جست‌وجوی استاد و پژوهشگر

• Robotics
• Mechatronics
• Medical Device Technology
• +۲ مورد دیگر

Felix Pancheri is an employee at the Chair of Microtechnology and Medical Device Technology (MiMed) at the Technical University of Munich (TUM) since February 2021. Holding an M.Sc. in Mechatronics and Information Technology, he actively contributes to research and teaching within the MiMed group, supervising courses including Mechatronic Device Technology (MGT), Development of mechatronic devices (SMG), and Mathematical Tools (MTT). His research spans interdisciplinary domains with core focus areas: Robotics : Specializing in bio-inspired quadruped locomotion, soft robotics, and medical robotics applications Mechatronic Systems : Integration of mechanical, electronic, and software components for medical devices Advanced Manufacturing : Leveraging topology optimization and additive manufacturing for rapid prototyping Computational Design : Developing automated systems for custom mechanical structures Analysis of his 2021-2024 publications reveals consistent innovation in topology-optimized robotic mechanisms, particularly for legged locomotion and gripper systems. His work demonstrates strong bio-inspiration trends, translating natural movement principles into compliant mechanical designs fabricated through 3D printing. Medical applications form a significant thread, including surgical instrument development and 3D digitization techniques for surgical planning, reflecting the MiMed group's translational research focus. No scientific awards are documented for Felix Pancheri in available records. As an academic contributor, he supervises key courses: Mechatronic Device Technology (MGT) exercises Development of mechatronic devices (SMG) seminars Mathematical Tools (MTT) instruction While no individual grants are specified, he participates in MiMed's funded projects including IndiPrint, Arburg Automated Design, and CarrierBot, which advance automated design methodologies and robotic applications. The MiMed research environment provides state-of-the-art facilities for robotics prototyping, medical device development, and additive manufacturing, supporting his work on bio-inspired mechanisms and surgical robotics systems with strong industry-academia collaboration.

• Artificial Intelligence
• 3D Scene Reconstruction
• Simultaneous Localization and Mapping (SLAM)
• +۳ مورد دیگر

Yiming Zhou is a researcher at Saarland University of Applied Sciences (htw saar) in Saarbrücken, Germany. Their work spans multiple engineering domains with emphasis on artificial intelligence integration, 3D scene reconstruction, and advanced imaging techniques. Contact: yiming.zhou@htwsaar.de Location: Goebenstraße 40, 66117 Saarbrücken Research Focus Zhou's research explores cutting-edge technologies in: AI applications for non-destructive evaluation Dynamic SLAM systems for robotics Next-generation 3D reconstruction methods Multimodal deception detection frameworks Novel Gaussian splatting techniques Semantic encoding for spatial data Recent Publications Trends Their scholarly output reveals a trajectory toward Real-time spatial mapping solutions Hybrid neural-implicit representations CAD-integrated building documentation AI-enhanced image translation pipelines Signal processing innovations Cross-modal data fusion Laboratory Affiliation Zhou contributes to the faculty laboratories at htw saar, focusing on engineering research through practical implementations and algorithm development.

• Generalized Nets
• Intuitionistic Fuzzy Sets
• Decision Making Under Uncertainty
• +۳ مورد دیگر

Assoc. Prof. Vassia Atanassova, PhD is an Associate Professor in the Bioinformatics and Mathematical Modelling Department at the Institute of Biophysics and Biomedical Engineering , Bulgarian Academy of Sciences. She holds a PhD in Informatics and Computer Sciences (2013) and has been actively contributing to generalized nets, intuitionistic fuzzy sets, and decision-making under uncertainty since 2002. Education : PhD in Informatics and Computer Sciences (2013), Institute of Information and Communication Technologies – BAS Master in Marketing (2009), University of National and World Economy Bachelor in Informatics (2004), Faculty of Mathematics and Informatics, Sofia University Her research focuses on intuitionistic fuzzy logic applications, generalized net modeling , and Wiki technologies for knowledge transfer . She has developed novel intuitionistic fuzzy operators and conducted extensive work on intercriteria analysis for complex systems. Recent publications emphasize fuzzy decision-making frameworks (2015-2019), with applications in Algorithm optimization Economic modeling Bioinformatics Medical data analysis Scientific Awards : Youngest Researcher Award 'Ivan Evstratiev Geshov' (2011) 2nd Award, VIII Youth Session of Federation of Scientific-Technical Unions (2010) She serves as Guest Editor for MDPI Mathematics and technical editor for international journals. Her teaching includes PhD-level courses on Wikipedia-based knowledge transfer and fuzzy set theory at Bulgarian universities.

• Visualization
• Computer-Aided Geometric Design
• Computer-Aided Design
• +۲ مورد دیگر

Georges-Pierre Bonneau is a Professor at University of Grenoble conducting research at Laboratoire Jean Kuntzmann (LJK) and INRIA in the MAVERICK research group. His academic career spans over three decades, beginning with his PhD from Universität Kaiserslautern in 1993, followed by positions as CNRS researcher (1994-2001), and Professor at University of Grenoble since 2001. Dr. Bonneau's research focuses on scientific visualization, computer-aided geometric design (CAGD), computer-aided design (CAD), and computational fabrication techniques. His work bridges theoretical geometric modeling with practical applications in structural monitoring, energy consumption visualization, and digital fabrication. He has developed innovative methods for surface reconstruction from sparse data, bending-active structures, and visualization of uncertain scalar data fields. His recent publications demonstrate a strong focus on immersive 3D modeling techniques, computational fabrication methods, and advanced visualization systems. Bonneau's research shows consistent evolution from foundational geometric modeling work in the 1990s-2000s toward contemporary applications in VR/AR interfaces, computational design of physical structures, and energy consumption visualization systems. Throughout his career, Bonneau has actively contributed to the academic community as conference organizer and program committee member for major venues including SPM, SMI, Eurographics, and IEEE Visualization conferences. He has supervised numerous PhD students whose work spans diverse applications of geometric modeling and visualization, with recent students focusing on surface creation from sparse strokes, bending-active structures, and structural monitoring systems. His former students have gone on to successful careers at institutions including AAC Clyde Space, Zurich Insurance, Pixminds, ISIR, and CEA.

Bartzos Evangelosمشاهده پروفایل

پژوهشگر

• Algebraic Modeling
• Graph Rigidity
• Distance Geometry
• +۱ مورد دیگر

Evangelos Bartzos is a researcher affiliated with the ΕρΓΑ Lab , a collaborative unit under the AROMATH team (joint with INRIA Sophia-Antipolis ) and the ATHENA Research Center . He earned his PhD in Telecommunications and Informatics from the National and Kapodistrian University of Athens under Prof. Ioannis Z. Emiris, as part of the ARCADES Marie Skłodowska-Curie Innovative Training Network . Education: PhD in Telecommunications and Informatics (University of Athens) His research focuses on algebraic modeling , graph rigidity theory , and distance geometry . His work investigates the maximal number of graph embeddings under length constraints in Euclidean spaces and spheres, leveraging real and complex algebraic geometry to bridge theoretical bounds and practical applications in computational mathematics. The 13 publications in his profile span 2015–2023 and reveal a trajectory from early computational biology modeling to advanced rigidity theory research. Key trends include the use of multi-homogeneous Bézout bounds, hypergraph orientations, and algebraic systems to analyze minimally rigid graphs and their realizations across dimensions. Evangelos has participated in numerous conferences and workshops including ISSAC , CASC , and the French Computational Geometry Days , presenting results on rigidity bounds and geometric constraint systems. His collaborations extend to institutions in France (ENS Lyon, Université Lyon 1), Austria, and Russia, particularly through the HEVEA project’s isometric reduction research.

• Artificial Intelligence
• Natural Language Processing
• Computer Vision
• +۳ مورد دیگر

Jiaang Li is a PhD Fellow and Guest Researcher at the Department of Computer Science , University of Copenhagen. His research spans Natural Language Processing , Computer Vision , and Multimodal Learning , focusing on vision-language models, word order sensitivity, and cross-modal understanding. PhD Fellow , Department of Computer Science, Pioneer AI (P1AI) Guest Researcher , Department of Computer Science, Natural Language Processing Research interests include: Vision-Language Model Analysis Multimodal Dataset Development Language Model Interpretability Human-Centred AI Applications Model Robustness and Ethics Recent publications highlight trends in: Vision-Language Concept Alignment Task-Oriented Model Evaluation Visual Culture Understanding Bias and Cultural Theory in AI Retrieval-Augmented Generation

Zachary Treismanمشاهده پروفایل

استادیار

• Topological and Geometric Data Analysis
• Analysis of Ecological Data
• Art and Mathematics
• +۲ مورد دیگر

Zachary Treisman is an Assistant Professor of Mathematics at Western Colorado University and serves as the Honors Program Director. He holds a PhD in Mathematics from the University of Washington (2006) and a BA in Mathematics from Reed College (1999). His academic career spans collaborations with mathematical artist Lun-Yi Tsai, work on high-profile legal cases involving statistical analysis, and postdoctoral research at the Tata Institute for Fundamental Research in Mumbai, India. PhD, University of Washington (2006) BA, Reed College (1999) His research bridges theoretical mathematics and practical applications, with a focus on: Topological and geometric data analysis Ecosystem modeling and ecological statistics Interdisciplinary art-mathematics projects Algebraic geometry fundamentals Treisman teaches a wide range of mathematics courses from foundational college algebra to advanced algebraic analysis, emphasizing mathematical typesetting and historical perspectives. He contributes to both the Math & Computer Science Department and the university's honors program.

Nils Morten Kriegeمشاهده پروفایل

دانشیار

• Data Mining
• Machine Learning with Graphs
• Combinatorial Optimization
• +۵ مورد دیگر

Dr. Nils Morten Kriege is an Associate Professor at the Faculty of Computer Science, University of Vienna, where he leads the Data Mining and Machine Learning research group. Previously, he served as Assistant Professor (2020-2023) at the same institution and held positions at TU Dortmund including Interim Professor (2019/2020) and Postdoctoral Researcher (2015-2020). His research focuses on graph-based machine learning methods with applications in cheminformatics and drug discovery. His educational background includes a Doctorate in Computer Science (2015) and Diploma in Computer Science (2009), both from TU Dortmund. He has also been a Visiting Researcher at the University of York, UK. Dr. Kriege's research centers on graph algorithms and machine learning with graphs, particularly focusing on graph neural networks, graph kernels, and their applications in cheminformatics and drug discovery. His work bridges theoretical computer science with practical applications, developing novel methods for graph similarity, graph classification, and network analysis. He has made significant contributions to understanding the expressivity and robustness of graph neural networks, as well as developing efficient algorithms for graph similarity search and molecular analysis. His recent publications (2023-2025) demonstrate a strong focus on graph neural networks, with particular attention to their expressivity, robustness against attacks, and practical applications in drug discovery. His work spans theoretical foundations (Weisfeiler-Leman hierarchy, graph isomorphism testing), practical implementations (efficient quantization, defense frameworks), and domain-specific applications (cheminformatics, drug discovery). Vienna Research Groups for Young Investigators (2019) - €1,466k funding for "Algorithmic Data Science for Computational Drug Discovery" Member of the Global Young Faculty V, Stiftung Mercator (2017) Dr. Kriege leads an independent research group funded through the Vienna Research Groups for Young Investigators program, focusing on computational drug discovery. He has served on program committees for major conferences including NeurIPS, ICML, IJCAI, AAAI, ICLR, and ICDM, and has reviewed for prestigious journals such as Transactions on Pattern Analysis and Machine Intelligence. His teaching portfolio includes courses on Data Mining, Graph Learning, and Introduction to Machine Learning. He leads the Machine Learning with Graphs work group within the Data Mining and Machine Learning Research Group at the University of Vienna, collaborating with researchers like Wilfried Gansterer and Petra Mutzel on graph-based methods for drug design and molecular analysis.

• Geometric Algebra
• Clifford Algebra
• Medical Imaging
• +۵ مورد دیگر

Silvia Giuseppina Franchini serves as a Contract Teacher in the Department of Biomedicine, Neuroscience and Advanced Diagnostics at the University of Palermo's School of Medicine and Surgery. Her academic career demonstrates a strong interdisciplinary focus bridging advanced mathematical frameworks with practical medical applications. Dr. Franchini's research interests include: Geometric and Clifford Algebra applications in medical imaging Hardware acceleration for geometric algebra operations Machine learning approaches for medical diagnosis, particularly for Crohn's disease Embedded systems design for real-time image processing Robotics control systems using conformal geometric algebra Medical image analysis and 3D reconstruction Her publication record spanning fifteen years reveals an evolution from foundational hardware implementations to sophisticated medical applications. She has developed specialized architectures including the GAPPCO system, ConformalALU coprocessor, and GAPP compiler, demonstrating innovative approaches to implementing geometric algebra in hardware for medical imaging. Her recent work emphasizes machine learning applications for Crohn's disease classification, showing how mathematical frameworks can translate to clinical diagnostic tools. Dr. Franchini maintains active research engagement with publications continuing through 2022, indicating ongoing contributions to the field of medical imaging technology development. Her work consistently addresses computational challenges while maintaining clinical relevance, particularly in gastroenterology through her Crohn's disease research.

• Artificial Intelligence in Education
• Learning Sciences
• Student Modeling
• +۷ مورد دیگر

Shayan Doroudi is an Assistant Professor at the School of Education, University of California, Irvine, where he conducts research at the intersection of artificial intelligence, learning sciences, and educational philosophy. His work bridges technical aspects of educational technology with critical perspectives on equity, history, and the philosophical foundations of learning. Doroudi's research interests focus on what he calls the "foundations of learning about learning" and how these relate to the design of socio-technical systems that improve learning. He examines the philosophical, historical, sociological, ethical, and critical perspectives underlying learning sciences and AI in education. His work spans multiple domains including the equitability of student modeling algorithms, the history of learning theories in AI and learning sciences, epistemologies underlying student models, and the theoretical limits of student modeling. His recent publications reveal a growing emphasis on generative AI in education, historical analysis of AI-education relationships, and equity considerations in educational technology. He has published extensively on the intertwined histories of artificial intelligence and education, examining how early AI pioneers were also influential in educational research. His work demonstrates a consistent concern with how beliefs about learning and knowledge influence educational technology design. "The intertwined histories of artificial intelligence and education" (2022/2023) "Equity and artificial intelligence in education" (2021-2022) "Technology in Education: Looking Back from 2020" (2019) "Fairer but not fair enough: On the equitability of knowledge tracing" (2019) Doroudi's approach combines technical expertise in AI and learning analytics with deep critical perspectives on educational technology. His work on socio-technical systems emphasizes designing technologies that combine insights from people and machines while being equitable and informed by critical perspectives on learning. He has been increasingly focused on how generative AI is reshaping educational landscapes and what this means for foundational understandings of learning.

Heather Liglerمشاهده پروفایل

استادیار

• Computational Design
• Shape Grammars
• Architectural Theory
• +۵ مورد دیگر

Heather Ligler is an Assistant Professor & Foundations Coordinator at the School of Architecture , Florida Atlantic University. Her work bridges computational and formal methods in architecture, particularly through shape grammars — visual algorithms enabling geometric rule-sets for design logic, criticism, and transformation. Education: Ph.D. & M.S. in Design Computation (Georgia Tech), B.Arch & B.Interior Arch (Auburn University) Her research focuses on shape computation to formalize design narratives, critique architectural history, and innovate future practices. She explores applications in adaptive reuse , refugee housing , and virtual reality . Recent publications address structural wall layouts in historic buildings, tile generation systems, and refugee shelter transformations. These align with interests in design automation , heritage conservation , and algorithmic aesthetics . Scientific Awards President’s Fellowship at Georgia Tech Hambidge Center for the Creative Arts & Sciences Fellowship Her work is supported by grants from the National Science Foundation , General Services Administration , and Stuckeman Center for Design Computing . She has taught at Penn State and Georgia Tech, and contributed to the Shape Machine software and CourtsWeb database .

Dena Bazazianمشاهده پروفایل

مدرس

• Robotics
• Machine Vision
• Artificial Intelligence
• +۳ مورد دیگر

Dr. Dena Bazazian is a Lecturer in Robotics and Machine Vision at the University of Plymouth's School of Engineering, Computing and Mathematics. Previously, she held positions as a Senior Research Associate at the University of Bristol's Visual Information Laboratory, Research Scientist at CTTC (Barcelona), and postdoctoral researcher at Universitat Autònoma de Barcelona, where she earned her PhD in 2018. Her international research includes visiting positions at Barcelona Supercomputing Center (2024), NAVER LABS Europe (2019), and University of Florence (2017). Her research focuses on robotic vision, underwater technologies, and AI-driven applications. Key interests include: 3D reconstruction and neural rendering techniques Anomaly detection using graph neural networks Underwater perception systems for marine environments Intergenerational co-design of immersive technologies Point cloud processing and geometric deep learning Publication analysis reveals strong emphasis on computer vision applications across robotics, industrial inspection, heritage preservation, and marine technology. Recent works increasingly integrate human-centered approaches with XR technologies and underwater systems. She currently supervises 8 PhD students across robotics, healthcare, and environmental monitoring domains: Offshore wind turbine maintenance drones (EPSRC-UDLA funded) Autonomous sea vessel swarms for climate forecasting (ARIA funded) AI for marine biodiversity monitoring (Cefas funded) Elderly fall detection systems (URS General funded) Healthcare anxiety detection through visual analysis (EPSRC DTP HMT funded) Dr. Bazazian leads modules including Artificial Vision and Deep Learning and Computer Vision . She co-organizes workshops including the Deep Learning for Geometric Computing series (CVPR/ICCV) and Women in Computer Vision workshops (CVPR/ECCV).

Henrik Schumacherمشاهده پروفایل

پژوهشگر

• Geometric Analysis
• Numerical Optimization
• Differential Geometry
• +۳ مورد دیگر

Dr. Henrik Schumacher is a researcher in the work group on Harmonic Analysis led by Prof. Dr. Philipp Reiter at TU Chemnitz. His research focuses on geometric variational problems, numerical optimization, and differential geometry, with applications in computer graphics and computational mathematics. He holds a PhD in Mathematics from TU Chemnitz (2015) and a Master's in Mathematics from the same institution (2010), where his thesis explored generalized Seiberg-Witten equations. His work bridges pure and applied mathematics, including contributions to self-avoiding energies, fractional Sobolev spaces, and algorithmic optimization for geometric problems. Notably, he co-developed the Repulsive Curves and Surfaces frameworks for collision-aware modeling, recognized by the SIGGRAPH 2024 Best Technical Paper Award . He also contributes to software tools like CoBarS and Repulsor for polygon sampling and energy optimization. Active in academic outreach, Schumacher supervises student computer tutorials on topics such as Euler elastica, finite element analysis, and geometric evolution equations. His teaching emphasizes computational methods and interdisciplinary applications, including contributions to TU Chemnitz's new Data Science programs. Awards: SIGGRAPH 2024 Best Technical Paper Award Software: CoBarS, Repulsor, ConformalBarycenter, PardisoLink Grants/Projects: Collaborations on applied analysis, geometric optimization, and inverse problems Lab/Team Affiliation: Harmonic Analysis Group at TU Chemnitz Faculty of Mathematics.

Leonard Brownمشاهده پروفایل

دانشیار

• Multimedia Database Management
• Information Retrieval
• Image Retrieval
• +۳ مورد دیگر

Dr. Leonard Brown serves as Department Chair and Associate Professor of Computer Science at The University of Texas at Tyler. He holds a Ph.D., M.S., and B.S. in Computer Science from The University of Oklahoma. Prior to academia, he worked as a Member of Technical Staff at AT&T in Oklahoma City. His teaching focuses on data management, including relational databases and advanced systems, while his research emphasizes multimedia database management, image retrieval, and machine learning applications. He received a NASA Summer Faculty Fellowship (2004) at JPL, exploring NFL sentiment analysis through social media and stock market data. His work spans interdisciplinary areas such as quantum computing and immersive VR applications in healthcare education. Dr. Brown is an active member of ACM and IEEE. Education: Ph.D. in Computer Science, The University of Oklahoma (2003) M.S. in Computer Science, The University of Oklahoma B.S. in Computer Science (with Special Distinction), The University of Oklahoma Research Interests: Dr. Brown’s research innovatively combines multimedia database systems with image retrieval techniques using editing operations. His work extends to stochastic optimization for equipment replacement, quantum computing methodologies, and the application of virtual reality in nursing education. Notable contributions include machine learning-driven phishing detection and predictive analyses of social media sentiment. Awards: NASA Summer Faculty Fellowship (2004) Grants & Advising: While no formal advisees are listed, his research portfolio suggests involvement in funded projects such as the NASA initiative and NSF-sponsored quantum computing conferences. His work integrates theoretical advancements with real-world applications in healthcare, cybersecurity, and operations research. Labs/Teams: Though specific lab affiliations are not detailed, his interdisciplinary collaborations imply participation in cross-departmental initiatives and industry partnerships.

Paolo Favaroمشاهده پروفایل

استاد

• Computer Vision
• Computational Photography
• Machine Learning
• +۴ مورد دیگر

Prof. Dr. Paolo Favaro is a full Professor and Head of the Computer Vision Group (CVG) at the University of Bern's Institute of Computer Science. He holds a Laurea (BSc+MSc) from the University of Padova, Italy, and M.Sc. and Ph.D. in Electrical Engineering from Washington University in St. Louis. His career includes postdoctoral roles at UCLA and Cambridge University, followed by work in medical imaging at Siemens Corporate Research. From 2006–2011, he was Lecturer and Reader at Heriot-Watt University, and Honorary Fellow at the University of Edinburgh before joining Bern in 2012. His research focuses on computer vision , computational photography , machine learning , and signal/image processing , with contributions to inverse problems and variational techniques. His work spans theoretical advancements and practical applications, such as video diffusion models, unsupervised segmentation, and image restoration. He is an active member of the IEEE Society. Recent research trends emphasize generative models (e.g., diffusion models, video generation) and self-supervised learning , addressing challenges in real-world image/video denoising and sparsity-driven methods. His articles explore topics like Kalman-based optimization, multimodal world models, and spatio-temporal representation learning. Prof. Favaro leads the CVG, fostering interdisciplinary research at the intersection of computer vision and AI. His lab develops cutting-edge solutions for 3D reconstruction, medical imaging, and autonomous systems.