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Sasan MatinfarView profile

Researcher

• Medical Sonification
• Sonic Interaction Design
• Extended Reality (XR)
• +4 more

Sasan Matinfar is a research scientist at the Technical University of Munich (TUM), affiliated with the Chair of Computer Aided Medical Procedures (Prof. Navab) and the Munich Center for Machine Learning (MCML). He serves as scientific staff at Rechts der Isar Hospital, developing XR and sonification systems for surgical environments since 2020. His educational background includes: Master’s and Bachelor’s in Computer Science, Ludwig Maximilian University of Munich (LMU) Musicology, Franz Liszt University of Music, Weimar Piano Interpretation, Art University of Tehran Matinfar pioneers medical sonification and multisensory XR, creating auditory interfaces that convert tissue properties into sound for surgical guidance. His work in user-centered design produces clinically viable tools like the Ocular Stethoscope for retinal procedures and physics-based BioSonix frameworks, merging computer vision with perceptual audio engineering to enhance intraoperative precision without visual overload. Analysis of his 12 recent publications (2017-2025) reveals an evolving research arc from foundational surgical soundtracks to sophisticated context-aware sonification. Current work integrates generative AI with real-time tissue deformation modeling, focusing on multimodal frameworks where auditory feedback complements visual navigation in complex surgeries like cardiac interventions and retinal peeling. Key recognitions include: The Data Sonification Award (2025) MICCAI 2023 Best Paper Nominee (top 3% of submissions) MICCAI Young Scientist Award (2017, top 2% of papers) As an educator, Matinfar mentors students through TUM courses including Medical Augmented Reality (WS 2025/26) and Surgical Robotics, while co-organizing the Medical Augmented Reality Summer School and IEEE ISMAR 2025’s MIX Workshop. His patented technologies emerge from collaborations with Politecnico di Milano, TU Dresden’s CeTI, and Balgrist Hospital Zurich, securing interdisciplinary grants in surgical data science. Matinfar operates within TUM’s NARVIS Lab for medical image analysis and RobUSt for robotics-ultrasound integration, leveraging the German Heart Center Munich (DHM) infrastructure to validate XR systems in live surgical workflows and advance vision-language models for intraoperative decision support.

Hannes RitschelView profile

Researcher

• Artificial Intelligence
• Reinforcement Learning
• Human-Robot Interaction
• +3 more

Dr. Hannes Ritschel is a Researcher at the Chair of Human-Centered Artificial Intelligence , Department of Computer Science, University of Augsburg , and a Professor at the University of Music Würzburg . His work focuses on adaptive AI systems for human-robot interaction, leveraging reinforcement learning to personalize robot behaviors based on social cues. His research interests span Artificial Intelligence , Reinforcement Learning , Natural Language Generation , and Multimodal Interaction . He has pioneered techniques for socially-aware robots, including humor and irony generation, adaptive linguistic styles, and real-time behavior synchronization. Recent publications highlight his work on generative AI for virtual agents , multimodal joke presentation , and privacy-by-design speech assistants . Key themes include emotion recognition, human-agent trust, and AI applications in health and music education. Scientific Awards: Best Technical Paper Award, PETRA 2019 Best Poster Award, PETRA 2019 He mentors students in projects related to reinforcement learning , social robotics , and creative AI , with advisees including Kathrin Janowski, Andreas Seiderer, and Thomas Kiderle. His teaching includes lectures on Content Creation for Virtual Environments and Reinforcement Learning , bridging art and computer science.

Tobias HallmenView profile

Researcher

• Multimodal Conversation Analysis
• Machine Learning
• Artificial Intelligence
• +3 more

Tobias Hallmen is a Researcher at the University of Augsburg 's Chair of Human-Centered Artificial Intelligence within the Faculty of Applied Computer Science . His work focuses on multimodal conversation analysis using machine learning and artificial intelligence in psychotherapy and medical/educational training contexts. Research interests include: Automated evaluation of conversational quality through multimodal data (audio, video, text) AI-based assessment systems for therapy sessions and parent-teacher interviews Development of real-time feedback mechanisms for skill improvement Integration of behavioral signal processing and empathy modeling Recent publications demonstrate expertise in vocal burst analysis , emotional mimicry prediction , and multimodal foundation models for behavioral annotation. Key technical domains: deep learning architectures , cross-modal data correlation , and computer vision applications . The Chair team under Prof. Dr. Elisabeth André currently includes 23 members with 10 projects active, including TherapAI (psychotherapy analysis) and KodiLL (medical training systems). Tobias Hallmen's work particularly addresses speaker classification , reception signal analysis , and remote physiological measurement techniques like video-based heart rate detection .

Lingling FanView profile

Associate Professor

• Software Security Analysis
• Software Testing and Analysis
• Big Data-driven Analysis
• +3 more

Lingling Fan is an Associate Professor (100 Young Academic Leaders of Nankai University) at Nankai University, China. Her research focuses on software security analysis, software testing and analysis, and big data-driven analysis, with significant contributions to mobile application security, particularly in Android security and accessibility. Her research interests include: Software Security Analysis, with emphasis on mobile application security and vulnerability detection Software Testing and Analysis, particularly for Android applications and accessibility issues Big Data-driven Analysis for security and quality assessment of software systems Dr. Fan's publication record shows strong trends in automated security testing, vulnerability detection in open-source ecosystems, and accessibility analysis for mobile applications. Her work spans multiple disciplines including software engineering, security, and human-computer interaction, with a particular focus on practical applications for Android ecosystem and measurable real-world impact. Her notable scientific awards include: ACM SIGSOFT Distinguished Paper Award (ASE 2022) ACM SIGSOFT Distinguished Paper Award (ICSE 2021) ACM SIGSOFT Distinguished Paper Award (ICSE 2018) Research Tool Award at NASAC 2018 National Scholarship from The Ministry of Education, China (2018) ACM SIGSOFT CAPS Award (ASE 2018) Dr. Fan has served in various academic service roles including as a program committee member for major conferences such as ASE, ICSE, FSE, and ISSRE. She has also been a reviewer for prestigious journals including IEEE Transactions on Information Forensics and Security (TIFS), IEEE Transactions on Dependable and Secure Computing (TDSC), IEEE Transactions on Software Engineering (TSE), and ACM Transactions on Software Engineering and Methodology (TOSEM).

Michael LyuView profile

Professor

• Software Engineering
• Cloud Computing
• AIOps
• +5 more

Michael Lyu is a Professor at The Chinese University of Hong Kong specializing in software engineering with a focus on cloud reliability, AIOps, and log analysis. His research bridges the gap between theoretical advances and practical industrial applications in large-scale cloud systems. His research interests span Software Engineering , Cloud Computing Reliability , AIOps , and Log Analysis . Dr. Lyu's work addresses critical challenges in modern cloud operations, including failure diagnosis, anomaly detection, and reliability engineering. His recent research has pivoted toward leveraging large language models for software engineering tasks, particularly in code generation and log analysis. His publication portfolio demonstrates consistent contributions to major software engineering conferences (ASE, ICSE, ESEC/FSE) from 2018-2025, with a noticeable increase in LLM-related research since 2023. The trend shows a clear evolution from traditional software engineering topics toward AI-driven approaches for cloud operations. ICSE 2021 Keynote: "Reliability-Driven AIOps for Cloud Resilience" ASE 2023: Maat: Performance Metric Anomaly Anticipation for Cloud Services ASE 2024: LILAC: Log Parsing using LLMs with Adaptive Parsing Cache Dr. Lyu actively mentors students, with numerous co-authored publications showing his advisees as first authors. His work receives significant attention in both academic and industrial software engineering communities, addressing practical problems faced by large-scale cloud service providers. His research group appears focused on developing data-driven approaches for improving cloud system reliability through advanced analytics of logs, traces, and KPIs.

• Human-Computer Interaction
• Virtual Reality
• Biomechanical Engineering
• +4 more

Sidney S. Fels is a Professor at the University of British Columbia, affiliated with the Human Communication Technologies Lab in Vancouver. His research spans Human-Computer Interaction (HCI), Virtual Reality, Biomechanical Engineering, Speech Synthesis, and Medical Imaging. He focuses on innovative interfaces, surgical simulation, and AI-driven educational tools. Recent work includes advancements in touch interaction systems (e.g., HaloTouch), chatbot-assisted learning, and biomechanical modeling for medical applications. His research interests emphasize bridging computational models with real-world applications, particularly in healthcare and education. Notable contributions include contributions to CHI conferences, SIGGRAPH, and INTERSPEECH, showcasing work on AI ethics in learning environments, vocal tract modeling, and pervasive computing systems. Fels collaborates extensively with researchers in engineering, medicine, and computer science, reflecting his interdisciplinary approach to solving complex human-centric challenges. Labs/Teams: Human Communication Technologies Lab at UBC.

• Computer Vision
• Multimedia Systems
• Machine Learning Applications
• +7 more

Timothy K. Shih is an active academic researcher with over 30 years of scholarly contributions, evidenced by his extensive publication record from 1991 through 2025. With more than 380 publications spanning numerous prestigious venues including IEEE Access, Multimedia Tools and Applications, and Lecture Notes in Computer Science, he maintains a robust research profile with consistent annual output (20+ papers in peak years). His work demonstrates leadership through frequent senior/corresponding author positions and collaborations with numerous researchers across international institutions. Dr. Shih's research interests encompass a diverse range of computer science disciplines with particular emphasis on Computer Vision , Human-Computer Interaction , and AI Applications . His work bridges theoretical advancements with practical implementations in educational technology, accessibility solutions, and multimedia systems. Recent publications reveal a strategic focus on applying deep learning techniques to solve real-world problems in sign language recognition, gesture analysis, and wireless sensing applications. Analysis of his publication trends over the past five years shows increasing specialization in multimodal AI systems, with significant contributions to sign language technology (including Arabic Sign Language recognition), WiFi-based human activity recognition, and music technology applications. His research demonstrates strong interdisciplinary connections between computer vision, machine learning, and human-centered computing, with practical applications spanning educational technology, accessibility solutions, and smart environments. Through his mentorship, Dr. Shih has guided numerous junior researchers who have become frequent collaborators, including Chih-Yang Lin, Hsin-Hung Cho, and Tipajin Thaipisutikul. His research program appears well-funded through consistent publication output across multiple project areas, suggesting successful grant acquisition in computer vision, AI, and educational technology domains. Current work indicates active involvement in cutting-edge research on diffusion models for audio processing, enhanced sign language recognition systems, and novel approaches to WiFi-based human interaction analysis.

• Human-Computer Interaction
• Ubiquitous Computing
• Sensing Technologies
• +6 more

Gierad Laput is an Associate Professor in the Human-Computer Interaction Institute within Carnegie Mellon University's School of Computer Science. His research spans the intersection of human-computer interaction, ubiquitous computing, and sensing technologies with a strong focus on practical applications for real-world problems. Laput has established himself as a leading researcher in wearable computing, gesture recognition, and accessibility technologies through consistent publication in top-tier venues including CHI, UIST, and other premier HCI conferences. Laput's research interests center on creating novel sensing techniques that enable more natural and accessible human-computer interactions. His work spans wearable computing systems, activity recognition, touch and gesture interfaces, and accessibility technologies. He has made significant contributions to sparse sensor constellations, acoustic activity recognition, and vision-based gesture systems that push the boundaries of what's possible with current hardware limitations. His research often bridges theoretical innovation with practical implementation, demonstrating real-world applicability of his sensing techniques. Analysis of Laput's recent publications reveals a strong trajectory toward more sophisticated interaction techniques that leverage AI and machine learning. His work has evolved from foundational sensing techniques (like Electrick and Vibrosight) to more complex systems integrating multiple modalities (vision, audio, and wearable sensors). Recent work shows increasing focus on accessibility applications, particularly sign language recognition and generation, and more personalized interaction techniques that adapt to individual users' movement patterns. The research demonstrates consistent innovation in making interaction more natural, accessible, and context-aware. Laput has been actively involved in mentoring and advising students, as evidenced by his role as first author on many papers with student collaborators. His work has received significant attention in the HCI community, with multiple papers appearing in top venues and influencing subsequent research directions in sensing and interaction techniques. While specific grant information isn't visible in the provided data, his consistent publication record suggests successful funding from major sources supporting HCI and computing research. Through his XRDS magazine columns ('Opening the black box,' 'Play ball,' 'The power of two'), Laput has also contributed to science communication and public understanding of technology. These writings demonstrate his commitment to making complex technical concepts accessible to broader audiences and exploring the social implications of emerging technologies.

Jörg OttView profile

Professor

• network architectures
• protocol design
• networked systems
• +7 more

Professor Jörg Ott holds the Chair for Connected Mobility at Technische Universität München (TUM) in the Faculty of Informatics since August 2015. He is also an Adjunct Professor at Aalto University, where he previously served as Professor for Networking Technology from 2005 through 2015. His academic career includes positions as Assistant Professor at Universität Bremen (1997-2005) and research staff with teaching responsibilities at TU Berlin (1992-1997). His research spans network architectures, protocol design, and networked systems , with current focus areas including network and system architectures, robust networking, mobile networked systems, adaptive real-time communication, and network measurements. He has made significant contributions to delay-tolerant networking, edge computing, and internet protocols. Professor Ott has served the networking community extensively, including as co-chair of IETF working groups (MMUSIC, SIP), co-chair of IRTF DTNRG, Treasurer of ACM SIGCOMM, Vice-chair of IEEE Comsoc TCCC, and General Co-Chair of major conferences including ACM SIGCOMM 2012, ACM MobiSys 2018, and ACM CoNEXT 2021. He is currently chair of the Steering Committee of the ACM CoNEXT conference and member of TUM Ethics Board for non-medical sciences. Best Paper Award at ACM ICN conference (2015) Best Student Paper Award at Packet Video Workshop (2012) Professor Ott has supervised numerous students and researchers, with current members of his research group including Wolfgang Wörndl, Ljubica Kärkkäinen, and Leonardo Tonetto. He has co-founded multiple technology companies including Tellique Kommunikationstechnik GmbH, Lysatiq GmbH, Spacetime Networks Oy, and NeMu Dialogue Systems Oy (callstats.io). His teaching portfolio includes courses on Connected Mobility Basics, Edge Computing and the Internet of Things, and Wireless Internet Communication.

• Signal Processing
• Machine Learning
• Acoustic Communication Systems
• +4 more

Rainer Martin is a Professor of Information Technology and Communication Acoustics at Ruhr-Universität Bochum, where he has been affiliated since 2003. He served as Dean of the Faculty of Electrical Engineering and Information Sciences from 2007 to 2009. He holds a Dipl.-Ing. and Dr.-Ing. from RWTH Aachen University and an M.S.E.E. from Georgia Institute of Technology. His research focuses on signal processing, machine learning, and acoustic systems for voice communication, hearing instruments, and human-machine interfaces. Key areas include speech enhancement, noise reduction, binaural processing, cochlear implant optimization, and acoustic sensor networks. His work integrates deep learning with traditional signal processing for real-world applications. Recent publications emphasize neural networks for noise control, hearing aid algorithms, radar interference mitigation, and auditory perception modeling. Trends show strong convergence of machine learning with acoustic engineering, particularly in low-power embedded systems and healthcare applications. Awards: Fellow of the IEEE He leads projects on acoustic sensor networks, automotive radar, and hearing technology, collaborating with industry partners. His lab develops algorithms for hearing aids, cochlear implants, and robust communication systems, with patents spanning noise reduction, echo control, and speech enhancement.

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.

• Bioacoustics
• Bird Conservation
• Ecology
• +3 more

Dr. Olaf Jahn is a Research Associate at the Museum of Natural History Leibniz Institute for Evolution and Biodiversity Research in Berlin, Germany. His work focuses on bioacoustic methods for biodiversity monitoring through the AMMOD project (Automated Multisensor Stations for Monitoring of Biodiversity) as part of Module 5: Automated Bioacoustic Monitoring. Research Focus: Bioacoustics, rapid detection of bird communities, ecological monitoring, and conservation biology Key Tools: Automated sound recognition systems, spectrogram analysis, temporal contextual integration algorithms His research spans tropical avian ecology in regions like Ecuador's Chocó forest while developing cutting-edge signal processing approaches for acoustic monitoring. Jahn's work bridges classical field biology with modern computational methods, creating robust tools for real-world biodiversity assessment. Through publications in journals like PLOS One, Applied Acoustics, and Expert Systems with Applications, he has advanced automated bird identification techniques using: Morphological filtering of spectrograms Temporal contextual information integration Direction of arrival estimation with microphone arrays Robust frame selection for audio parameterization Acoustic detection of endangered species

• Wireless Networking
• Sensor Networks
• Rural Networking
• +5 more

Bhaskaran Raman is a Professor in the Department of Computer Science and Engineering at the Indian Institute of Technology Bombay, where he leads research in networking systems with a focus on practical applications in developing regions. His work spans wireless networking, sensor systems, and mobile computing, with recent expansion into AI-assisted educational technologies. His research interests include wireless mesh networks for rural connectivity, transportation systems monitoring, mobile sensing applications, and educational technology. Notably, his lab has developed innovative solutions like Road-RFSense for traffic estimation in developing regions, FullStop for monitoring unsafe bus stopping behavior, and more recently, AI systems for automatic short answer grading with feedback. Analysis of his publication trends shows a consistent focus on practical networking challenges with strong emphasis on real-world deployment, particularly in resource-constrained environments. In recent years, he has expanded his research to include AI applications in education while maintaining his core expertise in systems research. Raman has mentored numerous graduate students who have become active researchers in networking and systems areas, with many continuing to collaborate with him on publications. His research has been supported by various grants focused on networking for developing regions and smart transportation systems. His lab at IIT Bombay focuses on building practical networking solutions with real-world impact, particularly for transportation safety and educational applications. The team combines expertise in wireless systems, mobile computing, and increasingly, artificial intelligence to address complex challenges in these domains.

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.