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Tobias StraußView profile

Lecturer

• Mathematics Education
• Historical Document Analysis
• Machine Learning
• +5 more

Dr. Tobias Strauß is a Lecturer at the Institute of Mathematics, Faculty of Mathematics and Natural Sciences, University of Rostock. He teaches courses such as Elementary Algebra and Number Theory and Analytical Geometry, focusing on foundational mathematical concepts and their applications. His research interests span historical document analysis, machine learning, and neural networks, with a particular emphasis on handwritten text recognition (HTR) and computational solutions for cultural heritage digitization. He actively contributes to the Mathematics Society RHO eV, supporting students in mathematics competitions and enrichment programs. Dr. Strauß’s research combines computer vision and machine learning to address challenges in document analysis, including text line detection, cursive script recognition, and keyword search in historical manuscripts. His work also explores semi-supervised learning techniques and neural network architectures tailored for HTR tasks. Through RHO eV, he promotes mathematics education through weekend seminars, district clubs, and game-based learning activities, fostering student engagement and problem-solving skills. His publications highlight advancements in HTR systems, such as the CITlab Recognition & Retrieval Engine, and address topics like regular expression-based decoding and Arabic handwriting recognition. These contributions underscore his expertise in bridging theoretical mathematics with practical applications in digital humanities and education. Dr. Strauß can be contacted via tobias.strauss@uni-rostock.de or through the university’s chat platform. No formal awards or grants are noted in the provided materials, though his involvement in international competitions (e.g., ICFHR, ICDAR) reflects his scholarly engagement.

Sidney BenderView profile

Researcher

• Counterfactual Explanations
• Generative Models
• Deep Learning
• +3 more

Sidney Bender is a Research Scientist at the IDA group of Technical University of Berlin and a doctoral researcher pursuing his PhD there. His work is supported by the BASLEARN collaboration between BASF and the IDA lab, focusing on applying state-of-the-art machine learning to address practical challenges in industry. He holds a B.Sc. (2018) and M.Sc. (2021) in Computer Science from the Karlsruhe Institute of Technology (KIT). His research centers on using deep generative models to create counterfactual explanations, enhancing model robustness against spurious correlations, and understanding neural network decision-making. His research interests include generative models (e.g., Diffusion Models, LLMs, VAEs, Normalizing Flows, GANs), counterfactual explanations, and model robustness. His recent work emphasizes aligning AI systems with human expectations through counterfactual analysis and knowledge distillation. His technical contributions include optimization algorithms like the MaxSat Tabu Search implemented in C++, showcasing his expertise in algorithmic development. While no formal awards are listed, his research bridges foundational AI theory with industrial applications through the BASLEARN initiative.

• Deep Learning
• Medical Image Analysis
• Pattern Recognition
• +4 more

Filipe R. Cordeiro is an Associate Professor at the Center of Informatics, Federal University of Pernambuco (UFPE), Brazil, with a distinguished research career spanning nearly two decades in machine learning, computer vision, and medical image analysis. He completed his PhD at UFPE in 2015 with research focused on semi-supervised techniques for tumor segmentation in digital mammography images. His primary research interests include deep learning with noisy labels, medical image analysis (particularly mammography and ECG), pattern recognition, and handwritten mathematical expression recognition. Dr. Cordeiro has pioneered several innovative approaches for handling label noise in deep learning systems, including ANNE (Adaptive Nearest Neighbors and Eigenvector-based sample selection), LongReMix, and ScanMix, which have gained significant attention in the computer vision community. His recent publication trend (2020-2025) shows remarkable productivity with over 30 papers, primarily in top venues like Pattern Recognition, IEEE Access, and SIBGRAPI. His work demonstrates a clear evolution from foundational medical image analysis to sophisticated noise-robust learning techniques, with increasing emphasis on practical applications in healthcare and education technology. A notable thread throughout his research is the development of methods that work effectively with limited or imperfect training data. Dr. Cordeiro actively supervises graduate students and collaborates extensively with researchers both within Brazil (particularly at UFPE) and internationally. His research group appears to focus on developing practical computer vision solutions for real-world medical and educational challenges, with recent emphasis on low-resource mobile applications for education and healthcare.

Seiji IsotaniView profile

Professor

• Artificial Intelligence in Education
• Gamification in Education
• Evidence-based Education Policies
• +3 more

Seiji Isotani is a Professor of Computer Science and Learning Technology at the University of São Paulo and a Visiting Professor at Harvard University. His research focuses on AI in Education, Gamification, and Evidence-based Policy. He holds a Ph.D. from Osaka University and a postdoc from Carnegie Mellon University. He advises government policies in Brazil, including the K-12 Computer Science Curriculum and the National Plan for Textbooks. His honors include ACM Senior Membership, IEEE Fellowships, and multiple awards for research and teaching excellence. His lab, Applied Computing in Education, explores AI-driven education equity and policy impact. Roles: Faculty Associate at Harvard’s Berkman Klein Center Education: Ph.D. (Osaka), Postdoc (CMU) Key Projects: Brazil’s National Textbook Program, Gamification Frameworks Research interests include AI for resource-constrained education, personalized gamification, and public policy design. Grants include over $5M for education tech projects. He leads teams developing systems like MeuTutor and Linkn, impacting 40M+ students.

• Computer Science
• Computational Geometry
• Computer Vision
• +7 more

Vasyl Tereshchenko is a Professor and Head of the Mathematical Informatics Department at Taras Shevchenko National University of Kyiv, Ukraine. His work spans computational geometry, computer vision, and algorithmic design, with a focus on applications in rehabilitation systems and real-time visualization. PhD (1993) and Doctor of Sciences (2000) in Physics and Mathematics from Taras Shevchenko National University of Kyiv Research interests include: Computational geometry for 3D surface modeling and mesh deformation Real-time eye-gaze tracking and handwriting recognition systems Augmented reality applications for robotics and navigation Algorithmic frameworks for common algorithmic space in visualization Recent publications emphasize geometric algorithms, machine learning coreset discovery, and 3D reconstruction techniques. His research group at the Samsung Advanced Information Technologies Research Joint Lab develops tools for: Medical rehabilitation systems Traffic accident warning technologies Mobile device computer vision Labs and teams under his leadership focus on cross-disciplinary challenges in: Medical technology integration Human-computer interaction Geometric modeling for engineering applications

• Automatic Speech Recognition
• Conversational User Interfaces
• Human-Computer Interaction
• +6 more

Prof. Hans-Peter Hutter is a Professor of Computer Science at the ZHAW School of Engineering, specializing in Deep Learning-based Automatic Speech Recognition, Conversational User Interfaces, and Human-Centered Computing. He leads the Human-Centered Computing research group at InIT/ZHAW and has held this position since 2005. His work focuses on accessibility technologies, mobile usability, and inclusive design for visually impaired users. Education: Dr. sc. techn. ETH in Computer Engineering (ETH Zurich, 1996) Dipl. El.-Ing. ETH in Electrical Engineering (ETH Zurich, 1986) Research Interests: Advancing accessibility in digital systems (e.g., accessible PDFs, navigation aids for visually impaired users) Speech recognition and dialogue systems Mobile application design principles Service engineering and platform development His recent work emphasizes multimodal interaction, accessible document remediation, and SLAM systems for navigation assistance. Projects: Leading the InCrowd-VI dataset project for indoor navigation Developing MathNet for mathematical expression recognition Creating accessible tourism services in Lake Constance region Grants & Labs: Active in EU-funded and industry collaborations, leading the InIT Institute founded in 2002. Collaborates with organizations like SwissICT and ACM.