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Isaac SkogView profile

Associate Professor

• Underwater Surveillance
• Signal Processing
• Navigation Systems
• +5 more

Isaac Skog is an Associate Professor in Communication Systems at KTH Royal Institute of Technology, part of the Digital Futures Faculty. He also holds adjunct positions at Linköping University (Automatic Control) and the FOI Swedish Defence Research Agency. His research focuses on underwater surveillance, signal processing, navigation systems, and sensor fusion, with a particular emphasis on autonomous systems and acoustic environments. Education: BSc and MSc in Electrical Engineering, KTH Royal Institute of Technology (2003, 2005) PhD in Signal Processing, KTH Royal Institute of Technology (2010) Research Interests: Underwater acoustic surveillance and tracking Navigation systems using magnetic fields and inertial sensors Autonomous underwater vehicles and multi-agent systems Sensor calibration and fusion techniques Machine learning for sensor data analysis Recent Projects: COAST Project: Complex Acoustic Surveillance and Tracking Joint Sensing, Localization, and Communication for Next-Gen Autonomous Underwater Systems (WASP-funded) Tensor-Field Based Localization Electromagnetic Navigation for Smaller Unmanned Underwater Vehicles Grants & Collaborations: Funded by CENIIT, WASP Sweden, Swedish Research Council, Vinnova, and industry partners like Saab Dynamics. Active collaborations with FOI, Lund University, and Indian Institute of Science. Technical Demonstrators: Lyra platform for elevator condition monitoring OpenShoe experimental platform for foot-mounted inertial navigation Tactical Locator (TOR) for first-responder tracking Movelo AB spin-off for smartphone-based traffic monitoring

• Seismic Data Processing
• Signal Processing
• Microseismic Monitoring
• +2 more

Jubran Akram is an Adjunct Assistant Professor in the Department of Earth, Energy, and Environment within the Faculty of Science at the University of Calgary. His research focuses on advanced geophysical signal processing techniques. Primary research areas include: Microseismic data processing and event detection Wavefield separation and noise suppression methods Machine learning applications in seismic analysis Velocity modeling and hypocenter localization His publications (2018-2024) demonstrate consistent focus on developing computational methods for seismic data enhancement, including machine learning approaches for phase picking, noise reduction, velocity calibration, and event location.

Md Nasir UddinView profile

Assistant Professor

• Neuroimaging
• HIV Neurology
• Multiple Sclerosis
• +6 more

Roles and Affiliations: Md Nasir Uddin is an Assistant Professor in three departments at the University of Rochester: Neurology, Biomedical Engineering, and Electrical and Computer Engineering. He holds academic appointments within the Hajim School of Engineering & Applied Sciences. Education: PhD in Electrical and Computer Engineering from the University of Alberta (2015). Research Interests: Dr. Uddin’s work focuses on advanced neuroimaging techniques to study neurological disorders such as HIV-associated neurocognitive disorders, multiple sclerosis, and inflammatory bowel disease (IBD). His research integrates diffusion MRI, functional connectivity analysis, and computational methods to explore brain microstructure, neuroinflammation, and neurovascular function. Key areas include: Cerebral small vessel disease mechanisms White matter pathway resilience Neuroimaging biomarkers for chronic pain and cognitive impairment Impact of vascular comorbidities on brain structure Research Trends from Articles: His recent work emphasizes: Linking neuroinflammation markers to HIV-associated cognitive decline Validation of brain white matter pathways in chronic pain resilience Exploring microstructural brain changes in IBD and vascular diseases Development of AI-driven MRI techniques for tissue microstructure analysis Advising and Grants: While specific grants and advisees are not detailed, his multi-departmental roles suggest involvement in interdisciplinary research initiatives. Labs/Teams: Likely collaborates with biomedical engineering and neurology labs at the University of Rochester Medical Center, though specific lab affiliations are not explicitly stated.

• Bioacoustics
• Environmental Acoustics
• Sound Propagation
• +3 more

Guillaume Dutilleux is a Professor in the Department of Electronic Systems at the Norwegian University of Science and Technology (NTNU). His research focuses on bioacoustics, environmental acoustics, sound propagation, and noise mitigation in contexts such as wind turbines, railways, and road traffic. He has contributed to interdisciplinary projects, including acoustic tracking of wildlife and automated biodiversity monitoring. Dutilleux teaches courses like TTT4181 (Acoustic Discovery), TTT4290 (Bioacoustics for Biodiversity), and TFE4595 (Electronic Systems Design). He has published extensively in journals such as Applied Acoustics , Journal of the Acoustical Society of America , and Bioacoustics , with recent work addressing time-varying acoustical systems and noise annoyance prediction. His research emphasizes practical applications of acoustics in environmental and technological domains. Leading acoustic experiments on long-range sound propagation and noise impact assessment Developing educational programs in acoustical measurement techniques Collaborating on automated acoustic monitoring for endangered species conservation His work bridges theoretical acoustics with real-world challenges, contributing to both academic discourse and policy-informing studies.

• Theoretical Computer Science
• Formal Methods
• Artificial Intelligence
• +3 more

M.Sc. Pascal Esser is a researcher at the Department of Informatics at Technical University of Munich (TUM). He specializes in theoretical computer science, formal methods, and machine learning, with a focus on neural networks and verification techniques. His teaching responsibilities include courses on theoretical computer science fundamentals such as Petri Nets, Automata and Formal Languages, Logic, and Model Checking. He has contributed to research in representation learning, graph neural networks, and probabilistic models, as evidenced by his recent publications. Esser is involved in the development of tools like Automata Tutor and has collaborated on projects such as PaVeS and ConVeY. His work bridges formal methods and artificial intelligence, emphasizing rigorous theoretical foundations while exploring practical applications in neural network verification and algorithm design. Education: Master of Science in Computer Science (degree details unspecified). Research Interests: Formal verification, machine learning theory, neural networks, representation learning, graph algorithms, and theoretical computer science. Professional Activities: Active in teaching advanced undergraduate and graduate courses since 2020, with a focus on foundational topics in informatics and emerging areas like neural network verification. Egger's research trends emphasize interdisciplinary approaches, combining insights from statistical learning theory with algorithmic analysis to address challenges in modern AI systems. His publications highlight advancements in understanding model dynamics, kernel-based methods, and graph neural network architectures. While no specific grants or awards are listed, his sustained academic contributions indicate active engagement in the informatics research community. He is part of a research group at TUM including notable figures like Javier Esparza and Jan Křetínský, contributing to tools and frameworks for automata theory and model checking. His work often intersects with practical software implementations such as the Automata Tutor educational platform and Strix verification tools.

• Diffusion MRI
• Neuroplasticity
• Brain Connectivity
• +5 more

Dr. Alfred Anwander is a Senior Researcher in the Department of Neuropsychology at the Max Planck Institute for Human Cognitive and Brain Sciences in Leipzig, Germany. His work focuses on diffusion MRI as a biomarker for brain development and plasticity, particularly in language-related pathways and neuroplasticity mechanisms during second language acquisition. He has held research positions at the institute since 2000, contributing to projects such as the DFG-funded "Human microstructural connectomics" and "Dynamic Connectome" initiatives. Education 1991–1996: Diploma in Electrical Engineering from the University of Karlsruhe 1995–2000: MSc (DEA) and PhD in Signal and Image Processing from INSA Lyon (France), with a thesis on model-based color image segmentation. Research Interests Anwander’s research integrates advanced neuroimaging techniques to study structural connectivity in the human brain. Key areas include: Diffusion MRI applications in developmental neuroscience and neuroplasticity Language-related white matter pathways Evolutionary comparisons of brain connectivity in primates Methodological advancements in high-resolution MRI and tractography Grants & Projects DFG project "Human microstructural connectomics: Computational modelling and validation with histology and CLARITY (MiCo-MRI)" DFG project "The dynamic connectome underlying language in the brain (DynaCon)" Evolution of Brain Connectivity (EBC) initiative Labs & Teams He collaborates within the institute’s Cortical Networks and Language Research groups, focusing on interdisciplinary approaches to connectomics and neuroimaging technology development.

Marc CASTELLAView profile

Lecturer

• Signal Processing
• Blind Source Separation
• Neural Networks
• +3 more

Prof. Marc CASTELLA is a Lecturer at Telecom SudParis, part of the SOP (Signal and Optimization Processing) unit. His research focuses on signal processing, particularly in blind source separation, nonlinear reconstruction, and optimization techniques. He has extensively contributed to areas such as sparse signal recovery, neural networks, and tensor decomposition. His work often addresses challenges in noisy environments and nonlinear systems. Recent publications include advancements in clipped signal recovery, motor torque estimation, and global optimization methods. Though no specific awards are listed, his prolific publication record reflects his impactful contributions to signal processing and applied mathematics. He collaborates with researchers like Jean-Christophe Pesquet and Arthur Marmin on projects involving polynomial systems and neural network models. His work is applied in diverse fields from industrial electronics to data analysis.

Todd YoungView profile

Professor

• Dynamical Systems
• Ordinary Differential Equations
• Random Differential Equations
• +10 more

Todd Young is a Professor and Chair in the Department of Mathematics at Ohio University , part of the College of Arts and Sciences . He is also a member of the Quantitative Biology Institute and the Infectious and Tropical Disease Institute . His research integrates dynamical systems theory with biological modeling, particularly in cell cycle regulation, clustering phenomena in yeast, and biomedical informatics. Education: Ph.D. in Mathematics, Georgia Institute of Technology, 1995 M.S. in Mathematics, University of California at Riverside, 1991 M.S. in Engineering Mechanics, University of Kentucky, 1987 B.S. in Mathematics, University of Kentucky, 1985 Research Interests: Todd Young's work centers on the qualitative theory of ordinary and random differential equations , with applications in cell cycle dynamics , biological feedback systems , clustering in populations , and binary classification in biomedical informatics . He develops mathematical models to understand how feedback mechanisms lead to synchronization in yeast and how dynamical systems principles apply to neural and immune responses. His research spans pure theory, such as bifurcation and ergodic theory, to applied problems in public health, like ventilator-associated pneumonia detection. Research Trends: His recent publications reveal a strong focus on mathematical biology , particularly in modeling the cell cycle with feedback and clustering behavior . He increasingly integrates numerical methods and tensor approximation dynamics into his work, collaborating across disciplines in physics, engineering, and medicine. His articles frequently appear in journals like SIAM Journal on Applied Dynamical Systems , Journal of Mathematical Biology , and Nonlinearity , reflecting a blend of rigorous analysis and biological relevance. Scientific Awards and Roles: Joint Editor-in-Chief, Dynamical Systems journal Recipient of the College of Arts and Sciences “Dean's Outstanding Teacher Award” Editorial Board memberships in Discontinuity, Nonlinearity and Complexity and Annual Review Chaos Theory, Bifurcations & Dynamical Systems Founding member of the Quantitative Biology Institute Active member of SIAM, AMS, and MAA Advising and Grants: Todd Young has advised numerous Ph.D., M.S., and undergraduate students, many of whom have pursued academic or data science careers. His research has been funded by the National Institutes of Health (NIH) and the National Science Foundation (NSF) , notably through the NIH-NIGMS R01GM090207 grant on mathematical biology. He emphasizes student training through structured research participation, including exploratory and paid assistantships. Labs and Teams: He leads the Dynamics in Biology Research Group , which includes students and collaborators working on projects ranging from theoretical dynamics to computational biology. The group fosters interdisciplinary collaboration, particularly with biologists and medical researchers at Ohio University and beyond.

Tozammel HossainView profile

Assistant Professor

• Bioinformatics
• Health Informatics
• Social Media Analytics
• +3 more

Tozammel Hossain is an Assistant Professor at the University of North Texas. His research focuses on Bioinformatics, Health Informatics, Social Media Analytics, Event Forecasting, and Cybersecurity. He holds a Ph.D. and M.S. from Virginia Tech and a B.S. from Bangladesh University of Engineering and Technology. Education: Ph.D., Virginia Tech M.S., Virginia Tech B.S., Bangladesh University of Engineering and Technology Research Interests: Hossain’s work spans interdisciplinary areas including healthcare analytics, geopolitical event prediction, cybersecurity, and machine learning applications. He develops models for continuous blood glucose prediction, gene expression analysis, and ransomware attack detection. His methods often integrate social media data and temporal modeling for public health and security applications. Research Trends: His articles highlight advancements in LSTM networks for healthcare and event forecasting, federated learning in healthcare data, and hybrid systems for geopolitical analysis. Themes include personalized medicine, conflict prediction, and leveraging social media for syndromic surveillance. Labs & Teams: While specific lab affiliations are not listed, his projects suggest collaboration with interdisciplinary teams in healthcare informatics, cybersecurity, and computational biology.

Antoine TilloyView profile

Assistant Professor

• Quantum Mechanics
• Quantum Field Theory
• Tensor Networks
• +7 more

Antoine Tilloy is an Assistant Professor at the Centre Automatique et Systèmes, Mines ParisTech, part of Paris Sciences et Lettres (PSL). He is also affiliated with the Quantic research team at Inria, reflecting his dual academic and research institute role. His work bridges theoretical physics, quantum information, and nuclear engineering, with a focus on tensor networks and quantum field theory. Research Interests: Quantum Field Theory (non-perturbative methods) Tensor Networks for many-body quantum systems Quantum Foundations and Measurement Theory Semiclassical and Quantum Gravity Nuclear Engineering and Fuel Cycle Physics His recent research has explored the application of tensor networks to solve quantum field theories, the philosophical and technical challenges in defining QFT rigorously, and the physics of nuclear fuel recycling. He advocates for a 'pile of dirt' approach—tackling messy, realistic models over highly structured but artificial ones. Scientific Contributions: Active contributor to non-perturbative QFT methods ERC-funded research and postdoctoral supervision Public engagement through blog posts and science communication Advising and Grants: While specific students are not listed, he leads an ERC-funded project and has advertised postdoctoral positions, indicating active mentorship. His research is supported by competitive European funding, underscoring its significance in the field. Labs and Teams: He is a member of the Quantic team at Inria, a leading research group in quantum information and technologies, and conducts his work at the Centre Automatique et Systèmes at Mines ParisTech.

Anders Nymark ChristensenView profile

Associate Professor

• Image Analysis
• Computer Vision
• Medical Imaging
• +7 more

Anders Nymark Christensen is an Associate Professor in the Department of Applied Mathematics and Computer Science at the Technical University of Denmark (DTU), specializing in Visual Computing. His work bridges computer vision, medical imaging, and materials science, with a strong focus on explainable AI in clinical applications and structural analysis of complex materials. Institution: Technical University of Denmark (DTU) School: Department of Applied Mathematics and Computer Science Department: Visual Computing Role: Associate Professor Christensen’s research interests include image analysis, computer vision, explainable AI, ultrasound imaging, fetal medicine, and fiber orientation in composites. He applies advanced computational techniques to solve real-world problems in healthcare and materials engineering, particularly through structure tensor analysis and deep learning. His recent publications highlight a strong trend in developing AI tools for clinical decision support in fetal medicine, structural analysis of food and composite materials, and ultrasound imaging. These works span journals such as Scientific Reports , Food Structure , and IEEE Access , reflecting interdisciplinary innovation. Clinical validation of explainable AI for fetal growth scans Characterization of anisotropy in mozzarella cheese Determining fetal orientations from ultrasound video Structure tensor analysis in composites He supervises several PhD students in projects related to AI in skin lesion analysis, cancer detection, and ultrasound imaging. His collaborative network includes key researchers at DTU such as Anders Bjorholm Dahl, Vedrana Andersen Dahl, and Mads Nielsen. He has been involved in significant research grants and is active in both medical and engineering domains, contributing to open datasets and reproducible research. Christensen leads and contributes to numerous active projects, including: Decision Support AI for Skin Lesions (2024–2027) Fighting Cancer with Generative AI (2024–2027) SONAI: Explainable AI in the Clinic (2022–2026) Deep learning for identifying biomarkers in medical images (2022–2025) His work supports UN Sustainable Development Goals related to health, innovation, and responsible consumption, particularly through advancements in medical diagnostics and sustainable materials.

Chris H.Q. DingView profile

Professor

• Machine Learning
• Data Mining
• Bioinformatics
• +7 more

Chris Ding is a Professor in the Department of Computer Science and Engineering at the University of Texas at Arlington, with a PhD from Columbia University. His research bridges Machine Learning, Data Mining, and Bioinformatics. Affiliations: University of Texas at Arlington; Lawrence Berkeley National Laboratory (staff computer scientist). Research Focus: Clustering algorithms (K-means, spectral clustering), matrix/tensor decompositions (NMF, Tucker), and bioinformatics applications (protein structure prediction, gene expression analysis). Professional Engagement: Organized KDD and ICDM workshops; served on NSF, DOE, and international grant review panels. Key Contributions: Established theoretical links between PCA/NMF and K-means; pioneered SVM use for protein 3D structure prediction. His work emphasizes practical applications in biological networks, dimensionality reduction, and optimization-based machine learning frameworks.

• Neurosurgery
• Pediatrics
• Molecular Biology
• +4 more

Oguz I Cataltepe is a Professor at the T.H. Chan School of Medicine, affiliated with UMass Chan Medical School. His primary appointment is in the Department of Neurological Surgery, with additional appointments in Pediatrics and Radiation Oncology. MD in Medicine from Hacettepe University, Ankara, Turkey His research focuses on neurosurgery and gene therapy applications for central nervous system disorders, particularly in pediatric populations. Additional interests include molecular biology, neurogenetics, and medical therapeutics. His work spans clinical neurosurgery, pathological analysis of neurological conditions, and medical imaging techniques. The 15 most recent publications reveal trends in neurological surgery, gene therapy for CNS diseases (e.g., Tay-Sachs research), and pediatric neurosurgical challenges. Key areas include neuroimaging applications, fatty acid binding protein studies in brain tumors, and innovative surgical techniques across various neurological conditions. Co-authors include prominent researchers like Terence Flotte, Heather Gray-Edwards, and Thomas Smith. Networks reflect collaborations in gene therapy, neurosurgical innovations, and pediatric neurological care.

• Tensor Decompositions
• Quantum Computing
• Fredholm Integral Equations
• +7 more

Patrick Gelß is a Postdoctoral Researcher at Zuse Institute Berlin, working in the AI in Society, Science, and Technology department. He leads the iol.QUANT research group focused on quantum-inspired and tensor-based methods, with affiliations to Freie Universität Berlin and collaborative projects like MATH+ Cluster of Excellence. Diploma in Mathematics/Physics (2013, Freie Universität Berlin) PhD in Mathematics (2017, summa cum laude, Freie Universität Berlin) Postdoctoral roles at Zuse Institute Berlin (2020-present) and CRC 1114 (2017-2022) His research bridges tensor decompositions, quantum computing, and dynamical systems, with notable contributions to: Graph Isomorphism: Continuous optimization approaches via doubly stochastic matrices and Frank-Wolfe algorithms Quantum Simulation: Tensor network representations for quantum circuits and the open-source WaveTrain package KvN Mechanics: Mathematical analysis of existence/uniqueness solutions for bounded domains Fredholm Networks: Novel training paradigms for neural networks using integral equations Scientific achievements include: Developing Scikit-TT for tensor-train computations Organizing major workshops (QML@SC2024, TMQS 2024) Leading the Thematic Einstein Semester 2024 on Mathematics for Quantum Technologies His work spans chemical kinetics, quantum dynamics, and quantum machine learning, with applications to CO oxidation models, exciton-phonon systems, and mutational hierarchies in medicine.

Monique LaurentView profile

Professor

• Discrete Optimization
• Semidefinite Programming
• Polynomial Optimization
• +4 more

Monique Laurent is a senior researcher at CWI Amsterdam in the Networks and Optimization Group and holds a part-time appointment as a full professor at the Department of Econometrics and Operations Research of Tilburg University. Her academic career spans institutions in France (CNRS, CNET), Germany (Humboldt Fellow at the University of Bonn), and the USA (visiting scientist at Yale). She is a SIAM Fellow (2017) and recipient of the Khachiyan Prize (2023) . PhD in Mathematics, University of Paris Diderot (1986) Research interests: discrete optimization, semidefinite programming, polynomial optimization, quantum information, and algebraic methods. Her work focuses on bridging continuous and discrete optimization, with applications to quantum information. She has organized major workshops such as the Learning Enhanced Optimization research semester at CWI and contributed to the TENORS EU project (2024-2027). Recent publications highlight her contributions to semidefinite programming hierarchies, polynomial optimization, and their applications to graph theory and quantum computing. Key article trends include: Analysis of sum-of-squares and moment matrix hierarchies for polynomial optimization Applications to quantum information, graph stability, and matrix factorization Convergence rates and error bounds for semidefinite relaxations Scientific awards: Khachiyan Prize (2023) SIAM Fellow (2017) Humboldt Fellowship (1991-1992) She has supervised numerous academic initiatives and students, including a master project on polynomial equation solutions at CWI. Her editorial roles include associate editorships for journals like SIAM Journal on Optimization and Numerical Algebra, Control and Optimization .