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Gunther CornelissenView profile

Professor

• Algebraic Geometry
• Arithmetic Geometry
• Automorphic Forms
• +4 more

Prof. Gunther Cornelissen is a Professor of Fundamental Mathematics and Academic Director at the Mathematical Institute of Utrecht University's Faculty of Science. His research focuses on algebraic and arithmetic geometry, automorphic forms, number theory, noncommutative geometry, and mathematical physics, with a thematic emphasis on Foundations of Complex Systems. He led the Utrecht Geometry Centre (2014–2020), a graduate program funded by NWO to support four PhD candidates in geometry. Cornelissen teaches advanced courses like Fields and Galois theory and serves as editor for Indagationes Mathematicae (Dutch Royal Mathematical Society). His professional roles include membership in the Royal Holland Society of Sciences (KHMW) and the ESF College of Expert Reviewers. His research integrates algebraic methods with dynamical systems, spectral geometry, and quantum statistical mechanics. Recent work explores isospectrality, endomorphisms of algebraic groups, and applications of automata theory to group structures. Cornelissen’s projects often bridge pure mathematics with computational methods, such as polynomial-time graph recognition algorithms and stable gonality analysis.

Kathlén KohnView profile

Associate Professor

• Algebraic Geometry
• Machine Learning
• Computer Vision
• +4 more

Kathlén Kohn is an Associate Professor in Mathematics at KTH Royal Institute of Technology in Stockholm, Sweden (since December 2024). She holds a PhD from Technische Universität Berlin (2018) and dual Master's degrees in Mathematics and Computer Science from Paderborn University (2015). Her research bridges algebraic geometry, geometric deep learning, and computer vision, focusing on algebraic structures in neural networks and geometric problems in AI. Education: PhD in Mathematics, TU Berlin (2015–2018) Master of Science in Mathematics & Computer Science, Paderborn University (2013–2015) Bachelor of Science in Mathematics & Computer Science, Paderborn University (2009–2013) Research: Kohn explores neural algebraic geometry , applying algebraic techniques to analyze deep learning architectures like polynomial neural networks and self-attention mechanisms. Her work includes minimal problems in computer vision (e.g., PLMP framework), metric algebraic geometry, and invariant theory connections to maximum likelihood estimation. She co-authored the book Metric Algebraic Geometry (2024) and leads the WASP-funded project on 3D scene perception. Recent Articles: Focus on geometric neural network analysis, self-attention mechanisms, and structure-from-motion problems. Key venues include ICML, ICLR, CVPR, and SIAM Journal on Applied Algebra and Geometry. Awards: Wallenberg Prize (2025), SIAM SIGEST Award (2024), Swedish L'Oréal-Unesco Award (2023), Göran Gustafsson Prize (2021), and multiple fellowships including Marie Skłodowska-Curie. Teaching & Outreach: Lectures on algebraic vision, nonlinear algebra, and cryptography. Active in promoting gender equality in STEM through ELLIS and Swedish Young Academy.

• Computer Graphics
• Geometry Processing
• 3D Reconstruction
• +3 more

Dr. Jing Ren is affiliated with the Department of Computer Science at ETH Zürich, holding a role within the Professorship for Computer Science. Their research focuses on computational geometry, 3D reconstruction, and computer graphics, with notable contributions to shape analysis, non-rigid matching, and fabric modeling. Dr. Ren’s work bridges theoretical advancements with practical applications in textile design, architectural modeling, and medical imaging. They collaborate extensively on projects involving functional maps, optimization algorithms, and geometric morphometrics. Key research interests include: Non-rigid shape correspondence and matching Computational modeling of woven fabrics and textiles 3D face and building reconstruction techniques Efficient spectral and discrete optimization methods Recent publications (2022–2024) emphasize innovations in fabric parameterization, Gaussian noise distribution, and rethinking 3D face reconstruction benchmarks. Their work often employs machine learning and functional map frameworks to solve geometric problems across disciplines. Laboratory and team affiliations are not explicitly detailed in the provided materials, but their research aligns with ETH Zürich’s broader initiatives in computer science and engineering. No grants or advising activities are specified in the current data.

Matthias SchröterView profile

Adjunct Associate Professor

• Granular Physics
• Statistical Mechanics
• Materials Science
• +3 more

Dr. Matthias Schröter is an Adjunct Associate Professor at Duke Kunshan University and a consultant at Hypatia - Science Consulting. His research focuses on granular physics, statistical mechanics, and imaging techniques, with emphasis on granular packing structures, fluid dynamics, and material characterization. He holds a habilitation (postdoctoral qualification) and has conducted postdoctoral research from 2015-2018. His work spans experimental and theoretical studies, including analysis of granular shear bands, X-ray tomography of particle systems, and neutron tomography for porous media. He has contributed to understanding contact networks in frictional ellipsoid packs and coarsening mechanisms in granular mixtures. His expertise includes advanced imaging methods such as superresolution microscopy and neutron tomography. Research interests include granular matter dynamics, phase transitions in granular systems, and structural analysis of amorphous materials. His publications frequently address experimental methodologies for granular media characterization and fluid dynamics in porous systems. He has advised on projects involving granular segregation, fluid flow in porous media, and thermal characterization of granular systems. His consulting work bridges academic research with industrial applications in materials science and imaging technology.

Joonas IlmavirtaView profile

Associate Professor

• Inverse Problems
• Mathematical Physics
• Applied Mathematics
• +4 more

Joonas Ilmavirta is an Associate Professor in the Department of Mathematics and Statistics at the University of Jyväskylä, affiliated with the Faculty of Mathematics and Science. He is part of the Centre of Excellence of Inverse Modelling and Imaging (2018–2025) and contributes to the Inverse Problems research group, focusing on mathematical frameworks for indirect measurements. His work intersects applied and pure mathematics, with applications in geophysics, material science, and astrophysics. Research interests include inverse problems, geometric analysis, and tomography. Key areas are the reconstruction of structures from boundary measurements (e.g., seismic imaging, elasticity tomography), spectral rigidity of manifolds, and quantum computing algorithms for inverse problems on graphs. He explores theoretical foundations and practical implementations of indirect measurement techniques, emphasizing stability, uniqueness, and computational methods. Notable contributions include studies on Finsler geometry, anisotropic elasticity, and low-regularity manifolds. His work addresses challenges in geophysical imaging, such as determining Earth’s internal structure or gas giant compositions using seismic or gravitational data. Collaborative projects involve the FAME Flagship initiative, advancing sensing, imaging, and modeling through inverse problem methodologies. Publications emphasize geometric tomography, ray transforms, and mathematical physics, with a focus on rigorous analysis and interdisciplinary applications. He engages with both theoretical developments and computational tools, bridging pure mathematics with real-world inverse problem solutions.

James PrittsView profile

Assistant Professor

• Computer Vision
• Underwater Robotics
• 3D Geometry
• +5 more

Dr. James Pritts is a computer vision researcher currently working at the Institute of Computer Science, Kiel University, within the Marine Data Science group. He is affiliated with the Faculty of Engineering and contributes to interdisciplinary marine research through the KiTE early-career fellowship program. His work bridges robotics, computer vision, and ocean science, focusing on enabling autonomous underwater vehicles (ROVs) to operate effectively in challenging, low-visibility environments. His research interests include: Underwater visual localization and mapping Robust 3D reconstruction in turbid waters Geometric calibration of camera systems Sensor fusion (camera, IMU, DVL) Non-rigid reconstruction (e.g., jellyfish) Autonomous monitoring of marine ecosystems Dr. Pritts' work supports applications in marine biology, underwater archaeology, and infrastructure inspection. He develops algorithms that allow robots to assess their own perception accuracy, enabling reliable long-term monitoring of seagrass beds and fragile marine species. His research is deeply collaborative, involving partnerships with GEOMAR and MBARI, and aligns with Kiel University’s priority research areas such as Kiel Marine Science and Ocean Health. He has previously held research roles at NASA, DARPA, and Facebook Reality Labs, and was an Assistant Professor at the Ukrainian Catholic University. His academic journey reflects a strong focus on geometric computer vision and real-world deployment of complex systems. Notable scientific contributions include advancing calibration techniques for underwater optics and developing robust localization methods under refractive distortions. His work often requires solving multiple vision challenges simultaneously, such as refraction, non-rigid deformation, and sparse features. Dr. Pritts is actively involved in hands-on robotics, working closely with hardware engineers to deploy ROVs in coastal waters near Kiel. He emphasizes practical utility in research and is driven by the principle of being useful. He holds a PhD from the Czech Technical University in Prague (2020) and has conducted postdoctoral research at Chalmers University and the Czech Institute of Informatics, Robotics & Cybernetics.

• Computer Vision
• Biometrics
• Face Recognition
• +3 more

Xiaoming Liu is a Professor at Michigan State University with extensive contributions to computer vision and biometrics. His research spans face recognition, 3D reconstruction, image forgery detection, and adversarial machine learning, with over 300 publications from 1999 to 2025. His primary research interests include Computer Vision , Biometrics , and Adversarial Machine Learning . Liu's work focuses on developing robust systems for face recognition at scale, detecting image manipulations, and creating 3D reconstruction techniques. Recent projects include SapiensID for human recognition, FRCSyn for synthetic face recognition, and proactive watermarking schemes. Liu's publication trends show increasing focus on multi-modal biometrics (combining face, body, and gait), image forgery detection using hierarchical approaches, and adversarial defense mechanisms . His 2023-2025 work emphasizes synthetic data applications and physics-driven recognition systems. Liu has mentored numerous students including Feng Liu, Minchul Kim, and Xiao Guo, who frequently co-author his papers. His research has been supported by grants enabling projects like FarSight (long-range biometrics) and ProMark (proactive watermarking). He leads research in biometrics security and computer vision, with recent work focusing on ethical AI applications and robust recognition systems. His team develops tools for detecting deepfakes and improving recognition in challenging conditions.

• Incremental Sheet Metal Forming
• Single Point Incremental Forming (SPIF)
• Manufacturing Engineering
• +5 more

Yannick Carette is a Researcher at KU Leuven's Department of Mechanical Engineering, affiliated with the MaPS (Manufacturing, Assembly and Productivity in Sheet metal forming) division, which serves as a core lab for Flanders Make - the strategic research center for Flanders' manufacturing industry. His work bridges advanced manufacturing techniques with biomedical applications, focusing on precision sheet metal forming processes. Dr. Carette's research expertise spans several interconnected domains: Single Point Incremental Forming (SPIF) process optimization and accuracy enhancement Digital Image Correlation for real-time process monitoring Multi-stage forming strategies using geometric decomposition techniques Application of manufacturing processes to medical implant production Statistical analysis of anatomical structures for implant design His publication trajectory reveals an evolution from fundamental manufacturing process understanding toward specialized biomedical applications. Early work focused on core SPIF mechanics and multi-step forming strategies, while recent research integrates machine learning for accuracy prediction and applies statistical shape modeling to anatomical analysis. This interdisciplinary approach has produced innovations like using non-rigid registration techniques - adapted from medical imaging - to improve SPIF process planning. Dr. Carette's collaborative research with medical professionals has significantly advanced understanding of anatomical variations in bones like the clavicle and tibia, explaining why standard implants often fail to fit properly. His work on thin-shell titanium clavicle implants demonstrates how manufacturing flexibility can address challenges in patient-specific medical device production.

Fred GittesView profile

Clinical Professor

• Statistical Physics
• Biological Physics
• Optical Trapping
• +7 more

Fred Gittes is a Clinical Professor in the Department of Physics and Astronomy at Washington State University, part of the College of Arts and Sciences. He is actively involved in both undergraduate and graduate education, teaching a broad range of physics courses including honors physics, electromagnetism, modern physics, and biological physics. His research lies at the intersection of statistical physics, biophysics, and theoretical methods in physics. His research interests include: Statistical Physics and Thermodynamics Biological Physics and Micromanipulation Optical Trapping and Laser-Based Techniques Differential Geometry and Gauge Theory in Physical Systems Mechanics of Biological Motors and Polymers His recent publications reflect a continued interest in theoretical modeling of physical phenomena, especially in biological contexts. He has developed methods in microscopy and optical trapping, and has explored the use of differential forms in classical physics. His work has been highlighted by Nature Physics and recognized with the JOSA Spotlight on Optics. A recurring theme in his research is the use of thermal fluctuations to extract mechanical properties of soft and biological materials. Notable scientific recognitions include: Excellence in Teaching by a Clinical Faculty Member (2020 Faculty Teaching Award) Highlighted by Nature Physics for his 2018 paper on deriving Einstein's results from the Jarzynski equality Selected for JOSA’s Spotlight on Optics distinction for work on nonconservative forces in optical traps Fred Gittes has mentored several award-winning undergraduate students, including multiple recipients of the Four Outstanding Senior Award who cited him as their most influential teacher. He has delivered numerous colloquia on topics ranging from falling cats and gauge theory to the physics of blood spatter and nuclear radiation in popular culture. His teaching includes graduate-level courses in thermal physics, electrodynamics, and differential geometry, as well as innovative interdisciplinary offerings.

• Virtual Reality
• Extended Reality (XR)
• Computer Graphics
• +5 more

Prof. Dr. Kiran Varanasi serves as a Professor in Virtual and Extended Reality at the Faculty of Computer Science and Media, Leipzig University of Applied Sciences. His research focuses on 3D shape processing, real-time facial animation, and convolutional neural network applications in computer vision. University: Leipzig University of Applied Sciences School: Faculty of Computer Science and Media Academic Rank: Professor Research Interests: Varanasi's work bridges virtual reality , machine learning , and interactive systems . Key areas include: 3D reconstruction using deep learning Facial animation pipelines Light field data processing Surface defect classification with CNNs Interactive character control Publication Trends (2018–2021): His recent articles emphasize monocular 3D reconstruction , real-time feature extraction , and immersive VR environments , often leveraging convolutional networks and geometric warping techniques. Advising and Grants: No specific students or advisory roles are listed in the provided text. The faculty participates in collaborative projects like website redesign for the Egyptian Museum, though Varanasi's direct involvement isn't specified. Labs and Teams: The faculty includes multimedia labs and research groups in digital transformation, but Varanasi's direct affiliations with these units are not explicitly stated.

ALİ NEHİRView profile

Assistant Professor

• Neurosurgery
• Brain and Nerve Surgery
• Minimally Invasive Surgical Techniques
• +3 more

Dr. ALİ NEHİR currently serves as an Assistant Professor at Gaziantep University Faculty of Medicine, Department of Surgical Medicine. His clinical and academic work focuses on Neurosurgery, with a specialization in Brain and Nerve Surgery. He is affiliated with the Turkish Neurosurgical Society, Middle East Spine Society, and the Nervous System Surgery Association. Dr. NEHİR completed his medical specialization at Gaziantep University (2016-2022) and holds a medical license from Uludağ University (2007-2014). His research spans multiple neurosurgical domains, including minimally invasive endoscopic techniques, pituitary adenoma complications, and molecular genetic studies in glial tumors. His publications highlight expertise in endoscopic approaches for orbital and sellar pathologies, oxidative stress analysis in subarachnoid hemorrhages, and surgical management of foramen magnum meningiomas. Recent projects include TIMP gene polymorphism investigations in glial tumors and pituitary adenoma studies. Key memberships: Middle East Spine Society (2023), Turkish Neurosurgical Society (2022) Non-academic clinical role: Brain and Neurosurgery Specialist at Turkey's Ministry of Health (2024) Training: Live animal spine surgery osteotomy course at Istanbul Acıbadem University (2024)

Fabrice HeitzView profile

Professor

• Signal and image processing
• Statistical modeling
• Markovian models
• +7 more

Professor Fabrice Heitz serves as Director of the ICube Laboratory at Telecom Physics Strasbourg, University of Strasbourg, where he leads the IMAGeS research team (Images, Modeling, Learning, Geometry and Statistics). His academic career spans several decades with significant leadership roles including Director of LSIIT UMR 7005 CNRS (2005-2012) and Deputy Director of ICube Laboratory (2013-2022). Professor Heitz's educational background is closely tied to Telecom Physics Strasbourg where he has taught foundational courses including Introduction to Signal Processing, Processing Random Signals, Statistics, and 2D Signal and Image Processing across undergraduate and Master's levels in the Imaging, Robotics, Engineering for the Living program. His research expertise centers on signal and image processing with specialization in statistical modeling of 3D+t images , Markovian and non-Gaussian models , appearance models , and deformable models . His work in machine learning addresses critical challenges in rigid and deformable registration of multimodal images, 3D image segmentation, 3D+t temporal tracking, and inter-image change detection, with primary applications in medical imaging and time sequence analysis . Current research directions include cryo reconstruction EM, 3D surface modeling, and preoperative planning for ARC needles. Professor Heitz has established himself as a prominent figure in medical image analysis, with his supervised doctoral research spanning applications from brain MRI analysis to lung CT segmentation. His former students have achieved notable positions at INRIA, Philips, Microsoft, and universities worldwide, demonstrating the impact of his mentorship. Director of ICube Laboratory (2023-present) Elected member of Research Commission of University of Strasbourg (2021-2025) Former Head of IMAGeS team at ICube (2016-2022) Associate Editor of IEEE Transactions on Image Processing (1996-1999) Professor Heitz leads the IMAGeS research team, which brings together experts working at the intersection of image processing, machine learning, geometry, and statistics to develop innovative solutions for medical diagnostics, surgical planning, and other critical applications. His leadership extends to numerous national and international committees including AERES/HCERES expert panels and ANR review boards.

• Medical Imaging
• Computer-Aided Diagnosis
• Machine Learning
• +7 more

Giovanni Luca Masala is a Senior Lecturer in the School of Computing at the University of Kent, Canterbury, UK. His verified institutional email is g.masala@kent.ac.uk, and he maintains an active ORCID profile (0000-0001-6734-9424). Dr. Masala's research spans multiple interdisciplinary domains at the intersection of computer science and healthcare. His primary research interests include medical imaging, particularly mammography and diagnostic x-ray imaging; computer-aided diagnosis systems; machine learning applications in healthcare; biometrics and cloud computing security; neural networks for visual tracking; and natural language processing. His work demonstrates a consistent focus on applying advanced computational techniques to solve real-world medical and security challenges. Analysis of his publication record spanning from 2003 to 2025 reveals a clear research trajectory evolving from medical imaging and computer-aided diagnosis toward broader applications of artificial intelligence in healthcare. Early work focused on mammographic screening, thalassemia detection, and medical image analysis. More recent publications show expansion into assistive robotics for elderly care, stress detection using wearable devices, and natural language processing applications. His collaborative work appears across multiple high-impact journals in computer science, medical physics, and healthcare technology. Dr. Masala has established significant research collaborations with institutions across Europe, particularly in Italy, with numerous co-authored publications in medical imaging and AI domains. His work has appeared in reputable journals including IEEE Transactions, Medical Physics, Computer Physics Communications, and various MDPI publications. His research has practical applications in healthcare technology, particularly in computer-aided diagnostic systems, biometric security for cloud services, and driver monitoring systems. The interdisciplinary nature of his work bridges computer science, medical physics, and clinical applications, demonstrating translational research impact.

• Sports Medicine
• Orthopedics
• Athletic Training
• +6 more

Dr. Janet Simon serves as Associate Professor and Associate Dean for Research in Ohio University's College of Health Sciences and Professions, with over 40 peer-reviewed publications and $5+ million in NIH/DoD grant funding. Her work bridges athletic training practice, neurocognitive science, and patient outcomes assessment. Her educational foundation includes: PhD in Biostatistics, Indiana University (2014) MS in Applied Statistics, Indiana University (2014) MS in Athletic Training, Ohio University (2010) BS in Athletic Training, Southern Connecticut State University (2008) Dr. Simon's research revolutionizes sports injury rehabilitation through three pillars: patient-reported outcome development for quality-of-life assessment, neurocognitive biomechanics of ACL/ankle recovery, and technology-enhanced assessment using virtual reality. Her 2025 publications reveal accelerating focus on brain-muscle interactions during movement, with 70% of recent work incorporating fMRI or motion-capture analysis of cognitive-motor function post-injury. Her publication trajectory demonstrates strategic expansion from foundational injury epidemiology (2014-2019) into neural mechanisms of recovery (2020-2025), now encompassing opioid use disorder in athletic contexts and Cirque du Soleil performer health—showcasing remarkable interdisciplinary range while maintaining core sports medicine expertise. Major recognitions include: Journal of Athletic Training Clint Thompson Manuscript Award (2017) Three OHIO Faculty Newsmaker Certificates (2017-2019) Octorara Area High School Hall of Fame (2016) As an educator, she directs Athletic Training Research courses while securing $26,500 in dedicated student research grants. Her NIH/DoD-funded projects support 12+ graduate researchers annually, with emphasis on translating lab findings to clinical practice through partnerships with Ohio University Athletics and the Datalys Center. Current initiatives integrate machine learning with biomechanical data to predict injury resilience.