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Hao XingView profile

Researcher

• Robot Vision
• Human Action Recognition
• Human-Object Interaction Recognition
• +4 more

Hao Xing is a scientific researcher and post-doctoral fellow at the Institute for Cognitive Systems (ICS), Technical University of Munich (TUM), working with Prof. Gordon Cheng since May 2024. Previously, he served as a research assistant at the Munich Institute of Robotics and Machine Intelligence (MIRMI) and completed his PhD under Prof. Darius Burschka from 2019. His educational background includes: Master of Science in Mechanical Engineering from Technical University of Munich Bachelor of Engineering in Mechanical Engineering from Hefei University of Technology, China Xing's research focuses on Robot Vision , Human Action Recognition , and Graph Convolutional Networks , with significant contributions to Human-Object Interaction Recognition , Scene Understanding , and Visual Depth Estimation . His work leverages machine learning to develop robust algorithms for human activity analysis and robotic perception, emphasizing spatio-temporal modeling and uncertainty handling in real-world scenarios. Analysis of his 13 recent publications (2019-2025) reveals a dominant focus on graph-based approaches for action recognition and segmentation, with increasing emphasis on open-world applications, uncertainty modeling, and healthcare integration. His research spans computer vision, robotics, and medical applications, demonstrating strong interdisciplinary impact through collaborations in surgical robotics and patient monitoring systems. Xing actively mentors students through master's thesis projects in Stereo Matching, Human Activity Segmentation, and Monocular Depth Estimation, requiring expertise in deep learning frameworks and computer vision algorithms. While no major grants are explicitly mentioned, his thesis topics indicate active research funding in geometric vision and human activity analysis. He operates within the Institute for Cognitive Systems (ICS) at TUM, a key unit in MIRMI's robotics ecosystem focused on advancing human-robot interaction through vision-based scene understanding and motion generation capabilities.

• Statistical Machine Learning
• High-Dimensional Data Analysis
• Computer Vision
• +3 more

Guy Lebanon joined Purdue University in August 2005 as an Assistant Professor with joint appointments in the Department of Statistics and the School of Electrical and Computer Engineering. His research focuses on statistical machine learning, high-dimensional structured data analysis, and geometric methods in statistics. Education : PhD in Computer Science from Carnegie Mellon University Industry Experience : 1 year at RAFAEL in automatic target recognition for infra-red images Research Interests : Geometry and ranking in statistical machine learning, computer vision

• Mathematical Statistics
• Data Science
• Algebra
• +3 more

Jukka Mikael Kohonen is a University Lecturer in the Department of Mathematics and Systems Analysis at Aalto University, Finland. He is affiliated with the Mathematical Statistics and Data Science, as well as Algebra and Discrete Mathematics research groups. His research interests span lattice theory and combinatorics, with recent work focusing on modular lattice reduction and enumeration techniques. He has contributed to algorithmic optimization and outlier correlation detection, collaborating across disciplines such as biomedical signal processing and computational mathematics. In his publications since 2017, Kohonen has explored topics ranging from symmetry reduction algorithms to additive number theory, with a notable emphasis on computational methods in discrete mathematics. Recent work (2025) advances techniques for simplifying modular lattices through elimination of irreducible elements. Research groups: Mathematical Statistics and Data Science, Algebra and Discrete Mathematics Email: jukka.kohonen@aalto.fi

Sándor Kisfaludi-BakView profile

Assistant Professor

• Algorithms
• Computational Geometry
• Geometric Optimization
• +4 more

Sándor Kisfaludi-Bak is an Assistant Professor in the Department of Computer Science at Aalto University, specializing in theoretical computer science with a focus on computational geometry. He develops algorithms for geometric problems involving points, curves, shapes, and spatial networks. His research interests include Algorithm design for geometric optimization Computational geometry fundamentals Spatial network analysis Hyperbolic and planar graph algorithms Parameterized complexity in geometric contexts Recent publications demonstrate expertise in Traveling Salesman Problem optimization, Steiner network construction, and hyperbolic graph analysis. Key research trends span computational geometry, graph theory, and algorithmic complexity in spatial domains. He has no listed scientific awards in the provided materials. No information about student advising or organizational affiliations beyond Aalto University was found.

Mohammad RafieeView profile

Adjunct Professor

• Advanced materials
• Sustainable materials
• Electronic materials
• +12 more

Mohammad Rafiee is an Adjunct Professor in the Department of Mechanical Engineering at the Faculty of Engineering, University of Ottawa. He holds affiliations with City University of Hong Kong, École Polytechnique de Montréal, National Research Council Canada, and Advanced Microelectronic Technologies, Sanmina. His research focuses on Advanced materials (graphene, carbon nanotubes, piezoelectric composites) Additive manufacturing (multi-material 3D/4D printing, coaxial deposition) Mechanical/thermal analysis of composite structures Smart devices (sensors, actuators, energy harvesters) Numerical modeling (finite element analysis, nonlinear dynamics) Recent publications highlight trends in Nonplanar additive manufacturing of piezoelectric devices Multiscale graphene/epoxy composites for vibration damping Thermal stability of nanomaterial-reinforced laminates 4D printing applications Structural health monitoring via machine learning Development of polymer-derived ceramics Scientific awards include Ontario Trillium Scholarship Fonds de Recherche du Québec – Nature et technologies (FRQNT) World’s Top 2% Scientists (Stanford, since 2020) Top 1% most cited authors in materials science

• 3D Computer Vision
• Geometric Deep Learning
• Topological Deep Learning
• +10 more

Tolga Birdal is an Assistant Professor (Lecturer) and UKRI Future Leaders Fellow in the Department of Computing at Imperial College London. As the Principal Investigator (PI) of the CIRCLE group , his research focuses on topological deep learning, geometric machine learning, and 3D computer vision, with theoretical interests in non-Euclidean inference and deep learning principles. Education: PhD and MSc in Computer Vision from Technical University of Munich (2018), BSc in Computer Science from Sabancı University (2008). Projects: PI for UKRI-EPSRC's UNTOLD (Topological Deep Learning), Royal Society's drug discovery initiative, and EPSRC's GNOMON (Generative Models in non-Euclidean Spaces). Leadership: Area Chair for CVPR, ICCV, and 3DV 2025 Publication Chair. His work bridges differential geometry, algebraic topology, and deep neural networks, with applications in quantum computer vision, 3D/4D generative priors, and medical imaging. Key contributions include novel frameworks for rotation forecasting, graph generation, and topological generalization bounds. Scientific Awards: UKRI Future Leaders Fellowship EMVA Young Professional Award

• Mathematics Education
• History of Mathematics
• Geometry
• +4 more

Magdalini Lada is an Associate Professor in the Department of Teacher Education at the Norwegian University of Science and Technology (NTNU) . Her work bridges mathematics education and history of mathematics , focusing on pedagogical strategies to enhance preservice teachers’ understanding of geometry, curriculum design, and reasoning-and-proving in mathematical instruction. Education: Ph.D. in Mathematics (NTNU, 2011) Recent research explores the integration of history and philosophy of mathematics into teacher training, with publications analyzing textbook reasoning items and designing courses that connect historical problems (e.g., construction problems of antiquity) to modern pedagogy. Her earlier work delved into advanced algebraic structures like Auslander generators and T-Koszul algebras. She has presented at international conferences such as the Europea Summer University on the History and Epistemology in Mathematics Education and ICME 14 , emphasizing innovative teaching methods including flipped learning models. No scientific awards or student advising details are mentioned in the provided texts.

• Distributed Algorithms
• Metric Graph Theory
• Theoretical Computer Science
• +3 more

Jérémie Chalopin is a Directeur de Recherche (DR CNRS) at LIS (Laboratoire d'Informatique et Systèmes) in Marseille, where he is a member of the DALGO research team. His position at CNRS is affiliated with Aix-Marseille University, one of France's largest multidisciplinary universities. As a senior researcher with habilitation to supervise research (awarded in 2020), he plays a significant role in the French theoretical computer science community. Chalopin's research primarily focuses on distributed algorithms and metric graph theory , with substantial contributions to understanding the theoretical foundations of distributed computing systems and the geometric properties of graphs. His work bridges theoretical computer science with practical applications in mobile agent systems, programmable matter, and network algorithms. He has developed fundamental results in areas including leader election, graph exploration, metric embeddings, and sample compression schemes for graph structures. Analysis of his recent publications reveals a strong trend toward interdisciplinary work combining distributed computing with geometric graph theory. His research on balls in graphs has applications in machine learning, while his work on programmable matter addresses cutting-edge challenges in distributed robotics. The consistent high-quality output in top theoretical venues demonstrates sustained research productivity across multiple subfields. As a habilitated researcher at CNRS, Chalopin supervises PhD students and participates in research projects, though specific details about current students or grants aren't provided in the source material. His long-standing collaboration with Victor Chepoi (appearing in numerous publications) represents a significant research partnership in metric graph theory. Chalopin is based at LIS in the TPR2 building (office 05.28, 5th floor) where he works as part of the DALGO team, which focuses on algorithms and theoretical computer science. His research environment provides strong connections to both the French and international theoretical computer science communities through collaborations with researchers across Europe and beyond.

Martin E. MüllerView profile

Professor

• Algebraic Logic
• Modal Logic
• Relational Algebra
• +7 more

Prof. Dr. Martin E. Müller is a Professor in the Department of Computer Science at Bonn-Rhein-Sieg University of Applied Sciences, specializing in the mathematical and theoretical foundations of informatics. His research bridges abstract algebraic structures with practical computational applications, particularly in knowledge representation and reasoning systems. Dr. Müller's primary research interests include Algebraic Logic , Modal Logic , Relational Algebra , Universal Algebra , and Logic Knowledge Discovery (also known as explainable machine learning). His work demonstrates how theoretical mathematical frameworks can provide robust foundations for practical computational problems, particularly in the areas of rough set theory, formal concept analysis, and inductive logic programming. He approaches machine learning through logical structures, emphasizing transparency and explainability in AI systems. Analysis of his publication record shows a consistent scholarly trajectory from 1994 through 2023, with increasing emphasis on applying formal logical methods to contemporary machine learning challenges. His work spans theoretical foundations of computing, relational methods in program semantics, and practical applications in user modeling and knowledge discovery. Recent publications demonstrate growing interest in making machine learning more interpretable through logical frameworks. Among his professional recognitions is the Seahorse award from 1975 . Dr. Müller serves as a reviewer for numerous academic journals and conferences and is an active member of several professional associations in computer science and logic. Dr. Müller has led significant research projects including PARIA (2004-2008), which developed the PACME architecture for concurrent processes; Rela-X (2010-2019), which implemented libraries for efficient relation calculus with the R-Lang programming language; and the ongoing COMPARE project (2024-) focusing on pairwise multidimensional comparisons for survey analysis. His current work with the 'Sets, Structures, Semantics' project (2018-) aims to create a comprehensive resource on discrete mathematics and logics. His research group maintains strong international collaborations, particularly with researchers in the relational and algebraic methods community, including notable figures like Tony Hoare, Peter Höfner, Peter Jipsen, and Bernhard Möller. The Rela-X project established a productive student working group environment that produced both theoretical insights and practical software tools for relational calculus visualization.

Jennifer ForresterView profile

Research Fellow

• Geometry representation for machine learning
• Robust design and uncertainty propagation
• Structural design with composites

Dr. Jennifer Forrester is a Research Fellow at the University of Southampton's Faculty of Engineering and Physical Sciences, specializing in advanced design methodologies for aerospace applications. She works with major aerospace partners to develop methods for incorporating measurement data into automated design processes, with emphasis on practical implementation of state-of-the-art methodologies alongside existing computational analysis codes. Her educational background includes: Master of Engineering Degree in Aeronautics and Astronautics from the University of Southampton (2001) PhD in "On the characterization of measured geometry for the facilitation of uncertainty propagation and robust design" (2019) Dr. Forrester's research program spans three interconnected domains. Her work in geometry representation for machine learning focuses on digital representation of geometry as discrete occupancy-based pixels or voxels to enable machine learning techniques in geometric design. In robust design and uncertainty propagation , she investigates how uncertainties in measurements affect probabilistic performance in design decisions. Her expertise in structural design with composites stems from her early career experience designing composite marine structures for high-profile projects. Her publication record reveals a clear research trajectory from traditional engineering design toward AI-integrated methodologies. Current work with the Rolls-Royce led COLIBRI project focuses on integrating machine learning and artificial intelligence with design processes, particularly examining integration of voxel-based representations with traditional CAD boundary representations and meshes. Dr. Forrester has secured significant research funding through: The COLIBRI project (co-funded by Aerospace Technology Institute and Innovate UK) The Strategic Investment in Low-carbon Engine Technology (SILOET II) programme Multiple Rolls-Royce plc sponsored research initiatives She has contributed to the academic community through PhD supervision and served three years on the Faculty of Engineering's Equality and Diversity Committee, working to maintain the ATHENA SWAN Bronze Award. As a chartered engineer, she maintains active industry connections while advancing academic research in computational design methodologies.

Andrea GuidolinView profile

Lecturer

• Topological Data Analysis
• Geometry and Topology for Machine Learning
• Applied Topology
• +1 more

Andrea Guidolin is a Lecturer in Mathematical Foundations of AI and Machine Learning at the School of Mathematical Sciences, University of Southampton, since 2025. His research focuses on applying geometric and topological methods to analyze data structures and enhance machine learning models. Education: PhD in Pure and Applied Mathematics, University of Turin (2018) Guidolin's work explores how topology and geometry characterize patterns in complex datasets, particularly in biological and machine learning contexts. He employs spectral systems and computational algebra to develop robust topological data analysis frameworks. Recent publications emphasize multi-persistence theory, Koszul complexes, and applications to neuroscience. His research bridges pure algebraic topology with practical data science challenges. Previous Appointments: Postdoctoral Researcher, KTH Royal Institute of Technology (2021–2024) Postdoctoral Researcher, Basque Center for Applied Mathematics (2018–2020)

Ryan LivelyView profile

Professor

• Energy-efficient separation processes
• Membrane technology
• Nanoporous materials
• +3 more

Ryan Lively is a Professor in the Department of Chemical and Biomolecular Engineering at Georgia Institute of Technology's College of Engineering, specializing in energy-efficient separation processes using advanced membranes and nanoporous materials. His research focuses on addressing global energy challenges through innovative materials and modular device design. B.S. (2006) Georgia Institute of Technology Ph.D. (2011) Georgia Institute of Technology Research interests include: Membrane and adsorbent development for carbon capture 3D-printed separation devices Polymer of intrinsic microporosity (PIM) modifications Natural gas liquid fractionation and solvent recovery Mechanistic studies of gas transport in hybrid membranes Techno-economic analysis of carbon capture systems Recent publications span 2025 and 2024 , with emphasis on: Direct air capture (DAC) optimization Organic solvent reverse osmosis (OSRO) systems Hybrid membrane synthesis techniques Computational modeling of transport phenomena Porous liquid material design 3D printing for contactor fabrication Scientific recognition includes: 2024 Stratis V. Sotirchos Memorial Award As advisor to PhD students and postdocs including Mingyu Song, Pavithra Shyam, and Younhwa Kim, Lively leads the Lively Lab at Georgia Tech. The lab operates advanced facilities for hollow fiber spinning, BET surface area analysis, membrane testing, and cyclic adsorption processes, while participating in the Georgia Tech Leadership Challenge and AIChE conferences.

Ulrike BückingView profile

Senior Lecturer

• Discrete Differential Geometry
• Conformal Geometry
• Discrete Surfaces
• +3 more

Ulrike Bücking serves as a Senior Lecturer in the Department of Mathematics and Computer Science at Free University of Berlin, where she is Deputy Head of the Primary School Mathematics Group within the Discrete Methods in Algebraic Geometry & Mathematics for Teacher Training working group. Her academic responsibilities include teaching mathematical professional knowledge courses for primary school teacher candidates since 2019, with recent expansion to advanced specialized instruction for mathematics-focused teacher trainees. Her research spans two interconnected domains: theoretical discrete differential geometry focusing on convergence of discrete conformal mappings, rigidity of planar patterns, and discrete minimal surfaces; and mathematics education specifically tailored for primary school teacher preparation. This dual expertise manifests in publications ranging from pure mathematical investigations of circle patterns and discrete Riemann surfaces to innovative pedagogical frameworks for mathematics teacher education. Analysis of her 15 most recent publications reveals a consistent trajectory in discrete complex analysis with increasing integration of mathematics education research since 2020. Her geometric work demonstrates methodological evolution from foundational circle pattern theory toward sophisticated discrete surface constructions, while her educational publications address practical curriculum development for teacher training programs. The 2023 MatheProfIL paper exemplifies her applied approach to bridging theoretical mathematics with classroom pedagogy. Dr. Bücking maintained continuous research activity through her decade-long participation (2012-2024) in the Collaborative Research Center/Transregio 'Discretization in Geometry and Dynamics' (Project A01: Discrete Riemann Surfaces). Her teaching portfolio shows significant evolution from advanced mathematics courses for physicists at TU Berlin (2002-2018) to specialized mathematics education for primary teachers at Free University of Berlin. She actively contributes to the Primary School Mathematics Group and collaborates within the Discrete Methods in Algebraic Geometry & Mathematics for Teacher Training working group, maintaining an office at Arnimallee 3 in Berlin's mathematics institute where she supervises course development and coordinates teacher education initiatives.

Angela BezoldView profile

Lecturer

• Mathematics Education
• Primary School Didactics
• Argumentation Competencies
• +5 more

Dr. Angela Bezold is a Lecturer at the Chair of Mathematics V (Mathematics Education) at the University of Würzburg . She specializes in primary mathematics didactics , with a focus on fostering argumentation competencies and self-differentiated learning environments . Her work is closely tied to the SINUS and CO-SINUS projects, which aim to improve mathematics education through research tasks and teacher training. Project leader of CO-SINUS (2024) for primary school research tasks Co-creator of the mathematics textbook series Zahlenzauber (2015–2021) Founder of Emil's Research Camp for children Her research emphasizes exploratory learning in geometry and arithmetic, including projects like Mathematik und Kunst and Modellieren und Problemlösen . Recent activities include workshops on LehrplanPlus implementation and digital teaching models like the Inverted Classroom . She actively collaborates with regional institutions on teacher training programs.

• Medical Imaging
• Magnetic Resonance Imaging (MRI)
• Diffusion MRI
• +5 more

Wenchuan Wu serves as Associate Professor in the Nuffield Department of Clinical Neuroscience within Oxford University's Medical Sciences Division and holds a Research Fellowship at Wolfson College. His work pioneers advanced MRI methodologies for elucidating brain structure and function through innovative acquisition and reconstruction techniques. Research focuses on submillimeter-resolution diffusion MRI, rapid brain connectivity mapping, computational artifact reduction, ultra-high field (7T) neuroimaging, and post-mortem studies of unfixed human brains. His methodologies integrate joint k-q acceleration, structured low-rank reconstruction, and Gaussian process modeling to overcome traditional resolution and distortion limitations in neurological imaging. Recent publications demonstrate accelerating trends in high-resolution diffusion imaging, with emphasis on motion correction, boundary artifact suppression, and multi-contrast integration. His work bridges computational innovation with clinical neuroscience, particularly in infant brain development and vascular imaging applications. Wu leads collaborative projects within Oxford's Diffusion Acquisition & Reconstruction and Ultra High-Field MRI Physics groups, working closely with Ziyu Li, Xinyu Ye, and Qijia Shen. His research is funded by the Royal Academy of Engineering and Wellcome Trust, supporting development of next-generation imaging protocols for both in vivo and post-mortem applications.