Find Academic Supervisors

Gjoreski HristijanView profile

Assistant Professor

• Artificial Intelligence
• Machine Learning
• Healthcare Technology
• +3 more

Gjoreski Hristijan is an Assistant Professor at the Faculty of Electrical Engineering and Information Technologies (Ss. Cyril and Methodius University in Skopje), with expertise in Artificial Intelligence and Machine Learning. He leads research in healthcare technology, focusing on developing intelligent systems using wearable sensors for activity recognition, fall detection, stress monitoring, and elderly care applications. His work bridges theoretical machine learning with practical healthcare solutions. PhD (2015): Jožef Stefan International Postgraduate School, Slovenia MSc (2011): Jožef Stefan International Postgraduate School, Slovenia BSc (2010): Ss. Cyril and Methodius University, Faculty of Electrical Engineering and Information Technologies, Skopje Research interests include: Machine learning for healthcare Wearable sensor systems Activity recognition algorithms Federated learning for privacy-aware applications Smart glasses integration for health monitoring His awards highlight impactful contributions, including first-place wins in international competitions like ChallengeUP (2019) and EvAAL (2013), and the prestigious Best Young Scientist Award (2017) from the President of Macedonia. Recent work focuses on OCOsense smart glasses for facial expression monitoring and real-time health diagnostics. He actively collaborates with institutions like the University of Sussex (UK) and Jožef Stefan Institute (Slovenia), advancing interdisciplinary research in AI and healthcare.

• Audio Signal Processing
• Music Information Retrieval
• Deep Learning
• +3 more

Alexander Schindler is an External Lecturer at the Department of Information Systems Engineering at Technische Universität Wien. His research focuses on audio signal processing, music information retrieval, and deep learning applications in multimodal analysis. He coordinates the approacH project (2010–2013) funded by the European Commission, exploring audio-visual search engines. His academic background includes a Dipl.-Ing. in Technical Engineering and a Dr.techn. from TU Wien. Key research areas include acoustic scene classification, deepfake detection, and music video analysis. He has supervised students on topics like bird song identification and machine outage prediction. Notable publications span from unsupervised cross-modal learning (2020) to multi-modal MIR frameworks (2019). He holds a Bakk.techn. and advanced technical qualifications. Recent work includes advancements in deepfake audio detection (2025) and audio-visual surveillance systems (2024). His projects bridge theoretical research with practical applications in forensic analysis and industrial predictive maintenance. He contributes to international conferences like ACM SAC and DCASE, focusing on neural network architectures for audio analysis.

• Affective Computing
• Natural Language Processing
• Multi-Modal Learning
• +3 more

Dr. Da-eun Ellen Lee is a Research Fellow at Yale University's School of Medicine, specifically within the Child Study Center, collaborating with Dr. Christine Cha. She holds a Ph.D. in Applied Artificial Intelligence from Sungkyunkwan University (2024) and a B.A. in Social Psychology from Sookmyung Women's University (2017). Her research focuses on Affective Computing, leveraging Natural Language Processing (NLP) and Multi-Modal Learning to develop AI-driven healthcare solutions for mental health challenges such as suicide prevention and mood disorder diagnosis. Education Ph.D. in Applied Artificial Intelligence, Sungkyunkwan University (2024) B.A. in Social Psychology, Sookmyung Women's University (2017) Research Interests Dr. Lee specializes in interdisciplinary AI applications for mental health, including: Deep learning models for suicide prevention Mood-aware NLP for bipolar disorder diagnosis Audiovisual feature analysis for depression detection Crisis communication via AI (e.g., fake news detection during pandemics) Key Research Trends Her work bridges computational methods with clinical psychology, emphasizing: Multi-modal fusion of text/audio/video data Temporal symptom tracking for mental health AI ethics in healthcare decision-making Labs & Affiliations She is part of the Cha Lab, focusing on translational AI research for child mental health. Her contributions include: Developing mood-aware multi-task learning frameworks Validating AI models in real-world clinical settings

• Speech Processing
• Machine Learning
• Affective Computing
• +6 more

Professor Julien Epps is the Dean of the Faculty of Engineering at the University of New South Wales (UNSW). He holds a Professorship and previously served as Head of the School of Electrical Engineering and Telecommunications. His academic journey includes roles as Senior Lecturer (2007), Associate Professor (2014), and Head of School (2019). Epps earned his BE (Hons I) and PhD in Electrical Engineering from UNSW in 1997 and 2001, respectively. His research focuses on speech processing, wearable sensor technology, affective computing, and mental health applications. Notable contributions include work on depression detection via speech analysis, multimodal emotion recognition, and the development of tools like the IREye4Task dataset for wearable mental state analysis. He also investigates human-computer interaction through eye-tracking and biometric sensors. Epps' publications (2021–2025) emphasize speech-based health screening, adaptive signal processing for cybersecurity, and the integration of large language models with acoustic landmarks. His work spans engineering education, including a pioneering undergraduate quantum engineering degree program introduced in 2023. His lab and team collaborations include projects on smartphone-based depression screening, real-time task load estimation, and the design of robust spoofing attack detection systems. Epps has led interdisciplinary efforts in healthcare technology and pedagogical innovation in engineering education.

• Speech Processing
• Machine Learning
• Computational Linguistics
• +4 more

Okko Räsänen is a Professor at the Department of Computing Sciences, Faculty of Information Technology and Communication Sciences, Tampere University. His research focuses on speech processing, machine learning, and computational models of child language development. He leads projects involving wearable sensor technology for tracking infant motor development and analyzing audiovisual data in naturalistic environments. Key research areas include automatic transcription of child-centered recordings, affective speech analysis, and zero-resource speech processing. He has contributed to tools like ALICE for automated language environment measurements and Meta-analysis frameworks for computational models. His work bridges signal processing, artificial intelligence, and developmental science, with applications in clinical speech assessment and educational technology. Recent studies explore the reliability of acoustic metrics in longitudinal recordings, the impact of audio quality in child-directed speech research, and the integration of multimodal data for speech emotion recognition. His interdisciplinary approach addresses challenges in natural language acquisition, wearable health monitoring, and cross-lingual generalization of speech models.

• Artificial Intelligence
• Machine Learning
• Constraint Programming
• +4 more

Siegfried Nijssen is a Professor at the Department of Computer Science within the Faculty of Engineering Science at KU Leuven. He is a core member of the Declarative Languages and Artificial Intelligence (DTAI) research group at the Arenberg campus and affiliated with Leuven.AI, the university-wide Institute for Artificial Intelligence. His academic position is designated as 'professor BOF', reflecting a specialized research-focused appointment. Nijssen's research centers on the integration of declarative programming paradigms with machine learning, particularly through constraint programming frameworks. Key focus areas include interpretable rule learning, neural-symbolic integration, and constraint-based optimization for combinatorial problems. His work bridges theoretical computer science with practical applications in bioacoustics, pandemic response modeling, and network analysis, emphasizing transparency and reliability in AI systems. Analysis of his 2021-2024 publications reveals a strong trajectory toward interpretable AI, with significant contributions like RL-Net (combining neural networks with rule-based reasoning) and novel approaches to NP-hard optimization using structured perceptrons. His research consistently targets the intersection of symbolic reasoning and statistical learning, addressing critical challenges in constraint imposition, model explainability, and stochastic optimization. Nijssen currently leads two major research initiatives: 'Declarative Languages for Imposing Constraints on Machine Learning Models' (2025-2027) and the long-term 'Declarative Programming for Machine Learning (DeclaLearn)' project (2025-2035), demonstrating sustained leadership and funding in his specialized domain. These projects extend his foundational work on constraint-based machine learning frameworks. Based at the DTAI research group, Nijssen contributes to KU Leuven's AI ecosystem through collaborative research in logic programming, constraint solving, and data mining. His work with Leuven.AI positions him at the forefront of institutional efforts to advance trustworthy and constraint-aware artificial intelligence systems.

• Computer Vision
• Multimodal Learning
• Action Recognition
• +3 more

Hilde Kuehne is a Full Professor at the University of Tuebingen's Tuebingen AI Center with significant affiliations at MIT-IBM Watson AI Lab, Goethe University Frankfurt, and University of Bonn. Her research leadership spans computer vision, multimodal learning, and artificial intelligence, with emphasis on video understanding and foundational model development. She actively collaborates with IBM Research and MIT across multiple high-impact projects. Her research program focuses on critical challenges in visual intelligence: Developing explainability methods for Vision Transformers to enhance model transparency Creating robust multimodal frameworks for audio-visual alignment and spatio-temporal grounding Addressing representation biases in video benchmarks through structured debiasing approaches Advancing zero-shot recognition capabilities using large language models Exploring associative memory mechanisms for next-generation foundation models Analysis of her 15 most recent publications (2024-2025) reveals three dominant research thrusts: (1) Multimodal foundation models showing strong emphasis on fine-grained audio-visual synchronization, (2) Explainable AI techniques targeting Vision Transformer interpretability, and (3) Systematic debiasing methodologies for video understanding benchmarks. Her work consistently bridges theoretical innovation with practical applications, particularly in instructional video analysis and training-free recognition systems. Key scientific recognition includes: NeurIPS 2024 Oral Presentation for "Convolutional Differentiable Logic Gate Networks" (top 2% acceptance rate) Professor Kuehne mentors a productive research group with notable PhD students including Walid Bousselham (ICCV 2025 first-author), Sivan Doveh (ICCV 2025 first-author), and Nina Shvetsova (CVPR 2025 first-author). Her research is supported through strategic partnerships with IBM Research and MIT, evidenced by consistent co-authorship on high-impact publications. She serves on the Scientific Advisory Board of the Carl-Zeiss-Foundation and contributed to Germany's 2024 Commission of Experts for Research and Innovation annual report. She leads research initiatives within the Tuebingen AI Center and MIT-IBM Watson AI Lab, while co-organizing influential workshops including the 3rd Workshop on What is Next in Multimodal Foundation Models (CVPR 2025) and New Frontiers in Associative Memories (ICLR 2025), demonstrating her leadership in shaping next-generation multimodal AI research directions.

• Music Information Retrieval
• Machine Learning
• Audio Signal Processing
• +3 more

Alicja Wieczorkowska is a Professor affiliated with the University of Gdańsk, specializing in Music Information Retrieval, Machine Learning, and Audio Signal Processing. She has edited multiple books and special issues in journals like Sensors and Springer's Studies in Computational Intelligence series. Her research focuses on audio processing applications in healthcare, music recommendation systems, and environmental monitoring. Education Background: Not explicitly stated in text. Research Interests: Development of audio and image sensing techniques for healthcare and environmental applications Machine learning models for music and speech analysis Data mining and pattern recognition in polyphonic audio Interpretable models for medical survival analysis Recent Research Trends: Her recent work emphasizes practical applications of machine learning in music recommendation systems, medical diagnostics (e.g., liver transplantation prognosis), and environmental pollen monitoring through image processing. She has pioneered methods for audio-based vehicle speed detection and pipe organ acoustics modeling. Labs/Teams: Actively collaborates with interdisciplinary teams including computer scientists, medical researchers, and acoustic engineers. Key collaborators include Z.W. Ras, E. Kubera, and M. Klec.

Roger ZimmermannView profile

Professor

• Multimedia and Distributed Systems
• Multimedia and Mobile Networking
• Spatio-temporal Data Management
• +8 more

Roger Zimmermann is a Full Professor at the School of Computing, National University of Singapore (NUS), where he is also a Co-PI at the Grab-NUS AI Lab and leads the Location AI project. He previously served as Deputy Director of the NUS Smart Systems Institute (SSI) and Co-Director of the Centre of Social Media Innovations for Communities (COSMIC), both funded by Singapore’s National Research Foundation (NRF). Before joining NUS, he was a Research Area Director and Research Assistant Professor at the University of Southern California (USC). Ph.D. in Computer Science, University of Southern California (1998) M.S. in Computer Science, University of Southern California (1994) His research focuses on multimedia systems , spatio-temporal data management , streaming media architectures (especially DASH), machine learning applications , AR/VR , and location-based services . He leads the Media Management Research Lab (MMRL) at NUS, which conducts cutting-edge work in distributed multimedia and intelligent systems. His work combines theoretical depth with real-world applications in urban computing, smart mobility, and immersive media. The recent publications reflect a strong trend toward multimodal learning , spatio-temporal AI , adaptive streaming , and urban intelligence . His team explores zero-shot learning, 3D scene understanding, traffic forecasting, and open-vocabulary audio-visual segmentation, often leveraging foundational models and deep neural architectures. There is a clear emphasis on real-time, scalable systems for smart cities and immersive experiences. Dr. Zimmermann has received numerous accolades, including: DASH-IF Excellence in DASH Award (multiple years) Best Paper Awards at ACM SIGSPATIAL, IEEE ICME, and ACM MMSys Silver Award at ACM MMSys 2020 Grand Challenge IEEE Communications Society Best Editor Award (2017) ACM Distinguished Member (2017) Top 1% Publons Reviewer in Computer Science (2018) He has advised numerous students and led major research initiatives funded by MOE, NRF, A*STAR, NSF, and industry partners like Seagate, Intel, and HP. He has served as General Chair for IEEE MIPR 2023, ACM Multimedia 2020, and IEEE ISM 2015, and as TPC Co-Chair for several top-tier conferences. His editorial roles include Associate Editor for IEEE Transactions on Multimedia (TMM), ACM TOMM, and IEEE OJ-COMS. He leads the Media Management Research Lab (MMRL) , which focuses on intelligent multimedia systems, spatiotemporal data mining, and immersive media technologies. The lab develops scalable solutions for real-world challenges in urban computing, smart transportation, and interactive media.

Mark SantolucitoView profile

Assistant Professor

• Program Synthesis
• Computer Music
• DevOps
• +4 more

Mark Santolucito is an Assistant Professor of Computer Science at Barnard College, Columbia University. He holds a PhD in Computer Science from Yale University, where his research focused on program synthesis and computer music. His current work explores program synthesis techniques to enhance programmer productivity, particularly in lowering barriers to entry for underrepresented groups and optimizing workflows for advanced developers. He leads the Barnard PL (Programming Languages) Labs, which develops tools like TSL (Temporal Stream Logic) for synthesizing reactive systems and analyzing infrastructure-as-code (IaC). His research intersects formal methods with creative applications in music and live coding, emphasizing accessibility and usability. Education: PhD in Computer Science (Yale University), focusing on program synthesis and computer music. Research interests include program synthesis, temporal logic specifications, infrastructure configuration analysis, and music technology. He emphasizes human-centered design in his work, aiming to make programming more accessible through tools like TSL and interactive synthesis environments. Projects include TSL Move Cube, Block-based Editor for Temporal Logic, and Spiral Analysis for medical software migration. Notable contributions include developing TSL synthesis pipelines, optimizing Arduino configurations, and exploring static analysis for cost prediction in cloud deployments. His work bridges theoretical foundations with practical applications in both software engineering and creative computing. Labs/Teams: Leads Barnard PL Labs, collaborating on projects like TSL Synthesis Engine and Static Analysis for IaC. Active in workshops such as SEConfig and FMCAD.

• Natural Language Processing
• Artificial Intelligence
• Data Science
• +3 more

Jorge Turmo Borras es profesor del Departament de Ciències de la Computació en la Universitat Politècnica de Catalunya. Forma part del Grup de Processament del Llenguatge Natural (GPLN) i del Centre de Tecnologies i Aplicacions del Llenguatge i la Parla (TALP). Su investigación se centra en procesamiento del lenguaje natural, inteligencia artificial y minería de datos clínicos. Investigación: NLP, Deep Learning, Salud Digital Colaboraciones: IDEAI-UPC, XarTEC Salut Sus intereses principales incluyen extracción de información de documentos médicos, resolución de anáforas mediante hipergrafos, y normalización léxica de microtextos. Ha publicado sobre modelado de temas para enlazado de entidades y detección de negaciones en textos clínicos. En los últimos 5 años ha trabajado en sistemas de asistencia para diagnóstico , procesamiento de tweets médicos , y redes neuronales para historiales de salud . Sus artículos destacan aplicaciones de Transformers, CRF y modelos híbridos. Patrocinadores: HORIZON 2020, Plan Estatal de Investigación 2021-2023, RIS3CAT. Colabora con investigadores como Alicia Ageno, Lluís Padró y Horacio Rodríguez-Hontoria.

Aidan SlingsbyView profile

Senior Lecturer

• Data Visualization
• Geographic Information Systems (GIS)
• Spatial Analysis
• +5 more

Dr. Aidan Slingsby is a Senior Lecturer in the Department of Computer Science at City, University of London, where he is part of the giCentre Research Centre. He serves as the Director of the MSc in Data Science programme and oversees the department's entire postgraduate offerings. His academic career at City spans over a decade, progressing from Research Fellow to his current position as Senior Lecturer since 2018. His educational background includes: PhD in Geography from University College London (2006) MSc in Geographical Information Science from University of Edinburgh (2002) BSc (Hons) in Geology from University of Edinburgh (2001) Dr. Slingsby's research focuses on data visualization for spatial and temporal data analysis. His work involves creating, designing, applying and evaluating both static and interactive information visualization techniques for data exploration and analysis. He specializes in integrating cartographic principles with information visualization methodologies to address complex analytical challenges. His research spans multiple application domains including insurance risk modeling, population studies, human movement patterns, animal tracking, and ecological monitoring. He has developed innovative visualization techniques such as glyphmaps, tilemaps, and OD maps that enable analysts to explore complex multivariate spatial datasets effectively. His publication record demonstrates a consistent focus on advancing visualization techniques for spatial-temporal data, with recent work exploring intersections with emerging technologies like large language models and advanced train delay modeling. His research shows a clear trajectory from foundational work in cartographic visualization to increasingly sophisticated applications in domains ranging from pandemic modeling to rail performance analysis. Notable scientific achievements include: Best VIS Short Paper award for 'Gridded Glyphmaps for Supporting Spatial COVID-19 Modelling' at IEEE VIS conference Multiple honorable mentions for visualization innovations Efficient Use of Visualization award for network monitoring visualization Dr. Slingsby has successfully supervised multiple PhD students to completion and currently mentors several doctoral candidates working on cutting-edge visualization research. He has secured funding for significant projects including an EPSRC-funded multi-university initiative on visual analytics for combating COVID-19 and industry collaborations with Risk Solutions on rail performance modeling. His professional service includes editorial roles with IEEE VIS and GIS Research UK, demonstrating his standing in the visualization research community. As part of the giCentre Research Centre, he contributes to a vibrant research environment focused on geographic information and visualization. His work bridges theoretical visualization research with practical applications across multiple domains, maintaining strong connections with both academic collaborators and industry partners including Leicestershire County Council and the Willis Research Network.

Florian MüllerView profile

Assistant Professor

• Extended Reality
• Human-Computer Interaction
• Mobile Interaction
• +4 more

Florian Müller serves as Assistant Professor for Mobile Human-Computer Interaction at Technical University of Darmstadt, leading the Urban Interaction Lab. His research pursues seamless integration of digital information, AI agents, and physical environments into unified Extended Reality (XR), with focus on novel interaction techniques for mobile XR and human-AI collaboration through body-centric, tangible, and haptic interfaces. Müller's primary research spans Extended Reality, Human-Computer Interaction, and Mobile Interaction with specialization in Haptic Interfaces, Tangible Interfaces, and Body-Centric Interaction. He investigates how multimodal sensory feedback enhances user experiences in immersive environments, particularly examining AI-driven adaptation for context-aware XR systems and the social implications of pervasive interactive technologies in urban settings. Recent publications (2024-2025) demonstrate strong interdisciplinary trends across multisensory XR (taste modulation, thermal feedback), social VR/AR applications (privacy, content sharing), and human-AI co-creation systems. Key domains include health (motor control assessment), education, and urban interaction contexts, with consistent emphasis on user experience, safety, and practical implementation challenges. Müller directs the Urban Interaction Lab at TU Darmstadt, which develops and evaluates interactive technologies for public spaces through prototyping novel mobile and XR interfaces. The lab's work emphasizes real-world deployment in urban environments, focusing on social engagement, situational awareness, and seamless interaction between physical and digital layers of city infrastructure.

Ovidiu ȘerbanView profile

Research Fellow

• Artificial Intelligence
• Natural Language Processing
• Interactive Systems
• +6 more

Ovidiu Șerban is a Research Fellow at the Data Science Institute, Imperial College London, leading the Data Observatory group. His work focuses on real-time Natural Language Processing, Data Curation, and Large Scale Visualization Systems. PhD in Computer Science (2013) - Joint from INSA de Rouen Normandy and Babeș-Bolyai University MSc in Artificial Intelligence (2009) - Babeș-Bolyai University BSc in Computer Science (2008) - Babeș-Bolyai University Research interests span Artifical Intelligence, Natural Language Processing, Interactive Systems, Affective Computing, and Deep Learning. Recent publications emphasize knowledge graph completion, temporal graph analysis, and multimodal data processing frameworks. Contributed to development of OVE (Open Visualization Environment) for scalable data rendering Created TKGQA dataset for temporal knowledge graph validation Advanced conflict-aware multilingual knowledge graph techniques Projects include SENTINEL for real-time event detection, Watchme for workplace analytics, Intuitel for e-learning enhancement, and Agentslang for distributed interactive systems. Affiliations include Imperial College London, University of Cambridge, and University of Reading.

• Music
• Auditory perception
• Categorization
• +7 more

Pascal Gaillard is an Associate Professor at the University of Toulouse - Jean Jaurès, where he conducts research at the CLLE Lab (Cognition, Languages, Language, Ergonomics, UMR5263) in the "Language and Cognitive Processes" Team, while teaching in the Music Department at the National Higher Institute for Teaching and Education in Toulouse - Midi-Pyrénées (INSPE). His academic career spans over two decades, beginning as an Assistant Professor in Musicology at Université de Toulouse - Le Mirail from 1998 to 2000, before becoming an Associate Professor at the University of Toulouse since 2002. His educational background includes: PhD in Musicology and Auditory Perception (1996-2000) from Université de Toulouse - Le Mirail / Université de Paris - Jussieu Master (DEA) in Musicology - Ethnomusicology (1993-1994) from Université de Toulouse - Le Mirail Maîtrise in Musicology - Ethnomusicology (1989-1990) from Université de Toulouse - Le Mirail Licence in Musicology (1985-1989) from Université de Toulouse - Le Mirail Dr. Gaillard's research focuses on auditory perception and cognitive processes, particularly the categorization of sounds. His work spans multiple domains including musical timbre perception, speech perception in individuals with age-related hearing loss, environmental sound recognition, and auditory processing in deaf individuals with cochlear implants. He has pioneered studies on how humans organize their sound world through categorization, drawing on prototypical categorization theory developed by Rosch (1976). A key insight from his research is that auditory categorization is dynamic rather than fixed, varying according to listener needs, tasks, and action goals. His recent publications demonstrate a strong interdisciplinary approach, bridging music cognition, clinical audiology, and cognitive neuroscience. There's a clear trend toward applied research with clinical implications, particularly for deaf children with cochlear implants, as evidenced by multiple studies on humanoid robots for speech-language training. His work also shows increasing integration of computational approaches, including transfer learning for music preference prediction and ontology-based data management. Dr. Gaillard has secured numerous research grants from prestigious funding bodies including the French National Research Agency (ANR), Occitanie Regional Council, and ANSES. Current projects include "The hospital and its 'Beeps': evaluation of the hearing health of caregivers" (2025-2028), "SILENCE - Comparative acoustics: earth, planets and extreme environments" (2024-2026), and "Rehabilitation of age-related hearing loss: evolution of cognitive load in a 3D virtual environment - AgeHear" (2020-2024). He directs the "Cognition, Behavior and Use" facilities (CCU) which include specialized auditory booths for research. Since 2003, he has developed TCL-LabX, experimental software for conducting categorization studies. His research collaborations span international boundaries, working with labs in France, Canada, and the Netherlands, as well as industry partners in aeronautics and healthcare sectors.