Find Academic Supervisors

• Computer Engineering
• Multimedia Security
• Embedded Systems
• +4 more

Cem Taşkın is an Assistant Professor at the Department of Computer Engineering, Trakya University, and has served as Department Head since January 2023. Previously, he was Assistant Professor at Tunca Vocational School (2011-2014) and held administrative roles as School Director (2013-2017) and Deputy Director (2012-2013). He earned his PhD in Computer Engineering from Trakya University in 2010. High School: Kırklareli Anatolian High School (1999) Bachelor's: Trakya University, Computer Engineering (2003) Master's: Trakya University, Computer Engineering (2006) PhD: Trakya University, Computer Engineering (2010) His research focuses on Computer Engineering , Multimedia Security , and Embedded Systems , with a strong emphasis on encryption techniques for MPEG video streams and mobile/web application development. Recent work includes applying interactive mobile applications for pediatric chemotherapy treatment (2022) and optimizing embedded systems as dashboards (2016). Publication trends reveal expertise in video encryption (I-frames, byte alignment, XOR operations), chaotic algorithms for sound data security, and software development for educational and administrative systems. His work bridges theoretical cryptography with practical implementations in multimedia and embedded environments. Supervised Master's Theses: Altuğ Yiğit - Human Insight in Business Processes with Deep Learning Temel Yavuz - Corporate Content-Information Management Systems Murat Emre Yücel - Text Classification with Active Learning Projects: Led initiatives at Trakya University on mobile application development laboratories (2016), campus automation systems (2016), and MPEG audio querying (2008). Participated in KOSGEB's R&D Innovation Project for Dream Cash Register/Barkot Systems (2012).

Nick JanßenView profile

Researcher

• Machine Learning
• Computer Science
• Signal Processing
• +3 more

Nick Janßen is a researcher at the Institute for Information Processing, Leibniz University Hannover, working towards his PhD since July 2024 in Dr. Melanie Schaller’s group. His research focuses on state space models and audio monitoring applications in biomedical contexts. Education: Master’s Degree in Computer Engineering (2024), Leibniz University Hannover Bachelor’s studies in Computer Engineering (2018–2024) His research interests include machine learning , signal processing , and probabilistic modeling , particularly applied to medical engineering and data segmentation . Recent work explores audio monitoring for surgical applications and conditional normalizing flows for error detection in segmentation data. Notable publications include a preprint on state space models for bone cement disintegration monitoring (2025) and a dataset on erosion depth analysis (2024). His methodological work bridges computer science and biomedical engineering .

Vijay ParsaView profile

Associate Professor

• Biomedical Engineering
• Acoustic Signal Processing
• Speech Processing
• +3 more

Vijay Parsa is an Associate Professor in the Department of Electrical and Computer Engineering at Western University , Canada. He holds the Oticon Foundation’s Chair in Acoustic Signal Processing, a joint position between the Faculties of Health Sciences and Engineering, focusing on interdisciplinary research in acoustic signal processing for audiology. Education: Ph.D. in Biomedical Engineering, University of New Brunswick M.E.Sc. in Electrical Engineering, University of New Brunswick B.Eng. in Electronics and Communication Engineering, Osmania University, India His research centers on acoustic signal processing for hearing aids, speech quality evaluation, and assistive listening devices. He develops algorithms for frequency compression, noise reduction, and envelope enhancement to improve speech perception for individuals with hearing loss. Prominent trends in his research include applications of machine learning and neural networks in speech processing, computational auditory modeling, and validation protocols for pediatric hearing aid fitting. His work bridges engineering and clinical audiology. Scientific Awards: Shaw Memorial Postdoctoral Award, Canadian Acoustics Association Dr. Parsa has contributed extensively to the field, with publications in journals like Ear & Hearing , Journal of the Acoustical Society of America , and IEEE Signal Processing Magazine . His work informs standards in hearing aid verification and wireless remote microphone systems.

Jean-Louis GutzwillerView profile

Researcher

• Image Processing
• Signal Processing
• Computer Vision
• +3 more

Jean-Louis Gutzwiller is a Researcher at the Lorrain Laboratory for Research in Computer Science and its Applications (LORIA), a joint research unit of CNRS, Inria, and the University of Lorraine, focusing on advanced image processing and compression techniques for hyperspectral data and transportation systems. His work bridges theoretical signal processing with practical applications in remote sensing and intelligent infrastructure. His research spans hyperspectral image compression, wavelet transforms, vehicle-to-road communication systems, and speaker diarization algorithms. Key contributions include developing exogenous quasi-optimal spectral transforms for MERIS satellite data, optimizing SPIHT coders for hyperspectral imaging, and pioneering electromagnetic loop-based vehicle detection systems. His methodology emphasizes low-complexity solutions suitable for onboard satellite processing and real-time applications. Analysis of his 11 publications (2022-2025) reveals a dominant focus on hyperspectral compression techniques, particularly using exogenous transforms and zero-tree coding for remote sensing data. Secondary themes include vehicle infrastructure communication systems and neural gas algorithms for audio processing. His work consistently addresses computational efficiency challenges in resource-constrained environments like satellite platforms. Gutzwiller operates within LORIA's collaborative framework, contributing to France's national research ecosystem through partnerships with CNRS and Inria. His laboratory affiliation enables interdisciplinary work spanning computer science, aerospace engineering, and transportation technology, with practical implementations in earth observation and smart mobility systems.

Hassan KaisView profile

Researcher

• Acoustics
• Signal Processing
• Machine Learning
• +2 more

Hassan Kais is a Teacher-researcher affiliated with the Institute of Acoustics at Le Mans University. His work bridges acoustics and telecommunications, focusing on advanced signal processing techniques. Key research areas: Acoustics, Machine Learning, Wireless Communications Institution: Le Mans University, France Hassan's research explores the intersection of acoustic signal analysis and next-generation communication systems. Notably, he has contributed to: Innovative microphone array designs for sound capture SCMA (Sparse Code Multiple Access) systems in telecommunications Machine learning applications in acoustic and wireless signal processing mmWave hybrid MIMO systems for 5G networks Thermo-acoustic and electroacoustic sensor development His publications demonstrate interdisciplinary expertise in: Acoustic wave propagation in complex media Deep learning for communication protocols Signal processing algorithms Metamaterials and transducer design Audio sensor networks Non-linear acoustics applications

Manuel MelonView profile

Professor

• Acoustics
• Electroacoustics
• Sound Field Synthesis
• +7 more

Manuel Melon is a Professor at the Acoustics Laboratory of the University of Le Mans , where he co-supervises the International Master in Electroacoustics (IMDEA) . His career spans institutions like the Conservatoire National des Arts et Métiers in Paris and focuses on advanced acoustic research. His research interests include electroacoustics , sound field synthesis , personalized listening zones , and acoustic material characterization using transducer arrays. He explores both theoretical and applied aspects of wave propagation in complex media. Recent publications highlight trends in acoustic metamaterials for directional sound control, MEMS speaker modeling , and adaptive filtering for automotive sound zones. He also contributes to online acoustic education through the ACOUCOU platform.

• Applied Data Science
• Human-centered Artificial Intelligence
• Machine Learning
• +5 more

Dr. Heysem Kaya is an Assistant Professor at Utrecht University's Faculty of Science , affiliated with the Social and Affective Computing research group in the Department of Information and Computing Sciences. With over 90 publications and 2600+ citations, his work bridges machine learning, speech processing, and affective computing to advance applications in mental health, child behavior analysis, and explainable AI. PhD in computational paralinguistics from Bogaziçi University (2015) Editorial board member of IEEE Transactions on Affective Computing Keynote speaker at University of Glasgow Social AI Workshop (2025) Leader of the student wellbeing task force at Utrecht's Faculty of Science (2023-2025) Research spans applied data science , human-centered AI , and computational paralinguistics , with notable contributions to emotion recognition, bias mitigation, and multimodal analysis of mental health indicators. Recent articles focus on fairness in AI, explainable models for depression severity, and synthetic data generation. Awarded six ComParE Challenges (INTERSPEECH), three ChaLearn Awards , and first runner-up in EmotiW 2015 , Kaya's work combines technical rigor with societal impact. His projects include the Turkish Audio-Visual Bipolar Disorder Corpus and federated learning for psychiatric violence prediction.

• Computer Vision
• Machine Learning
• Human Behavior Analysis
• +3 more

Mohamed Daoudi serves as Full Professor of Computer Science at IMT Nord Europe and leads the Image group at CRIStAL Laboratory (UMR CNRS 9189). With over 150 publications in top-tier journals and conferences, his research pioneers computer vision and machine learning approaches for human behavior understanding, particularly through 3D geometric analysis and Riemannian manifold frameworks. His research spans computer vision, machine learning, and affective computing with core expertise in 3D face/body modeling, unregistered data analysis, and depression/pain assessment. Key contributions include Riemannian geometry applications for facial expression recognition, motion dynamics analysis, and geometric generative models. His work bridges theoretical computer vision with clinical applications in mental health and animal welfare. Recent publications (2023-2025) reveal intense focus on unregistered 3D data analysis, with 70% of articles addressing depression/pain biomarkers through body/facial dynamics. Dominant methodologies include geometric deep learning (45%), diffusion models (25%), and transformer architectures (20%), applied to medical diagnostics, surgical training, and affective computing challenges. Scientific Awards: IAPR Fellow AAIA Fellow Professor Daoudi has graduated 30 doctoral students including Yujin WU, Baptiste Chopin, and Emery Pierson, with many now leading industry/academic roles. His leadership extends to editorial positions (Image and Vision Computing, IEEE Transactions on Multimedia), conference organization (IEEE FG 2019 General Chair, FG 2025 General Chair), and 12+ specialized workshops on human analysis. He directs significant research grants through CNRS collaborations and EU projects. As Head of the Image group at CRIStAL Laboratory, he oversees interdisciplinary teams developing geometric vision solutions for healthcare, biometrics, and human-computer interaction. Current initiatives include the REACT 2025 challenge for facial reaction generation and depression biomarker discovery using multimodal physiological sensing.

• Natural Language Processing
• Machine Translation
• Statistical Machine Translation
• +4 more

Cristina España i Bonet is a Professor in the Department of Computer Science at the Polytechnic University of Catalonia (UPC), working within the Natural Language Processing group (GPLN) of the Center for Technologies and Applications of Language and Speech (TALP). She holds a Physics degree and a PhD in Cosmology from the University of Barcelona, later transitioning to Natural Language Processing and Machine Translation. Her academic journey includes teaching at both the Faculty of Physics of the University of Barcelona and currently at the Barcelona School of Informatics of UPC. Her research spans multiple areas of computational linguistics with a strong focus on Machine Translation. She has extensive experience in statistical and hybrid translation systems, document-level translation, multilingual systems, and machine learning applications in NLP. Her work often addresses real-world challenges with diverse text genres including news, patents, Wikipedia articles, and social media content. She has made significant contributions to developing translation systems that leverage context beyond the sentence level to improve coherence and quality. Cristina has been actively involved in numerous European and national research projects including OPENMT, OPENMT2, MOLTO, and TACARDI, where she has contributed both research and project coordination. Her recent work shows a growing interest in sign language translation, low-resource language processing, and the intersection of large language models with traditional machine translation paradigms. She has supervised multiple PhD and Master's students in topics related to machine translation and multilingual systems. Member of the Natural Language Processing group (GPLN) at TALP Research Center Supervisor of doctoral and master's theses in NLP and Machine Translation Lead researcher in multiple EU-funded projects on multilingual translation Developer of resources and tools for Wikipedia-based multilingual corpora Her research has evolved from statistical machine translation to incorporate neural approaches while maintaining focus on document-level context and multilingual applications. She has made significant contributions to understanding translation artifacts, developing methods for low-resource language translation, and creating resources for sign language processing. Her work bridges theoretical advances with practical applications across diverse language pairs and domains.

Fabian OstermannView profile

Researcher

• Artificial Intelligence
• Computer Music
• Algorithmic Music Composition
• +5 more

Fabian Ostermann is a researcher at the Chair 11: ALGORITHM ENGINEERING within the Department of Computer Science at Technical University of Dortmund. His work focuses on the intersection of artificial intelligence and music technology, with particular expertise in algorithmic composition and evolutionary approaches to music generation. His primary research interests include: Artificial Intelligence for Music Applications Computer Music and Algorithmic Composition Reinforcement Learning and Evolutionary Algorithms Neuroevolution and Procedural Content Generation Music Information Retrieval Systems Ostermann's research output demonstrates a strong focus on applying AI techniques to music creation and analysis. His recent work explores the use of large language models in evolutionary music generation, adaptive video game music systems, and novel approaches to instrument recognition in polyphonic audio. He has developed significant resources for the research community including the AAM dataset of artificial audio multitracks, which contains 3,000 algorithmically generated music tracks with rich annotations. His scientific contributions have been recognized through invitations to serve on program committees for major conferences including EvoMUSART (2024, 2025) and IJCAI's Special Track on AI, the Arts, & Creativity. He has also contributed to journals such as Computer Music Journal and Transactions of the International Society for Music Information Retrieval. Ostermann has supervised numerous student theses on topics ranging from transformer-based music generation to evolutionary approaches for recreating vector graphics. His teaching portfolio includes courses on practical optimization, music informatics, and digital entertainment technologies across multiple semesters from WS20/21 through SS25.

• Computer Science
• Medical Imaging
• Artificial Intelligence
• +3 more

Asmaa Shati is a researcher at the School of Physics, Mathematics and Computing, King Khalid University, with a focus on applying computer science and artificial intelligence to medical diagnostics. Her work bridges disciplines such as machine learning, image analysis, and public health through innovative research in disease prediction. Her research output centers on developing advanced algorithms for medical imaging analysis. Using techniques like residual networks, DenseNet, and texture descriptors, she contributes to improving detection systems for pneumonia, tuberculosis, and COVID-19 based on cough audio signals. Shati's publications demonstrate expertise in computer science and biomedical engineering, with a strong emphasis on practical applications in healthcare. Her work leverages methods such as GLCM, wavelet transforms, and k-NN to enhance diagnostic accuracy.

• Biomedical Engineering
• Optical Spectroscopy
• Medical Instrumentation
• +3 more

B.H.W. Hendriks is a Professor in the Department of Mechanical Engineering at Delft University of Technology, specializing in Medical Instruments & Bio-Inspired Technology. His research develops optical and signal processing solutions for medical applications, particularly in surgical environments and tissue analysis. His primary research domains include: Biomedical Engineering Optical Spectroscopy (Diffuse Reflectance) Medical Device Development Surgical Technology Cardiac Signal Processing Tissue Characterization Analysis of his 55+ publications reveals dual expertise: (1) Intraoperative optical sensing (e.g., fiber-optic tissue identification during spine surgery and electrosurgery), and (2) Advanced signal processing for cardiac diagnostics (atrial fibrillation mapping via electrograms/ECG). His work bridges mechanical engineering with clinical practice through real-time surgical workflow analysis and tissue-mimicking phantoms. Hendriks actively supervises research students and has generated 3 significant datasets for tissue characterization. His fingerprint shows dominant activity in spectroscopy (100%), surgery (93%), and tissue analysis (91%), with emerging work in human pose tracking for cardiac catheterization laboratories.

Coralie VogelaarView profile

Lecturer

• Human-Machine Interaction
• Behavioral Studies
• Affective Computing
• +7 more

Coralie Vogelaar is an interdisciplinary artist and lecturer at the ArtScience Interfaculty , affiliated with Leiden University . Her work bridges behavioral science with artistic practice, focusing on human-machine relationships through algorithmic systems and biometric data. 2021 Prix de Rome nominee Collaborates with experts in data analysis, choreography, and sound design Exhibited at Stedelijk Museum Amsterdam, ZKM Karlsruhe, and Science Gallery Dublin Her research explores: Power dynamics in responsive technologies Biometric data translation into sensory experiences Algorithmic biases in image recognition systems Human compliance with fitness-machine hybrids Technical collaborations with V2_Lab , TU/e Innovation Space , and Digital Methods Initiative demonstrate her cross-disciplinary approach. Works utilize Teachable Machine , SuperCollider , and facial action coding systems to interrogate digital-physical interfaces. Scientific Recognition: Nominated for Prix de Rome (2021) Featured in V2_ Lab's 40-year retrospective

• Virtual Reality
• Multimedia Systems
• Network Protocols
• +7 more

Mario Montagud Climent is a researcher at Centrum Wiskunde & Informatica (CWI) , focusing on virtual reality (VR), extended reality (XR), and multimedia systems. His work bridges network protocols, human-computer interaction, and immersive technologies. Key contributions: Multi-party holographic meetings, Edge rendering architectures, and XR quality of experience (QoE) optimization Collaborations: Sergi Fernández, Pablo César, Gianluca Cernigliaro, and other international experts in VR/XR Research spans network programmability for adaptive XR, holographic communications , and explainable AI for spatial audio analysis. Publications highlight QoE assessment and low-cost immersive solutions . Recent articles address real-time multiuser systems, neural 3D reconstruction for UAVs, and 6G federation concepts. His work integrates 5G/6G technologies with applications in cultural heritage and social VR.

Matt GoodersonView profile

Senior Lecturer

• Music Business
• Artist Development
• Music Production
• +3 more

Matt Gooderson is an award-winning British music artist, producer, composer, and academic currently serving as Programme Director for the MA in Music Management at City St George's, University of London. Formerly a founding member of the indie-electro band Infadels, he has performed over 500 shows globally including Glastonbury and Coachella, and contributed music to A24 films, Gran Turismo, FIFA, and CSI: Miami. His academic work bridges industry practice and education through real-world applications of music business principles. Gooderson's educational background includes: Senior Fellowship of the Higher Education Academy (SFHEA) from University of the Creative Arts (2025) MA in Music Education from University College London (2013-2014) BA (Hons) in Commercial Music from University of Westminster (1996-1999) His research centers on the intersection of music technology, production, and creativity, with particular emphasis on artificial intelligence's impact on artistic processes. Gooderson investigates how AI tools influence diversity and inclusivity in music creation while examining tensions between academic songwriting pedagogy and industry practices. He advocates for entrepreneurial approaches in music education through authentic assessments that promote equality and critical industry engagement, aiming to empower creators through technological innovation. Analysis of Gooderson's publications from 2015-2024 reveals an evolving trajectory from live electronic performance techniques toward AI-driven composition. His scholarship increasingly addresses ethical dimensions of machine creativity, with recent work exploring bias in AI music generation across cultural contexts. Recurring themes include collaborative songwriting dynamics, producer cognition in digital workflows, and immersive audio environments, demonstrating consistent engagement with evolving music technologies and their sociocultural implications. His professional recognition includes: Impala Silver Award for Infadels' independently released single selling over 50,000 copies Senior Fellowship of the Higher Education Academy (SFHEA) awarded 2023 As Programme Director for the MA in Music Management, Gooderson oversees curriculum development and student progression through industry-integrated learning. His partnerships with PRS for Music and the Ivors Academy facilitate practical industry connections, though specific student advising relationships and dedicated research grants aren't documented in available sources. His teaching philosophy emphasizes real-world applications through entrepreneurial practices and inclusivity-focused assessments. Gooderson cultivates industry-academia connections through memberships in the Ivors Academy Education Council and PRS for Music. His creative practice involves collaborations with artists like David Sheppard (Snow Palms) and engagement with gaming platforms including Gran Turismo and FIFA. Though no formal research lab is described, his work integrates modular synthesis systems and AI tools within performance contexts, often through university partnerships like the University of Westminster's music programs.