Find Academic Supervisors

• Artificial Intelligence
• Computer Learning and Pattern Recognition
• Audio and Image Processing
• +5 more

Prof. Dr. Rifat EDİZKAN is a distinguished Professor at Eskisehir Osmangazi University's Faculty of Engineering-Architecture, Department of Electrical and Electronics Engineering. He currently serves as the Head of Department since 2023 and previously held the position of Vice Rector from 2019-2022. With a career spanning over three decades at the university, he has progressed from Research Assistant to his current professorship. His educational background includes: Bachelor's Degree: Anadolu University (1987) Master's Degree: Anadolu University (1990) PhD: Eskisehir Osmangazi University (2000) Prof. EDİZKAN's research focuses on cutting-edge areas in artificial intelligence and signal processing. His work primarily centers around Artificial Intelligence, Computer Learning and Pattern Recognition, with specific expertise in Audio and Image Processing, Pattern Classification, and Digital System Design. His research has evolved from traditional pattern recognition techniques to modern deep learning applications across diverse domains including medical imaging, agricultural technology, and robotics. An analysis of his recent publications reveals a strong trend toward practical AI applications, particularly in healthcare technology (fall detection systems, medical imaging), agricultural monitoring (plant disease detection), and industrial automation. His work demonstrates a consistent evolution from theoretical pattern recognition to applied machine learning solutions addressing real-world problems. Prof. EDİZKAN has served as Principal Investigator on numerous research projects, including several TUBITAK-funded initiatives. His leadership extends beyond research to academic administration, having served as Department Head twice and as Vice Rector. He has also been actively involved in peer review activities for journals and conferences, and has served on academic promotion committees. His laboratory work appears to focus on practical implementations of AI systems, with recent projects involving elderly care robots, plant disease detection systems, and industrial monitoring solutions. His research group seems to maintain strong industry connections, particularly in the automotive and healthcare sectors.

Emrah IrmakView profile

Associate Professor

• Biomedical Image Processing
• Signal Processing
• Medical Image Registration
• +3 more

Assoc. Prof. Emrah Irmak serves as Associate Professor and Deputy Head of Department (2020-2023) at the Department of Electrical and Electronic Engineering, Rafet Kayış Faculty of Engineering, Alanya Alaaddin Keykubat University, where he currently holds board membership (2024). Previously, he was Doctor Lecturer and Research Assistant at Karabük University's Department of Biomedical Engineering (2012-2019). His academic credentials include: Doctorate in Electrical-Electronic Engineering, Karabük University (2018) Master's in Electrical and Electronics Engineering, Gaziantep University (2014) Bachelor's degree, Opole University of Technology (2011) Dr. Irmak's research pioneers biomedical image processing with applications in brain tumor progression analysis, cancer diagnosis, and Alzheimer's disease detection. His work integrates deep learning with traditional image processing techniques for medical applications, while extending to tribocorrosion analysis of orthopedic implants and IoT systems for healthcare and mining safety. Key innovations include 3D medical image registration frameworks and novel convolutional neural network architectures for automated pathology detection. His publication trajectory demonstrates consistent advancement in applying AI to critical healthcare challenges, with recent focus on multi-modal diagnostic systems for cancer and neurological disorders through integration of image processing, signal analysis, and deep learning. His scientific recognitions include: Distinction in Scholarship in Physiological Genomics (2020) TÜBİTAK Publication Incentive Award (2021) The World's Most Influential Scientists (Stanford University, 2023 & 2024) Dr. Irmak has supervised Master's students Mustafa Burak Turköz (3D medical image registration for brain tumors) and Ergun Ercelebi (medical image registration for tumor growth analysis), and served as main jury for five graduate thesis defenses at Karabük and Gaziantep Universities. His research projects include: National project on Tribocorrosion Properties of Titanium Implants (2021-2022) National project on 3D Medical Image Registration for Brain Tumor Evolution (2017-2018) He actively contributes to scientific advancement as reviewer for Current Medical Imaging, Health Informatics Journal, Plos One, and Turkish Journal of Electrical Engineering and Computer Sciences, while maintaining international collaboration through Erasmus exchange programs.

• Parallel Programming
• GPU Programming
• Image Processing
• +5 more

Sándor Szénási is a Professor at the Department of Informatics within the Faculty of Economics and Informatics at J. Selye University, where he serves as the person responsible for the Applied Informatics study program. With over two decades of academic experience, he has established himself as a leading researcher in parallel programming, GPU programming, and image processing, with recent expansion into machine learning applications. Eötvös Loránd University, Faculty of Science and Informatics (2001-2004): Information technology teacher Budapest Polytechnic, John von Neumann Faculty of Information Technology (1997-2001): B. Engineer in Information Technology Óbuda University (2010-2013): PhD in Applied Informatics Habilitation at Óbuda University (2019): Information Science and Technology Professor inauguration at Óbuda University (2022) Szénási's research spans computational methods with practical applications across multiple domains. His early work focused on parallel and GPU programming for image segmentation and heat transfer simulation. More recently, he has integrated machine learning techniques with traditional computational approaches, particularly in metaheuristic optimization, speech processing, and inverse problem solving. His interdisciplinary research bridges computer science with transportation safety, manufacturing, and medical applications. His recent publications reveal a clear evolution toward hybrid computational approaches that combine machine learning with traditional algorithms. There is a strong emphasis on optimization techniques, particularly metaheuristics enhanced with machine learning components. His work spans diverse application areas including speech emotion recognition, autonomous vehicle control, additive manufacturing, and heat transfer simulation, while maintaining a core focus on computational efficiency and parallel processing. Szénási has been actively involved in multiple EFOP-funded research projects including 'Improvement of higher education institutes for better teaching quality and accessibility,' 'Dynamics and control of autonomous vehicles,' and 'Solving the Inverse Heat Conduction Problem with Machine Learning.' His collaborative work with researchers like Gábor Kertész, Zoltán Vámossy, and Imre Felde demonstrates his commitment to interdisciplinary research.

• Robotics
• Auditory Perception
• Multimodal Perception
• +3 more

Sylvain Argentieri serves as an Associate Professor at the Institute for Intelligent Systems and Robotics (ISIR) within Sorbonne University, where he has been a member of the ASIMOV research group ("Architectures and Models for Adaptation and Cognition") since 2008. His office is located at ISIR, Campus Pierre et Marie Curie, 4 place Jussieu, BC173, 75005 Paris. He maintains an active research presence with publications spanning from 2006 to 2024. His educational background includes obtaining the highest teaching diploma in France (Agrégation externe) in Electrical Engineering from the École Normale Supérieure in 2002, followed by a Ph.D. in Computer Science from Paul Sabatier University (Toulouse) in 2006, and his Habilitation à Diriger des Recherches (HDR) in 2018. Argentieri's research focuses on artificial perception in robotics with special emphasis on auditory perception, active multimodal perception approaches, and sensorimotor integration. His work demonstrates a consistent trajectory exploring how robots can develop perceptual capabilities through sensorimotor experiences, with particular attention to binaural sound localization, head movement control, and the emergence of spatial representations from uninterpreted sensory signals. His publications reveal a strong interest in how naive agents can structure their understanding of the world through active exploration and sensory prediction. His publication record shows a clear progression from foundational work on sound source localization to more complex investigations of sensorimotor contingencies and embodied cognition. The research spans multiple venues including IEEE conferences, Robotics and Autonomous Systems, and Frontiers journals, with recent work incorporating transformer architectures and neural network approaches to auditory processing. Argentieri maintains active collaborations with researchers including Nicolas Obin, Bruno Gas, and Valentin Marcel, as evidenced by his co-authored publications. His laboratory work takes place within the ASIMOV team at ISIR, which focuses on architectures and models for adaptation and cognition in robotic systems.

• Embedded Machine Learning
• Neural Architecture Search
• Signal Processing
• +4 more

Christopher Ringhofer serves as a Researcher and PhD candidate at the Intelligent Embedded Systems department within the Faculty of Engineering and Computer Science at the University of Duisburg-Essen since April 2020. His work focuses on developing energy-efficient AI solutions for embedded platforms with current projects funded by the German Federal Ministry of Education and Research. He earned his BSc in Applied Informatics (2017) and MSc in Distributed Dependable Systems (2020) from the same institution, following three years of industry experience in IoT development at ithinx GmbH. His doctoral research centers on automated neural architecture search for signal processing on constrained devices. Ringhofer's research explores evolutionary algorithms for constructing latency-optimized neural networks targeting microcontrollers and embedded FPGAs, with primary applications in digital audio processing for studio/live environments. His work bridges hardware constraints with deep learning requirements through techniques like precomputed convolutional layers and hardware-aware NAS. He actively contributes to academic instruction through the Bachelor's course 'Embedded Systems' and specialized student projects on 'AI-based Neurosignal Processing', maintaining consistent teaching involvement since Winter Semester 2020/21. Current research projects include 'TransfAIr: Transfer Approaches for Artificial Intelligence in Industry' (since May 2024) and previous work on 'LUTNet' and 'KI-LiveS' initiatives. His technical contributions focus on the IoT Garage infrastructure and Elastic AI ecosystem development for pervasive computing environments.

Toral RuizView profile

Assistant Professor

• Machine Translation
• Computational Linguistics
• Natural Language Processing
• +3 more

Dr. Toral Ruiz is an Assistant Professor at the University of Groningen 's Faculty of Arts . Her research focuses on Machine Translation , Computational Linguistics , and Natural Language Processing , with particular emphasis on translation quality assessment, literary adaptation, and ethical automation frameworks. Expertise: Machine Translation, Computational Linguistics, NLP, Translation Quality, Literary Adaptation, Ethical Automation Contact: a.toral.ruiz@rug.nl Her recent work explores speech-text discrimination , tokenization strategies , and lexical diversity in literature . She contributes to the European Association for Machine Translation and collaborates on projects like LT-LiDER for digital literacy in translation. Press engagement includes discussions on machine translation limitations in creativity and cross-lingual literary reception . Notable collaborations include studies on Catalan/Dutch translation reception and sustainability frameworks for translation automation.

Stefan BalkeView profile

Researcher

• Music Information Retrieval
• Deep Learning
• Jazz Music Analysis
• +3 more

Stefan Balke is a Researcher at the AudioLabs Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), focusing on wind music research and Music Information Retrieval (MIR). Previously, he held a PostDoc position at the Institute of Computational Perception, JKU Linz (2018–2019) and later worked as a Data Scientist and Team Lead in industry. He temporarily served as a professor at Hochschule Weserbergland in 2023/24. Education: PhD (Dr.-Ing.) in MIR from FAU (2018), Electrical Engineering studies at Leibniz Universität Hannover (2008–2013). Research Interests: Music Information Retrieval, Deep Learning, Jazz and Wind Music Analysis, Dataset Development. His work includes creating datasets like ChoraleBricks (wind music) and JSD (jazz structure analysis), and tools like trackswitch.js for audio visualization. Grants & Projects: Secured 35k€ funding for his orchestra through the 'Engagiertes Land' program (2024). Collaborated on exhibits such as 'The Listening Machine' (Ars Electronica) and 'Con Espressione!' (mathematics of music exhibition). Contributions: Active on GitHub with repositories for datasets (choralebricks, jsd) and tools. Co-developed web-based audio tools and contributed to open-source projects like Sonic Visualiser and librosa.

Bassam ShaerView profile

Professor

• Embedded System Design
• VLSI Design and Test
• Digital Signal Processing
• +5 more

Dr. Bassam Shaer is a Professor in the Department of Electrical and Computer Engineering at the University of West Florida (UWF), affiliated with the Hal Marcus College of Science and Engineering. He has over 20 years of experience in academia, with a focus on embedded systems, VLSI design, and distance learning methodologies. Shaer earned his Ph.D. and M.S. in Electrical Engineering from the University of South Florida and a B.S. from the University of Florida. Educational Background: Ph.D. in Electrical Engineering, University of South Florida (Tampa, FL) M.S. in Electrical Engineering, University of South Florida (Tampa, FL) B.S. in Electrical Engineering, University of Florida (Gainesville, FL) Research Interests: Dr. Shaer's work spans embedded system design , VLSI testing and partitioning , analog/digital electronics , and distance-learning laboratory development . His innovations include energy conservation systems, robotic ball collection mechanisms, and intelligent tracking telescopes. He emphasizes practical applications of microcontrollers and FPGA-based solutions. Teaching: Shaer teaches courses such as Electronic Circuits , VLSI , Digital Computer Architecture , and Microprocessor Applications , integrating hands-on lab experiences. He has developed interactive distance-learning systems to enhance accessibility in engineering education. Professional Contributions: He has reviewed over 10 academic journals and contributed to projects like solar array positioning systems and LabVIEW-based plotter controls. His research often bridges theoretical engineering principles with real-world applications in robotics, renewable energy, and automated systems. Labs/Teams: He collaborates with initiatives like the Science Olympiad and Science Showcase , promoting interdisciplinary engineering education and innovation.

Isabella C.F.S. CondottaView profile

Assistant Professor

• Precision Agriculture
• Machine Learning in Animal Husbandry
• Environmental Sustainability
• +3 more

Dr. Isabella C.F.S. Condotta is an Assistant Professor at the University of Illinois, holding affiliations in Animal Sciences, Agricultural and Biological Engineering, and the Center for Digital Agriculture at the National Center for Supercomputing Applications (NCSA). Her interdisciplinary research focuses on applying technologies like computer vision, audio processing, and machine learning to enhance animal welfare, reduce environmental impact, and improve precision management in animal agriculture. Her work emphasizes continuous monitoring of animal behavior, health, and well-being through automated systems. Recent contributions include advancements in facial recognition for cattle, AI-driven decision-making frameworks, and the application of depth cameras for livestock monitoring. These efforts aim to provide farmers with actionable insights for efficient management practices. Condotta’s research intersects computer science, engineering, and animal science, reflecting her role in bridging technological innovation with agricultural challenges. While no specific grants or awards are listed, her collaborative projects with institutions like FAPESP and the ASABE Annual Meetings highlight her active engagement in the academic community.

Gabriel BibbóView profile

Research Fellow

• Audio Signal Processing
• Sound Event Detection
• Embedded AI
• +4 more

Gabriel Bibbó is a Research Fellow in Sound Sensing at the University of Surrey's CVSSP, part of the Faculty of Engineering and Physical Sciences. He holds a BSc in Electrical Engineering from Universidad de la República (Uruguay) and an MSc in Sound & Music Computing from Universitat Pompeu Fabra (Spain). His research focuses on audio signal processing, embedded AI systems, and ethical AI applications in sound environments. Prior to academia, Gabriel worked in industry roles including R&D Engineer at Ikatu/Bang & Olufsen, Technical Support Engineer at Google, and IT Auditor at KPMG. His practical engineering background combines with DJ experience to inform his work on sound event detection, real-time AI tools, and sustainable AI model compression. Key research areas include developing real-time sound recognition systems (e.g., PiSoundSensing device), creating residential audio datasets (Sounds of Home), and exploring AI-driven workplace soundscapes. His work emphasizes real-world deployment challenges on embedded hardware like Raspberry Pi, addressing thermal management and microphone quality issues. Bibbó collaborates on EPSRC-funded 'AI for Sound' projects and supervises postgraduate students like Sam Watts. His future directions include interactive music generation and AI ethics in sound technologies.

Berker BanarView profile

Researcher

• Generative Deep Learning
• Music Composition
• Artificial Intelligence
• +3 more

Berker Banar is a Researcher and PhD student at Queen Mary University of London's School of Electronic Engineering and Computer Science, affiliated with the Centre for Digital Music (C4DM) under the Music Informatics Research theme. His research focuses on developing generative deep learning systems for composing contemporary classical music, emphasizing interpretable control over musical attributes and expanding aesthetic boundaries through novel compositional methods. He explores intersections of AI, music generation, and artistic creativity, with applications in co-creative tools and potential robotic musicianship. His work spans technical innovations such as variational autoencoders, latent space regularization, and neuro-symbolic approaches, alongside philosophical inquiries into creativity and machine learning ethics. He has presented tutorials on transformer-based music generation and published extensively on generative AI in music, sound event detection, and adaptive acoustic systems. Berker's research portfolio demonstrates a focus on both technical advancements in machine learning architectures and their artistic implications, aiming to bridge computational methods with creative expression. His publications reflect a commitment to interdisciplinary exploration at the nexus of computer science, musicology, and AI ethics.

• Artificial Intelligence
• Machine Learning
• Audio Signal Processing
• +3 more

Shubhr Singh is a Visiting Professor at Queen Mary University of London's School of Electronic Engineering and Computer Science, based in the Peter Landin building (Room CS 403). His research focuses on artificial intelligence, machine learning, audio signal processing, and their applications in bioacoustics and financial technology. He has contributed to advancements in graph neural networks (GNNs) for audio identification and classification, few-shot learning for bioacoustic event detection, and AI-driven innovations in banking systems. His work spans interdisciplinary areas such as perceptual music similarity metrics, intelligent audio control systems, and agricultural studies on crop growth optimization. Singh collaborates on international challenges like the DCASE series, exploring cutting-edge methods in sound event detection and domain adaptation. Key research trends in his publications include leveraging GNNs for complex audio analysis tasks, addressing data scarcity via few-shot learning, and integrating AI into real-world applications like neobanks and agricultural sustainability. His articles reflect a blend of theoretical advancements and practical implementations across diverse domains.

Koen HindriksView profile

Professor

• Artificial Intelligence
• Robotics
• Human-Robot Interaction
• +4 more

Prof. Koen Hindriks is a Full Professor at Vrije Universiteit Amsterdam, affiliated with the Faculty of Science's Department of Artificial Intelligence. He holds additional roles at the Network Institute and Social AI research groups. His research focuses on social robotics, human-robot interaction, and multi-agent systems, with applications in healthcare, education, and retail. He leads projects like SOROCOVA (social robotics for educational support post-COVID) and Droomrobot (a robot companion for pediatric medical stress reduction). Key research interests include dialogue management, empathy elicitation via robots, and long-term human-robot relationships. He teaches courses such as Multi-Agent Systems and Socially Intelligent Robotics. Recent work emphasizes ethical AI integration and practical deployment of social robots in real-world scenarios. His contributions span over 220 publications, addressing topics like robot learning from human demonstrations and automated speech processing in HRI contexts. Projects: Principal Investigator for SOROCOVA (2021-2023) Robot Companion for Childhood Cancer Patients (2019-2021) Current Droomrobot project (2024-2026) Awards: None explicitly listed, but recognized through extensive peer-reviewed contributions. Advising: Supervised 4 PhD theses, focusing on child-robot interaction and healthcare robotics. Prof. Hindriks also engages in ancillary roles, including Director at Ex Machina (Utrecht) and speaker at the Amsterdam Speakers Academy. His work aligns with UN SDGs, particularly in improving health and education through technology.

Sajjad Bagheri Baba AhmadiView profile

Lecturer

• Multimedia forensics
• Multimedia Security
• Information Hiding
• +7 more

Sajjad Bagheri Baba Ahmadi is a Lecturer in Computing at the University of the West of Scotland, affiliated with the School of Computing, Engineering and Physical Sciences. Previously, he served as a research assistant at Nanjing University of Science and Technology and a lecturer at IAU University. His research focuses on cybersecurity, artificial intelligence, and multimedia forensics, with specialties in anomaly detection, watermarking, and machine learning applications. He actively contributes to academic communities through editorial roles, conference reviews, and workshop organization. His academic journey spans from high school through a PhD, consistently centered on computer science and digital technology. Key research interests include multimedia security, pattern recognition, and signal processing. He has published in top-tier venues such as Applied Intelligence , Annals of Telecommunications , and The Visual Computer , with notable work on blind dual-color image watermarking and IoT anomaly detection. Award-winning research includes a highly cited paper on robust watermarking schemes (64 citations) and contributions to contextual anomaly detection methodologies. He currently mentors PhD students and advises on immersive learning technologies, reflecting his commitment to education and innovation. His work bridges theoretical advancements and practical applications, addressing challenges in cybersecurity, multimedia authentication, and AI-driven solutions for modern digital systems.

Vincent LostanlenView profile

Researcher

• Machine Learning
• Audio Content Analysis
• Computer Music
• +11 more

Vincent Lostanlen is a Researcher (chargé de recherche) at CNRS, affiliated with the LS2N laboratory located at Centrale Nantes engineering school. He also serves as a visiting scholar at New York University. His research focuses on developing artificial intelligence for audio analysis, emphasizing machine listening systems, bioacoustic monitoring, and computer music. Key projects include the BirdVox platform for avian migration monitoring and the Kymatio Python library for wavelet scattering transforms. Education: Ph.D. in Mathematics from École Normale Supérieure (2017), supervised by Stéphane Mallat; MSc in Acoustics & Music Informatics from Université Pierre-et-Marie-Curie (2013); Dipl. Ing. in Signal Processing from Télécom ParisTech (2013). Research interests span audio representation learning, environmental sensor networks, and generative models for sound. Notable contributions include the SONYC-UST urban sound dataset and the PCEN signal frontend for robust acoustic sensing. Scientific awards include the 2019 AFIM Young Researcher Award, 2016 SFA Best Poster Prize, and 2015 IEEE MLSP Best Paper Award. He advises graduate students across computer science and music technology domains, with former advisees working in academia and industry. Current activities include developing green AI for audio processing and collaborating on bioacoustic sensor networks for biodiversity monitoring. His work bridges signal processing theory with practical applications in music, ecology, and biomedical engineering.