering with joint appointments in Electro-Optics and Photonics and Bioengineering . His academic journey began with a B.S. in Engineering Science from Loyola College (1988), followed by M.S. and Ph.D. in Electrical Engineering from the University of Delaware (1990, 1992). Prior to joining the University of Dayton in 1993, he served as a Senior Scientist at Earth Satellite Corporation (now MDA Federal). Research Interests : Digital signal/image processing, medical imaging, super-resolution techniques, hyperspectral/infrared imaging, pattern recognition Key Awards : 2006 Alumni Award in Teaching (University of Dayton) 1998 Rudolf Kingslake Medal (SPIE) 1999 School of Engineering Excellence in Teaching 2002 IEEE Professor of the Year 1997 Epsilon Delta Tau Engineering Professor of the Year Recent Work : Focuses on machine learning applications for medical imaging (lung segmentation, nodule detection), atmospheric turbulence mitigation, and hyperspectral data analysis. His 15 most recent publications span topics from zero-shot chest X-ray analysis to methane plume detection and turbulence-corrected imaging systems. Contact: rhardie1@udayton.edu
Dr. Alexander Belyaev is an Associate Professor at the Institute of Sensors, Signals & Systems within the School of Engineering & Physical Sciences at Heriot-Watt University. His research focuses on advanced mathematical and computational methods in image processing, signal processing, and geometry processing. He has contributed to areas such as distance function approximation, image deblurring, dehazing, and texture analysis. His work often integrates variational methods and partial differential equations (PDEs), addressing challenges in computer vision and geometric modeling. Research interests include image enhancement techniques like multiplicative deblurring and reverse filtering, as well as geometric algorithms for shape interrogation and surface reconstruction. His publications span over 15 years, with recent emphasis on deep learning applications in image dehazing and variable exponent diffusion for texture separation. Collaborations involve international teams, particularly in signal processing and computer graphics. No scientific awards are explicitly listed. His advising and grants are not detailed in the provided texts, but his lab work is centered at the Institute of Sensors, Signals & Systems, focusing on interdisciplinary research in sensing technologies and computational methods.
Steve Presse is a Professor at Arizona State University (ASU), affiliated with the School of Molecular Sciences and the Department of Physics. He holds roles in the Biodesign Center for Mechanisms of Evolution, Mechanisms of Evolution Researchers, and the Center for Biological Physics. His research focuses on integrating Bayesian inference, machine learning, and statistical physics to study living systems, with experimental work on bacterial hydrodynamics and predation. Education: Ph.D. in Chemical Physics from MIT (2008). Research Interests: Bayesian methods, ML/AI in biophysics, single-molecule analysis, and stochastic processes. Experimental studies include bacterial interactions and predator-prey dynamics. Research Trends: Recent work emphasizes single-molecule reaction-diffusion, Bayesian inference for binding kinetics, and deep learning applications in biophysical data. Themes include superresolved tracking, fluorescence microscopy analysis, and modeling complex biological systems. Lab & Collaborations: The Pressé Lab actively recruits postdocs, graduate, and undergraduate students. Collaborations focus on developing computational tools for data modeling, as seen in his textbook Data Modeling for the Sciences .
Dr. Karen Eguiazarian is a Professor of Signal Processing at the Department of Computing Sciences , Tampere University . He leads the Computational Imaging research group and has served as head of the Signal Processing Research Community (SPRC) at Tampere University of Technology (2016-2018). Education: M.Sc. in Mathematics, Yerevan State University, Armenia (1981) Ph.D. in Physics and Mathematics, Moscow State University, Russia (1986) Doctor of Technology in Signal Processing, Tampere University of Technology, Finland (1994) His research focuses on Computational Imaging , Compressed Sensing , and Efficient Signal Processing Algorithms , with significant contributions to Image/Video Restoration and Compression . Recent work includes AI-driven phase imaging, hyperspectral reconstruction, and noise-robust algorithms for remote sensing and biomedical applications. Scientific Awards: Service Award from the Society for Imaging Science and Technology (IS&T) (2014) Honorary Doctoral Degree from Don State-Technical University, Russia (2015) Dr. Eguiazarian has supervised 25 doctoral theses and published over 650 papers. He serves as Editor-in-Chief of the Journal of Electronic Imaging and associate editor of the IEEE Transactions on Image Processing , while co-founding Noiseless Imaging Oy , a Tampere University spin-off.
