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Sergio RojasView profile

Senior Lecturer

• Numerical Analysis
• Scientific Computing
• Mathematical Modeling
• +4 more

Sergio Rojas is a Senior Lecturer in the Applied and Computational Mathematics section at Monash University's School of Mathematics. His expertise spans numerical analysis, scientific computing, and mathematical modeling, focusing on residual minimization-based methods for solving complex partial differential equations. He holds a PhD and MSc in Engineering Sciences from Pontificia Universidad Católica de Chile, a Master's in Mathematics from the University of Pavia, and a Bachelor's in Mathematics from Pontificia Universidad Católica de Valparaíso (PUCV). Education: PhD and MSc in Engineering Sciences, Pontificia Universidad Católica de Chile Master's in Mathematics, University of Pavia, Italy Bachelor's in Mathematics, PUCV, Chile Research Interests: Dr. Rojas develops advanced numerical methods including Finite Element, Discontinuous Galerkin, and Physics-Informed Neural Networks. His work emphasizes robust algorithms for complex PDEs, with applications in computational fluid dynamics, geophysics, and solid mechanics. Recent studies explore adaptive stabilization techniques and solver optimization for parallel architectures. Article Trends (2023-2025): Recent publications highlight robust neural network approaches for PDEs, adaptive finite element methods, and computational efficiency in parallel solvers. Key themes include residual minimization, stability analysis, and integration of machine learning in numerical simulations. Supervision: Accepts undergraduate, master’s, and PhD students in numerical analysis and scientific computing. Current PhD projects include variational physics-informed schemes and residual minimization methods. Labs/Teams: Collaborates on interdisciplinary projects involving computational mathematics and engineering applications, though specific lab affiliations are not detailed in the provided text.

• Algebra
• Algebraic Coding Theory
• Boolean Algebras
• +7 more

Massimiliano Sala is a Full Professor at the Department of Mathematics of the University of Trento. His expertise spans Algebra, Algebraic Coding Theory, Boolean Algebras, Cryptography, and Computational Algebra. He specializes in cryptographic protocols, post-quantum security, and elliptic curve cryptography. His recent work includes advancements in blockchain-agnostic multi-signature schemes, searchable encryption, and elliptic curve group structures. Research Focus: Dr. Sala's research integrates mathematical foundations with applied cryptography, emphasizing secure communication protocols, cryptographic algorithm design, and blockchain technologies. He has contributed to the analysis of Boolean functions, APN permutations, and secure multi-party computation. Recent Contributions: His 2024 publications address topics like cubic Boolean functions, blockchain-agnostic asset management, and randomized searchable encryption. He also explores post-quantum cryptographic frameworks and elliptic curve-based security mechanisms. Awards & Recognition: None explicitly listed in the provided materials. Teaching & Academic Leadership: Teaches Algebraic Cryptography at the Master's level, focusing on public-key systems and post-quantum cryptography. His academic contributions include editorial work for cryptographic journals and conference proceedings.

Heather ReadView profile

Professor

• Sensory Neuroscience
• Sound Perception
• Auditory Processing
• +3 more

Heather Read is a Professor at the University of Connecticut's Department of Psychological Sciences, where she leads research in sensory neuroscience and auditory processing. Her work investigates how mammals perceive sound frequency, shape, and rhythm, with implications for understanding cortical developmental disorders like dyslexia and autism. She directs the Read Lab, focusing on neural mechanisms from single neurons to behavior, aiming to develop innovative diagnostic tools and prosthetic hearing devices. Research spans neurobiology of sound perception, including: Neural circuit anatomy underlying auditory processing Perceptual deficits in developmental disorders Computational models of natural sound encoding Novel neurotechnologies for auditory rehabilitation Her publications (2018-2024) demonstrate evolving focus: earlier work established thalamocortical auditory pathways and neural coding principles, while recent research emphasizes closed-loop neuromodulation systems, wearable EEG technology, and translational applications for hearing disorders. Studies consistently integrate neurophysiological recordings, computational modeling, and behavioral analysis across species. Awards and honors include: Kavli Frontiers Fellow (National Academy of Science) Schmitt Dissertation Award Marine Biological Laboratories Undergraduate Fellowship Graduate Merit Fellowship She teaches undergraduate courses in Sensation/Perception (PSYC 3501) and Sensory Neuroscience Lab (PSYC 3253). Contact: heather.read@uconn.edu | Office: Bousfield 114, Storrs Campus.

• Statistics
• Machine Learning
• Environmental Data Analysis
• +3 more

Nikolaus Umlauf is an Associate Professor at the Department of Statistics, University of Innsbruck. He specializes in Bayesian distributional regression, structured additive models, and spatiotemporal analysis, with applications in public health, climate science, and real estate valuation.

Gianluigi RozzaView profile

Professor

• Numerical Analysis
• Computational Fluid Dynamics
• Scientific Computing
• +6 more

Gianluigi Rozza is a Full Professor in Numerical Analysis and Scientific Computing at SISSA (International School for Advanced Studies) in Trieste, Italy. He leads the SISSA mathLab research group and serves as Coordinator of the Mathematics Area at SISSA. He holds roles such as SISSA Director's Delegate for Innovation and Knowledge Transfer and member of various committees including the SISSA HPC Committee and SIAM Student Chapter. His research focuses on numerical analysis, computational fluid dynamics, reduced order methods (ROM), and their applications in engineering and biomedical fields. Education: Laurea cum Laude in Aerospace Engineering from Politecnico di Milano (2002), PhD in Applied Mathematics from EPFL (2005). Postdoctoral research at MIT (2006-2008), followed by roles at EPFL and SISSA. He has supervised over 30 PhD and Master’s students, with expertise in reduced order modeling and computational mechanics. Research Interests: Reduced basis methods, parametrized PDEs, fluid-structure interaction, cardiovascular simulations, environmental flows, machine learning integration, and industrial collaborations with companies like Danieli and Fincantieri. He coordinates EU projects such as the ERC Consolidator Grant AROMA-CFD (2016-2021) and the ITN ROMSOC project. Publications: Over 90 scientific papers, two books as editor, and contributions to open-source software like rbMIT and ITHACA-FV. Awards include the ECCOMAS Young Investigator Award (2014), SIAM Fellowship (2025), and the Gili Agonistelli Prize (2025). Grants and Projects: Principal Investigator of ERC AROMA-CFD and ARGOS PoC. Coordinates regional projects like UBE (Under Water Blue Efficiency) and TRIM. Engages in environmental modeling (e.g., SOPHYA, PRELICA) and industrial collaborations under MAR tc FVG. Labs/Teams: SISSA mathLab develops open-source tools for computational science, including ROM libraries and fluid dynamics solvers. Active in organizing workshops, serving on editorial boards (e.g., SIAM Journals), and advisory roles for agencies like ARPA FVG.

Volker Christian BehrView profile

Associate Professor

• Magnetic Particle Imaging
• Magnetic Resonance Imaging
• Biophysics
• +3 more

Prof. Dr. Volker Christian Behr serves as an Associate Professor at the University of Würzburg's Faculty of Physics and Astronomy, where he leads the Magnetic Particle Imaging group within the Chair of Experimental Physics (Biophysics). Located in Building P1, Room F-076 at the Am Hubland campus in Würzburg, Germany, Prof. Behr contributes significantly to the advancement of medical imaging technologies with expertise in Magnetic Particle Imaging and Magnetic Resonance Imaging. Prof. Behr's research focuses on Magnetic Particle Imaging (MPI), a novel medical imaging technique that detects superparamagnetic iron oxide nanoparticles with high sensitivity and specificity within the human body. His work explores the nonlinear magnetization function of these particles to develop marker-based applications that avoid radioactive nuclides, offering significant potential for clinical applications. He has contributed to the development of MPI-MRT hybrid systems where MPI captures the marker signal while MRT provides the anatomical background. His research extends to scanner technology, wave propagation in high-field MRI, and applications in non-destructive material testing. Prof. Behr's publications over the past two decades demonstrate a consistent focus on medical imaging technologies, with particular emphasis on Magnetic Particle Imaging since 2009. His work spans from fundamental scanner components and hardware to novel reconstruction techniques and clinical applications. The evolution of his research shows progression from general MRI technology (2003-2011) to specialized MPI development (2015-2018), reflecting the emergence of MPI as a distinct field within medical imaging. Prof. Behr leads the Magnetic Particle Imaging research group within the larger Magnetic Resonance Imaging group at Experimental Physics 5. His team works on advancing MPI technology, developing novel reconstruction techniques, and exploring clinical applications of magnetic particle imaging. The group is part of the Chair of Experimental Physics (Biophysics) at the University of Würzburg, which maintains strong connections with medical researchers for translational applications of imaging technologies.

Perc MatjažView profile

Professor

• Phase transitions
• Complex networks
• Stochastic processes
• +7 more

Matjaž Perc is a Full Professor of Physics at the University of Maribor and holds leadership roles including Vice Dean of Natural Sciences at the European Academy of Sciences and Arts. His research focuses on complex systems, nonlinear dynamics, and socio-physical phenomena. He has been recognized with prestigious awards such as the Young Scientist Award for Socio- and Econophysics (2015) and the Zois Award (2018), Slovenia's highest national research honor. Perc is also a Fellow of the American Physical Society and among the world's top 1% most cited physicists. Research Interests : Phase transitions in social systems, evolutionary game theory, complex networks, stochastic processes, and self-organization mechanisms. Awards : Extensive list includes recognition for refereeing excellence, societal memberships (APS, EPS), and national accolades. Publications : Over 300 articles in top journals like Physical Review E , Chaos , and Scientific Reports , focusing on interdisciplinary applications of physics to social, biomedical, and environmental challenges. His work bridges physics, computer science, and social sciences, addressing global issues through mathematical modeling and network analysis.

Sina KhanmohammadiView profile

Assistant Professor

• Neural Data Science
• Neural Dynamics
• Network Neuroscience
• +3 more

Sina Khanmohammadi is an Assistant Professor in the School of Computer Science at the University of Oklahoma. His research focuses on neural data science, machine learning, and network neuroscience, aiming to understand brain dynamics and develop data-driven methods for neural implants to improve cognitive function. He holds a Ph.D. in Systems Science from SUNY Binghamton, M.Sc. in Manufacturing Management from the University of Hertfordshire, and B.Sc. in Computer Science from the University of Tabriz. Before joining OU, he was a Postdoctoral Associate at Washington University in St. Louis (Electrical & Systems Engineering) and an Adjunct Lecturer at SUNY Binghamton. His work integrates neuroimaging data with advanced computational techniques to study brain disorders and cognitive mechanisms. Key research interests include EEG analysis, functional connectivity, and applying machine learning to biomedical problems. His lab, 3SigmaLab (www.3sigmalab.com), explores interdisciplinary applications in neuroscience and systems science. He has contributed to over 25 peer-reviewed articles since 2012, focusing on neurological disorders, signal processing, and network dynamics. Notable achievements include developing novel methods for seizure detection, EEG modulation analysis, and predictive models of cognitive decline post-brain injury. His long-term goal is to translate computational insights into clinical tools for improving patient outcomes and cognitive health.

• Biomedical Engineering
• Medical Imaging Diagnostics
• Ultrasound Technologies
• +7 more

Prof. Demi Libertarian is an Associate Professor at the University of Trento’s Department of Information Engineering and Computer Science, and an Adjunct Professor at Queensland University of Technology. Active since 2018, she leads the ULTRa Lab focusing on lung ultrasound, nonlinear acoustics, and biomedical imaging. She holds a PhD in Applied Physics from Delft University of Technology (2013) and has held visiting roles at Stanford, KAIST, and others. Her research emphasizes lung ultrasound diagnostics, quantitative imaging techniques, and AI-driven medical data analysis. Notable contributions include the ICLUS project for pandemic-related lung monitoring and the development of lung ultrasound spectroscopy. She has authored over 190 peer-reviewed publications and holds editorial roles at journals like Ultrasound Journal and Journal of the Acoustical Society of America . Prof. Libertarian’s work spans signal processing innovations, beamforming algorithms, and collaborations with global clinical partners. Her lab’s equipment includes advanced ultrasound platforms, clinical probes, and lung phantoms. She supervises PhD/PostDoc teams on topics like neonatal lung disease and AI-driven image analysis. Education: BSc/MSc Telecommunication Engineering, University of Pisa (2006/2008) PhD Applied Physics, Delft University of Technology (2013) Research Highlights: Lung ultrasound standardization and AI scoring systems Ultrasound localization microscopy and super-resolution techniques Quantitative assessment of lung diseases via spectroscopy Grants & Leadership: EU ERC Starting Grants evaluator Editorial roles at Springer Nature, Elsevier, and IEEE International Lung Ultrasound Symposium organizer

Michael MuehlebachView profile

Researcher

• Machine Learning
• Dynamical Systems
• Control Theory
• +3 more

Michael Muehlebach is a Research Group Leader at the Max Planck Institute for Intelligent Systems in Tuebingen, Germany, leading the Learning and Dynamical Systems group. He holds a B.Sc. and M.Sc. in Mechanical Engineering from ETH Zurich (2010, 2013) and a Ph.D. from the Institute for Dynamic Systems and Control (2018), advised by Prof. R. D'Andrea. He later pursued postdoctoral research under Prof. Michael I. Jordan at UC Berkeley. Research Interests: Machine Learning, Dynamical Systems, Control Theory, Optimization, Physics-Informed Learning for Cyber-Physical Systems. Scientific Awards: Outstanding D-MAVT Bachelor Award, Willi-Studer Prize, ETH Medal, HILTI Prize, Branco Weiss Fellowship (2018), Emmy Noether Fellowship (2020), Amazon Fellowship (2024). Students: Supervised Julien Kohler (Master's thesis on Quadrotor Control). Projects: Developed Floaty (wind-energy robot), Flying Platform (ducted fan actuation testbed), One-Wheel Cubli (3D inverted pendulum), and electromagnetic navigation systems.

Guillaume TochonView profile

Assistant Professor

• Mathematical Morphology
• Statistical Signal Processing
• Machine Learning
• +4 more

Guillaume Tochon is an Assistant Professor at EPITA since 2016 and an Invited Researcher at the Institut de Mécanique Céleste et de Calcul des Éphémérides (IMCCE, Observatoire de Paris) since 2022. He holds a M.Sc. and Ph.D. in electrical engineering and signal/image processing from Grenoble Institute of Technology and Université Grenoble Alpes, with postdoctoral experience at Carnegie Institution for Science and UCLA. His research focuses on mathematical morphology, statistical signal processing, and machine/deep learning, applied to space imagery (e.g., astrometry) and satellite data analysis. He leads projects on morphological neural networks, Sentinel-2 time series dynamics, and meteor/satellite detection using 1-second exposure imagery. Education: M.Sc. in Electrical Engineering, Grenoble Institute of Technology (2012) Ph.D. in Signal and Image Processing, Université Grenoble Alpes (2015) Visiting Scholar, UCLA Department of Mathematics (2013) Research Interests: Combines mathematical morphology with machine learning to address challenges in remote sensing and space imagery. Key areas include hierarchical representations, noise analysis, and applications in astrometry and hyperspectral unmixing. Current projects involve neural Koopman operators for forecasting, Sentinel-2 time-series assimilation, and meteor detection in night-sky imagery. Teaching: At EPITA, he teaches Mathematics for Signal Processing, Data Compression, Image Processing, Convex Optimization, Machine Learning, and Constrained Optimization across undergraduate and graduate levels. Collaborations: Works with researchers at IMCCE (e.g., V. Lainey, J. Vaubaillon), IMT-Atlantique, and Grenoble-INP. Supervises PhD students in topics such as noise estimation, spectral dynamics, and Cassini image analysis. Labs/Teams: Active in EPITA Research Laboratory's Image Processing and Pattern Recognition team and IMCCE's PEGASE team.

Lateef JolaosoView profile

Lecturer

• Continuous Optimization
• Machine Learning
• Image Processing
• +3 more

Dr. Lateef Jolaoso is a Lecturer in Mathematical Sciences at the University of Southampton, specializing in the Operational Research group. He joined the university as a Research Fellow in 2021 and was promoted to Lecturer in 2023. His research focuses on developing algorithms for continuous optimization, machine learning, and image processing, with over 40 peer-reviewed publications (1,700+ citations, h-index 27). He holds a PhD in Mathematics from the University of KwaZulu-Natal (2019) and a Postgraduate Certificate in Academic Practice from the University of Southampton (2023). He teaches undergraduate and postgraduate modules in Operational Research, Financial Portfolio Theory, and Python Programming. As a member of CORMSIS (Centre for Operational Research, Management Sciences, and Information Systems), he supervises postgraduate students on optimization, data analytics, and machine learning projects. His work includes collaborations with international teams and contributions to journals as a referee. Key research areas include proximal point frameworks for bilevel programming, gradient-based algorithms for machine learning, and image restoration techniques. He has been recognized as a Fellow of the Institute of Mathematics and its Applications (FIMA) and the Higher Education Academy (FHEA).

Andras ZsakView profile

Lecturer

• Functional Analysis
• Banach Space Theory
• Operator Theory
• +2 more

Dr. Andras Zsak serves as an Affiliated Lecturer in the Department of Pure Mathematics and Mathematical Statistics at the University of Cambridge. His academic profile shows consistent engagement with the department, maintaining an office in Room C0.05 and active contact information including email A.Zsak@dpmms.cam.ac.uk and telephone 01223 764279. His professional homepage is available at https://www.dpmms.cam.ac.uk/~az10000. Dr. Zsak's primary research focus lies in functional analysis, particularly Banach space theory and operator theory. His work demonstrates significant contributions to metric geometry, geometric functional analysis, and the structure of operators on sequence spaces. His research often explores deep connections between geometric properties of Banach spaces and their operator algebras, with particular attention to closed ideals and embedding properties. An analysis of his publication record spanning over two decades reveals consistent research productivity with a clear thematic focus. His work frequently examines structural properties of Banach spaces, operator ideals, and metric embeddings, often in collaboration with prominent mathematicians including Thomas Schlumprecht, Edward Odell, and Daniel Freeman. The publications show increasing sophistication in handling complex geometric and algebraic structures within functional analysis. Dr. Zsak maintains active research collaborations across international mathematical communities, contributing to the advancement of geometric functional analysis. His work has appeared in prestigious journals including the Quarterly Journal of Mathematics, Bulletin of the London Mathematical Society, and Journal of the London Mathematical Society, reflecting the significance of his contributions to the field.

Nicolas BoulleView profile

Assistant Professor

• Numerical Analysis
• Numerical and Computational Mathematics
• Knowledge Representation
• +4 more

Nicolas Boulle is an Assistant Professor in Applied Mathematics at Imperial College London's Department of Mathematics within the Faculty of Natural Sciences. His research focuses on the intersection of numerical analysis and deep learning, particularly in discovering mathematical models (e.g., partial differential equations) from data and developing theoretically grounded numerical techniques. Education: DPhil (PhD) in Numerical Analysis from the University of Oxford (2018–2022) Research interests include numerical analysis, operator learning, machine learning, and their applications in solving complex mathematical problems. His work emphasizes data-driven methods for Green’s functions and the development of rational neural networks for enhanced accuracy in deep learning. Recent publications explore topics like dynamic mode decomposition, Koopman operators, and large language models' behavior. He advises PhD students and visiting researchers on projects involving operator learning and neural networks. Boulle contributes to open-source projects such as GreenLearning (for PDE Green’s functions) and RationalNets (rational activation functions in neural networks), hosted on GitHub.

• Cell Mechanics
• Tissue Dynamics
• Mechanobiology
• +4 more

Raimon Sunyer Borrell is a Researcher at the Institute for Bioengineering of Catalonia (IBEC), where he leads investigations within the Integrative Cell and Tissue Dynamics group. His work bridges biophysics, bioengineering, and cell biology to decipher how mechanical forces govern cellular and tissue behavior in physiological and pathological contexts. His primary research focuses on mechanobiology, particularly collective cell migration, durotaxis (stiffness-guided migration), and tissue dynamics. He investigates how cells sense mechanical cues through adhesion complexes like E-cadherin, with applications in understanding developmental processes, sleep apnea pathophysiology, and circadian rhythm disruptions. His interdisciplinary approach combines advanced experimental techniques—including atomic force microscopy, microfluidic platforms, and 3D tissue models—with computational modeling to quantify force transmission and tissue remodeling. Analysis of his 15 most recent publications reveals consistent innovation in developing physiomimetic models for disease study (e.g., intermittent hypoxia systems for sleep apnea) and fundamental discoveries about collective cell behaviors. His work demonstrates how mechanical coordination across cell clusters enables optimal migration through complex environments, with implications for regenerative medicine and tissue engineering. Dr. Sunyer actively contributes to IBEC's collaborative ecosystem, working within the Integrative Cell and Tissue Dynamics group that develops cutting-edge tools for probing cellular mechanics. His research directly supports IBEC's mission to translate fundamental discoveries into biomedical applications through interdisciplinary collaboration.