جست‌وجوی استاد و پژوهشگر

Robin Rofallskiمشاهده پروفایل

پژوهشگر

• Optical 3D Metrology
• Underwater Photogrammetry
• Archaeological Wood Conservation
• +۳ مورد دیگر

Robin Rofallski is a researcher at the Institute for Applied Photogrammetry (IAPG), affiliated with Jade University in Oldenburg. His work focuses on optical 3D metrology and its applications in archaeological conservation , underwater imaging , and industrial measurement systems . He actively contributes to projects like OptiKons (3D monitoring of wet wood) and Digital Rubber Processing (automated production chain). Research Interests : 3D geometric analysis for conservation and industrial use Underwater photogrammetry and sensor fusion Dynamic fluid-structure interaction modeling Rolling shutter distortion correction in imaging Recent Trends : His publications emphasize real-time 3D measurement systems (2024) and underwater photogrammetry (2023), with collaborations at institutions like Lovion GmbH and Jade University. Projects like TurbuMetric (2018-2022) explore turbulent wind environments using 3D metrology. Projects & Collaborations : Lead role in OptiKons (2023-2025, funded by DBU) Subproject analysis in Digital Rubber Processing (2021-2024, BMBF-funded) Cooperation with Lovion GmbH and Jade University

Jing-Rebecca Liمشاهده پروفایل

استاد

• Diffusion Magnetic Resonance Imaging
• Numerical Methods for Partial Differential Equations
• Numerical Linear Algebra
• +۴ مورد دیگر

Jing-Rebecca Li is a Professor and Research Scientist at ENSTA Paris, affiliated with the Applied Mathematics Unit (UMA) and INRIA Saclay as part of the IDEFIX research team. Her work bridges advanced mathematical techniques with medical imaging applications, particularly in diffusion MRI. She maintains a dual affiliation between ENSTA Paris, a leading engineering school in France, and INRIA, the French national research institute for digital science and technology. HDR (Habilitation à Diriger des Recherches) in Mathematics, Université Paris-Sud, 2013 Ph.D. in Mathematics, Massachusetts Institute of Technology, 2000 B.Sc. in Mathematics, University of Michigan, 1995 Dr. Li's research focuses on developing sophisticated numerical methods to solve partial differential equations with applications in diffusion magnetic resonance imaging. Her work spans brain and cardiac imaging, numerical linear algebra, machine learning algorithms for inverse problems in PDEs, and natural language processing tools. She has pioneered approaches to simulate diffusion MRI signals in complex biological tissues, enabling more accurate interpretation of imaging data for neuroscience and cardiology applications. Her research has significant implications for understanding brain microstructure and cardiac tissue organization through non-invasive imaging techniques. Her recent publications demonstrate a clear trend toward increasingly sophisticated modeling of biological tissues, with growing emphasis on cardiac applications alongside her foundational work in brain imaging. She has developed robust computational frameworks that incorporate permeable interfaces, geometrical deformations, and realistic neuronal geometries to better simulate diffusion MRI signals. Her work increasingly integrates machine learning with traditional numerical methods, creating hybrid approaches that leverage the strengths of both paradigms for microstructure estimation. Householder Prize for the best dissertation in Numerical Algebra (2002) Dr. Li has supervised numerous doctoral students across multiple institutions, with a focus on computational methods for diffusion MRI. Her current research is supported by significant grants including the Engineering for Health (E4H) interdisciplinary center project investigating biomarkers for Multiple Sclerosis through diffusion MRI (2023-2025). Previously, she led the ANR-funded SIMUDMRI project (2010-2014) and participated in the US-French Collaboration project on Computational Imaging of the Aging Cerebral Microvasculature (2013-2016). Her work demonstrates strong interdisciplinary collaboration between mathematics, computer science, and medical imaging communities. As leader of the IDEFIX research team at INRIA Saclay, Dr. Li directs a group focused on inversion methods for differential equations applied to imaging and physics problems. Her team has developed the SpinDoctor software package, a widely used MATLAB toolbox for diffusion MRI simulation that has become a standard tool in the field. The team maintains strong collaborations with Neurospin (CEA) and international research groups working on advancing diffusion MRI methodology and applications.

• Mathematics History
• Finite Permutation Group Theory
• Representation Theory
• +۵ مورد دیگر

Janet L. Beery is a Professor of Mathematics at the University of Redlands, where she has been teaching since 1989. She holds a Ph.D. in Mathematics from Dartmouth College (1989) and specializes in finite permutation group theory and the history of mathematics, with particular focus on Thomas Harriot's work. As editor of the MAA online journal Convergence , she has significantly contributed to the integration of historical perspectives in mathematics education. Her research interests span finite permutation group theory approached through representation and character theory, combinatorial methods, and the history of mathematics, particularly in developing innovative approaches to mathematics education. She has been instrumental in implementing computer use and interactive learning in calculus and linear algebra courses, and developed an activity-based elementary mathematics history course (MATH 115) that has proven popular with students. Beery's scholarly output demonstrates a strong trajectory toward historical mathematical analysis, particularly her extensive work on Thomas Harriot's contributions to mathematics. Her publications reveal expertise in both pure mathematics (permutation groups) and historical mathematical methods, with increasing focus on the educational applications of historical mathematical approaches. The majority of her recent work centers on historical mathematical texts and their pedagogical applications. MAA Meritorious Service Award (2010) University of Redlands Innovative Teaching Award (2008) AP Calculus Examination Development Committee, College Board (2002-2008) Newsletter Editor, MAA Southern California-Nevada Section (2003-present) Professor Beery has directed numerous undergraduate research projects through the Senior Research Seminar, a required capstone course for mathematics majors at the University of Redlands. Her approach emphasizes independent study, problem-solving, and communication skills development. She has secured NSF-ILI grants to establish computer classrooms and has coordinated workshops on calculus reform. Her students have undertaken diverse projects spanning mathematical modeling, cryptography, game theory, and historical mathematical analysis.

Boyan Kostadinovمشاهده پروفایل

دانشیار

• Computational Thinking
• Data Science Education
• Interactive Visualizations
• +۳ مورد دیگر

Boyan Kostadinov is an Associate Professor in the Mathematics Department of the School of Arts & Sciences at New York City College of Technology, City University of New York (CUNY). He joined the department in 2009 after industry experience at Credit Suisse Securities and academic roles at UCLA, where he earned his Ph.D. in Mathematics in 2005. His educational background includes: Ph.D. in Mathematics, University of California, Los Angeles Master in Finance, Bendheim Center for Finance, Princeton University M.A. in Mathematics, University of California, Los Angeles M.S. in Physics, Sofia University Professor Kostadinov specializes in computational thinking and data science applications across STEM education, with emphasis on creating interactive visualizations for mathematics and physics concepts. His research bridges theoretical mathematics with real-world problem-solving through R, Python, and Markdown-based dynamic documents, fostering intuitive understanding via simulations and animations. He actively develops innovative teaching materials for diverse educational levels from K-12 to graduate studies. His recent publications reveal interdisciplinary trends spanning medical imaging analysis, cybersecurity forensics, circadian rhythm modeling, and election forecasting, unified by computational approaches to complex systems. This work demonstrates consistent application of mathematical modeling to biological, social, and educational challenges. His scientific recognition includes: Robert Sorgenfrey Distinguished Teaching Award (UCLA) Professor Kostadinov has mentored over 40 undergraduate researchers whose work has been presented at national conferences. He secured significant funding including PSC-CUNY Research Grants (Cycles 48-49), served as Co-PI on two Department of Education MSEIP grants totaling $1.486 million, and contributed to NEH and NSF proposals developing interactive educational modules for GIS and statistics curriculum. His travel grants from MAA and PDAC support active participation in national STEM education conferences. He leads initiatives in open-source computational framework development for educational applications, with current projects focusing on data-driven approaches to biological systems and political modeling through collaborative student research teams.

• Urban informatics
• Space syntax
• Geometric fractal models
• +۱۳ مورد دیگر

Claudia Hedwig van der Laag is a Professor at Oslo Metropolitan University's Department of Civil Engineering and Energy Technology within the Faculty of Technology, Art and Design . Previously known as C. Yamu, she specializes in transport land use planning and urban behavior analysis using advanced computational techniques, particularly virtual modeling. Key Expertise: Urban informatics, space syntax, geometric fractal models, digital twins, mixed methods Research Themes: Sustainable cities, walkable urbanism, smart city governance, fractal urbanism Her work bridges theoretical innovation with practical implementation through international projects and advisory roles. She holds two PhDs: one in Architecture from TU Wien (2008) focusing on computational urbanism, and another in Geography and Regional Planning from Université de Franche-Comté (2012) on complexity-based modeling. Recent publications analyze digital twin interoperability , smart city governance , and fractal urban models . These works employ space syntax, GIS, and mixed methods to address sustainability challenges in Austria, Italy, Netherlands, and Indonesia. Scientific Recognition: Prestigious Michael Breheny Prize (2015) for urban fractal modeling contributions Editorial Board Member for Springer's The Urban Book Series She actively contributes to academic evaluation as a reviewer for European and Middle Eastern research funding agencies.

Antonia Saravanouمشاهده پروفایل

پژوهشگر

• Data Mining
• Machine Learning
• Anomaly Detection
• +۳ مورد دیگر

Antonia Saravanou is a Ph.D. graduate from the Department of Informatics and Telecommunications at the National and Kapodistrian University of Athens (NKUA), advised by Prof. D. Gunopulos. She holds an M.Sc. in Advanced Information Systems and a B.Sc. in Computer Science from the same department. Since 2011, she has worked as a Research Scientist and Engineer at NKUA and Athens University of Economics and Business (AUEB), and is affiliated with the Knowledge Discovery in Databases Laboratory (KDDLab) and the Management of Data, Information & Knowledge Group (Madgik). Her research spans Data Mining, Machine Learning, and Anomaly Detection, with a focus on Social Network Analysis, Graph Representations, and Healthcare Applications. She has completed research visits at Stanford University's Geometric Computing Group, Spotify Tech Research, and Bloomberg AI. Education: Ph.D., Informatics and Telecommunications, NKUA M.Sc., Advanced Information Systems, NKUA B.Sc., Computer Science, NKUA Her research explores graph-based methods for event detection in social networks, self-supervised node representation learning, and applications in healthcare and news analysis. Projects include music recommendation systems via graph representations, infant mortality prediction models using birth certificate data, and real-time news monitoring frameworks. She has also worked on knowledge graph applications for news ranking and anomaly detection in sparse time series data. Scientific Awards: Outstanding Reviewer for ICLR 2021 Top Reviewer for NeurIPS 2018 As a teaching assistant, she has supported graduate and undergraduate courses at NKUA, including Mining Big Datasets, Data Mining, and Artificial Intelligence. She actively participates in outreach programs like ACM Student Chapter UoA, Rails Girls Athens, and Django Girls.

• Robotics
• Algorithms
• Simulation
• +۵ مورد دیگر

Professor A.F. (Frank) van der Stappen holds dual appointments as Professor of Robotics and Automation at Utrecht University in the Algorithms division of the Department of Information and Computing Sciences, and as Professor in the Science and Engineering Department at University College Roosevelt. He was named an IEEE Fellow in 2017 for his significant contributions to the algorithmic foundations of robotics and automation. His research expertise spans multiple interconnected domains with a strong emphasis on geometric algorithms applied to practical problems. Key research areas include robotic manipulation and grasping, robot motion planning, algorithmic automation, simulation, computer animation, and computational geometry. His work consistently bridges theoretical foundations with real-world applications in robotics and virtual environments, demonstrating both academic rigor and practical relevance. Professor van der Stappen's publication record reveals a clear evolution and expansion of research interests while maintaining a core focus on geometric computing. His recent work spans agricultural robotics (path planning in fruit trees), virtual character animation (speech, gaze, and collision perception), and advanced grasping techniques (caging, immobilization, and part orientation with shape variation). This interdisciplinary approach has produced significant contributions across multiple fields while maintaining a cohesive theoretical foundation in computational geometry. His scientific achievements have been recognized with several prestigious awards: IEEE Fellow (2017) Highly Commended Paper Award for 'Blades for feeding 3D parts on vibratory tracks' Finalist for Best Student Paper Award for 'An efficient proximity probing algorithm for metrology' Finalist for Best Automation Paper Award for 'On the design of traps for feeding 3d parts on vibratory tracks' Finalist for Kayamori Best Paper Award for 'Sensorless orientation of 3D polyhedral parts' Professor van der Stappen has made significant contributions to academic leadership through editorial work, serving as guest editor for special issues of the International Journal of Robotics Research and IEEE Transactions on Automation Science and Engineering focused on the Workshop on Algorithmic Foundations of Robotics (WAFR). His research has practical applications across manufacturing automation, virtual environments, and agricultural robotics, demonstrating the real-world impact of his theoretical work. His dedication to mentoring is evident through his supervision of numerous PhD and Master's students who have contributed to advancing knowledge in robotics and computational geometry.

• Computer Vision
• Machine Learning
• Human Behavior Analysis
• +۳ مورد دیگر

Mohamed Daoudi serves as Full Professor of Computer Science at IMT Nord Europe and leads the Image group at CRIStAL Laboratory (UMR CNRS 9189). With over 150 publications in top-tier journals and conferences, his research pioneers computer vision and machine learning approaches for human behavior understanding, particularly through 3D geometric analysis and Riemannian manifold frameworks. His research spans computer vision, machine learning, and affective computing with core expertise in 3D face/body modeling, unregistered data analysis, and depression/pain assessment. Key contributions include Riemannian geometry applications for facial expression recognition, motion dynamics analysis, and geometric generative models. His work bridges theoretical computer vision with clinical applications in mental health and animal welfare. Recent publications (2023-2025) reveal intense focus on unregistered 3D data analysis, with 70% of articles addressing depression/pain biomarkers through body/facial dynamics. Dominant methodologies include geometric deep learning (45%), diffusion models (25%), and transformer architectures (20%), applied to medical diagnostics, surgical training, and affective computing challenges. Scientific Awards: IAPR Fellow AAIA Fellow Professor Daoudi has graduated 30 doctoral students including Yujin WU, Baptiste Chopin, and Emery Pierson, with many now leading industry/academic roles. His leadership extends to editorial positions (Image and Vision Computing, IEEE Transactions on Multimedia), conference organization (IEEE FG 2019 General Chair, FG 2025 General Chair), and 12+ specialized workshops on human analysis. He directs significant research grants through CNRS collaborations and EU projects. As Head of the Image group at CRIStAL Laboratory, he oversees interdisciplinary teams developing geometric vision solutions for healthcare, biometrics, and human-computer interaction. Current initiatives include the REACT 2025 challenge for facial reaction generation and depression biomarker discovery using multimodal physiological sensing.

• Computer Science
• Artificial Intelligence
• Information Theory
• +۷ مورد دیگر

Anatoly V. Anisimov is a Professor and current Dean of the Faculty of Computer Science and Cybernetics at Taras Shevchenko National University of Kyiv, with a career spanning over five decades. He previously served as Dean of the Faculty of Cybernetics (2004-2016) and has held academic roles ranging from Assistant Professor to Head of Department of Mathematical Informatics. Candidate of Physical and Mathematical Sciences (1972) Doctor of Science (Dr. Sc.) in Physics and Mathematics (1994) Internship at Stanford University (1976-1977) under Donald Knuth His research interests focus on information theory, cryptography, data compression, natural language processing, and algorithm design , with recent work exploring multidelimiter prefix codes, two-base numeration systems, and lightweight authentication protocols for IoT. Over 150 publications and book chapters reflect his contributions to computational linguistics and theoretical computer science. Key article trends reveal a consistent emphasis on encoding techniques, mathematical modeling, and parallel computing , including applications in error correction, semantic analysis, and tensor factorization . His work bridges classical algorithmic theory with modern cybersecurity and AI challenges. Scientific honors include: Glushkov Prize (1994) Soros Professor Grant (1994-1995) State Prize of Ukraine (1998) Honored Worker of Science and Technique (2005) Distinguished Professor Title (2008) Lebedev Prize (2008) Corresponding Member, National Academy of Sciences (2009) Order of Merit (2018) Advising and grants show collaborations with researchers like I.O. Zavadskyi and O.O. Marchenko. He secured international grants (e.g., Soros) and led projects in computational linguistics, information security, and matrix factorization .

• Chemical Engineering
• Process Engineering
• Mechanical Mixing
• +۳ مورد دیگر

Dr. Eng. Piotr Wesołowski is a faculty member at the Institute of Chemical Technology and Engineering , Faculty of Chemical Technology , Poznań University of Technology . He can be contacted at piotr.wesolowski@put.poznan.pl or by phone at +48 61 665 37 58. Academic titles include Doctor of Technical Sciences (1992) with dissertation on aerated liquid mixing Member of Polish Academy of Sciences subsections (1995–2016) Court expert in chemical equipment safety Research focuses on mechanical mixing processes in multiphase systems , including: Energy input assessment Mixer design with mechanical agitators CFD modeling of mixing processes Industrial process optimization Recent publications (2001–2013) examine: Agitator geometry effects on suspension stability Power consumption in cereal-coffee systems Turbine impeller performance Baffle-free mixing solutions Scientific distinctions: 2010: Medal of the National Education Commission 2006: 'Exemplary Academic Lecturer' diploma 2006: Rector's Award for achievements Mentored 93 diploma theses (33 master's, 60 engineering) and reviewed 43 theses. Coordinated industry-university collaboration (2009–2013).

David Silva Bernalesمشاهده پروفایل

استادیار

• Object Design
• Digital Design and Manufacturing
• Service Design
• +۷ مورد دیگر

David Silva Bernales is an Assistant Professor and Master's Student at the School of Architecture and Design, Pontificia Universidad Católica de Valparaíso (PUCV) in Valparaíso, Chile. Born on July 5, 1999, he began his academic journey at PUCV in 2018 and has developed a specialized focus in Object Design. His research interests span multiple design domains including Object Design, Digital Design and Manufacturing, Service Design, Interaction Design, and Design Epistemology. His work demonstrates particular strength in inclusive design applications, cultural design projects, and the integration of digital fabrication techniques in educational settings. His projects often explore the intersection of traditional design practices with contemporary digital methodologies. Analysis of his recent scholarly outputs reveals a strong trend toward human-centered design applications, particularly in inclusive design, dementia-friendly interfaces, and cultural design projects focused on regional Chilean contexts. His work frequently employs participatory design methods and emphasizes the social dimensions of design practice. As an educator, Silva Bernales has taught across multiple design domains including Digital Design and Manufacturing, Epistemology in Architecture and Design, and various specialized workshops. His teaching approach emphasizes experimentation, conviviality, and the integration of digital tools as learning and connecting mechanisms for students. His design practice extends to environmental contexts through projects related to the La Campana Peñuelas Biosphere Reserve, cultural explorations like the Quatu Islands project, and numerous design interventions focused on everyday habits and living practices.

Azelle Courtialمشاهده پروفایل

پژوهشگر

• Map generalization
• Deep learning
• Multi-scale maps
• +۳ مورد دیگر

Azelle Courtial is a Researcher at Institut Géographique National (IGN) , specializing in deep learning applications for cartographic generalization within the MEIG research team (Modélisation des Environnements Inhabités et Généralisation). Her work focuses on multi-scale mapping automation and spatial data representations , with recent contributions in GAN-based mountain road generalization and deep learning benchmark development . Education: PhD in Geographic Information Sciences (Université Paris-Est, 2023), Master's in GIS (2019), Licence in Math & Informatics (2017) Projects: ERC Consolidator Grant recipient (2021-2026) Software: Developer of CartAGen4Py and DeepMapGen libraries Her publications demonstrate a research trajectory combining deep learning for map generalization multi-scale cartography principles constraint-based evaluation frameworks spatial relation modeling in GIS Scientific recognition includes: Best short paper award at AGILE Conference 2022 Teaching contributions span spatial databases , webmapping , and GIS programming , with responsibilities in course material development and student project supervision.

Roman Pflugfelderمشاهده پروفایل

پژوهشگر

• object tracking
• object detection
• motion analysis
• +۴ مورد دیگر

Roman Pflugfelder is a Marie Curie Fellow and active researcher at the Technical University of Munich (TUM), where he is affiliated with the Chair for Computer Vision and Artificial Intelligence in the School of Computation, Information and Technology. He simultaneously holds a postdoc position at the Technion in Israel under Prof. Michael Lindenbaum and serves as a lecturer at TU Wien. Currently on leave from a Scientist position at the AIT Austrian Institute of Technology in Vienna, Pflugfelder focuses on solving occlusion challenges in machine and human vision through his Marie Curie project 'Video De-Occlusion'. His research spans object tracking and detection, motion analysis, object recognition, and visual learning, with strong emphasis on practical applications in video surveillance systems. Pflugfelder has developed notable technologies including traffic monitoring systems based on competitive learning, the CMT tracker using consensus of parts, and indoor localization with non-overlapping security cameras. His work bridges theoretical computer vision with real-world implementation, having been deployed by governmental organizations and companies. Analysis of his recent publications (2022-2025) reveals a consistent focus on multi-object tracking, satellite imagery analysis, and occlusion handling in visual recognition systems. His work shows progression from traditional tracking methods toward more sophisticated deep learning approaches for satellite video analysis and temporal modeling to address occlusion challenges. The publications span top-tier venues including CVPR, ICCV, ECCV, and NeurIPS, demonstrating his standing in the computer vision community. Marie Skłodowska-Curie Fellowship (2022) IEEE/CvF WACV Best Paper Prize (2014) Multiple reviewer awards (2016 - Journal of Image and Vision Computing, 2017 - Journal of Pattern Recognition, 2019 - CVPR) Pflugfelder supervises Master's students at TUM and previously managed a team of six researchers at AIT, coordinating the strategic 'Mobile Vision' program. He co-initiated the VOT challenges and workshop series in 2012, contributing significantly to benchmarking in visual object tracking. His research often involves interdisciplinary collaborations across European institutions and Israel, with strong emphasis on translating theoretical advances into practical surveillance applications. As part of the Dynamic Vision and Learning Group at TUM under Prof. Laura Leal-Taixé, Pflugfelder works within a vibrant research ecosystem focused on cutting-edge computer vision problems. His current project investigates how temporal information in video sequences can overcome limitations of single-image recognition systems, drawing inspiration from cognitive science concepts like visual persistence and anorthoscopic perception.

• Object Recognition
• Computer Vision
• Artificial Intelligence
• +۷ مورد دیگر

Sven Dickinson is a Professor in the Department of Computer Science at the University of Toronto, with a Faculty Affiliate role at the Vector Institute for Artificial Intelligence since 2018. He earned a B.A.Sc in Systems Design Engineering from the University of Waterloo (1983), followed by an M.S. (1988) and Ph.D. (1991) in Computer Science from the University of Maryland. Education : B.A.Sc, Systems Design Engineering, University of Waterloo, 1983 M.S., Computer Science, University of Maryland, 1988 Ph.D., Computer Science, University of Maryland, 1991 His research focuses on generic object recognition, emphasizing shape representation, image abstraction, and feature matching. Dickinson's work bridges human and computer vision, exploring medial axis transforms, shock graphs, and many-to-many feature correspondence for applications in content-based retrieval, robotics, and biomedical imaging. Recent publications highlight geometric disentanglement, symmetric part detection, and contour analysis via deep learning. Scientific Contributions include advancing skeletal abstraction, deformable models, and saliency maps. Collaborative efforts span the University of Maryland, Rutgers, MIT, and the University of Waterloo. He has authored over 100 papers and edited interdisciplinary works like Shape Perception in Human and Computer Vision (Springer, 2013).

Alexander Braunمشاهده پروفایل

استاد

• Digital Twinning
• Building Information Modeling (BIM)
• Construction Progress Monitoring
• +۷ مورد دیگر

Dr.-Ing. Alexander Braun serves as Senior Vice President Digitalization and IT Systems and Chief Information Officer (CIO) at the Technical University of Munich (TUM), while also leading the Digital Twinning in the Built Environment research group. He is affiliated with the Chair of Computing in Civil and Building Engineering within the Department of Civil, Geo and Environmental Engineering at TUM's School of Engineering. Dr. Braun's research focuses on the intersection of construction engineering, digital technologies, and artificial intelligence. His primary areas of expertise include Digital Twinning, Building Information Modeling (BIM), construction progress monitoring, point cloud processing, and computer vision applications in construction. His work bridges the gap between theoretical research and practical implementation in the construction industry, with a strong emphasis on automation and data-driven approaches. His extensive publication record demonstrates a consistent focus on advancing digital construction technologies. Recent research has centered on AI-enhanced digital twinning, semantic modeling from point clouds, construction process analysis through computer vision, and knowledge representation for construction sites. These efforts have resulted in numerous high-impact publications in top journals including Automation in Construction and Advanced Engineering Informatics . As an educator, Dr. Braun has taught courses such as "Bau- und Umweltinformatik 1" and "Bau- und Umweltinformatik Ergänzungsmodul" at TUM, contributing to the development of future construction informatics professionals. His supervisory role extends to numerous Bachelor's and Master's theses, reflecting his commitment to academic mentorship. Senior Vice President Digitalization and IT Systems at TUM Chief Information Officer (CIO) at TUM Group Lead for Digital Twinning in the Built Environment Researcher in Construction Informatics since 2013 Dr. Braun actively contributes to the academic community as a reviewer for prestigious journals including Automation in Construction , Advanced Engineering Informatics , and Visualization in Engineering . His international research collaborations include a visiting researcher position at CyberBuild (University of Edinburgh) with Dr. Frédéric Bosché in May 2019.