جست‌وجوی استاد و پژوهشگر

• Applied Data Science
• Human-centered Artificial Intelligence
• Machine Learning
• +۵ مورد دیگر

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.

Navin Raj Prabhuمشاهده پروفایل

پژوهشگر

• Speech Processing
• Affective Computing
• Machine Learning
• +۳ مورد دیگر

Navin Raj Prabhu is a researcher affiliated with the University of Hamburg under the Faculty of Mathematics, Informatics and Natural Sciences , Department of Informatics, within the Signal Processing (SP) Research Group . He is based in Room F-124 and can be contacted via navin.raj.prabhu@uni-hamburg.de . Research Interests include advanced speech processing techniques, affective computing, Bayesian neural networks, diffusion models, and multimodal analysis of group affect. His work focuses on modeling label uncertainty, emotion conversion in speech, and the dynamics of spontaneous interactions. Publication Trends indicate a strong emphasis on speech emotion recognition with uncertainty quantification, diffusion-based emotion conversion for real-world data, and collective affect dynamics in groups. He leverages self-supervised learning, neural vocoders, and semantic information to enhance emotion recognition systems. Collaborations span researchers like Timo Gerkmann , Nale Lehmann-Willenbrock , and Danilo de Oliveira , with publications in venues such as Interspeech , ACII , IEEE Transactions on Affective Computing , and ICASSP .

• Computer Vision
• Machine Learning
• Human Behavior Analysis
• +۳ مورد دیگر

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
• +۴ مورد دیگر

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 Ostermannمشاهده پروفایل

پژوهشگر

• Artificial Intelligence
• Computer Music
• Algorithmic Music Composition
• +۵ مورد دیگر

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.

• Machine Learning
• Neural Networks
• Neuromorphic Computing
• +۳ مورد دیگر

Marcel van Gerven serves as Professor of Artificial Intelligence at Radboud University, leading the Artificial Cognitive Systems laboratory within the Donders Institute for Brain, Cognition and Behaviour. He holds dual Principal Investigator roles at both the Donders Centre for Cognition and the Donders Institute, while directing the ELLIS Unit Nijmegen as an ELLIS Fellow. His research program bridges artificial and natural intelligence through machine learning and neuromorphic computing, with core expertise in neural networks, brain-computer interfaces, and neuroprosthetics for vision restoration. Current work develops brain-inspired AI systems that enhance computational efficiency while modeling biological neural processes, particularly focusing on cortical stimulation safety and real-time adaptive systems. Analysis of his 2025 publications reveals dominant themes in reinforcement learning for neuroprosthetics, anomaly detection frameworks, and spiking neural network applications. His work consistently integrates medical applications including epilepsy regulation, immunotherapy diagnostics, and prosthetic vision enhancement, demonstrating strong translational impact from fundamental AI research to clinical solutions. Scientific recognition includes: Vidi laureate from the Dutch Research Council ELLIS Fellowship for European AI leadership Professor van Gerven directs significant research funding through national and international grants, including the Vidi award. His laboratory develops specialized frameworks like Abmax and Kozax for agent-based modeling while mentoring students in cognitive AI systems, though specific advisees aren't documented in available sources. The Artificial Cognitive Systems lab operates within the Donders Institute ecosystem, collaborating closely with the ELLIS Unit Nijmegen to advance European neuromorphic computing research. Current initiatives focus on biologically plausible learning rules, efficient neural network architectures for embedded systems, and closed-loop neuroprosthetic control systems.

• Computer Science
• Medical Imaging
• Artificial Intelligence
• +۳ مورد دیگر

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
• +۳ مورد دیگر

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 Vogelaarمشاهده پروفایل

مدرس

• Human-Machine Interaction
• Behavioral Studies
• Affective Computing
• +۷ مورد دیگر

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
• +۷ مورد دیگر

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 Goodersonمشاهده پروفایل

مدرس ارشد

• Music Business
• Artist Development
• Music Production
• +۳ مورد دیگر

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.

Kristy Martireمشاهده پروفایل

استاد

• Forensic Psychology
• Evidence Evaluation
• Criminal Trials
• +۵ مورد دیگر

Professor Kristy Martire is a distinguished academic in the School of Psychology at the University of New South Wales, specializing in forensic psychology and evidence evaluation within criminal justice contexts. Her research bridges psychological science with legal practice, focusing on how lay decision-makers interpret complex forensic evidence. Education: PhD in Forensic Psychology, University of New South Wales, Sydney (2008) Master of Psychology (Forensic), University of New South Wales, Sydney (2008) BA(hons), majoring in Psychology and History, University of Sydney (2004) Professor Martire's research examines the development of expertise, processes of evidence evaluation in criminal trials, and communication between experts and lay decision-makers in forensic settings. Her work employs online crowd-sourcing platforms and electronic questionnaires to assess how evidence characteristics impact belief-updating among international samples of lay decision-makers. She collaborates extensively with legal practitioners, forensic scientists, psychologists, and statisticians to ensure experimental validity and maximize practical implications for the criminal justice system. Her recent publications reveal a strong focus on juror comprehension of statistical evidence, transparent reporting in forensic science, and the psychological mechanisms underlying expertise development. The research consistently addresses critical gaps identified in the 2009 NAS report on forensic science, with particular emphasis on improving the reliability and communication of forensic evidence in legal contexts. Research Funding: ARC Discovery Project (DP220102412) 2022-2024: Misinformation: Evidence evaluation in an alternate fact reality ARC Linkage Project (LP160100008) 2016-2019: Improving the communication of forensic science evidence to courts ARC Discovery Early Career Researcher Award (DE140100183) 2014-2016: Unravelling the golden thread: Presumption of innocence, fair trial, and lay belief change NH&MRC Project Grant (APP1021862) 2012-2015: Reducing smoking in socioeconomically disadvantaged groups Professor Martire actively supervises PhD students interested in forensic evidence validity, lay comprehension of scientific evidence, and expertise development. Her lab culture includes PhD, Masters, and Honours students alongside academics and legal professionals, with weekly meetings to discuss research projects and emerging questions. She teaches courses including Forensic Psychology: Crime, Courts and Corrections (PSYC1027), Psychology & Law (PSYC3301), and Applications of Forensic Psychology (PSYC7402).

Anupam Dasمشاهده پروفایل

دانشیار

• Security and Privacy
• Cyber Security
• Internet of Things (IoT)
• +۶ مورد دیگر

Anupam Das is an Associate Professor in the Department of Computer Science at North Carolina State University and a member of the Wolfpack Security and Privacy Research (WSPR) lab. He serves as the director of the IoT Smarthome Lab and holds a Ph.D. in Computer Science from the University of Illinois at Urbana-Champaign (UIUC), with prior postdoctoral experience at Carnegie Mellon University (CMU). Education: Ph.D. in Computer Science (UIUC, 2016) Das conducts interdisciplinary research in security and privacy, focusing on designing secure and privacy-preserving technologies for the Internet of Things (IoT) era. His work explores privacy-preserving IoT analytics, network traffic analysis for device fingerprinting, and frameworks for controlling information sharing in data analytics. Key areas include cybersecurity, human-computer interaction (HCI), machine learning, signal processing, and public policy. His recent publications address challenges in IoT security, web tracking, and voice assistant privacy. Notable awards include the 12th Annual Privacy Papers for Policymakers (PPPM) Award and two Meta Research Awards. Das actively collaborates with students in the IoT Smarthome Lab and mentors graduate and undergraduate researchers. Scientific Awards 12th Annual PPPM Award winner Meta Research Award for social VR privacy studies Meta Research Award for voice assistant security Das has secured significant grants from the National Science Foundation (NSF) and the Center for Accelerated Real-Time Analytics (CARTA) for projects like collaborative IoT analytics and defense against web tracking. His lab serves as a testbed for IoT device analysis, network traffic studies, and emerging communication protocols.

• Signal Processing
• Communications Engineering
• Biomedical Signal Processing
• +۴ مورد دیگر

Dr. Jesús Bernardino Alonso Hernández is a Professor in the Department of Signals and Communications at the University of Las Palmas de Gran Canaria , affiliated with the College of Engineering . His research spans signal processing, biomedical applications, and communications engineering, with a focus on real-time systems and acoustic analysis. Key research areas include: Speech processing for Alzheimer's detection Neural network design for hardware acceleration Biometric identification via neural signatures Acoustic methodologies for voice and machinery diagnostics Markovian transformations in shape recognition He has supervised doctoral theses in diverse topics such as: Reverse emission techniques for musical instruments Cellophane-based vocal emission analysis Vibration signal monitoring for preventive maintenance Task-independent brainwave identification Markovian contour transformation methodologies

• Data Sonification
• Audio Engineering
• Design Research
• +۵ مورد دیگر

Sara Lenzi is a Lecturer at the Faculty of Engineering, University of Deusto, where she has been employed full-time since 2017. Her academic activities encompass undergraduate and postgraduate teaching, research, and extensive service contributions to the international design and sonification communities. She co-founded the Data Sonification Archive, a leading crowdsourced repository for sonification projects, and serves on the International Advisory Council of the Design Research Society. Her research integrates data sonification, audio engineering, and design methodologies with applications spanning urban soundscapes, healthcare environments, climate adaptation, and AI-driven anomaly detection. Key interdisciplinary themes include: Multimodal data representation through integrated sonification-visualization frameworks Machine listening for analyzing urban and medical soundscapes Design-driven tools for enhancing human-data interaction Transdisciplinary approaches to climate resilience and sustainable infrastructure Recent publications (2022-2025) demonstrate a strong focus on applied sonification in healthcare, environmental science, and cybersecurity. Her work frequently employs AI/ML techniques, emphasizes user-centered design, and addresses real-world challenges like sleep quality analysis and climate adaptation. Theoretical contributions include foundational frameworks for autographic sonification and cross-sensory data mapping. Dr. Lenzi has chaired six international conferences in design research and audio engineering. She has supervised six Master's theses and one PhD dissertation. As Co-Principal Investigator, she contributed to grant applications totaling €1.2M with institutions including City University of Hong Kong and TU Delft. Entrepreneurially, she previously secured €3M in private investment prior to her academic career. She leads initiatives through the Data Sonification Archive and collaborates on projects involving networked sensing systems, participatory design, and sensory ethnography. Her future research directions likely involve scaling sonification frameworks for global environmental monitoring and enhancing AI-driven soundscape analysis tools.