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Christian Theobalt is a Professor of Computer Science at Saarland University and Scientific Director of the Visual Computing and Artificial Intelligence Department at the Max Planck Institute for Informatics . He leads the Saarbruecken Center for Visual Computing as a strategic partnership between Google and MPI. PhD in Computer Science (2005) from MPI-INF/Saarland University Postdoctoral Researcher at MPI (2005-2007) Visiting Assistant Professor at Stanford (2007-2009) His research focuses on the intersection of Computer Graphics, Computer Vision, and Artificial Intelligence , with specializations in: 3D/4D Human Reconstruction Neural Rendering Performance Capture Geometric Deep Learning Volumetric Video Quantum Visual Computing Recent article trends show emphasis on: Quantum computing applications in visual reconstruction Neural rendering with radiance fields Human motion capture from egocentric views Gesture-language interaction modeling Multi-modal scene understanding Real-time free-viewpoint rendering Scientific Awards : Fellow of EUROGRAPHICS (2022) CVPR Best Student Paper Honorable Mention (2020) ERC Consolidator Grant (2017) Karl Heinz Beckurts Award (2017) Busy Beaver Teaching Award (2016) ERC Starting Grant (2013) Advising over 50 students and researchers including: Current researchers: Viktor Rudnev, Linjie Lyu, Mohit Mendiratta Postdocs: Kwang In Kim, Kiran Varanasi Alumni: Franziska Mueller (Google), Dushyant Mehta (Qualcomm), Ayush Tewari (MIT) Grants include ERC grants, Google Glass Research Award, and multiple industry partnerships. His lab maintains cutting-edge facilities with: Multi-camera capture systems Quantum annealing infrastructure HDR radiance field technology GPU clusters for AI research Time-of-flight imaging systems Event camera arrays

• Computer Science
• Artificial Intelligence
• Computer Vision
• +5 more

Prof. Peter Scholze is a leading mathematician at the University of Bonn and the Max Planck Institute for Mathematics (MPIM). He holds a professorship in the Mathematical Institute at the University of Bonn and is affiliated with MPIM as a principal investigator. His research focuses on arithmetic geometry, representation theory, and p-adic geometry, with notable contributions to perfectoid spaces, prismatic cohomology, and the geometrization of the local Langlands correspondence. Scholze has organized several advanced seminars (ARGOS) and taught courses on topics like condensed mathematics, p-adic geometry, and algebraic geometry. He collaborates widely with researchers such as Laurent Fargues and Bhargav Bhatt. His recent work includes groundbreaking papers on wild Betti sheaves, motivic geometrization of the local Langlands correspondence, and the Habiro ring of number fields. Scholze has pioneered the use of prismatic cohomology to unify p-adic Hodge theories and has developed foundational frameworks in derived algebraic geometry. His research also intersects with representation theory and topological cyclic homology. Affiliations: University of Bonn (Mathematical Institute), Max Planck Institute for Mathematics (MPIM) Research Groups: Arbeitsgruppe Arithmetische Geometrie und Darstellungstheorie Teaching: Recent courses include 'Selected Topics in Algebraic Geometry' and 'Six-Functor Formalisms'. His work frequently explores connections between algebraic geometry and number theory, with a focus on geometric approaches to arithmetic problems. Scholze is a key figure in modern arithmetic geometry, influencing both foundational theory and applied areas like the Langlands program.

• Arithmetic Geometry
• Algebraic Geometry
• Representation Theory
• +4 more

Prof. Kai S. Exner is a Professor of Theoretical Inorganic Chemistry at the University of Duisburg-Essen's Faculty of Chemistry and a member of the Zentrum für Medizinische Biotechnologie (ZMB). His research focuses on electrocatalysis , particularly optimizing electrode materials for energy applications such as batteries, electrolyzers, and fuel cells. He employs computational methods like ab initio calculations and microkinetic modeling to bridge theoretical insights with experimental and industrial applications, emphasizing sustainable catalyst design without relying on noble metals. His work integrates basic research with applied medical biotechnology through ZMB collaborations, though his primary affiliation lies in the Faculty of Chemistry. Exner has pioneered concepts like the electrochemical-step asymmetry index and free-energy span model, which are critical for screening electrocatalysts. His research group, 'Theoretical Inorganic Chemistry,' actively explores mechanisms behind oxygen and chlorine evolution reactions, emphasizing selectivity control and reaction pathway complexity. Exner received the Gottschalk-Diederich-Baedeker-Preis (2024) for advancing the energy transition through foundational electrocatalysis research. He leads Aktuelles Projekt funded by third-party grants and collaborates with facilities like the Analytics Core Facility Essen (ACE) and Imaging Center Campus Essen (ICCE). His advisory role focuses on guiding theoretical and computational approaches in electrochemistry, and he has established a reputation for interdisciplinary research connecting computational chemistry with practical energy solutions.

• Electrocatalysis
• Inorganic Chemistry
• Materials Science
• +7 more

Prof. Stefan Leutenegger is a tenure-track Assistant Professor at Technische Universität München (TUM), leading the Machine Learning for Robotics group within the TUM School of Computation, Information, and Technology. Previously, he held roles as Senior Lecturer (2018–2021) and Lecturer (2014–2018) at Imperial College London's Dyson Robotics Lab, where he founded the Smart Robotics Lab. He earned his PhD (2014) from ETH Zurich, focusing on autonomous solar-powered aircraft navigation, and holds BSc (2006) and MSc (2009) in Mechanical Engineering from ETH Zurich. His research centers on mobile robotics, particularly enabling robots (e.g., drones) to perceive and navigate complex environments using machine learning and sensor data fusion. Key focus areas include SLAM, event-based vision, 3D reconstruction, and autonomous exploration. He has pioneered algorithms like BRISK (2011), OKVIS (2014), and ElasticFusion (2016), advancing real-time robotics perception. Notable Awards: Imperial College President's Award (2018), Best ECCV Paper (2016), ETH Medal for Dissertations (2015). Labs: TUM's Machine Learning for Robotics Group, Imperial's Smart Robotics Lab. Publications: Over 100 papers, including seminal works in CVPR, ECCV, and Robotics: Science and Systems. Current projects include DigiForests (forest inventory via robotics), aerial additive manufacturing, and object-centric semantic mapping. His work bridges theory and practice, with applications in autonomous drones, construction robotics, and human-robot interaction.

• Mobile Robotics
• SLAM (Simultaneous Localization and Mapping)
• Computer Vision
• +7 more

Jinhyung Park is an Associate Professor in the Department of Mathematical Sciences at KAIST, Daejeon, Korea. He obtained his Ph.D. from KAIST in 2014 under Prof. Sijong Kwak and was mentored as a CMC Fellow at KIAS (2014–2019). His research focuses on Algebraic Geometry , particularly syzygies of algebraic varieties , secant varieties , and positivity of line bundles . Education: Ph.D., KAIST (2014); MS/BS, KAIST and University of Chicago (2002/2001). Research Interests: His work explores geometric properties of projective manifolds, singularities in algebraic geometry, and applications of Okounkov bodies to divisors and syzygies. Recent trends include Castelnuovo-Mumford regularity bounds (e.g., for threefolds with rational singularities) and subadditivity of Okounkov bodies in fiber spaces. Teaching: Courses include Matrix Groups, Algebraic Geometry, and Calculus. He previously taught at Sogang University and Purdue University. Professional Affiliations: Member of the Korean Mathematical Society, American Mathematical Society, and other international societies. Organized the 8th East Asia Number Theory Conference at KAIST (2019).

• Algebraic Geometry
• Syzygies of Algebraic Varieties
• Secant Varieties
• +6 more

Andrea Tagliasacchi is an Associate Professor in the School of Computing Science at Simon Fraser University (SFU), where he holds the Visual Computing Research Chair. He is also a part-time (20%) Staff Research Scientist at Google DeepMind in Toronto and holds an associate professor (status only) appointment in the Department of Computer Science at the University of Toronto. Education: PhD in Computing Science – Simon Fraser University (NSERC Alexander Graham Bell Fellow) Postdoctoral Research – École Polytechnique Fédérale de Lausanne (EPFL) MSc in Computer Science – Politecnico di Milano (Gold Medalist) His research lies at the intersection of computer vision, computer graphics, and machine learning, with a focus on 3D visual perception. Key areas include neural radiance fields (NeRF), 3D Gaussian splatting, inverse rendering, and geometric deep learning, with applications in robotics, augmented reality, and autonomous systems. His work emphasizes robust and efficient scene understanding and reconstruction from visual data. His recent publications, appearing in top venues like CVPR, SIGGRAPH, NeurIPS, and ECCV, demonstrate a strong emphasis on neural fields, 3D reconstruction, and generative modeling. Trends include improving rendering efficiency, enhancing robustness to noise and distractors, and enabling controllable and 3D-aware generation. His group has made significant contributions to Gaussian splatting, NeRF optimization, and diffusion-based 3D/4D synthesis. Scientific Awards: 2024 CVPR Best Paper Award (Honorable Mention) 2020 CVPR Best Student Paper Award 2015 SGP Best Paper Award NSERC Alexander Graham Bell Canada Graduate Scholarship MITACS Best Paper Award (SIGGRAPH Asia 2009) NSF Best Poster Award (SGP 2012) He has advised numerous PhD and MSc students, many of whom are now researchers at leading institutions and companies. His research has been supported through collaborations with Google, Intel, and academic partners. He serves the community as a Senior Area Chair for CVPR 2025, Associate Editor for IEEE TPAMI (2024–2026), Guest Editor for IEEE TPAMI on 3D GenAI, and Program Chair for 3DV 2024. He leads a vibrant research lab at SFU focused on pushing the boundaries of 3D scene understanding with machine learning.

• Computer Vision
• Computer Graphics
• Machine Learning
• +11 more

Adam Runions is a researcher in the Department of Computer Science at the University of Calgary, leading the MPG Partner Group in computational analysis of leaf development through collaborative work with Miltos Tsiantis. His group is embedded in the Graphics Cluster, focusing on interdisciplinary problems at the intersection of computer science and developmental biology. University of Calgary - Department of Computer Science MPG Partner Group (2022) Graphics Cluster affiliation His research explores computational modeling and analysis of plant form and development across multiple scales, integrating geometric modeling, physically-based simulation, and computer-aided design. Key themes include plant morphogenesis, self-organization of natural forms, and cross-disciplinary applications in computer graphics and animation. Recent publications emphasize plant development (leaf shape, bark patterning), mathematical modeling (auxin-driven patterning), and geometric techniques (subdivision surfaces, PUPs). Collaborations span institutions like the Max Planck Institute for Plant Breeding Research. Scientific Awards Marie Sklodowska-Curie Fellowship Best Paper Award (International Conference on Cyberworlds 2015) Best Student Paper Award (Computer Graphics International 2011) The group actively recruits BSc, MSc, and PhD students with backgrounds in computer science and mathematics for projects on plant form simulation and digital content creation. Research integrates evolutionary biology, biomechanical modeling, and computational techniques.

• Computational Modeling
• Developmental Biology
• Computer Science
• +4 more

Prof. Laura Leal-Taixé is a Professor at the Technical University of Munich (TUM) in the Department of Informatics, leading the Dynamic Vision and Learning group. She holds the Rudolf Mößbauer Tenure Track Assistant Professorship, promoted to W3 Associate Professorship in 2022. Her research focuses on computer vision and machine learning, particularly video analysis, multi-object tracking, and autonomous driving applications. Education: B.Sc./M.Sc. in Telecommunications Engineering (Technical University of Catalonia, UPC) Ph.D. in Information Processing (Leibniz University of Hannover, Germany) Postdoctoral Research at ETH Zürich (Switzerland) and TUM Research Interests: Laura’s work addresses challenges in video analysis, including motion analysis, semantic segmentation, and integrating social dynamics into urban traffic modeling. Her project socialMaps , funded by the Sofja Kovalevskaja Award, explores decoupling vehicle and pedestrian traffic using dynamic maps. Her research combines optimization techniques, deep learning, and sensor data for real-world applications like autonomous systems. Recent Trends: Her publications highlight advancements in multi-object tracking, 3D LiDAR segmentation, and trajectory forecasting, emphasizing neural networks and geometric approaches. Collaborations with industry and academia underscore her work’s practical impact. Awards: 2017 Sofja Kovalevskaja Award (€1.65M, Humboldt Foundation) 2017 DAAD Australia-German Joint Research Grant Travel grants from Women in CV, CVPR Doctoral Consortium, and Vodafone Foundation Labs & Projects: Leads the Dynamic Vision and Learning group at TUM, focusing on vision-based AI for autonomous systems and environmental monitoring. Active in developing datasets like DynamicEarthNet for semantic change analysis.

• Computer Vision
• Machine Learning
• Autonomous Driving
• +4 more

Prof. Dr. Thomas Brinkhoff is Chair of the Institute Board and Chair of Geoinformatics at Oldenburg University of Applied Sciences. He leads the Institute for Applied Photogrammetry and Geoinformatics (IAPG) and contributes to institutions like the Association for the Promotion of Geoinformatics in Northern Germany (GiN e.V.) and the Oldenburg Research and Development Institute for Computer Science (OFFIS e.V.). Education: Diploma in Computer Science (Informatik), Universität Bremen (1990) Doctorate in Computer Science (Dr. rer. nat.), Ludwig Maximilian University of Munich (1994) Brinkhoff's research spans geodatabase systems, spatiotemporal data processing, geosensor analytics, and location-based services. His work addresses Volunteered Geographic Information (VGI), web-based geospatial visualization, and mobile data integration, with applications in traffic management and forensic science. Recent projects include ProSaDi (Digital Provenance and Collection Research) and contributions to the Laboratory for optical 3D metrology . He has served on program committees for ACM SIGSPATIAL (2002-2019), AGILE conferences (2010-2025), and editorial boards of journals like GeoInformatica and TGIS. Notable Lectures: 2024: Forensic applications of tachograph data 2023: Geoinformatics in homicide investigations 2022: Spatiotemporal analysis for sustainability projects 2015: Open geodata standards at FOSSGIS 2014: Mobile sensor data processing

• Geoinformatics
• Geodatabase Systems
• Spatio-temporal Data Processing
• +5 more

PD Dr. Christian Zillinger is a researcher at the Karlsruhe Institute of Technology (KIT), specifically within the Department of Mathematics. He leads the Junior Research Group "Stability and Instability in Fluids and Materials" (AP6) as part of the CRC 1173. His office is located at Kollegiengebäude Mathematik (20.30), room 2.024 in Karlsruhe, Germany. Dr. Zillinger obtained his PhD under the supervision of Herbert Koch at the University of Bonn. Following his doctorate, he served as an assistant professor (NTT) at the University of Southern California and was a postdoctoral fellow at BCAM (Basque Center for Applied Mathematics). He recently completed his habilitation thesis titled "On Mixing and Resonances in Fluid Systems" at KIT in 2023. Dr. Zillinger's research focuses on partial differential equations motivated by physical problems, particularly in fluid dynamics and material sciences. His work encompasses several key areas: Mixing as a (de)stabilizing mechanism in fluids and inviscid damping Cascades of resonances and instabilities in fluids and plasmas Convex integration and microstructures in materials, including rigidity and flexibility phenomena Magnetic fluids and magnetohydrodynamics Partial dissipation in the Boussinesq equations His recent publications demonstrate a strong focus on stability and instability phenomena in fluid systems, with particular attention to mathematical analysis of PDEs governing fluid behavior. He has made significant contributions to understanding echo chains, resonance phenomena, and damping mechanisms in various fluid models. His work bridges theoretical mathematics with applications in physics and materials science, often employing advanced analytical techniques to address challenging problems in nonlinear PDEs. Dr. Zillinger actively teaches courses at KIT, including "Klassische Methoden für partielle Differentialgleichungen" (Classical Methods for Partial Differential Equations), "Introduction to convex integration," "Introduction to Kinetic Equations," and seminars on microstructure in materials and fluid dynamics. He leads the Junior Research Group "Stability and Instability in Fluids and Materials" which is part of the Collaborative Research Centre (CRC) 1173 at KIT, focusing on wave phenomena. This research group investigates mathematical aspects of stability and instability in physical systems, with applications to fluid dynamics and material science.

• Partial differential equations
• Fluid dynamics
• Material sciences
• +5 more

Jan von Delft is a Professor (chair) at Ludwig-Maximilians-University (LMU) Munich, working in the Faculty of Physics within the Chair of Theoretical Solid State Physics. His research group consists of postdocs, PhD students, and master's students working on various aspects of strongly correlated electron systems, with physical space located at Theresienstr. 37 (Room A420) in Munich. von Delft's research focuses on correlated electron and spin systems, with particular interest in dynamical and transport properties, quantum impurity models, Hund metals, unconventional superconductors, quantum magnets, and quantum criticality. His methodological expertise includes many-body field theory, parquet formalism (FRG), DMFT, and tensor networks (NRG, DMRG, PEPS, XTRG, etc.). His work bridges theoretical concepts with computational approaches to understand complex quantum phenomena in condensed matter systems. He has developed a distinctive emphasis on real-frequency calculations and numerical methods for studying quantum critical phenomena. Analysis of von Delft's recent publications reveals a strong focus on developing and applying advanced computational methods to study strongly correlated electron systems. His group has made significant contributions to numerical renormalization group techniques, tensor network methods, and the parquet formalism for calculating real-frequency correlation functions. His research shows increasing sophistication in handling quantum criticality, particularly in heavy-fermion systems, and exploring unconventional superconductivity mechanisms. Notably, his group has developed specialized computational libraries like KeldyshQFT to make these advanced methods more accessible to the broader physics community. von Delft actively mentors a substantial research group consisting of one postdoc (Markus Scheb), eleven PhD students (Anxiang Ge, Sasha Kovalska, Mathias Pelz, Marc Ritter, Nepomuk Ritz, Changkai Zhang, Markus Frankenbacher, Felipe Picoli, Simone Fodera, Ming Huang), and two master's students (Ester Pages, Gianluca Grosso). His detailed Style Guide for scientific communication demonstrates his commitment to high-quality research presentation. The group appears well-funded with ongoing research activities spanning theoretical development, computational implementation, and physical interpretation of complex quantum phenomena.

• Correlated electron systems
• Quantum impurity models
• Quantum criticality
• +7 more

Prof. Peter Scholze is a leading mathematician at the Max Planck Institute for Mathematics in Bonn, specializing in algebraic geometry and arithmetic geometry. He holds the academic rank of Professor and is part of the Arbeitsgruppe Algebraische Geometrie und Darstellungstheorie. His research focuses on foundational questions in algebraic geometry, number theory, and representation theory, particularly through the lens of the Langlands program, p-adic Hodge theory, and perfectoid spaces. Scholze has pioneered geometric approaches to the local Langlands correspondence and introduced revolutionary concepts like prismatic cohomology and condensed mathematics. He actively contributes to academia through advanced courses on topics such as geometrization of the Langlands program, étale cohomology, and condensed mathematics. His work bridges algebraic geometry with representation theory, addressing fundamental problems in arithmetic geometry and p-adic analysis. Scholze’s research outputs include seminal papers on perfectoid spaces, prismatic cohomology, and the geometrization of local Langlands correspondence. He collaborates extensively with leading mathematicians globally, contributing to collaborative research initiatives like the ARGOS seminar and the Habiro ring project. His teaching engagements include advanced lectures on algebraic geometry, representation theory, and p-adic geometry, reflecting his commitment to training the next generation of researchers. Despite no explicitly listed awards in the provided text, Scholze is widely recognized as a Fields Medalist (2018) and a leading figure in modern mathematics.

• Algebraic Geometry
• Arithmetic Geometry
• Number Theory
• +5 more

Cumrun Vafa is an Iranian-American theoretical physicist and the Hollis Professor of Mathematicks and Natural Philosophy at Harvard University. His research spans string theory, quantum gravity, and the interplay between geometry and quantum field theories. Born in Tehran, Iran (1960) BS in Mathematics and Physics from MIT (1981) PhD in Physics from Princeton University (1985) under Edward Witten His work includes foundational contributions to F-theory, black hole entropy via solitonic states, and geometric engineering of quantum field theories. He has published over 300 articles with collaborators like Robbert Dijkgraaf and Hirosi Ooguri. Recent research focuses on string dualities, mirror symmetry, and applications to unresolved particle physics problems. His articles emphasize connections between Toda theories, matrix models, and supersymmetric gauge systems. Notable awards include: ICTP Dirac Medal (2008) Dannie Heineman Prize for Mathematical Physics (2016) Breakthrough Prize in Fundamental Physics (2017) Mustafa Prize (2021) Election to American Academy of Arts and Sciences (2005) Election to National Academy of Sciences (2009) He has advised prominent students such as Freddy Cachazo and Daniel Jafferis. Vafa is also a trustee of the Network of Iranians for Knowledge and Innovation (NIKI).

• Theoretical Physics
• String Theory
• Quantum Gravity
• +6 more

Prof. Dr. Frank Pollmann is a Full Professor (W3) at the Department of Physics PH-I, Technical University of Munich (TUM), leading the Chair of Theoretical Solid-State Physics since 2022. His research focuses on condensed matter theory and quantum information concepts , particularly in systems of correlated electrons and quantum many-body dynamics . PhD: Max Planck Institute for the Physics of Complex Systems / TU Ilmenau (2006) Postdoc: UC Berkeley (2008-2010) Group Leader: MPIPKS Dresden (2011-2016) Associate Professor: TUM (2017-2022) His work spans topological phases , frustrated spin systems , and non-equilibrium quantum dynamics , utilizing tensor network methods and quantum information theory to study phenomena like many-body localization and Hilbert space fragmentation . His publications demonstrate trends in quantum scar states , Kardar-Parisi-Zhang hydrodynamics , and quantum transport anomalies . Scientific Awards : ERC Consolidator Grant (2017) Walter Schottky Prize (2015) Otto-Hahn Medal (2007) He teaches courses including Advanced Methods in Quantum Many-Body Theory , Solid State Theory , and Topology in Condensed Matter , while leading the Pollmann Group under the TUM School of Natural Sciences.

• Condensed Matter Theory
• Quantum Information Theory
• Correlated Electrons
• +6 more

Xiaoxiao Long is a Tenure-Track Associate Professor at the School of Intelligence Science and Technology, Nanjing University. He joined NJU as an associate professor in February 2024. Previously, he earned his Ph.D. from the University of Hong Kong (HKU) under the supervision of Prof. Wenping Wang (IEEE & ACM Fellow) and Prof. Taku Komura. His educational background includes: Ph.D. in Computer Science from University of Hong Kong Bachelor's degree in Control Science & Engineering from Zhejiang University Dr. Long's research focuses on computer graphics and 3D computer vision, with particular emphasis on 3D Vision, Physical AI, and World Models. His long-term goal is to develop General-Purpose AI with spatial capabilities. His work bridges theoretical understanding of 3D spaces with practical implementations of spatial AI systems, with applications spanning robotics, virtual reality, and augmented environments. He employs innovative neural network approaches and geometric constraints to advance 3D scene understanding and reconstruction. His publication record shows strong momentum with multiple papers accepted to top-tier conferences including CVPR (5 papers in 2025 alone), ICML, ICLR, ECCV, and TPAMI. His research demonstrates a clear progression from foundational geometric estimation techniques (ASN++) toward more comprehensive spatial AI systems. His scientific recognition includes: Excellent Young Scholars Fund (Overseas) from NSFC Dr. Long has successfully mentored numerous students who have published at major venues and gone on to pursue advanced degrees at prestigious institutions including USTC, Beihang University, HKU, UCAS, Virginia Tech, and HKUST. He is currently recruiting Ph.D. and master's students for Fall 2026, seeking candidates interested in pushing the boundaries of 3D computer vision and spatial AI. His laboratory focuses on developing advanced techniques for 3D scene understanding, neural rendering, and physical AI. Current projects span Gaussian-based representations, neural radiance fields, and geometric estimation, with applications in robotics, virtual environments, and spatial reasoning systems.

• Computer Graphics
• 3D Computer Vision
• 3D Vision
• +4 more