Jerry Eriksson is an Associate Professor at the Department of Computer Science , Umeå University. He is also affiliated with the High Performance Computing Center North (HPC2N) at the same institution, focusing on computational methods and applications. Academic Rank : Associate Professor Primary Affiliation : Department of Computer Science, Umeå University Secondary Affiliation : High Performance Computing Center North (HPC2N) Email : jerry.eriksson@umu.se Dr. Eriksson's research spans interdisciplinary domains where computer science intersects with applied physics and network engineering . His work includes: Medical Imaging : Fluorescence optical tomography and electromagnetic shape tomography using advanced mathematical models Computational Methods : Implicit radial basis function techniques, Gauss-Newton optimization, and regularization schemes Network Engineering : Collision detection algorithms in wireless networks and peer-to-peer streaming protocols 3D Measurement : Bundle adjustment formulations for photogrammetry and robotics applications Article trends show a focus on applied inverse problems (2003-2017) with emphasis on: Medical imaging reconstruction algorithms Wireless network optimization techniques Geometric computing for 3D modeling Mathematical methods in computational engineering
Dario Lodi Rizzini is an Associate Professor at the Department of Engineering and Architecture, University of Parma, where he conducts research and teaching in robotics, computer vision, and autonomous systems. He has held academic positions including Research Assistant (2015–2020) and Assistant Professor (2021–2023) before becoming Associate Professor. His work spans industrial, agricultural, and underwater robotics applications. Research Interests: Localization, Mapping, and Navigation (SLAM) for mobile robots 3D perception and point cloud processing Robot manipulation and grasping Perception-aware control and pose estimation Applications in industrial automation, precision agriculture, and underwater intervention His recent publications show a strong trend in geometric algorithms for rotation and point cloud registration, particularly using the Angular Radon Spectrum, as well as practical deployments in warehouse automation and agricultural robotics. He has developed scalable systems for variable-rate irrigation and industrial depalletizing, demonstrating real-world impact. Scientific Awards and Recognition: Team leader of the University of Parma team that won Sick Robot Day in 2012 and 2014 Active IEEE member with publications in top-tier journals including IEEE Transactions on Robotics, IEEE RA-L, and IEEE ICRA/IROS Advising and Grants: Dario Lodi Rizzini has mentored numerous PhD students and postdocs, including Riccardo Monica, Ernesto Fontana, and Asad Ullah Khan. He has collaborated on multiple industrial and national research projects focusing on robotics in agriculture, warehouse automation, and underwater intervention, including the Italian MARIS project on underwater robotics. Labs and Teams: He is a key member of the Robotics and Intelligent Machines Lab (RIMLab) at the University of Parma, contributing to research in autonomous systems, perception, and manipulation. He has led teams in robotic competitions and collaborative industrial projects.
Dr. Yingxin Lin is a Researcher at the School of Mathematics and Statistics , University of Sydney , specializing in Bioinformatics and related fields. Their work spans interdisciplinary applications in biomedical engineering , biostatistics , and materials science , focusing on bone regeneration , bioceramics , and computational modeling of bone metabolism . Current Position: Researcher, Department of Statistics Email: yingxin.lin@sydney.edu.au Research Interests include: Bioinformatics applied to gene expression , signal transduction , and statistical modeling in bone biology . Biostatistical analysis of biomaterials and regenerative medicine outcomes. Computational modeling of bone remodeling and metastatic processes . Recent Publications reveal a focus on 3D-printed scaffolds , mitochondrial-targeting drugs , and biomechanical factors in bone repair and cancer progression . These works employ bioinformatics , biostatistics , and materials science to address osteoporosis , bone metastasis , and biomedical device development . Collaborative Networks include Supervisor: YH Yang Auxiliary Supervisor: JT Ormerod with no current students listed.
Francesca Pistilli is a Researcher at the Department of Control and Computer Engineering (DAUIN) at Politecnico di Torino. She is affiliated with the College of Computer, Film, and Mechatronics Engineering and contributes to the VANDAL Laboratory (Visual and Multimodal Applied Learning). Her research focuses on artificial intelligence, robotics, graph learning, and computer vision. She teaches courses including Introduction to Graph Learning (PhD), Robot Learning (MSc), and Programming Techniques (BSc). She supervises PhD student Gaetano Salvatore Falco (Artificial Intelligence, 40th cycle, 2024–ongoing). Her publications highlight expertise in image segmentation, egocentric video understanding, neural architecture search, and point cloud compression. Recent projects include Scene Understanding (2025–2026) funded by commercial contracts.
Yann Busnel is a Full Professor at IMT Atlantique in France, where he serves as Head of the Network Systems, Cyber Security and Digital Law (SRCD) department at the Rennes Campus since January 2017. He also leads the D2-NTS department (Networks, Telecommunications and Services) within UMR IRISA. His academic journey includes positions as Assistant Professor at Université de Nantes (2009-2014), Associate Professor and Head of Computer Science Department at ENSAI (2014-2016), and postdoctoral work at Università "La Sapienza" in Rome. Busnel's research focuses on models for large-scale distributed systems and networks, with particular expertise in data stream analysis over massive datasets. His work spans cybersecurity, dependability, medical data analysis (including pharmacovigilance and genomic sequence analysis), and the self-organized coordination of drone fleets for disaster management. He has published over 100 peer-reviewed papers and coordinated numerous national and international research projects. His recent scholarly output demonstrates a clear progression toward applied cybersecurity solutions, particularly in federated learning for intrusion detection, blockchain analysis, and wireless network security. The research shows strong interdisciplinary connections between distributed systems theory and practical security applications, with increasing emphasis on medical data analysis and drone coordination systems. His work bridges theoretical computer science with real-world security and networking challenges. Busnel actively mentors PhD students across multiple institutions, with current advisees working on topics including AI in cybersecurity using game theory, threat intelligence tools for IoT, and trust in distributed network services. He has coordinated substantial research funding through projects like CMA TCE (France 2030), EDIH Bretagne (Horizon Europe), and various ANR-funded initiatives. He leads several research chairs including OM&AI2MD (Operations Models and Artificial Intelligence to Manage Disasters), PRACom (Pôle de Recherche Avancée en Communications), and Cyber CNI (Cyber Security of Critical Infrastructures), demonstrating his leadership in both academic research and industry collaboration. His work with these chairs focuses on practical applications of his theoretical research in disaster management, communications technology, and critical infrastructure protection.
Xavier Pennec is a Senior Research Scientist (Directeur de Recherche) at Inria since 2007, holding the 3IA Côte d'Azur Chair on Geometric statistics and geometric subspace learning. He is affiliated with the Inria centre at Université Côte d'Azur and leads the Epione team in Sophia Antipolis. His educational background includes a PhD from Ecole Polytechnique (1996), Habilitation from University of Nice-Sophia Antipolis (2006), Master's degree from Ecole Polytechnique and Ecole Normale supérieure (1993), and Engineer Degree from Ecole Polytechnique (1992). He previously served as a Research Scientist at INRIA (1998-2007) and Post-doctoral associate at MIT (1997). His research focuses on the intersection of statistics, differential geometry, computer science and medicine, particularly in computational anatomy. He has made significant contributions to geometric statistics involving statistical computing on Riemannian manifolds and other geometric structures. His work includes mathematically grounded methods for medical image registration, statistics on shapes, and clinical research applications. He co-edited the first reference book on Riemannian Geometric Statistics in Medical Image Analysis (2020) and was awarded the ERC Advanced Grant G-Statistics in 2018. His publications demonstrate expertise across geometric statistics, manifold-valued image processing, medical image registration, and computational anatomy, with recent work focusing on implementing theoretical frameworks through the Geomstats software. ERC Advanced Grant G-Statistics (2018) He teaches in the Master MVA (Mathematics, Vision, Learning) program at ENS Saclay (28h module) and the Master of Data Science and Artificial Intelligence at Université Côte d'Azur (30h module), both with H. Delingette. His research has been presented at numerous conferences including MICCAI, IPMI, and workshops on Mathematical Foundations of Computational Anatomy (MFCA) which he animated from 2006 to 2019. He has contributed to EU projects including Health-e-Child, developing computational anatomy models for brain and heart shape variability using geometric currents and diffeomorphisms. His current research through the ERC G-Statistics project aims to ground the mathematical foundations of geometric statistics and demonstrate their impact on life science applications.
Jean-Yves Tinevez is the Head of the Image Analysis Hub (IAH) at the Institut Pasteur in Paris, France. He serves as a Principal Investigator for key image analysis software projects and is a central figure in the institute's bioimage analysis community. His research is fundamentally centered on bioimage analysis and open-source software development . His primary interests include single-particle tracking , machine and deep learning for image segmentation , the creation of custom analysis pipelines , and the development of extensible software platforms like TrackMate, MaMuT, and JDLL. His work bridges the gap between complex biological imaging data and quantitative scientific discovery. The recent publications he is associated with highlight a strong trend in methodological innovation for bioimage analysis. The focus is on creating powerful, accessible software tools (e.g., SAMJ, CellTracksColab, JDLL, GeNePy3D) and enhancing existing ones (e.g., TrackMate 7) to leverage state-of-the-art AI and computational techniques. While his core expertise is in developing the tools, these tools are applied to diverse biological problems, such as host-pathogen interactions , single-cell analysis in microfluidics , and cellular and tissue morphometrics . Head of Facility, Image Analysis Hub, Institut Pasteur (Current) Principal Investigator, TrackMate, Institut Pasteur (Completed) Principal Investigator, MaMuT, Institut Pasteur (Completed) Member, Advanced Light Microscopy initiative, Institut Pasteur Member, Artificial Intelligence at the Institut Pasteur initiative Steering Committee Member, NEUBIAS Tinevez plays a vital role in training and knowledge dissemination . He is a regular instructor for the Institut Pasteur's PhD training programs and has led numerous workshops on Fiji/ImageJ, Python for image analysis, and advanced bioimage analysis. The IAH, under his leadership, operates as a collaborative core facility with a strong commitment to open science and quality management (ISO-9001:2015 certified). He fosters extensive collaborations with research units across the Institut Pasteur and beyond. The facility provides infrastructure, walk-in support, and develops custom tools to empower researchers, effectively acting as a grant-funded service that enables a vast amount of research across the campus. The Image Analysis Hub, led by Tinevez, is a core technological facility within the Institut Pasteur's Center for Technological Resources and Research (C2RT) and the Research and Resource Centre for Scientific Informatics (C2RI). It operates a dedicated analysis room with specialized workstations and offers remote access via virtual machines. The hub is involved in several transversal projects, including the Advanced Light Microscopy initiative and the application of Artificial Intelligence in biomedical research at the institute.
Elizabeth Munch is an Associate Professor at Michigan State University with appointments in the Department of Computational Mathematics, Science and Engineering and the Department of Mathematics. Her research focuses on Applied Topology and Topological Data Analysis (TDA), with applications across biology, neuroscience, and dynamical systems. PhD in Mathematics, Duke University (2013) Previous roles: Assistant Professor at University at Albany - SUNY, Postdoctoral Fellow at University of Minnesota's IMA Her work leverages TDA to study complex data in plant morphology, neural activity, and network dynamics. Recent publications highlight advancements in mapper graphs, persistent homology, and geometric deep learning. Selected awards include the 2022 NSF CAREER award . She teaches courses in TDA (CMSE 890) and optimization (CMSE 382) and co-organizes the MSU TDA Seminar.
Shenghan Guo is an Assistant Professor in the School of Manufacturing Systems and Networks at Arizona State University, specializing in data-driven solutions for smart manufacturing. She leads the DAIM (Data Analytics & Insights in Manufacturing) research lab, which focuses on human-AI collaboration in manufacturing processes. Her lab utilizes specialized equipment including an OPTOMEC Aerosol Jet Printer for flexible 3D printing applications. Education: Ph.D. in Industrial and Systems Engineering, Rutgers University (2021) M.S. in Engineering Sciences and Applied Mathematics, Northwestern University (2016) M.S. in Financial Mathematics, Johns Hopkins University (2014) B.S. in Financial Engineering, Jilin University (2013) Her research integrates knowledge-informed AI with human-centered approaches to advance manufacturing systems. Key areas include: Human-in-the-Loop ML : Developing collaborative AI systems that incorporate human expertise Additive Manufacturing : In-situ monitoring and defect detection for 3D printing Process Optimization : Real-time quality control using computer vision and signal processing Publications (2017-2025) demonstrate a consistent focus on machine learning applications in manufacturing, with recent emphasis on generative AI for process simulation, contrastive learning for worker monitoring, and multi-modal data fusion for quality prediction. Over 60% of recent works involve additive manufacturing applications. Awards and Honors: ASU FWA Outstanding Mentor Award (2023) INFORMS Best Paper Runner-up (2021) IISE Data Challenge Winner (2019-2020) NSF Conference Support Awards Advising and Grants: Currently mentors three PhD students (Hasnaa Ouidadi, Pius Gyamenah, Boyang Xu) whose work has received SME 30 Under 30 recognition and AEC awards. Secured lab funding for advanced manufacturing equipment supporting electronic printing and human-machine interaction research. Teaching includes courses on computational modeling (EGR 219), machine learning (RAS 585), and specialized manufacturing topics. Developed new curriculum integrating real-world manufacturing data with Python-based ML implementations.
Jan Sher Akmal is an Assistant Professor at the Department of Energy and Mechanical Engineering, Aalto University. His research focuses on additive manufacturing (AM), digital manufacturing, and integrating artificial intelligence into metal AM processes. He actively contributes to advancements in 3D/4D printing, self-sensing materials, and industrial AM adoption strategies. Current affiliation: Aalto University Department: Energy and Mechanical Engineering Email: jan.akmal@aalto.fi Research Interests Jan's work spans Additive Manufacturing , Materials Science , and Digital Transformation . Key areas include defect detection using AI, 4D printing of smart materials, exposure measurement systems, and legal/strategic aspects of AM adoption. His research often intersects mechanical engineering with industrial applications, emphasizing sustainability and cost efficiency. Scientific Awards Doctoral dissertation award, Finnish Production Planning and Control Society (2022-2023) Aalto Doctoral Incentive Scholarship (2023) Labs & Teams Member of the Materials to Products research group at Aalto University, collaborating on interdisciplinary projects involving AM process optimization and functional material development.
Thomas Rylander is an Assistant Professor in the Signal Processing research group at Chalmers University of Technology. His research focuses on electromagnetics, computational methods, and microwave engineering, with applications in antenna modeling, wireless power transfer, and electromagnetic compatibility. He has led projects such as Modeling of RF emissions from e-axis (MORFex) (2024–2028) and Säker induktiv energiöverföring för elfordon (2014–2017). His work integrates advanced numerical techniques like the Method of Moments (MoM) and Finite Element Method (FEM) to solve complex electromagnetic problems. Education: PhD in Electromagnetics (2001, Chalmers University) Research Keywords: Electromagnetics, Computational Electromagnetics, Microwave Engineering, Signal Processing, Wireless Power Transfer, Finite Element Method, Method of Moments Projects: MORFex (2024–2028, funded by Energimyndigheten), Virtual Electric Driveline (2018–2022, Vinnova), FFI SAWE (2014–2017, Energimyndigheten), Model-Based Reconstruction (2011–2014, VR) His recent publications emphasize efficient electromagnetic modeling techniques, including macro basis functions for wire antennas and compressed sensing for microwave imaging. Despite extensive collaboration with researchers like Matthys M. Botha and Johan Winges, no specific scientific awards or advisees are mentioned in the provided data.
Götz Pfander is a Professor of Mathematics at the Catholic University of Eichstätt-Ingolstadt , holding the Chair of Mathematics - Scientific Computing . He has held previous academic roles at Philipps-Universität Marburg (W2 Numerical Analysis), Jacobs University Bremen (Associate/Assistant Professor), and visiting positions at institutions including MIT, NYU Courant, and TU München. His leadership roles include Dean and Vice Dean of the Faculty of Mathematics and Geography (2019-2023) and Speaker of the Mathematical Institute of Machine Learning and Data Science (MIDS) since 2022. PhD in Mathematics (University of Maryland, 1999), advised by John J. Benedetto Master of Arts in Mathematics (University of Maryland, 1998) Studies in Mathematics and Psychology (Johannes Gutenberg University Mainz, Freie Universität Berlin) Pfander's research focuses on numerical harmonic analysis, operator sampling theory, and time-frequency analysis, with applications in digital communications and signal processing. His work bridges Gabor frames, wavelet transforms, and uncertainty principles to solve problems in OFDM channel modeling, sparse signal recovery, and quantum information theory. Notable contributions include: Sampling theory for pseudodifferential operators with bandlimited Kohn-Nirenberg symbols Uncertainty principles for joint time-frequency representations on finite Abelian groups Design of robust Gabor systems for wireless communication channels Wavelet-based periodicity detection for biomedical signals His recent publications (2022-2024) emphasize exponential bases for interval partitions, cube tiling constraints, and complex-valued neural network approximation. Scientific awards include the Max Kade Fellowship and John von Neumann Visiting Professor title. He serves as Editor in Chief of Sampling Theory, Signal Processing, and Data Analysis and chairs the International Conference on Sampling Theory and Applications .
Torben Peters is a Lecturer in the Department of Civil, Environmental and Geomatic Engineering at ETH Zürich. His research focuses on 3D computer vision, deep learning, and generative models applied to geospatial analysis and photogrammetry. Research Focus: Peters develops computational tools for processing LiDAR point clouds, aerial imagery, and satellite data. His work enables automated environmental monitoring (e.g., forest inventories and avalanche mapping) and urban modeling through advanced segmentation and 3D reconstruction techniques. Generative models like TetraDiffusion expand capabilities in geometric deep learning. Publication Trends: Recent articles emphasize scalable geospatial AI, including war damage assessment in Ukraine, global biomass datasets, and self-supervised shape completion. Methodological innovations center on reducing annotation dependencies and improving geometric accuracy. Teaching: Leads courses on image-based mapping and geodetic data processing at ETH Zürich.
Thomas Leimkuehler is a Senior Researcher at the Max Planck Institute for Informatics in Saarbrücken, Germany, holding dual appointments in the Department of Computer Graphics and the Department of Visual Computing and Artificial Intelligence. His work bridges computer graphics and machine learning, focusing on neural rendering techniques for image synthesis and manipulation within the Saarland Informatics Campus ecosystem. Education: PhD in Computer Science, Saarland University (2019) Leimkuehler's research centers on neural signal representations and generative models for visual computing. He develops data-driven approaches to physically-based rendering, inverse rendering, and high dynamic range imaging, emphasizing efficient parallel algorithms. His work frequently integrates deep learning with traditional graphics pipelines to solve longstanding challenges in image synthesis and 3D scene representation, with applications spanning cinematic effects, automotive visualization, and computational photography. Analysis of his publication record reveals dominant trends in neural radiance fields (particularly Gaussian splatting), diffusion-based HDR generation, and multi-scale image synthesis. His research consistently addresses the tension between physical accuracy and neural efficiency, with recent work focusing on uncertainty modeling in radiance fields and real-time cinematic effects. Award highlights: Best Paper Award at SIGGRAPH 2023 for pioneering 3D Gaussian Splatting Eurographics PhD Award and Otto Hahn Medal (2019) Multiple student paper awards at EGSR, Graphics Interface, and ACM SAP Leimkuehler actively mentors through his Image Synthesis and Machine Learning research group, serving as primary advisor for PhD candidates and postdocs. He holds editorial positions at IEEE TVCG and Computer Graphics Forum while reviewing for major conferences including SIGGRAPH, CVPR, and ECCV. His group maintains strong collaborations with Snap, Princeton University, and the Visual Geometry Group at Oxford. Based at the Saarland Informatics Campus, his research group operates within the Max Planck Institute for Informatics' Department 4 and Department 6 frameworks, leveraging infrastructure from the Saarbrücken Research Center for Visual Computing and the European Laboratory for Learning and Intelligent Systems.
Emmanuel Cledat is an Associate Professor of photogrammetry at the National Institute of Geographic and Forest Information (IGN) and lecturer at the ENSG (National School of Geographic Sciences), where he teaches courses in sensor technology, mathematics for photogrammetry, and applied photogrammetry. As a member of the UMR Lastig research unit and ACTE research team, he conducts interdisciplinary work spanning photogrammetry, geomatics, and transportation safety. His research interests focus on photogrammetry , sensor calibration , and measurement of risks faced by cyclists . Dr. Cledat specializes in macro-photogrammetry of small objects, drone-based mapping systems, and GNSS-denied environment navigation. His methodological expertise includes camera calibration models, 3D reconstruction, and fusion of photogrammetric and LiDAR data. Dr. Cledat's publication record demonstrates consistent contributions to photogrammetry and geospatial sciences since 2016, with recent work exploring AI applications in geomatics and historical bridge modeling. His research shows a clear trajectory from foundational work on drone photogrammetry calibration to more applied projects addressing transportation safety and cultural heritage preservation. ISPRS Best Young Author Award 2020 As principal investigator, Dr. Cledat leads the CycloSafe project which quantifies cycling risks using LIDAR-equipped bicycles, and the EntrePonts project focused on 3D modeling of historical bridge models from the 17th-19th centuries. His teaching portfolio spans undergraduate and graduate courses in photogrammetry fundamentals, underwater photogrammetry, and climate change workshops. His laboratory work centers around the UMR Lastig research unit, with projects involving drone mapping systems, 3D TOF camera calibration, and photogrammetric fieldwork methodologies for both small objects and large-scale environmental mapping.
