Find Academic Supervisors

Nils HolzenbergerView profile

Assistant Professor

• Natural Language Processing
• Artificial Intelligence
• Legal AI
• +3 more

Nils Holzenberger is an Assistant Professor at Télécom Paris, France, since February 2023, affiliated with the Data Intelligence Graphs (DIG) research team within the Information Processing and Communication Laboratory (LTcI). His work bridges artificial intelligence, natural language processing, and legal domains through neuro-symbolic approaches to statutory reasoning, particularly in tax law. Education: PhD in Computer Science, Johns Hopkins University (2017-2022) Master's in Engineering, Mines ParisTech (2013-2017) Preparatory Classes, Lycée Louis-le-Grand (2011-2013) Holzenberger's research centers on legal artificial intelligence with emphasis on statutory reasoning limitations in large language models. He pioneered the SARA dataset for tax law reasoning and LegalBench benchmark, developing hybrid symbolic-neural frameworks that expose LLMs' shortcomings in precise legal interpretation. His work integrates Prolog solvers with NLP techniques to create executable tax code mappings and contract analysis tools, establishing foundational methods for verifiable legal AI systems. Analysis of his 15 most recent publications (2019-2024) reveals three dominant research thrusts: (1) Tax law reasoning benchmarks exposing LLM hallucinations, (2) Neuro-symbolic integration for statutory interpretation, and (3) Low-resource template extraction for legal documents. His work consistently demonstrates that pure neural approaches fail at precise legal reasoning, necessitating symbolic grounding for reliable legal AI applications. The DIG research team at Télécom Paris, where Holzenberger leads legal AI initiatives, is actively hiring faculty for neuro-symbolic projects. While specific grant details aren't public, his collaborations with HEC Paris, Copilex startup, and featured podcast appearances indicate substantial industry-academia engagement in legal tech development. Holzenberger directs the legal AI vertical within LTcI's DIG team, focusing on data intelligence for statutory reasoning. His group develops tools for tax minimization strategy discovery, contract analysis, and legal information extraction, maintaining close ties with legal practitioners through projects like the Prolog-based tax code interpreter. The team's infrastructure supports both academic research and startup partnerships in computational law.

Benjamin RicaudView profile

Associate Professor

• Machine Learning
• Graph Signal Processing
• Explainable AI
• +3 more

Benjamin Ricaud is an Associate Professor and Group Leader in Machine Learning at UiT The Arctic University of Norway's Department of Physics and Technology. His core affiliations include membership in the Machine Learning Group, Visual Intelligence center, and co-directorship of the Digital Technology Innovation Lab focused on Arctic-region tech startups. He also co-chairs the annual Northern Light Deep Learning conference. Ricaud's research spans: Fundamental ML : Graph signal processing, explainable AI, and generative models Applications : Microfossil classification, medical diagnostics (retinal aging), drug analysis, and climate data interpretation Emerging domains : Self-supervised learning and biological data analysis using Raman spectroscopy His recent publications (2020-2025) cluster in three domains: Graph ML methodologies (35%) Biomedical/biological applications (40%) Geoscience/climate informatics (25%) with consistent focus on interpretability and real-world data challenges. Teaching includes Image Processing (FYS-2010), Pattern Recognition (FYS-3012), and Machine Learning (FYS-2021). He leads outreach initiatives developing AI exhibits for Tromsø Science Centre.

Wei LiuView profile

Associate Professor

• Adversarial Machine Learning
• Generative AI
• Cybersecurity
• +6 more

Wei Liu is an Associate Professor in Machine Learning and Director of the Future Intelligence Research Lab at the University of Technology Sydney's School of Computer Science. He holds a PhD in Machine Learning from the University of Sydney and maintains active roles as a senior IEEE member and area chair for top AI conferences including KDD, AAAI, and ICDM. Education: PhD in Machine Learning, University of Sydney His research focuses on adversarial machine learning, generative AI, cybersecurity, and multimodal learning, with particular emphasis on AI security, robustness of algorithms, and model fairness. Liu's work addresses critical challenges in developing next-generation AI systems that can withstand cyber attacks while maintaining performance with multi-modal data and balanced outcomes despite data imbalances. Analysis of his recent publications reveals a strong trend toward securing large language models against novel attack vectors while advancing multimodal learning techniques. His work spans both theoretical contributions in adversarial frameworks and practical applications in cybersecurity, transportation, and industrial systems. Scientific Awards: 3 Best Paper Awards Most Influential Paper Award at PAKDD Nominee for NSW Premier's Prizes for Early Career Researcher (2017) Liu actively supervises numerous PhD students working on adversarial attacks, robust AI models, and agricultural applications. He has secured substantial funding including ARC Discovery Projects, government grants, and industry partnerships with organizations including Agriwebb, CSIRO Data61, and AVEVA. His Future Intelligence Research Lab specifically targets three emerging challenges: AI security against cyber attacks, robustness with multi-modal data, and model fairness with imbalanced datasets. The Future Intelligence Research Lab produces next-generation AI algorithms addressing AI security vulnerabilities, multi-modal robustness challenges, and fairness issues in real-world deployment scenarios, with multiple representative papers demonstrating practical applications in each domain.

Mark MynettView profile

Senior Lecturer

• Metal Music Production
• Heavy Metal
• Music Technology
• +3 more

Mark Mynett is a Senior Lecturer in Music Technology and Production at the University of Huddersfield, affiliated with the School of Computing and Engineering and the Department of Media, Humanities and the Arts. He holds positions in the Centre for Audio and Psychoacoustic Engineering and the Centre for Research in Music and its Technologies. With a PhD in engineering and a BSc in Popular Music Production, his expertise spans music production, heavy metal genres, and audio engineering. His research focuses on 'heaviness' in metal music production, explored through projects like the 2020-2023 'Heaviness in Metal Music Production' initiative. He is a Fellow of the Higher Education Academy and author of Metal Music Manual (2016), Routledge. Mark has a dual career as an academic and professional musician/producer, with six commercial album releases and collaborations with renowned producers like Jens Bogren. His work includes producing, engineering, and mixing for indie, rock, and metal genres, alongside contributions to publications like Sound on Sound and Computer Music . He actively supervises PhD students and presents at international conferences, including the 2025 Audio Engineering Society Convention. His research outputs emphasize genre-specific production techniques, historical analysis of metal music, and listener perception of sonic elements. Key projects include netnographic studies of online metal communities and investigations into mixing conventions. His work bridges artistic practice with academic rigor, addressing topics like dynamic range management, frequency balancing, and the cultural evolution of metal production. Collaborations span academic and industry contexts, reflecting his commitment to advancing both music education and professional production standards.

• Audio and Speech Processing
• Signal Processing
• Image Processing
• +3 more

Serkan KESER is an Associate Professor in the Department of Electrical and Electronics Engineering at the Faculty of Engineering and Architecture, Ahi Evran University, Turkey. He has been serving as a full-time faculty member since 2018 and previously served as Head of Department from 2018-2021. His educational background includes a PhD in Electrical and Electronics Engineering from Eskişehir Osmangazi University (2009-2018), a Master's degree in the same field from the same institution (2005-2008), and a Bachelor's degree in Electrical and Electronics Engineering from Mustafa Kemal University (1999-2005). Dr. KESER's research focuses on three main areas: Audio and Speech Processing : Including speaker identification, isolated word recognition, and speech coding techniques Signal Processing : With applications in fiber optic sensor systems and acoustic positioning Image Processing : Covering face recognition, image compression, and denoising techniques His recent publications demonstrate a strong trend toward integrating machine learning and deep learning approaches with traditional signal processing methods. He has made significant contributions in applying subspace methods to various domains including speech recognition, image processing, and sensor technologies. His work often bridges theoretical signal processing concepts with practical applications in areas like smart home systems, medical imaging, and environmental monitoring. Dr. KESER has successfully supervised five Master's students to completion, with thesis topics spanning deep learning applications for class attendance systems, photovoltaic systems, speaker identification, brain tumor classification, and speech-controlled robotic arms. He has led four research projects, including development of fiber optic motion sensors, smart home models using Arduino microcontrollers, and science outreach initiatives. His teaching portfolio includes advanced courses in digital image processing, artificial neural networks, digital signal processing, and machine learning at both undergraduate and graduate levels. Dr. KESER's research impact is reflected in his publication metrics: 30 total publications with 117 citations (h-index 5) through the UNIS system, 25 publications with 180 citations (h-index 6) on Google Scholar, and strong representation in Scopus and Web of Science databases.

Armando BarretoView profile

Professor

• Digital Signal Processing
• Biomedical Signal Processing
• Adaptive Digital Signal Processing
• +5 more

Armando Barreto is a Professor in the Department of Electrical & Computer Engineering at Florida International University (FIU). He holds a Ph.D. in Electrical Engineering from the University of Florida (1993), an M.S.E.E. from FIU (1989), and a Mechanical-Electrical Engineering degree from the National Autonomous University of Mexico (1987). His research focuses on digital signal processing with applications in biomedical systems, including EEG analysis, adaptive algorithms, neural networks, and real-time embedded systems. He also explores human-computer interaction through sensor technologies like MEMS, inertial measurement units, and multi-touch interfaces. His work addresses accessibility challenges for visually impaired users through dynamic image precompensation and assistive technologies. Key contributions include innovations in signal denoising, affective computing via pupil dynamics analysis, and multimodal data fusion for early detection of neurodegenerative diseases like Alzheimer’s. His research bridges theoretical signal processing with practical applications in healthcare, virtual reality, and wearable devices. Barreto’s publications span over three decades, demonstrating sustained leadership in sensor-based systems, medical signal processing, and computational neuroscience. He collaborates with interdisciplinary teams to advance technologies addressing clinical and accessibility challenges.

• 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.

Michelle TempleView profile

Associate Professor

• Music Technology
• Sound Studies
• Experimental Music
• +6 more

Dr. Michelle Temple is an Associate Professor at Temple University's Boyer College of Music and Dance, specializing in interdisciplinary research that bridges music technology, disability studies, and acoustic materiality. Her work interrogates the political dimensions of sound measurement standards and advocates for inclusivity in sonic arts. PhD, Rensselaer Polytechnic Institute (2022) MPS, New York University (Interactive Telecommunications Program) BA, Prescott College (Fine Arts) Her research explores the societal implications of audio hardware, focusing on phased arrays, ultrasonic technologies, and piezoelectric materials. She investigates how marginalized communities interact with sound systems and challenges traditional acoustic hierarchies through DIY electronics and collaborative workshops. Temple's artistic output includes international installations like Sonic Spells (2022) and Directional Speaker (2010), supported by institutions such as EMPAC and RPI's Center for Biotechnology. She received the 2021 Malcom S. Morse award for continuing Pauline Oliveros' research. 2021 Malcom S. Morse research enhancement award As a community educator, she develops assistive technologies and conducts workshops on electronics fabrication. Her collaborations include projects with Kathy High and Eric Rosenthal, merging artistic practice with technical innovation.

Esther SayersView profile

Senior Lecturer

• Arts Education
• Skateboarding Culture
• Equity in Education
• +4 more

Esther Sayers is a Senior Lecturer in Education at Goldsmiths, University of London, affiliated with the Department of Educational Studies and the Centre for Arts and Learning. Her work bridges art-based pedagogies, skateboarding culture, and social justice. She leads the MA Arts and Learning programme and supervises PhD students in Education and Arts Practice & Learning. Education: PhD in Education (2014), MA Fine Art (1995), BA Three Dimensional Design (1993), and Art & Design Foundation (1989). Prior to academia, she worked as a gallery educator at Tate Modern and Camden Arts Centre. Research focuses on participatory pedagogies, embodied learning through skateboarding, and equity in arts education. Notable projects include the City Mill Skate project (UCL collaboration) and 'ArtScapers' which reimagines creative learning in schools. Current work explores skateboarding's mental health benefits for marginalized youth and the ethics of risk-taking in motherhood. Grants include Sport England funding for UK skateable space audits and SeNSS/CHASE support for postgraduate research training. She actively engages with skate communities through public talks and media appearances.

• Environmental Art
• Sculpture
• Performance Art
• +3 more

Becky Šik is a Lecturer in Sculpture and Environmental Art at The Glasgow School of Art. Her work spans interdisciplinary practices including sculpture, performance, sound art, and installation, often exploring themes of queer identity, environmental sustainability, and material experimentation. She has exhibited widely in solo and group shows, including notable exhibitions at Collective Gallery Edinburgh, Glasgow Project Room, and Glasgow International Festival. Šik’s research frequently involves collaborative projects with artists, musicians, and curators, as seen in works like Queereal Secretions and Carbon-Mercury: Thinking Through Holes . Her performances and multimedia works challenge conventional boundaries between art forms, emphasizing temporal and spatial engagement with audiences. Šik has presented at festivals such as Supernormal Festival and has produced audio works that blend experimental soundscapes with narrative themes. Her exhibitions often address socio-environmental issues, as seen in Mercury (2021), which interrogates materiality and ecological critique. Collaborations with institutions like GAMIS Glasgow and Collective Gallery highlight her role as a bridge between artistic innovation and community engagement. Šik’s practice is marked by a commitment to queer aesthetics and interdisciplinary dialogue, reflected in her participation in events like T A P E x Invisible Women Present After Hours (2022).

• Machine Learning
• Deep Learning
• Neural Networks
• +7 more

Sung-Bae Cho is a prolific researcher in computer science with a publication record spanning over three decades from 1990 to 2025, demonstrating sustained academic productivity and research leadership. Their work primarily focuses on machine learning, neural networks, and their applications across diverse domains including cybersecurity, healthcare, and intelligent systems. Cho's research interests center around advanced machine learning techniques including deep learning architectures, Bayesian networks, and context-aware systems. Their work shows a consistent evolution from traditional neural networks to modern deep learning approaches, with recent publications emphasizing graph neural networks, federated learning, and anomaly detection systems. The research demonstrates strong methodological rigor with applications in practical domains such as traffic prediction, medical diagnosis, and security systems. The publication pattern reveals significant research productivity with multiple high-impact papers annually in reputable venues including IEEE Access, Neurocomputing, and Expert Systems with Applications. Recent work shows particular emphasis on addressing contemporary challenges in machine learning including continual learning, few-shot learning, and privacy-preserving approaches in federated settings. The research demonstrates both theoretical contributions and practical implementations across various application domains. Cho has cultivated an extensive collaborative network with numerous co-authors including Kyung-Joong Kim, Satchidananda Dehuri, Jin-Hyuk Hong, and Seok-Jun Bu, suggesting leadership in research groups and projects. The collaborative pattern indicates supervision of junior researchers and students, though specific advisee relationships aren't explicitly documented in the publication metadata. The researcher maintains active contributions to laboratory and team-based research, with recent publications indicating involvement in projects related to healthcare AI, cybersecurity systems, and intelligent transportation. Current research directions appear focused on addressing limitations in deep learning including catastrophic forgetting, data scarcity, and privacy concerns through novel architectural and training approaches.

Jukka HäkkinenView profile

Researcher

• Psychology
• Visual Cognition
• Human-Computer Interaction
• +4 more

Jukka Häkkinen is a researcher at the University of Helsinki's Medicum (Faculty of Medicine) and a supervisor in the Doctoral Program in Cognition, Learning, Teaching and Communication. His research focuses on visual cognition, eye movements, virtual reality (VR), immersion, simulator sickness, stereoscopic perception (3D/S3D), and image quality. Key Research Areas : Psychology, Human-Computer Interaction, Cognitive Neuroscience, and Perception Projects : Humor: Human Optimized XR (Business Finland, 2020–2022), PEPI: Post-digital Epistemologies of Photography (2019–2022), Smarter Glasses (2015–2018) Public Engagement : Häkkinen has delivered numerous public talks, including presentations on conspiracy theories, cognitive biases, and virtual reality at events like the Audio Engineering Society Convention. Scientific Awards : Notable accolades include the 2019 IS&T Charles E. Ives Journal Award and a 2018 nominee for Science Book of the Year. His work has generated over 344 research outputs, including datasets like CID2013 and CVD2014, and he has participated in 14 projects since 2001. Teaching & Advising : As a doctoral program supervisor, he contributes to mentoring students in cognition, learning, and communication. He has also reviewed manuscripts, served on committees, and provided expert commentary in media outlets like Yle and Tiedetuubi.fi.

Tove Noorjahaan DaleniusView profile

Senior Lecturer

• Holography
• Direct-Write Digital Holography
• Analogue Holography
• +14 more

Tove Noorjahaan Dalenius is a Senior Lecturer in Applied Holography at De Montfort University, affiliated with the School of Arts, Design and Architecture. She leads the Modern Holography Laboratory and lectures across disciplines including Holography, Fine Art, Video/Moving Image, and Digital Art. Her work bridges artistic practice, technological innovation, and scientific visualization. Education: PhD in Holography, Art and Design, De Montfort University, UK (2023) MSc in Creative Technologies, De Montfort University, UK (2013) BA Hons in Fine Art, Combined Media Arts, Sheffield Hallam University, UK (1993) History of Ideology and Ideas, University of Gothenburg, Sweden (1989) Her research focuses on Direct-Write Digital Holography (DWDH) as an artistic medium, integrating full-parallax digital holography with scientific and medical visualization. She employs practice-based research to explore transdisciplinary intersections between art, technology, and science. Her work enables 3D visualization without glasses, using RGB laser technology and CHIMERA holography systems. She investigates anatomical imaging, data representation, and creative 3D display techniques such as lenticular imaging and Pepper's ghost. Her recent publications and presentations at SPIE Photonics West and the International Symposium on Display Holography reflect a strong trend toward using holography for medical education, public engagement, and data communication. The works often visualize complex anatomical structures or energy data in immersive, accessible formats, emphasizing clarity and artistic expression. She is an active member of SPIE – The International Society for Optics and Photonics – reflecting her engagement with the technical and scientific dimensions of display technologies. Tove supervises PhD students such as Oliver Peacock (holographic Schlieren imaging) and Tal David Stokes (accessible holography), contributing to next-generation research in applied holography. She has led numerous community arts projects since 1999, empowering marginalized youth through digital media and filmmaking. Her research is supported through institutional affiliation with the Research and Innovation Institute (RII) in Arts, Design and Performance. Her recent exhibitions include Emergence at The HoloCenter (New York), Women in Tech (Stockholm), and San Francisco, highlighting the public and educational impact of her holographic art. She is also certified in laser safety, ensuring responsible laboratory practices.

Johannes RegnierView profile

Lecturer

• Electronic Music
• Computer Music
• Digital Signal Processing
• +4 more

Johannes Regnier is a Lecturer at the Central Institute for Continuing Education and Transfer at Berlin University of the Arts (UDK Berlin). He also serves as a Computer Music Designer at IRCAM (Institute for Research and Coordination in Acoustics/Music) and as a research associate at Fraunhofer Institute for Telecommunications, Heinrich-Hertz-Institut, HHI, Vision and Imaging Technologies department. His educational background includes: MA in Sound Studies from the UDK Berlin Ph.D. in Computer Music from the University of California, San Diego Regnier specializes in electronic and computer music techniques, with research interests spanning digital signal processing, sound synthesis, spatial audio, and Wave Field Synthesis. His work bridges the gap between artistic expression and technological innovation, focusing on creating immersive sonic experiences through advanced audio processing techniques. He has developed expertise in live electronics and spatialization, enabling performers to engage with sound in three-dimensional space. His recent creative output shows a consistent focus on interdisciplinary collaborations, particularly at the intersection of music, theater, dance, and visual arts. Regnier's technical expertise in computer music design has been applied across diverse performance contexts, from solo instrumental works to large-scale multimedia productions and film. His spatial audio techniques have become a signature element across his portfolio. Regnier has been actively involved in numerous significant artistic collaborations, working with renowned institutions like IRCAM and contributing to major productions across Europe. His technical expertise has supported performances of works by prominent composers including Pierre Boulez, Roger Reynolds, and Fausto Romitelli. His work spans both academic research and professional artistic production, with strong connections to the Berlin arts scene and international collaborations. Regnier maintains an active presence in both the European and American new music communities, contributing to the advancement of computer music techniques through practical application in performance contexts.

Friedrich Wilhelm RöddingView profile

Visiting Professor

• Musical Acoustics
• Artistic Music Production
• Sound Engineering
• +2 more

Friedrich Wilhelm Rödding is a Visiting Professor and Lecturer at the Robert Schumann University of Music Düsseldorf's Institute for Music and Media. He teaches Musical Acoustics (since 2019) and serves as Visiting Professor for Artistic Music Production (since winter 2021/22). Rödding studied musicology and sound engineering at Detmold University of Music, graduating in 1991. His research focuses on: Advanced audio recording techniques Three-dimensional music reproduction systems Acoustic properties of performance spaces High-fidelity music production workflows Rödding received industry recognition including the German Record Critics' Award and over 30 ECHO Klassik awards , notably the 2015 ECHO for his multi-channel recording of Ravel's Daphnis et Chloé .