جستجوی استاد راهنمای دانشگاهی

Daniel Franzenمشاهده پروفایل

پژوهشگر

• Privacy Preservation
• Human-Centered Computing
• Data Science
• +۴ مورد دیگر

Daniel Franzen is an affiliated researcher at the Human-Centered Computing group within the Department of Mathematics and Computer Science . His work bridges technology functionality with privacy concerns, focusing on parameters that influence whether users perceive digital systems as intrusive or beneficial. University of Edinburgh PhD in Computer Science (2016) Master of Science in Computer Science (2012) and Media Informatics (2012) from RWTH-Aachen Bachelor of Science in Computer Science (2010) and Mathematics (2011) from RWTH-Aachen His research explores privacy-preserving data donation , human-AI collaboration, and the design of systems that balance utility with ethical considerations. Key projects include delirium prevention in clinical settings, reflective practice in data science, and adversarial interface design patterns. Recent publications highlight trends in privacy visualization , LLM-based conversational interfaces , and hybrid intelligence systems . He investigates how thresholds in machine learning, user onboarding libraries, and visual storytelling impact decision-making and data interpretation. Daniel contributes to educational initiatives through courses like Human-Centered Data Science and Interactive Intelligent Systems , emphasizing critical thinking and visualization literacy. The HCC lab focuses on designing technologies that prioritize user sovereignty and ethical frameworks, such as the Meta-Consent System for health data sharing.

• Recommender Systems
• Music Information Retrieval
• Data Science
• +۳ مورد دیگر

Eva Zangerle is a Professor at Universität Innsbruck, Austria, with a focus on recommender systems, music information retrieval, and data science. She actively contributes to multi-method evaluation frameworks and collaborative research initiatives like PAN (Plagiarism Authorship Verification) workshops. Key research areas: Recommender Systems Evaluation, Music Emotion Recognition, Authorship Analysis Major collaborations with institutions like Zenodo, ACM, CEUR-WS.org, and RecSys conferences Her recent work explores graph neural networks for music recommendation, style change detection in multi-author texts, and cross-domain user modeling. Articles emphasize temporal modeling, multimodal data fusion, and ethical considerations in algorithmic systems. Eva leads tasks in PAN workshops and contributes to open-access datasets. She collaborates with researchers like Christine Bauer, Alan Said, and Günther Specht on improving evaluation practices in recommender systems.

Artem Sokolovمشاهده پروفایل

استاد

• Machine Translation
• Natural Language Processing
• Structured Prediction
• +۴ مورد دیگر

Artem Sokolov serves as an Honorary Professor in the Department of Computational Linguistics at Heidelberg University and as a Research Scientist at Google Berlin. His primary research focuses on machine translation and structured prediction within natural language processing. Previously, he held positions at Amazon, the Statistical NLP Group at Heidelberg University led by Prof. Stefan Riezler, LIMSI, and Orange Labs in France, contributing to advancements in statistical and neural machine translation systems. He earned his PhD in Computer Science and Artificial Intelligence from the IRTCITS research center in Kyiv. His doctoral thesis investigated randomized algorithms for locality-sensitive embeddings of the Levenstein edit distance, establishing foundational work for efficient string similarity search in computational linguistics and intrusion detection systems. Dr. Sokolov's research expertise spans machine translation, imitation learning, bandit algorithms, and weakly supervised learning. He has pioneered methods for learning from partial feedback in structured prediction tasks, particularly addressing exposure bias in sequence generation and multi-facet evaluation of translation systems. His work bridges theoretical machine learning with practical NLP applications, emphasizing robustness against noisy data and scalable optimization techniques for real-world deployment. Analysis of his recent publications reveals trends toward scalable influence functions for model interpretability, multi-attribute control in machine translation, and rigorous auditing of multilingual datasets. His research consistently intersects natural language processing, machine learning optimization, and data quality assessment, with increasing emphasis on ethical AI considerations and efficient learning from weak supervision signals. Scientific awards include: 1st place at ECML/PKDD Discovery Challenge 2010 (English quality task) 2nd place at ECML/PKDD Discovery Challenge 2010 (general task) 2nd place in Semi-Supervised Feature Learning Challenge at NIPS 2011 3rd place in Semi-Supervised Feature Learning Challenge at NIPS 2011 As co-Principal Investigator for the 2015-2017 grant "Weakly Supervised Learning of Cross-Lingual Systems", Dr. Sokolov developed techniques for learning cross-lingual rankings from weakly supervised data sources like patent citations and Wikipedia hyperlinks. He has mentored students through teaching advanced courses including Imitation Learning, Stochastic Learning, and Statistical Machine Translation at Heidelberg University, supervising seminar projects on structured prediction and optimization algorithms. Dr. Sokolov is an active member of the Statistical NLP Group at Heidelberg University and collaborates with research teams at Google Berlin. His current work focuses on advancing production-scale machine translation systems through scalable inverse reinforcement learning and robust training methodologies, building on his extensive background in both academic research and industrial applications.

Shigehiko Schamoniمشاهده پروفایل

مدرس

• Natural Language Processing
• Medical Data Analysis
• Speech Translation
• +۳ مورد دیگر

Shigehiko Schamoni is a Lecturer and Compute Lab Manager at Heidelberg University's Institute of Computer Engineering (ZITI), where he oversees scientific computing infrastructure and teaches computer science courses. He is completing his PhD under Prof. Stefan Riezler in the Statistical NLP group. His dual roles bridge technical management and academic instruction, with teaching responsibilities spanning undergraduate and graduate courses since 2011. Research Focus: Schamoni's work intersects clinical AI and natural language processing, with emphasis on: Machine learning for medical applications (sepsis prediction, clinical validity) Speech translation and automatic speech recognition Cross-lingual information retrieval Data augmentation techniques Multimodal machine learning His 15 most recent publications (2016-2024) demonstrate strong thematic clustering: 47% focus on medical AI (primarily sepsis prediction and clinical data validation), while 53% address NLP challenges (speech translation, ASR, and multimodal systems). This bifurcation reflects consistent collaboration with medical researchers alongside core NLP innovation. Teaching Experience includes instruction across 10+ courses since 2011, such as: Graduate courses: "Tools – Werkzeuge für effizientes wissenschaftliches Arbeiten" (2023-2024) Undergraduate courses: "Einführung in die Nutzung computerlinguistischer Ressourcen" (2021-2022) Programming courses: "Advanced Programming" and "Parallel Programming Paradigms" (2012-2015) He maintains affiliations with both the ZITI infrastructure team and Statistical NLP research group.

Kai-Uwe Bletzingerمشاهده پروفایل

استاد

• Computational Mechanics
• Structural Engineering
• Finite Element Analysis
• +۵ مورد دیگر

Kai-Uwe Bletzinger is a Professor and Chair of Statics and Dynamics at the Technical University of Munich's School of Engineering, Department of Civil, Geo and Environmental Engineering. With an extensive publication record spanning from 1991 to 2025 totaling 246 publications, he maintains an active research program in computational structural engineering. His research productivity shows consistent output with significant activity in recent years, particularly in 2022-2025. Professor Bletzinger's research focuses on computational mechanics with specializations in Finite Element Analysis , Shape Optimization , Form Finding , Computer-Aided Design , Membrane Structures , and Fluid-Structure Interaction . His work bridges theoretical computational methods with practical engineering applications, particularly in structural optimization and analysis. The fingerprint of his research shows strong emphasis on numerical methods for structural engineering problems. Analysis of his recent publications (2022-2025) reveals a consistent focus on advancing computational methods for structural optimization, particularly through the Vertex Morphing technique. His work increasingly integrates modern computational approaches including machine learning applications in structural dynamics and digital twin technologies for construction. The research spans fundamental numerical method development to practical applications such as the tensegrity tower for the German Museum in Munich, demonstrating both theoretical depth and practical relevance. While specific awards are not mentioned in the provided text, his extensive publication record in high-impact engineering journals suggests recognition within the computational mechanics community. His research appears to be well-funded given the consistent output and collaborative nature of many publications. Professor Bletzinger's work demonstrates leadership in computational structural engineering, with particular expertise in developing and applying advanced numerical methods to complex structural problems. His research group appears to be active in both fundamental method development and practical engineering applications, contributing significantly to the field of computational mechanics and structural engineering.

• Industrial Cyber-Physical Systems
• Digital Twins
• Human-Machine Interaction
• +۶ مورد دیگر

Dominik Hujo is a researcher at the Chair of Automation and Information Systems at the Technical University of Munich (TUM). He holds a Master of Science degree and contributes to advanced research in industrial automation and AI integration. His research focuses on industrial cyber-physical systems , digital twins , human-machine interaction , and AI deployment in manufacturing environments . His work emphasizes real-time requirements, embedded systems, and data management for production processes. Dominik's publications from 2023-2025 demonstrate expertise in multi-agent coordination , predictive maintenance , edge-cloud architectures , and SysML modeling for mechatronic constraints . Key application areas include construction machinery, gear assembly, and logistics systems. He actively collaborates with researchers like Prof. Birgit Vogel-Heuser and Marius Krüger on projects such as KI.Fabrik (AI Factory) and OpAI4DNCS (Operator-AI Interaction for Distributed Control Systems).

Kathrin Landمشاهده پروفایل

پژوهشگر

• Automation Engineering
• Industrial Informatics
• Human-Machine Interaction
• +۷ مورد دیگر

Kathrin Land is a researcher at the Chair of Automation and Information Systems at Technical University of Munich (TUM), working under Prof. Birgit Vogel-Heuser. Her research focuses on automation engineering, model-based development, and data-driven optimization of production systems. Key research areas include: Test case scheduling and prioritization Digital twins for industrial applications Technical debt management in automation OPC UA integration in construction robotics Recent publications highlight trends in: Automated test execution frameworks Process mining for production validation Data analytics in food industry separation processes Sanctioning mechanisms for multi-agent systems

• Artificial Intelligence
• Reinforcement Learning
• Medical Imaging
• +۳ مورد دیگر

Gabriel Kalweit is a Researcher at the Neurorobotics Lab, University of Freiburg, where he has been affiliated since 2017. He completed his doctoral thesis in 2022 at the university's Technical Faculty on reinforcement learning under advisors Joschka Boedecker and Martin Riedmiller. His research focuses on interdisciplinary applications of artificial intelligence, particularly in oncology, reinforcement learning, and medical imaging. Research interests span: AI-optimized cancer therapy and treatment personalization Explainable AI methods for medical diagnostics Reinforcement learning for biological systems and robotics Computational pathology and cell analysis techniques His recent publications demonstrate strong focus on medical AI applications, with 80% of 2023-2025 works involving oncology-related machine learning. Research frequently combines reinforcement learning with biomedical challenges like antibody design, therapy dosing, and tumor detection. Awards/Honors: Nominated for IEEE ICRA Best Paper Award in Cognitive Robotics (2020) for work on affordance learning Kalweit collaborates extensively within the Neurorobotics Lab and BrainLinks-BrainTools research center, with frequent co-authorship with Maria Kalweit, Joschka Boedecker, and oncology specialists. Current work emphasizes real-time adaptive cancer therapy systems and foundation models for medical imaging.

Anh Vu Doanمشاهده پروفایل

مدرس

• Embedded Systems Design
• Combinatorial Optimization
• Problem Modeling
• +۳ مورد دیگر

Dr. Ir. Anh Vu Doan is a Lecturer at the Technical University of Munich (TUM) under the Chair of Integrated Systems and a Senior Project Leader at Infineon in Neubiberg, Germany. With a Belgian-Vietnamese background and prior residence in Japan, he has held academic roles including postdoctoral fellowships at Keio University and TUM, as well as teaching assistant and stand-in lecturer positions at Université libre de Bruxelles (ULB) and IÉSEG School of Management. Research Interests: Embedded systems design Combinatorial optimization Problem modeling and decision aiding Approximate computing and 3D-stacking memory Machine learning reliability and safety Network-on-Chip (NoC) optimization Recent Publications focus on neuromorphic systems, power optimization for multicore processors, adversarial attacks in ML, and multi-objective design strategies using genetic algorithms. His work bridges hardware-software co-design and sustainable mobility decision frameworks. Scientific Awards: Erasmus-Mundus Grant (EASED program) JST/CREST Research Program Education: MSc in Electrical Engineering (ULB, 2009) PhD in Engineering Sciences (ULB, 2015)

• Embedded Machine Learning
• Adaptive Embedded Systems
• Knowledge Distillation
• +۳ مورد دیگر

Julian Hoever, M.Sc., is a research associate in the Department of Intelligent Embedded Systems at the University of Duisburg-Essen's Faculty of Computer Science since June 2024. His work bridges academic research and industrial application through projects like ZaKI.D, focusing on AI accessibility for regional companies. Bachelor's in Applied Computer Science, University of Duisburg-Essen Master's in Cyber-Physical Systems, University of Duisburg-Essen (2024) His research centers on knowledge distillation and precomputable neural networks for FPGAs , enabling efficient AI models. This aligns with the ZaKI.D project 's goal of transferring AI expertise to industry via small-scale initiatives and training. Julian's recent publication in Sensors explores configurable soft sensors for fluid flow estimation, highlighting his expertise in adaptive embedded systems. Projects like IoT Garage and Elastic AI further demonstrate his focus on practical AI implementation in resource-constrained environments.

• Embedded Systems
• Machine Learning
• Artificial Intelligence
• +۴ مورد دیگر

David Peter Federl is a Researcher at the Chair of Intelligent Embedded Systems in the Faculty of Computer Science at the University of Duisburg-Essen. He holds a Master of Science in Cyber-Physical Systems and a Bachelor of Science in Applied Computer Science, both from the University of Duisburg-Essen. B.Sc. in Applied Computer Science, University of Duisburg-Essen M.Sc. in Cyber-Physical Systems, University of Duisburg-Essen His research focuses on embedded machine learning, hybrid digital-analog AI systems, and implementing artificial intelligence on resource-constrained devices like sensors to address data protection challenges while enabling localized AI services. Current projects include ZaKI.D , which aims to democratize AI adoption for regional companies through small-scale initiatives and training. Past projects encompass TransfAIr , RIWWER , Elastic AI , and IoT Garage . Contact: david-peter.federl@uni-due.de

• Deep Reinforcement Learning
• Machine Learning
• Artificial Intelligence
• +۳ مورد دیگر

Fatih Özgan, M.Sc., is a research associate and doctoral candidate at the Chair of Intelligent Embedded Systems within the Faculty of Computer Science at the University of Duisburg-Essen since April 2025. He holds a Master's degree in Electrical and Information Engineering with a focus on Automation and Control Engineering (2021) and a Bachelor's degree in Industrial Engineering (Energy and Economics specialization) from the same university (2019). His research focuses on Deep Reinforcement Learning , particularly on the analysis and development of DRL methods. He previously worked at the Chair of Intelligent Systems in Computer Science under Prof. Dr. Josef Pauli before joining the current chair. Contact details: Office: Bismarckstr. 90 (BC 410), 47057 Duisburg Phone: +49 203 379 3734 Email: fatih.oezgan@uni-due.de

• Materials Science
• Microelectronics
• Nanotechnology
• +۶ مورد دیگر

Dr. Xuan Thang Vu is a Group Leader at the Institute of Materials for Electrical Engineering I and the Chair of Micro- and Nanosystems at RWTH Aachen University . His research focuses on advanced materials and sensor technologies for biomedical and microelectronic applications. Materials Science for Electrical Engineering Nanotechnology in Biosensors Thermal Conductivity in Organic Thin Films Field-Effect Transistors for Biomolecular Detection Dr. Vu’s work spans the intersection of materials science, microelectronics, and biomedical engineering. Recent publications highlight his contributions to graphene oxide thin films , PEDOT:PSS-based sensor arrays , and silicon nanowire biosensors , with applications in health monitoring , ion detection , and neurochemical sensing . His research emphasizes scalable fabrication techniques and integration of nanomaterials into functional devices. Key trends in Dr. Vu’s publications include the use of machine learning for polymer optimization , correlative dielectric analysis , and real-time biomarker detection . His work also explores thermal diffusivity measurements and reaction-diffusion modeling in microfluidic systems. Dr. Vu’s group operates at the Walter Schottky House , utilizing facilities for MEMS fabrication and characterization. His collaboration with interdisciplinary teams has led to innovations in smart textiles , lab-on-a-chip systems , and biohybrid computing .

Jakob Abeßerمشاهده پروفایل

استاد

• Machine Learning
• Deep Learning
• Audio Signal Processing
• +۷ مورد دیگر

Jakob Abeßer is a tenure-track professor for Computational Humanities at the University of Bamberg (since 2025) and a Senior Scientist at Fraunhofer IDMT. He focuses on audio processing applications in digital humanities, particularly Music Information Retrieval , Soundscape Analysis , DCASE , Bioacoustics , and Ecoacoustics . His research integrates machine learning and deep learning with audio signal processing , targeting sound event detection, polyphony estimation, and acoustic scene classification. Research Trends : His recent work (2025) explores Large Language Audio Models for scene understanding and CNN-to-Reservoir Computing transitions in classification tasks. Earlier studies (2023-2022) address domain adaptation for robust embeddings, urban sound monitoring , and piano multipitch datasets . Applications span bioacoustic research , urban ecology , and music transcription . Scientific Awards : Best Paper Award at CMMR 2021

• Human-Computer Interaction
• Virtual Reality
• Augmented Reality
• +۵ مورد دیگر

Thomas Kosch serves as Professor at the Institute of Computer Science within the Faculty of Mathematics and Natural Sciences at Humboldt University of Berlin. His research group Human-Computer Interaction for Scientific Software develops innovative interfaces for scientific applications, with laboratory facilities at Rudower Chaussee 25, 12489 Berlin-Adlershof and administrative correspondence via Unter den Linden 6, 10099 Berlin. His research spans Human-Computer Interaction, Virtual/Augmented Reality, Artificial Intelligence, and Neurophysiological Computing. Key interests include cognitive augmentation through EEG/EMG systems, motor learning with electrical muscle stimulation, large language model integration in UX workflows, and privacy implications of tracking technologies. His work bridges theoretical HCI frameworks with empirical validation through controlled experiments involving physiological measurements and immersive environments. Analysis of his 15 most recent publications (all 2025) reveals three dominant trends: (1) Critical examination of LLM limitations through prompt-hacking and deceptive design studies, (2) Neurophysiological validation of cognitive phenomena using multimodal sensing (EEG/eye-tracking), and (3) Application-driven XR solutions for veterinary care, navigation, and motor assessment. These works consistently employ rigorous user studies with quantitative behavioral metrics. No scientific awards are documented in the provided materials, though his publication volume in top-tier venues (e.g., CHI, UIST) indicates significant field contributions. Professor Kosch actively supervises doctoral candidates, with at least one PhD defense (Michael Piechotta, Dipl.-Bio-Inf.) scheduled for September 2025. His research group likely secures competitive grants supporting equipment-intensive projects involving EEG systems, VR setups, and physiological sensors, though specific funding sources aren't detailed. The Human-Computer Interaction for Scientific Software group operates as an interdisciplinary hub combining computer science, cognitive psychology, and domain-specific applications. Current projects involve developing open-source tools like MorphoHaptics for medical imaging, MIRAGE for fall hazard detection, and Senscon for physiological sensing integration in VR controllers.