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Amar HekaloView profile

Researcher

• Medical Image Processing
• Natural Language Processing (NLP)
• Information Extraction
• +6 more

Amar Hekalo is a researcher at the Chair of Computer Science VI - Artificial Intelligence and Knowledge Systems (1992-2025) at the University of Würzburg , within the Faculty of Mathematics and Computer Science . Since November 2018, he has collaborated with the University Hospital of Würzburg to enhance medical documentation through speech/image processing , deep learning , and rule-based methods . Education: Master's degree in Physics His research focuses on automated image processing and NLP , covering information extraction , image classification , segmentation , object recognition , and medical applications . Recent work includes federated learning for private data reconstruction and AI applications in cardiac/radiological imaging. His publications reveal expertise in medical AI , particularly gastroenterology , cardiology , and radiology . He supervises Bachelor's and Master's students in AI applications for healthcare, logistics, and education. He contributes to open-source tools and actively seeks student assistants with Angular/NodeJS skills for radiology-focused documentation and e-learning tools.

Jörn OstermannView profile

Professor

• Video Coding
• Computer Vision
• Machine Learning
• +9 more

Prof. Jörn Ostermann is a Full Professor and Head of the Institut für Informationsverarbeitung at Leibniz Universität Hannover since 2003, with prior roles at AT&T Bell Labs and AT&T Labs-Research. He served as Dean of the Faculty of Electrical Engineering and Computer Science (2011–2013) and member of the Senat (since 2020). His research spans video coding, computer vision, machine learning, 3D modeling, and computer-human interfaces , with applications in SAR imaging, predictive maintenance, children's speech analysis, and cochlear implants. Key projects include Next Generation Video Coding , Conditional Coding for Learned Compression , and GreenAutoML4FAS . Notable trends in his recent publications (2025–2023) include Neural network-based video compression Uncertainty estimation in speech recognition Zero-delay coding for cochlear implants Domain adaptation for aerial image segmentation 3D mesh compression standards Error concealment in VVC coding Scientific recognitions: AT&T Standards Recognition Award (1998) ISO Award (1998) IEEE Fellow (2005) Distinguished Lecturer, IEEE CAS Society (2002/2003) MPEG Convenor (2020–2023) He co-authored a graduate textbook on Video Communications , holds >30 patents, and has led >20 research projects. His work bridges academic research and industrial standardization, particularly in MPEG and IEEE committees.

Marcel AachView profile

Researcher

• Artificial Intelligence
• High-Performance Computing
• Machine Learning
• +3 more

Marcel Aach is a researcher at the Jülich Supercomputing Centre (JSC) within Research Centre Jülich, focusing on high-performance computing (HPC) and artificial intelligence (AI) integration. His work spans hyperparameter optimization, quantum-classical hybrid systems, and scalable deep learning architectures. Research Focus: AI-driven CFD simulations, medical imaging analysis, and edge AI optimization Infrastructure: Leverages JSC's HPC systems for large-scale computational tasks Recent publications highlight his contributions to resource-adaptive AI training, turbulent flow prediction using GRU models, and enhancing CT data accuracy for respiratory simulations. His work intersects HPC scalability, machine learning efficiency, and interdisciplinary applications. While no scientific awards are explicitly documented in the provided texts, his research demonstrates technical leadership in merging classical and quantum computing paradigms for AI optimization.

Chelsea Maria JohnView profile

Researcher

• Natural Language Processing
• Artificial Intelligence
• High-Performance Computing
• +5 more

Chelsea Maria John is a Researcher at the Accelerating Devices Lab within the NOVAS division of the Jülich Supercomputing Centre (JSC) at Forschungszentrum Jülich. Her work focuses on high-performance computing and artificial intelligence, specializing in efficient machine learning model training, hardware accelerator benchmarking, and optimization strategies for communication and computation. Her research spans: Natural Language Processing and Large Language Models (LLMs) Scientific Machine Learning with Neural Operators High-Performance Computing system optimization Benchmarking frameworks for emerging AI hardware European open-source AI initiatives like OpenGPT-X Analysis of her 15 publications (2021-2025) reveals pioneering work in training LLMs on HPC systems, development of benchmarking suites (JUPITER, CARAML), and European LLM projects (Teuken series). Her research bridges AI and computational science, notably applying Fourier Neural Operators to fluid dynamics simulations. As part of the Accelerating Devices Lab, she contributes to advancing AI acceleration through hardware-software co-design, focusing on communication efficiency and computational optimization for next-generation scientific computing workloads.

Jürgen PeissigView profile

Professor

• Signal Processing for Audio and Acoustics
• Machine-to-Machine Communication
• Psychoacoustics
• +5 more

Prof. Dr. Jürgen Peissig is a Full Tenure Professor at the Institute for Communications Technology within the Faculty of Electrical Engineering and Computer Science at Leibniz University Hannover since 2014. He supervises a research group with 1 post-doctoral researcher and 12 PhD students across subgroups: Data Communications Systems, Audiocommunications and Acoustics, and Statistical Signal Processing. PhD in Physics (1992) from University of Göttingen Researcher at AT&T Bell Labs (1990-1991) Research Associate at Universities of Göttingen and Oldenburg (1992-1996) Industry leadership at Sennheiser (1996-2014) His research spans audio signal processing for hearing aids, cochlear implants, and immersive audio systems; machine-to-machine communication with robust waveforms (FBMC); and hybrid VLC-RF systems . Key sub-fields include noise cancellation, psychoacoustic modeling, MIMO interference alignment, and 3D audio reproduction. Recent publications focus on deep learning for sound source localization, spatial audio measurement frameworks, and wind turbine noise analysis. Trends show integration of neural networks , higher-order ambisonics , and mobile measurement systems . Recipient of Werner v. Siemens Excellence Award 2006 for thesis supervision Awarded Best Technical Paper at AES 156th Convention (2024) Best Student Paper Award at FRUCT 2020 and AES Poster Award 2019 He has taught courses on Signals and Systems , Digital Data Transmission , and Acoustical Transducers , supervising over 30 diploma/master's theses and 15 PhD students. His lab, the Immersive Media Laboratory , develops advanced audio systems for live events and virtual environments.

Bastian GoldlückeView profile

Associate Professor

• Computer Vision
• Variational Methods
• Light Field Analysis
• +3 more

Bastian Goldlücke serves as an Associate Professor in the Department of Computer Science at the University of Konstanz since 2014. His academic journey includes postdoctoral positions at the University of Heidelberg (2011), TU Munich (2010), and the University of Bonn (2009), following a Ph.D. from the Max Planck Institute for Computer Science in Saarbrücken (2006). His research centers on variational methods and their applications in computer vision, with specialized focus on light field analysis and inverse problems . Key contributions include novel frameworks for light field decomposition, real-time 3D reconstruction, and attention-based neural representations. His work bridges mathematical optimization with practical computer vision challenges in autonomous systems and 3D imaging. Notable scientific recognition includes: ERC Starting Grant 'Light Field Imaging and Analysis' (2013) Funding through SFB Transregio 'Quantitative Methods for Visual Computing' Prof. Goldlücke actively contributes to the academic community as a reviewer for major conferences and journals, co-organizer of the 'Light Fields for Computer Vision' workshops at ECCV 2014 and CVPR 2017, and area chair for ACCV 2014 and 3DV 2016. His current research is supported by significant European and German research grants focusing on computational imaging and visual analysis.

• Numerical Analysis
• Scientific Computing
• Mathematical Modeling
• +12 more

Patricio Farrell is an applied mathematician and Research Group Leader at the Weierstrass Institute Berlin (WIAS), focusing on numerical analysis, scientific computing, and mathematical modeling for semiconductor devices. His work bridges applied analysis and practical applications, particularly through structure-preserving numerical methods for drift-diffusion systems and nonlinear PDEs. Key applications include next-generation semiconductors, perovskite photovoltaics, and neuromorphic computing. He actively collaborates with institutions like Inria Lille, the University of Oxford, and the Helmholtz Zentrum Berlin. Vice Chair of KOMSO (Committee for Mathematical Modeling, Simulation and Optimization) Editor of Open Mathematics Scientific Ambassador for Brain City Berlin Developed ChargeTransport.jl , an open-source Julia tool for semiconductor simulations used in academia and industry His research interests span numerical analysis, nonlinear PDEs, finite volume methods, and meshfree techniques, with applications in perovskites, nanowires, memristors, quantum wells, and laser design. He has secured ~€1.5M in third-party funding from organizations like the Leibniz Association and MATH+. Scientific Awards: Capital's Top 40 under 40

Felix FischerView profile

Researcher

• Information Security
• Usable Security
• Machine Learning Applications
• +3 more

Felix Fischer is a Researcher at the Professorship of Cyber Trust within the Department of Computer Science, Technical University of Munich, affiliated with the School of Computation, Information and Technology. His work focuses on the intersection of information security , privacy technologies , and human-computer interaction . Research interests include: Usability and acceptance of security systems Machine learning applications for secure coding Cryptographic implementation challenges in software development Code reuse vulnerabilities in Android applications Recent publications demonstrate his focus on developer security behavior and machine learning interventions across platforms like GitHub and Stack Overflow. He actively contributes to empirical security studies through software tools such as APDG (Attributed Program Dependency Graph generator) and FixTest. Contact: flx.fischer@tum.de | GitHub | Twitter

Dominik FuchsgruberView profile

Researcher

• Uncertainty Estimation
• Spatio-Temporal Deep Learning
• Graph Neural Networks
• +3 more

Dominik Fuchsgruber is a PhD Student at the Technical University of Munich (TUM) , affiliated with the TUM School of Computation, Information and Technology and the Department of Computer Science . His research focuses on uncertainty estimation in machine learning models and spatio-temporal deep learning applications. Educational background: M.Sc. and B.Sc. in Informatics from TUM, with an exchange semester at ETH Zürich Research experience: PhD work at TUM, internships at University of Alberta and Fraunhofer ESK Research Highlights : Developed energy-based uncertainty estimation frameworks for graph neural networks Created orientation-equivariant graph neural networks for edge signal analysis Contributions to efficient inference methods in large-scale machine learning Laboratory Affiliation : Part of the Data Analytics and Machine Learning group led by Prof. Dr. Stephan Günnemann at TUM.

Vladimir GolkovView profile

Researcher

• Deep Learning
• High-Dimensional Data
• Geometric Data
• +6 more

Dr. Vladimir Golkov is a Postdoctoral Researcher at the Technical University of Munich (TUM) within the School of Computation, Information and Technology, specifically in Informatics 9 (Computer Vision Group). He works under the supervision of Prof. Dr. Daniel Cremers and maintains an active research profile in deep learning applications for medical imaging and biomedical data analysis. His primary research interests focus on deep learning since 2014, with specialization in high-dimensional and geometric data structures, data-processing goals beyond supervised learning (including clustering and anomaly detection), and applications in biomedicine and physics. Dr. Golkov has successfully mentored students who have gone on to pursue PhD studies at prestigious institutions including TUM, LMU, Mila, ETHZ, Cambridge, and Stanford. Analysis of his recent publications reveals a strong trend toward medical imaging applications, particularly in MRI technology, where he combines deep learning approaches with traditional physics-based methods. His work frequently bridges the gap between theoretical machine learning advances and practical medical applications, with significant contributions to diffusion MRI, sequence optimization, and 3D data processing. He has also expanded into emerging areas including large language models for medical applications and equivariant deep learning architectures. Dr. Golkov receives grant support from the Deutsche Telekom Foundation and maintains an active publication record with numerous contributions to leading conferences including ISMRM, MICCAI, and NeurIPS workshops. His research demonstrates a consistent trajectory of innovation at the intersection of computer vision, deep learning, and biomedical applications. He is actively involved in teaching and mentoring, with students from his projects advancing to top PhD programs worldwide. His office is located at Boltzmannstrasse 3, 85748 Garching, Germany (Office: 02.09.061), and he can be contacted at vladimir.golkov@tum.de.

Xi WangView profile

Researcher

• Computer Vision
• Computer Graphics
• Vision Science
• +7 more

Dr. Xi Wang is a postdoctoral researcher at the Technical University of Munich (TUM) within the Computer Vision Group (Informatics 9) under Prof. Daniel Cremers, and a researcher at the Computer Vision and Geometry Lab at ETH Zurich led by Prof. Marc Pollefeys. He collaborates with Prof. Luc Van Gool at INSAIT and has affiliations with MIT and Adobe Research. His research spans computer vision, computer graphics, vision science, and vision-language multimodal learning , focusing on modeling human common sense, intent-driven behavior, and egocentric scene understanding. Research Highlights: Developed multimodal world models (GEM) for fine-grained ego-motion and object dynamics Created vision-language interior design systems (I-Design) and driver trajectory prediction models Advanced 3D human-object interaction reconstruction (ROMEO) and eye-tracking-based action anticipation Explored Gaussian splatting for dynamic scene understanding (EgoGaussian) Scientific Recognition: Recipient of the ECCV 2024 Workshop Best Paper Award for ROMEO $1.6 million grant from BMBF for establishing his independent TUM research group Accepted papers at top venues: CVPR, ECCV, ICLR, ETRA, ISMAR

Marten LienenView profile

Research Fellow

• Generative Modeling
• Time Series Analysis
• Physics-Informed Machine Learning
• +3 more

Marten Lienen is a Research Fellow at the Technical University of Munich, affiliated with the TUM School of Computation, Information and Technology in the Department of Computer Science (I26 - Data Analytics and Machine Learning). He works within Prof. Stephan Günnemann's research group located at Boltzmannstr. 3 in Garching. His research focuses on the intersection of generative modeling and physical systems simulation. Lienen specializes in developing machine learning approaches for time-dependent physical phenomena, particularly turbulent fluid flows. His work bridges deep learning with numerical methods for differential equations, creating novel frameworks that respect physical constraints while leveraging data-driven approaches. Analyzing his publication record reveals a clear trajectory toward building physics-informed generative models capable of simulating complex dynamical systems. His most recent work on UnHiPPO demonstrates sophisticated integration of uncertainty quantification with state space modeling, while his publications on fluid dynamics simulation showcase practical applications of generative ML to longstanding challenges in computational physics. Lienen maintains active online presences through his personal website (martenlienen.com), GitHub, and Twitter accounts, sharing research code and engaging with the machine learning community. His torchode library for PyTorch demonstrates commitment to creating practical tools for scientific machine learning.

Wenchao GuView profile

Research Fellow

• Artificial Intelligence
• Software Engineering
• Computational Linguistics
• +5 more

Dr. Wenchao GU is a Postdoctoral Researcher at the Chair of Software Engineering & AI at the Technical University of Munich (TUM) , working under the supervision of Prof. Chunyang Chen. His academic journey includes a PhD in Computer Science and Engineering from The Chinese University of Hong Kong (CUHK) (2024), an MSc in Information Science from Tohoku University (2017), and a B.Eng in Mechanical and Aerospace Engineering from Tohoku University (2015). Research Interests: Dr. GU's work focuses on Artificial Intelligence for Software Engineering (AI4SE) , leveraging Large Language Models (LLMs) to advance source code understanding, analysis, and generation. Key areas include code optimization , vulnerability detection , and UI code generation . His research bridges theoretical innovations with practical tools for automated code efficiency improvement, semantic code retrieval, and natural language generation in software development workflows. Publication Trends : His recent work emphasizes iterative LLM refinement via evolutionary search (2025), segmented deep hashing for scalable code retrieval (2025), and contrastive learning in code search (2023). Earlier studies explored AST-guided transformers for code summarization (2022) and semantic dependency learning in code retrieval (2021). Scientific Awards: Distinguished Paper Award, ICSE 2025 The Aoba Foundation Scholarship (2015) JASSO Scholarship (2012) Tohoku University President Fellowship (2012) Mentorship: Dr. GU supervises 11 current students (including 1 PhD) and has advised 5 graduated Master's students. He actively seeks candidates with web/Android development expertise for UI code generation projects.

Hao XingView profile

Researcher

• Robot Vision
• Human Action Recognition
• Human-Object Interaction Recognition
• +4 more

Hao Xing is a scientific researcher and post-doctoral fellow at the Institute for Cognitive Systems (ICS), Technical University of Munich (TUM), working with Prof. Gordon Cheng since May 2024. Previously, he served as a research assistant at the Munich Institute of Robotics and Machine Intelligence (MIRMI) and completed his PhD under Prof. Darius Burschka from 2019. His educational background includes: Master of Science in Mechanical Engineering from Technical University of Munich Bachelor of Engineering in Mechanical Engineering from Hefei University of Technology, China Xing's research focuses on Robot Vision , Human Action Recognition , and Graph Convolutional Networks , with significant contributions to Human-Object Interaction Recognition , Scene Understanding , and Visual Depth Estimation . His work leverages machine learning to develop robust algorithms for human activity analysis and robotic perception, emphasizing spatio-temporal modeling and uncertainty handling in real-world scenarios. Analysis of his 13 recent publications (2019-2025) reveals a dominant focus on graph-based approaches for action recognition and segmentation, with increasing emphasis on open-world applications, uncertainty modeling, and healthcare integration. His research spans computer vision, robotics, and medical applications, demonstrating strong interdisciplinary impact through collaborations in surgical robotics and patient monitoring systems. Xing actively mentors students through master's thesis projects in Stereo Matching, Human Activity Segmentation, and Monocular Depth Estimation, requiring expertise in deep learning frameworks and computer vision algorithms. While no major grants are explicitly mentioned, his thesis topics indicate active research funding in geometric vision and human activity analysis. He operates within the Institute for Cognitive Systems (ICS) at TUM, a key unit in MIRMI's robotics ecosystem focused on advancing human-robot interaction through vision-based scene understanding and motion generation capabilities.

Christiane HassenrückView profile

Researcher

• Molecular ecology
• Biodiversity
• Omics approaches
• +4 more

Dr. Christiane Hassenrück is a Bioinformatician and Researcher at the Leibniz Institute for Baltic Sea Research Warnemünde (IOW) in Rostock, Germany, where she leads a working group focused on bioinformatics and 'omics data science. Her research integrates molecular ecology, biodiversity assessment, and computational approaches, with emphasis on FAIR data management, multivariate statistics, and environmental genomics. She previously held postdoctoral positions at MARUM (University of Bremen), the University of Copenhagen, the Leibniz Centre for Tropical Marine Research, and the Max Planck Institute for Marine Microbiology. Her work spans marine microbial ecology, biogeochemical cycling, and environmental bioinformatics. Key themes include the dynamics of microbial communities in upwelling systems, deep-sea hydrothermal vents, polar oceans, and polluted habitats (e.g., microplastics). She also develops methodologies for DNA recovery from challenging substrates and contributes to pathogen modeling (e.g., Vibrio vulnificus ) for ecosystem management. Her research leverages omics technologies to explore microbial adaptation, cryptic speciation, and nutrient limitation responses. Hassenrück collaborates on cross-disciplinary initiatives like the 2024 '10 Must-Knows from Biodiversity Research' and co-authors frameworks for research data management in Mecklenburg-Vorpommern. She maintains active profiles on ORCID, Google Scholar, ResearchGate, and GitHub to promote open science.