Rohan Hitchcock is a Researcher in the Department of Electrical and Electronic Engineering, focusing on interdisciplinary research at the intersection of machine learning and computational physics. His work emphasizes developing physically informed ML architectures to accelerate particle-based models, as reflected in his thesis titled 'Beyond the Black Box.' He is supervised by Prof Jonathan Manton. His research interests span machine learning, computational physics, statistical mechanics, and algebraic topology. Recent articles highlight contributions to stochastic processes, neural network physics simulators, and theoretical frameworks in category theory. Rohan's publications reflect a blend of applied and theoretical work, including studies on stochastic gradient Langevin dynamics (SGLD), entropy in thermodynamic systems, and advanced mathematical constructs like Landau-Ginzburg models and matrix factorizations.
Dr. Qingguo Hong is an Assistant Professor in the Department of Mathematics and Statistics at Missouri University of Science and Technology. His research focuses on numerical analysis, numerical PDEs, applied and computational mathematics, and deep learning. He holds a Ph.D. in Computational Mathematics from Peking University (2012), an M.S. from Xiangtan University (2008), and a B.S. in Information and Computational Science from Xiangtan University (2005). Prior to his current position, he held roles as a Research Scientist at the Johann Radon Institute for Computational and Applied Mathematics (Austria), Postdoctoral Scholar at the University of Duisburg-Essen (Germany), and Assistant Research Professor at Pennsylvania State University (USA). His work emphasizes innovative numerical methods, including extended Galerkin techniques, neural network-driven PDE solvers, and robust approximation frameworks for complex physical models like superconductivity and poromechanics. Research Interests: Numerical methods for partial differential equations (PDEs) Discontinuous Galerkin methods and finite element analysis Deep learning applications in scientific computing Stability analysis of numerical algorithms Phase-field modeling and material science simulations Publications Trends: Dr. Hong's recent work (2021-2025) emphasizes advancements in numerical PDEs through hybrid approaches combining classical methods (e.g., Korn’s inequalities, Galerkin methods) with modern machine learning tools (neural networks). Key themes include: Development of efficient algorithms for time-dependent and coupled PDE systems Stability frameworks for perturbed saddle-point problems Applications in superconductivity, poromechanics, and elasticity Robust discretization techniques for fluid-structure interactions Grants & Advising: While specific grants are not listed, his academic trajectory reflects sustained support through postdoctoral and research roles. He currently advises students in numerical analysis and computational mathematics.
Ivan Nourdin is a Full Professor of Stochastic Modelling in the Department of Mathematics at the University of Luxembourg. He holds a PhD in Mathematics from Université de Lorraine (2004) and has held academic positions at Université Pierre et Marie Curie (2005–2010) and Université de Lorraine (2010–2014). His research focuses on probability theory, stochastic analysis, and their applications to statistics, geometry, and data science. He co-founded GrewIA, a startup focused on AI and mathematics education. **Research Interests**: Malliavin calculus, Stein’s method, functional inequalities, free probability, rough paths theory, inference for high-dimensional problems. He has authored/co-authored over 80 journal articles and two monographs, including the award-winning Normal Approximations with Malliavin Calculus (2012). **Awards**: 2015 FNR Award for Outstanding Scientific Publication, 2013 France Scopus Researcher Award, 2011 Fondation des Sciences Mathématiques de Paris Prize. **Advising & Teams**: Leads a research group including postdocs and PhD students. Former advisees include Simon Campese, Federico Dalmao, and Guangqu Zheng. His team explores topics like stochastic processes, limit theorems, and applications in AI. **Contact**: Office MNO E05 0515090, Maison du Nombre, University of Luxembourg. Phone: (+352) 46 66 44 6380. Email: ivan.nourdin@uni.lu.
Shuyan Li is a Lecturer at the School of Electronics, Electrical Engineering and Computer Science, Queen’s University Belfast. Her research focuses on foundational computer vision methods and their healthcare applications, including unsupervised learning, multi-modal learning, and medical imaging analysis. She actively mentors early-career researchers through the Cambridge Trinity College Postdoctoral Mentorship Program and serves as Director of the Tsinghua Alumni Association (UK) and Secretary-General of the UK Association of Distinguished Young Scholars. Education Background: While specific degree details are not explicitly listed, Dr. Li has received prestigious awards such as the First Prize Scholarship (Tsinghua University, 2020) and the National Scholarship (Ministry of Education, PRC, 2013), indicating a strong academic foundation. Research Interests: Central themes include unsupervised learning, digital twins for construction and healthcare, video understanding, representation learning, and domain adaptation. Her work bridges theoretical advancements and practical applications, such as medical image translation and point cloud-based building digitization. Awards and Recognition: Key achievements include the Athena Postdoctoral Fellowship (NSF AI Research Center), Forbes’ Top 100 Most Influential Chinese (2024), and the Excellent Doctorate Dissertation Award (2023). She is also a Guest Editor for Innovation and Technology of Computer Vision . Advising & Grants: Currently supervising PhD students Ben Redden (UK) and Shurui Xu (China). She offers multiple funded PhD opportunities, including EPSRC and CSC scholarships, and collaborates with institutions like Cambridge and UCL. Labs & Collaborations: Active in interdisciplinary projects involving digital twin construction, medical AI, and point cloud analysis. Recent collaborations include work with the University of Cambridge on digital construction modules and Newcastle University on AI-driven data analysis.
Drew A. Torigian is a Professor of Radiology at the Perelman School of Medicine, University of Pennsylvania, with extensive contributions to medical imaging research. His work focuses on developing advanced methodologies for PET/CT image analysis, disease quantification, and anatomical segmentation through innovative applications of deep learning and computer vision techniques. Dr. Torigian's research interests span multiple critical areas in medical imaging including quantitative image analysis, PET/CT applications, deep learning in medicine, radiation therapy planning, and anatomical segmentation. His work has significantly advanced the field of medical image analysis through the development of novel algorithms for disease quantification without explicit object delineation, standardized anatomic space frameworks, and attention-based neural networks for medical image interpretation. His recent publications demonstrate a strong trend toward integrating artificial intelligence with medical imaging, particularly in developing gaze-guided neural networks, geographical attention mechanisms, and hybrid transformer-convolutional architectures for improved medical image analysis. These works consistently address critical challenges in radiology including disease quantification, anatomical segmentation, and the development of clinically interpretable AI models. Dr. Torigian has received recognition through his substantial publication record in top-tier medical imaging venues including Medical Image Analysis, IEEE Transactions on Biomedical Engineering, and leading medical imaging conferences. His research has been consistently funded through various mechanisms supporting innovation in medical imaging technology. Through his mentorship and collaborative research, Dr. Torigian has contributed to advancing the field of medical imaging with practical applications in radiation therapy planning, disease quantification, and the development of standardized methodologies for image analysis. His work bridges the gap between computer science innovation and clinical radiology applications.
Hao Zhang is a Distinguished Professor and IEEE Fellow at Simon Fraser University's School of Computing Science, leading the GrUVi (Graphics & Vision) Lab. He holds a Ph.D. from the University of Toronto's Dynamic Graphics Project and degrees from the University of Waterloo. His research focuses on computer graphics, geometric modeling, and visual computing, with over 200 publications, including 70+ in SIGGRAPH/TOG. He directs research in 3D representation learning, generative models, and fabrication-aware design. Awards include the ACM SIGGRAPH Academy induction (2025) and IEEE Fellow (2024). He advises numerous students, many of whom have become professors or industry leaders. His work spans theoretical contributions (e.g., Test of Time Award for 2013 paper) and applied innovations like Slice3D and ArcPro. He is Technical Papers Chair for SIGGRAPH 2025 and has held roles at Amazon as an Amazon Scholar. Education: Ph.D., Dynamic Graphics Project, University of Toronto MMath and BMath, University of Waterloo Research Interests: Geometric deep learning, 3D vision, CAD representation, spatial AI, computational fabrication. Key projects include neural implicit fields (IM-Net), structured 3D synthesis (BSP-Net), and generative models (LOGAN). Collaborations include Adobe, Autodesk, and Amazon.
Simo Alami is a Ph.D. candidate and Tutor at Ecole Polytechnique, affiliated with the LIX computer science laboratory under the supervision of Professor Jesse Read. He teaches tutorials for the Advanced Machine Learning and Autonomous Agents course (Computer Science, Ingénieur 3A/Master1) since 2020, focusing on practical implementations of machine learning algorithms and autonomous systems. His academic background includes undergraduate and graduate studies in Mathematics and Computer Science at Université Pierre et Marie Curie and Ecole CentraleSupélec. Ph.D. Candidate, Ecole Polytechnique (2020-Present) Master's Studies, Mathematics and Computer Science, Université Pierre et Marie Curie and Ecole CentraleSupélec Alami's research centers on developing algorithms that learn from experience through self-learned metrics, with core expertise in Deep Learning, Inverse Reinforcement Learning, and Metric Learning. His work targets robotic applications where agents must perform novel tasks by understanding logical concepts, while also extending to energy management systems through non-invasive load monitoring. He explores how meta-learning frameworks can enable transferable reward structures across domains. His publication record reveals a strong trajectory in distributional reinforcement learning and inverse reinforcement learning using generative models, with significant contributions to metric learning for clustering and energy disaggregation. The consistent focus on transferable methodologies across robotics and smart grid applications demonstrates his interdisciplinary approach to solving real-world problems through advanced machine learning techniques. Scientific recognition includes: Nvidia Grant awarded in 2022 for research on Meta-Inverse Reinforcement Learning 3rd Prize at Hi Paris Hackathon (2021) for a reinforcement learning-based smart grid energy management solution While not supervising graduate students as a Ph.D. candidate, Alami has secured competitive external funding including the Nvidia research grant. His industry collaboration with Accenta during a visiting researcher position resulted in ECML 2022 publications on non-invasive load monitoring, highlighting his ability to bridge academic research with practical engineering applications. As an active member of the LIX laboratory at Ecole Polytechnique, Alami participates in cutting-edge computer science research while maintaining external collaborations. His visiting researcher position at Accenta demonstrates engagement with industry partners, and his organization of the IRT SystemX Electromobilité et territoires webinar in 2021 reflects leadership in knowledge dissemination within the energy and mobility research community.
Steve Oudot is a Senior Researcher (Directeur de Recherche) at Inria where he leads the GeomeriX research group, and serves as an Adjunct Professor at École Polytechnique. His research focuses on topological and geometric approaches to data analysis. Research Interests: Primarily works on persistence theory and its connections to homological algebra and representation theory, topological data analysis with applications to statistics and machine learning, multimodal time series analysis, and manifold learning/sampling theory. His research bridges theoretical mathematics with computational applications in data science. Publication Trends: Recent works (2024-2025) focus on multiparameter persistence theory, stability analysis of topological descriptors, and computational methods for persistence module decomposition. His publications demonstrate strong emphasis on theoretical foundations with algorithmic implementations for geometric data analysis. Student Advising: Currently supervising 3 PhD students (Michel, Li, Mordacq) and has graduated 7 doctoral students since 2014. Alumni now hold positions in academia (Lacombe at LIGM, Carrière at Inria) and industry (Berkouk at CNIL, Solomon at Deep Detection). Teaching: At École Polytechnique, teaches courses on topological data analysis (INF556), algorithms for data analysis (INF442), and computational geometry/topology (MPRI). Also taught at international schools in Luxembourg (2018), TUM (2016), and La Marsa (2016).
Dr. Shusen Pu is an Assistant Professor in the Department of Mathematics and Statistics at the University of West Florida (UWF) , within the Hal Marcus College of Science and Engineering . He joined UWF in 2022 after serving as a Lecturer at Vanderbilt University. His academic journey includes a Ph.D. in Applied Mathematics from Case Western Reserve University (2020), advised by Dr. Peter J. Thomas, and postdoctoral research with Dr. Christos Constantinidis. Dr. Pu's educational background includes: Ph.D. in Applied Mathematics, Case Western Reserve University B.S. in Mathematics and Statistics, Beijing Normal University His research spans computational neuroscience , mathematical statistics , data analytics , and deep learning . He investigates neural network models, working memory, stochastic processes in biological systems, and generalized statistical distributions. He collaborates with biologists to analyze experimental data and integrates deep learning into neuroscience applications. His work aims to bridge microscopic neural activities with macroscopic behaviors. His recent publications (2024–2018) reflect a strong focus on both neuroscience modeling and statistical distribution theory . Articles in journals like Nature Communications , Entropy , and Biological Cybernetics demonstrate expertise in prefrontal neuronal dynamics, neural noise modeling, and novel distribution generators. The consistency in topics indicates a dual research thrust: understanding brain function through computational models and advancing statistical tools for real-world data analysis. Scientific contributions include: Published in high-impact journals such as Nature Communications , Neural Computation , Eneuro , and Biological Cybernetics Reviewer for multidisciplinary journals including iScience (Cell Press) Active participant and presenter at international conferences in Canada, China, and the USA Organizer of academic conference sessions and workshops Dr. Pu is deeply involved in academic mentoring and teaching. He advises students in capstone projects integrating mathematical modeling, data science, and statistical analysis. He teaches courses such as Analytic Geometry and Calculus, Linear Algebra, Deep Learning, and Mathematical Statistics. He emphasizes problem-solving methodology, collaborative learning, and real-world applications. He leads the Computational Statistics and Data Analytics (CSDA) Lab , hosting weekly lab meetings and Python/data science tutorials, fostering a collaborative research environment. His lab and team activities include: Weekly lab meetings (Fridays 9:00–10:00, Building 4, Room 212) Weekly Python and Data Science Tutorials (Fridays 10:00–11:00, same location) Supervision of capstone projects in data science and mathematical modeling Active research in neural connectivity inference and statistical distribution development
Hrushikesh Mhaskar is a Research Professor of Mathematics at Claremont Graduate University (CGU) since 2012, with a prior 32-year tenure at California State University, Los Angeles. He holds a PhD in Mathematics from Ohio State University, alongside an MS in Computer Science and an MSc from the Indian Institute of Technology, Mumbai. His research focuses on approximation theory, computational harmonic analysis, machine learning, and signal processing, with significant contributions to neural network theory and kernel-based methods. Mhaskar has authored over 150 papers, two books, and five edited volumes. His work includes pioneering studies on weighted polynomial approximation, Fourier domain conversions, and manifold learning. He currently serves on editorial boards for journals like Applied and Computational Harmonic Analysis and Journal of Approximation Theory , and collaborates with institutions like the University of California, Santa Barbara. Awards include five Alexander von Humboldt Fellowships and a John von Neumann Distinguished Professorship. His research is supported by the NSF and previously by the U.S. Air Force and intelligence agencies. Notable contributions include developing eignets for function approximation on manifolds and analyzing deep vs. shallow networks' approximation capabilities. His work bridges theoretical mathematics with practical applications in biomedical data analysis (e.g., blood glucose prediction) and signal processing.
Elias N. Zois is an Associate Professor in the Department of Electrical & Electronics Engineering at the University of West Attica, where he has taught since 2019. Previously, he held positions as Lecturer (since 2009), Assistant Professor (2015-2022), and Adjunct Professor at multiple institutions including the Hellenic Army Academy and Hellenic Police Academy. He received his B.Sc. (1994), M.Sc. (1997), and Ph.D. (2000) in Physics and Electronics Engineering from the University of Patras, Greece. His research focuses on digital signal processing , image processing , pattern recognition , and specialized applications in handwriting biometry and offline signature verification . Recent work extends to smart grid optimization , including load forecasting and non-technical loss detection using machine learning techniques. Analysis of his 15 most recent publications reveals strong interdisciplinary trends: 60% focus on advanced biometric verification using Riemannian geometry and metric learning, while 40% apply machine learning to energy systems. Key methodologies include sparse coding, manifold learning, and neural network ensembles, with consistent applications in security systems and smart grid resilience. He has led multiple funded projects including: BioControl (2021-2023): Experienced Researcher for biometric systems Ireact-NG (2018-2021): Experienced Researcher in smart grid technologies ESA SimSat Engine Enhancement (2011-2015): Theoretical Project Manager for aerospace simulations He directs research at the TELSIP Laboratory (Building Z, University of West Attica), specializing in signal processing and pattern recognition systems.
Kristian Muri Knausgård is a Lecturer at the Department of Engineering Sciences , University of Agder , Norway. He teaches courses in embedded systems, software development, and robotics. Current courses: MAS245 (Embedded Computer Systems), MAS417 (Software Development), MAS418 (Robotics Programming) Previous courses: MAS218 (Electrical Circuits), MAS234 (Embedded Systems) His research focuses on embedded systems , real-time systems , and technical cybernetics , with applications in artificial intelligence , computer vision , and systems engineering . He contributes to the university's research groups on Robotics and Automation and Systems Engineering and Modeling . Recent publications show a strong emphasis on: Autonomous systems (robotics, docking algorithms) Deep learning applications in marine ecology 3D reconstruction and computer vision techniques Fish detection/classification using neural networks Industrial automation for aquaponic systems His work bridges theoretical research with practical implementations in mechatronic systems and environmental monitoring.
Dr. Marcos Loureiro Garcia is an Assistant Professor at the University of Vigo, affiliated with the Department of Mathematics within the Faculty of Education and Sport Sciences. He obtained his PhD in 2020 with a thesis on "Modeling and numerical approach of aortic valve stages: healthy, stenotic and transcatheter replaced (TAVI)". His career demonstrates interdisciplinary expertise combining mathematics, cardiology, and educational innovation. Doctorate in Mathematics Applied to Cardiology (2020) Master's in Industrial Mathematics Master's in Teacher Training Bachelor's Degree in Mathematics His research focuses on applying mathematical methods to cardiology, particularly for simulating aortic valve pathologies and transcatheter aortic valve implantation (TAVI) procedures. He has developed biomechanical models of aortic valves, investigated TAVI outcomes through numerical simulation, and explored educational technology applications for mathematics teaching. His academic work spans both computational cardiology and mathematics education. He has published on topics including deep learning for TAVI characterization, digital valve twins, and augmented reality applications in teacher training. His research addresses clinical challenges through mathematical modeling while contributing to innovative teaching methodologies. Scientific achievements include: Best Poster Prize in Numerical Simulation at ESC Congress (2018) Six-Year Research Career Recognition Grants from Ministry of Science, Xunta de Galicia, GAIN He collaborates with leading institutions like the Czech Technical University in Prague and the Center for Computational Medicine in Cardiology (Lugano), developed his doctoral research at the Galicia Sur Health Research Institute (IISGS), and currently participates in the RITME research group focused on educational innovation and mathematical modeling.
MARIA BLANCA IBAÑEZ ESPIGA is an Associate Professor at the Telematics Engineering Department of Carlos III University of Madrid , specializing in educational technology and immersive learning environments. Her research focuses on integrating augmented reality (AR), virtual reality (VR), and artificial intelligence (AI) into educational practices to enhance student engagement and learning outcomes. Research Focus: Dr. Ibañez Espiga explores the application of augmented reality in STEM education, gamification for learning activities, and learning analytics to model student behavior in immersive systems. She has developed tools like BabloXR for WebXR applications, XploRe for solar system education, and Reaq for chemistry tutoring, demonstrating her commitment to innovative pedagogical solutions. Publications: Recent work includes studies on generative AI in education , cloud-based universities , and AR simulations in problem-based learning . Her research spans from 2010 to 2025, showing sustained contributions to technology-enhanced learning methodologies. Labs & Teams: She is affiliated with the eMadrid Network , a research group focused on learning methodologies, gamification, and quality in educational technology. Her projects often involve interdisciplinary collaboration, leveraging telematics engineering to create scalable educational solutions.
Marc Donias is an Associate Professor at the University of Bordeaux, affiliated with the IMS (Laboratory of Integration, Material to System) which is part of the College of Engineering. He is a member of the Signal and Image Processing research group and works within the MOTIVE team, focusing on advanced image processing and computer vision applications. His research interests span across multiple domains including image processing, signal processing, computer vision, machine learning, and seismic data analysis. Dr. Donias has made significant contributions in texture analysis, image colorization algorithms, and geological data interpretation. His work often bridges theoretical advances with practical applications in agriculture, materials science, and geophysics. Analysis of his recent publications reveals a strong trend toward deep learning applications in image processing, particularly generative models for image colorization and object detection. His work demonstrates interdisciplinary reach, connecting computer vision with agricultural technology, materials science, and geophysical interpretation. The publications show consistent collaboration with researchers from the IMS laboratory, particularly with Yannick Berthoumieu and other members of the MOTIVE team. Dr. Donias has been actively involved in developing innovative approaches for seismic horizon reconstruction, texture analysis, and agricultural image processing. His work on SPD manifold learning for image colorization represents a sophisticated mathematical approach to a challenging computer vision problem. The practical applications of his research are evident in precision agriculture technologies and geological interpretation tools.
