Find Academic Supervisors

• Affective Computing
• Explainable AI
• Data Privacy
• +4 more

Dr. Olya Hakobyan is a Researcher at the University of Bielefeld, working in the Faculty of Engineering within the Human-Centered Artificial Intelligence Group at the Center for Cognitive Interaction Technology (CITEC). She has been with the university since June 2021 as a research associate in the Multimodal Behavior Processing lab. Dr. Hakobyan's educational background includes: PhD in Computational Neuroscience (2016-2020) from the Institute for Neural Computation at Ruhr University Bochum Master's degree in Cognitive Science (2014-2016) from Ruhr University Bochum Bachelor's degree in Linguistics (2010-2014) from Yerevan Brusov State University of Languages and Social Sciences Her research focuses on the intersection of artificial intelligence, cognitive science, and human-computer interaction. Dr. Hakobyan specializes in affective computing, explainable AI systems, and the development of privacy-preserving data donation platforms. Her work on the Dona platform represents a significant contribution to ethical data collection methods for social interaction analysis. She investigates how AI systems can interpret human emotions through audio and visual channels while maintaining transparency in decision-making processes. Her research also extends to cognitive modeling, particularly in understanding memory processes and recognition mechanisms. Dr. Hakobyan's publication record demonstrates a strong trajectory in developing methods for analyzing social interactions through digital footprints while addressing critical privacy concerns. Her work bridges technical innovation with ethical considerations in data collection. Her scientific contributions have focused on: Privacy-preserving data donation frameworks Explainable AI for emotion recognition systems Analysis of social interactions through digital communication Cognitive modeling of memory processes Ethical considerations in AI and data science

• Computer Science
• Artificial Intelligence
• Machine Learning
• +7 more

Sio Kei Im is an active researcher with a focus on computer science, machine learning, and human-computer interaction. His recent work spans multiple domains including image processing, quantum computing, and virtual reality. Publications address advanced data augmentation (LogicMix), multi-modal quantum watermarking (MMQW), and efficient neural decoding algorithms (TRHyper). Research interests include time series optimization, dialogue summarization, and haptics in VR environments. Collaborations with experts in linguistics, electrical engineering, and software development indicate interdisciplinary expertise. Key contributions involve adaptive algorithms for AI model protection, speaker recognition systems, and real-time 3D rendering techniques.

Shlomo DubnovView profile

Professor

• Music Technology
• Audio Signal Processing
• Machine Learning for Audio
• +3 more

Shlomo Dubnov is a Professor in the Department of Music at the University of California, San Diego. With an h-index of 36 and over 4,803 citations, his research spans the intersection of music, audio processing, and machine learning, making significant contributions to music information retrieval, sound synthesis, and speech recognition technologies. Dr. Dubnov's primary research interests focus on Music Technology and Audio Signal Processing , with particular expertise in: Machine learning applications for music and audio analysis Sound texture synthesis and modeling Music improvisation systems and computational creativity Audio adversarial examples and security Pitch tracking in noisy environments Multimodal learning for audio-text alignment His recent work demonstrates a strong trend toward applying transformer architectures and contrastive learning techniques to audio processing tasks, with publications in top conferences like IEEE International Conference on Acoustics, Speech, and Language Processing. Dr. Dubnov's research bridges theoretical music concepts with practical audio engineering applications, creating innovative solutions for music generation, analysis, and security. Dr. Dubnov has established productive collaborations with researchers across disciplines, particularly with: G. Assayag (102 Publications • 2,475 Citations) - music technology and improvisation systems Taylor Berg-Kirkpatrick (31 Publications • 1,459 Citations) - machine learning applications K. Chen (18 Publications • 1,150 Citations) - audio transformer models Cheng-i Wang (21 Publications • 305 Citations) - music information retrieval His work is supported by grants focused on advancing the state-of-the-art in music technology, audio processing, and machine learning applications for creative domains. Dr. Dubnov leads research in the Music Technology laboratory at UC San Diego, where his team develops innovative systems for music analysis, synthesis, and human-computer interaction in musical contexts. The lab focuses on creating intelligent systems that can understand, generate, and interact with musical content in real-time applications.

• Machine Learning
• Distributed Machine Learning
• Medical Signal Processing
• +3 more

Dr. Mahdi Barhoush is a Postdoc researcher and teaching assistant at RWTH Aachen University since 2023. His work focuses on applying machine learning to medical domains, particularly in distributed learning systems and signal processing. PhD (2023): "Using machine learning in the medical field: speaker signal processing and distributed learning systems" MSc (2015): Communication Engineering, RWTH Aachen University BSc (2013): Telecommunication Engineering, Arab American University His research spans medical machine learning, edge computing, and IoT optimization, with recent publications on federated learning, split learning architectures, and privacy-preserving ECG classification systems. He contributes to advancements in energy-efficient AI for resource-constrained environments and speaker localization in hospitals. Scientific achievements include: Best Paper Award at RADAR 2024 He collaborates with the INDA Institute in Aachen, contributing to active research projects in distributed learning, 6G technologies, and biomedical AI applications.

Dominic HenzeView profile

Professor

• Cyber-Physical Systems
• Smart Environments
• Machine Learning Applications
• +1 more

Dominic Henze is a Professor at the Technical University of Munich (TUM), affiliated with the Faculty of Informatics and the Chair of Software Engineering . He leads research initiatives in Cyber-Physical Systems , Smart Environments , and Machine Learning Applications , with a particular focus on Fog Computing architectures. His research spans theoretical frameworks and real-world implementations, including collaborations with institutions like Carnegie Mellon University and industry partners such as Siemens AG and Zeiss IMT . His work addresses challenges in resource allocation , predictive maintenance , and blockchain integration within industrial contexts. Dr. Henze's publications reveal a consistent focus on Fog Computing architectures, IoT resource management, and educational software engineering. Key trends include self-organizing network systems , decentralized supply chain traceability , and smart environment coordination . He has advised numerous students on topics ranging from QoS negotiation to autonomous drone coordination . As an educator, he has taught multiple iPraktikum courses and seminars on iOS development and Agile Project Management since 2014, with publications exploring team composition strategies and distributed programming pedagogy.

• Internet of Things
• RFID and antenna design
• Wireless sensor networks
• +5 more

Turke Althobaiti is an active researcher and faculty member whose recent work is concentrated in electrical and computer engineering, with strong interdisciplinary links to computer science and biomedical informatics. Based on co-author affiliations and publication scopes, he is associated with King Saud University, College of Engineering, Department of Electrical Engineering . Research Interests: Design of UHF RFID antennas and Internet-of-Things sensing systems. Localization and communication in smart cities, including non-line-of-sight mitigation and 5G/6G networks. Machine-learning-driven healthcare applications—ranging from COVID-19 detection via chest X-rays to arrhythmia and pneumonia screening. Assistive technologies for the visually impaired, employing contactless RF sensing and AI-based navigation aids. Cloud-security solutions, specifically ensemble intrusion-detection systems against flash-crowd attacks. Cross-disciplinary forays into metabolomics biomarkers and human-animal affective computing. Across 15 recent publications (2019-2025), Althobaiti demonstrates a clear trajectory toward AI-enabled sensing and communication . Workflows combine hardware-level innovations (antennas, RFID tags, USRP radios) with data-level advances (deep learning, ensemble methods, privacy-preserving techniques) to address real-world problems in healthcare, smart cities, and assistive living. Scientific Awards & Recognition: No specific awards are listed in the provided text. Advising & Grants: While no explicit list of students or funded projects is given, the high volume of multi-institutional collaborations and senior-author positions suggest active supervision of graduate researchers and participation in funded projects, most likely supported by the Deanship of Scientific Research at King Saud University or similar Saudi funding bodies. Laboratories & Teams: Though no formal laboratory names are provided, the breadth of hardware prototyping, RF experimentation, and AI model development implies access to well-equipped laboratories in RF/microwave engineering, embedded systems, and computational intelligence.

Marcus HandteView profile

Researcher

• Computer Science
• Pervasive Computing
• Sustainable Mobility
• +3 more

Dr. Marcus Handte is a Senior Researcher at the University of Duisburg-Essen, focusing on networked embedded systems, context-aware computing, and sustainable mobility. His academic journey includes a Habilitation in Computer Science (2013) and a PhD in Natural Sciences (2009) from Universität Stuttgart, alongside a Master's degree from Georgia Institute of Technology (2002). Research Interests Context-aware applications Localization and location-based systems Sustainable mobility solutions Internet of Things (IoT) Smart city infrastructure Privacy-preserving technologies His recent work involves developing platforms for multimodal mobility analysis (MOBYDEX), wireless EV charging systems (TALAKO, FAIR), and innovative approaches to indoor localization. Publications span journals like Machine Vision and Applications and conferences in pervasive computing. Scientific Recognition Best Poster Award at ACM KMIS 2023 Dr. Handte has contributed to projects such as ATMo2, INNAMORUHR, and GAMBAS, and maintains active collaborations across institutions. His expertise in adaptive middleware and distributed systems continues to shape research in smart mobility and ambient intelligence.

Timo GerkmannView profile

Professor

• Statistical Signal Processing
• Machine Learning
• Speech and Audio Processing
• +3 more

Prof. Timo Gerkmann is a Professor at the University of Hamburg's Department of Informatics, leading the Signal Processing Research Group. His research focuses on statistical signal processing and machine learning for speech and audio applications, including communication devices, hearing aids, audiovisual media, and human-machine interfaces. He previously held roles at Technicolor Research & Innovation, KTH Royal Institute of Technology, and Siemens Corporate Research. His work emphasizes generative models, diffusion-based approaches, and acoustic signal enhancement. He currently serves as Senior Area Editor of the IEEE/ACM Transactions on Audio, Speech, and Language Processing. Research Interests: Statistical Signal Processing for Speech and Audio Machine Learning Applications in Acoustic Environments Diffusion Models for Audio Restoration Audio-Visual Speech Enhancement Human-Machine Interaction Systems Acoustic Scene Analysis Publications Highlight Trends: Recent works focus on diffusion models for speech enhancement, generative approaches to dereverberation, and audiovisual multimodal analysis. He has pioneered frameworks like ReverbFX datasets and FlowDec codecs, emphasizing perceptual quality and unsupervised domain adaptation. Advising & Grants: While no specific students or grants are listed, his research group actively publishes in top venues, indicating sustained academic contributions. His work bridges theoretical signal processing with applied systems engineering. Labs/Teams: Leads the Signal Processing (SP) Research Group at UHH, specializing in cutting-edge audio technologies and human-centric signal processing solutions.

• Knowledge Representation
• Spatial and Temporal Cognition
• Multimodal Interaction
• +3 more

Prof. Christopher Habel is a Professor in the Department of Computer Science at the University of Hamburg, leading the WSV work area. His research focuses on spatial and temporal cognition, multimodal interaction, and accessibility technologies. He holds the formal title of Head of Department WSV and is affiliated with the Faculty of Mathematics, Computer Science and Natural Sciences. Habel's work bridges cognitive science, artificial intelligence, and human-computer interaction with an emphasis on assistive technologies for visually impaired individuals. Key research projects include the CINACS International Research Training Group, exploring auditory-haptic access to spatial graphics, and the development of multimodal comprehension systems integrating text, graphics, and tactile feedback. His contributions span theoretical frameworks in cognitive linguistics and practical implementations of verbal assistance systems for tactile maps and haptic graph exploration. Over 30 years of academic activity is reflected in his extensive publication record (1984–2015), with recent work emphasizing cross-linguistic studies, event recognition in virtual environments, and the design of multimodal interfaces for cognitive robotics. His research integrates computational models with empirical studies on human perception and language processing.

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.

Tim FingscheidtView profile

Professor

• Signal Processing
• Machine Learning
• Speech Processing
• +6 more

Prof. Tim Fingscheidt is a Professor at the Technical University of Braunschweig, heading the Signal Processing and Machine Learning Group within the Institute for Communications Technology. His career spans academia and industry, including roles at AT&T Labs (USA), Siemens, and leadership in research teams. He specializes in speech processing, audio technologies, and communication systems quality assessment. Education : 1987-1993: Dipl.-Ing. in Electrical Engineering (RWTH Aachen University, with distinction) 1998: PhD (RWTH Aachen University, with distinction) Research Interests : His work focuses on signal processing fundamentals, machine learning applications in audio/speech systems, error concealment in mobile transmissions, and automotive hands-free system standards. He actively contributes to ITU-T recommendations and chairs ITG committees on speech communication and audiotechnology. Awards : Received prestigious recognition including the IEEE Fellow (2025), ITG Fellow (2023), and multiple ITG/VDE prizes. Professional Activities : Leadership roles in EURASIP, VDE ITG, and IEEE. Standardization expertise in automotive communication systems (ITU-T P.1100/P.1110). Labs & Teams : Leads the Signal Processing and Machine Learning Group, collaborating on cutting-edge projects in audio/speech technologies and machine learning.

• 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.

• 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.

Sebastian NagelView profile

Researcher

• Audio Signal Processing
• Spatial Audio
• Binaural Recording
• +3 more

Dr.-Ing. Sebastian Nagel is a scientist at the Institute of Communication Systems and Data Processing (IKS) under the Faculty of Electrical Engineering and Information Technology at RWTH Aachen University . His research focuses on immersive binaural audio systems, spatial audio signal processing, and real-time adaptation of head-related transfer functions (HRTFs). Key research areas include: Interactive binaural reproduction for moving listeners Coherence-adaptive binaural cue algorithms Acoustic head-tracking with unconstrained movement Dereverberation and noise reduction in spatial audio His recent work (2025) extends binaural cue adaptation for hearable devices, while earlier publications (2024-2018) address multi-microphone integration, HRTF modeling, and real-time signal enhancement. Teaching activities include Laboratory Real-Time Audio Processing and Selected Topics in Communications Engineering seminars. Contact: Room 314 | Phone: +49 241 80-26961 | Email: nagel@iks.rwth-aachen.de