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Gerard de MeloView profile

Professor

• Artificial Intelligence
• Large Language Models
• Natural Language Processing
• +9 more

Gerard de Melo is a Professor and Chair of Artificial Intelligence and Intelligent Systems at the Hasso Plattner Institute (University of Potsdam, Germany). He also serves as a member of the Cognitive Sciences Research Focus and ELLIS Unit Potsdam . Research Interests: AI and Machine Learning Natural Language Processing Cross-modal AI (vision-language models, knowledge graphs) Societal and philosophical aspects of AI Medical Informatics Recommendation Systems Graph Neural Networks Recent Article Trends: His work spans generative AI (Vector Grimoire), medical LLM evaluation (CliMedBench), distributed training (Efficient Parallelization), and social media analysis (WallStreetBets) with a focus on multimodal systems and ethical AI . Scientific Recognition: Rutgers CSGSS Best Professor Award (Teaching, Advising) Best Paper Awards: NeurIPS Workshop, CIKM, EACL LANTERN IEEE TCSE Distinguished Paper Award First Runner-Up ACM WebSci Advising & Grants: Mentored students like Rohan Sawahn (Better World Award) and Maximilian Schall (IEEE TCSE Award). Secured major funding including German BMBF Grant for AI Service Center Berlin-Brandenburg and DARPA SocialSim Program support.

• Computer Science
• Electrical Engineering
• Robotics
• +7 more

Pengfei Li is a prolific researcher affiliated with multiple academic institutions, including Harbin Medical University, Yale University, Beihang University, Zhejiang University, and others. His work spans interdisciplinary domains such as machine learning, robotics, remote sensing, and biomedical engineering. Research interests focus on Machine learning and deep learning for industrial and medical applications Signal processing and sensor technologies Remote sensing and geospatial data analysis Robotic control systems and exoskeleton design Code search and software engineering optimization His recent publications highlight trends in FPGA-based real-time systems, multimodal machine learning, and AI-driven diagnostics. While awards and student advising details are absent in the provided data, his contributions to IEEE journals and conferences underscore his expertise in algorithm design and applied informatics.

• Sound Synthesis
• Physical Modeling
• Virtual Reality Audio
• +6 more

Cumhur Erkut is an Associate Professor in the Sound and Music Computing group within the Department of Architecture, Design and Media Technology at Aalborg University, Copenhagen, Denmark. With a publication record spanning over two decades from 2000 to present, his research bridges computer science, audio engineering, and creative arts. His primary research interests include Sound Synthesis, Physical Modeling of musical instruments, Virtual Reality Audio, Sonic Interaction Design, and Embodied Interaction. His work demonstrates a consistent focus on the intersection of technology and human experience, particularly in how sound and movement interact in digital environments. His recent publications show a strong shift toward AI-driven audio processing, voice conversion, and differentiable digital signal processing techniques. Dr. Erkut's publication trends reveal an evolution from traditional physical modeling of musical instruments (particularly string instruments like tanbur, clavichord, and guitar) toward more contemporary applications in virtual reality, embodied interaction, and AI-powered audio processing. His work consistently emphasizes real-time performance considerations and human-centered design principles. He has served in significant academic roles, including organizing the 17th International Conference on New Interfaces for Musical Expression (NIME 2017) at Aalborg University, demonstrating his leadership within the international research community. His collaborative network is extensive, with frequent co-authorship with Stefania Serafin, Vesa Välimäki, Antti Jylhä, Rolf Nordahl, and other prominent researchers in the sound and music computing field. These collaborations span multiple institutions across Europe, reflecting the international nature of his research.

• Human-Computer Interaction
• Social Robotics
• Artificial Intelligence
• +3 more

Büsra Sarigül serves as a research associate and PhD student at the Leibniz Institute for Knowledge Media (IWM) in Tübingen since June 2022, affiliated with the Everyday Media Lab. She concurrently teaches Master's-level courses at the International School of Management (ISM) Stuttgart. Her academic foundation includes: B.Sc. in Psychology, Ankara University (2016) M.Sc. in Interdisciplinary Social Psychiatry, Ankara University (2020) Research focuses on communication dynamics of social agents through multimodal integration, examining how voice characteristics, social roles, and relationship types influence user perceptions of AI systems. Her work bridges human-robot interaction, credibility assessment, and psychological responses to voice assistants. Publication trends (2020-2025) reveal concentrated investigation into trust mechanisms within voice-based AI, analyzing cross-modal perception in human-robot contexts and ChatGPT's dual impact on information credibility and creative cognition. Key methodological approaches include longitudinal studies and experimental designs measuring predictive processing. Active within IWM's collaborative ecosystem, she contributes to the Everyday Media Lab while partnering with Multimodal Interaction, Perception and Action, and Knowledge Construction labs on projects exploring human-AI interaction dynamics.

• Artificial Intelligence
• Neuroscience
• Machine Learning
• +3 more

Nikola Milosevic is a doctoral researcher at the Max Planck Institute for Human Cognitive and Brain Sciences , affiliated with the International Max Planck Research School NeuroCom and working within the Methods and Development Group Neural Data Science and Statistical Computing . His research spans interdisciplinary topics at the intersection of artificial intelligence, neuroscience, and computational modeling. His work focuses on active matter systems , reinforcement learning architectures , and neural data science methodologies . He has developed the ARC toolbox for artificial speech rhythmicity controls and contributed to explainable AI frameworks. Recent publications highlight his interests in embodied cognition, trust region optimization, and deep learning applications for biomedical imaging. Nikola's methodological expertise includes information processing , representation learning , and algorithmic safety in computational systems. His research combines theoretical approaches with practical implementations in Python and machine learning frameworks.

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.

Kristian KerstingView profile

Associate Professor

• Data Mining
• Machine Learning
• Statistical Relational Artificial Intelligence
• +5 more

Kristian Kersting is an Associate Professor in the Computer Science Department at Technical University of Darmstadt, Germany, having moved from TU Dortmund in 2017. He holds additional affiliations including Adjunct Assistant Professor roles and has contributed to institutions like the University of Bonn and Wake Forest University. His research focuses on data mining, machine learning, and statistical relational AI, with applications in medicine, plant phenotyping, and traffic analysis. He has authored over 150 peer-reviewed publications and received numerous awards, including the ECCAI Dissertation Award 2006 and Best Student Paper Award at ECML 2006. Education includes a Ph.D. from the University of Freiburg and postdoctoral research at MIT. He has held roles such as Assistant Professor at the University of Bonn and co-chaired major conferences like ECML PKDD 2013. His work emphasizes interdisciplinary applications, including collaborations with industries like goedle.io and pflegix.de. Scientific contributions span probabilistic logic learning, lifted inference, and neuro-symbolic systems. His research group's activities include projects on plant disease detection, traffic prediction, and collective attention analysis. Key awards highlight his impact in AI and data science.

Oliver BehrendView profile

Professor

• Amphibian lateral line system
• Auditory processing
• Neuroethology
• +3 more

Oliver Behrend is a Professor at the Department of Biology II within the Faculty of Biology at Ludwig-Maximilians-Universität München, where he serves as Managing Director of the Munich Center for Neurosciences - Brain & Mind . His research focuses on comparative neurobiology and auditory processing mechanisms across vertebrates. Behrend's work examines: 1) Amphibian lateral line systems for surface wave localization, 2) Mammalian sound localization through interaural time difference processing, and 3) Auditory feedback mechanisms in echolocating bats. His studies span neuroethology, auditory neuroscience, and sensory-motor integration. Publications reveal multidisciplinary approaches to sensory neuroscience with keywords: Neuroethology Computational neuroscience Auditory pathways Sensory integration Neural coding Comparative neuroanatomy Contact: o.behrend@lmu.de

• 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.

• Neurocognition of Music and Language
• Perception and Production of Sound
• Interpersonal Interaction
• +4 more

Daniela Sammler is an Adjunct Professor at Goethe University Frankfurt am Main and leads the Neurocognition of Music and Language Research Group at the Max Planck Institute for Empirical Aesthetics. Her work bridges cognitive neuroscience, linguistics, and musicology to explore shared neural mechanisms in music and language processing. PhD in Psychology from the University of Leipzig and Max Planck Institute (2008) Habilitation on intonation in speech and music (2018) Venia Legendi in Frankfurt (2022) Her research focuses on neurocognitive overlaps between music and language, including syntax, prosody, and interpersonal interaction. Key findings include cortical tracking of rhythm, neural hubs for melodic processing, and evidence of universal links in speech-song perception despite cultural diversity. Recent publications analyze audio-motor coordination in duets, prosody-emotion mapping, and dyslexia-related auditory processing deficits. Awards include the Otto Hahn Medal (2010) and European historic preservation recognition (2012). Co-led studies with institutions like Western Sydney University and University of Glasgow Editor for The Oxford Handbook of Language and Music (under contract) Grants from Max Planck Society and Heisenberg Foundation

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.

Christiane AntweilerView profile

Researcher

• Digital Signal Processing
• Speech and Audio Processing
• Acoustic System Identification
• +6 more

Dr.-Ing. Christiane Antweiler is a Researcher at the Institute of Communication Systems (IKS) , part of RWTH Aachen University 's Faculty of Electrical Engineering and Information Technology. Her work focuses on algorithms for digital speech/audio processing, multi-channel system identification, head-related transfer functions (HRTFs), and acoustic/echo cancellation in medical and teleconferencing applications. Research topics: Medical diagnostics signal processing , time-variant system identification , HRTF measurement , and real-time audio implementation . Current projects: Connected Visual Reality (CoVR) , Acoustic Tube Endoscopy , and Spatial Rendering with quasi-continuous HRTFs. Her 15 most recent publications (2025–2011) span adaptive filtering , acoustic system identification , and 3D audio rendering , with a strong emphasis on perfect sequence excitation and medical applications . She received the Best Paper Award at ICMCIS 2022 for her work on spectrum monitoring. Teaching roles include Information Theory and Source Coding (since 2009) and Digital Speech Processing 1 (2014–2015). Tools used: Matlab , C/C++ , and RT Proc for real-time audio processing.

René PeinlView profile

Professor

• Multimodal Artificial Intelligence
• Resource-efficient AI
• Speech Recognition
• +7 more

René Peinl is a Professor of Business Informatics at the Institute for Information Systems (iisys) within the Faculty of Computer Science at Hof University of Applied Sciences. He serves as the Institute Director and leads the research group on Multimodal Artificial Intelligence. His academic career spans over 15 years of teaching in master's programs including 'Master Informatik' and 'Master Applied Research in Computer Science', with a focus on resource-efficient AI for text understanding since 2021. Dr. Peinl's research spans multiple cutting-edge domains in AI and information systems. His primary focus is on multimodal artificial intelligence systems that integrate various data modalities. He specializes in resource-efficient AI models that can operate without cloud resources, with particular emphasis on German language processing for speech recognition, text understanding, and speech synthesis. His work extends to practical applications in Industry 4.0 through customizable manufacturing execution systems (HiCuMES) and immersive learning applications using virtual reality technology for fields like electrical engineering and healthcare training. Analysis of his recent publications reveals a strong trend toward practical, efficient AI implementations with a focus on German language processing and multimodal systems. His work addresses critical challenges in model compression, bias mitigation in generative AI, and multimodal integration of text and image understanding. A significant portion of his research focuses on making AI more accessible and practical for small and medium enterprises through resource-efficient implementations that don't require cloud infrastructure. Professor Peinl actively participates in academic service and industry collaboration. He serves as a member of Hof University's senate and supervisory board. His research is supported through EFRE projects including 'Wirtschaft im Mittelstand 4.0' and 'Digitale Transformation des Mittelstands mit KI'. He frequently engages with industry through speaking engagements, consulting, and collaborative projects with companies across various sectors. At Hof University, Peinl leads the Institute for Information Systems and the Multimodal Artificial Intelligence research group. His team works on developing privacy-friendly speech assistance systems that function without cloud resources, highly customizable manufacturing execution systems for Industry 4.0, and immersive VR learning applications for domains that are expensive, dangerous, or difficult to replicate in physical reality.