Find Academic Supervisors

• Ontology Engineering
• Knowledge Representation
• Natural Language Processing
• +4 more

Dr. C. Maria Keet is a Professor in the Department of Computer Science at the University of Cape Town, South Africa. With a PhD from the Free University of Bozen-Bolzano (2008), she has established herself as a leading researcher in ontology engineering and multilingual natural language processing, particularly for African languages. Her research interests span Ontology Engineering , Knowledge Representation , Natural Language Processing for African languages (especially isiZulu), Conceptual Data Modeling , and Temporal Data Modeling . She has developed significant frameworks for ontology modularization, competency questions, and multilingual knowledge representation. Her work bridges theoretical computer science with practical applications for linguistic diversity in Africa, addressing the critical need for technology that serves low-resourced languages. Her recent publications (2023-2025) reveal a strong focus on advancing ontology engineering methodologies, improving multilingual language processing capabilities, and developing tools for temporal data modeling. She has pioneered approaches for representing African languages in computational systems, with particular attention to noun classification, verb conjugation, and part-whole relations in Bantu languages. Dr. Keet has made substantial contributions to educational resources in her field, including her 2023 book The What and How of Modelling Information and Knowledge - From Mind Maps to Ontologies , which serves as a comprehensive guide to knowledge representation techniques. She actively supervises graduate students and has developed in-house tools specifically for ontology engineering education, demonstrating her commitment to advancing both research and teaching in her field.

• Accelerator Physics
• Beam Dynamics
• Radio-Frequency Photoinjectors
• +5 more

Thorsten Kamps is a Professor of Physics at Humboldt University of Berlin and Head of the Department of High-Brilliance Electron Beams at Helmholtz-Zentrum Berlin . He is also Deputy Project Manager for the Superconducting RF Electron Accelerator Laboratory SEALAB . Education: Graduate Physicist (Dipl.-Phys.) from TU Dortmund (formerly University of Dortmund), Doctor rerum naturalium (Dr. rer. nat.) from Humboldt University of Berlin Research Interests: Particle accelerator beam dynamics, diagnostics, superconducting radio-frequency photoinjectors (SRF photoinjectors), photocathode growth, instrumentation for bright electron beams, free-electron lasers, ultrafast scattering sources, and future trends of accelerator-driven light sources Expertise: Accelerator physics, beam physics of electron beams, photoinjectors, storage rings, electron beam diagnostics, free-electron lasers, and project management of large-scale accelerator projects Labs/Teams: Involved with SEALAB (Superconducting RF Electron Accelerator Laboratory), BERLinPro (Berlin Energy Recovery Linac Project), and collaborations with institutions like Royal Holloway University of London, DESY, and DELTA/University of Dortmund Publications Trends: Thorsten Kamps’ recent works focus on SRF photoinjectors, beam diagnostics, photocathode development, and thermal load studies. His research spans applications in ultrafast electron diffraction, laser-driven acceleration, and energy-recovery linacs, with collaborations across Europe and the US. Topics include interferometric beam monitoring, multi-alkali antimonide materials, and solenoid alignment for beam control. Teaching: Promotes education in accelerator physics through BSc and MSc courses, and supervises BSc, MSc, and PhD students. Develops practical experiments for internships in accelerator physics.

• Autonomous Systems
• Computer Vision
• Robotics
• +3 more

Markus Enzweiler serves as Professor of Computer Science and Autonomous Systems and Director of the Institute for Intelligent Systems at Esslingen University of Applied Sciences, Germany, within the Department of Computer Science and Engineering. His leadership role positions him at the forefront of intelligent systems research in applied academic settings. His research spans autonomous systems, computer vision, and robotics with emphasis on visual-inertial SLAM, semantic segmentation, and collective perception. Key contributions address real-world challenges in unstructured environments like agricultural fields and urban settings through efficient perception systems. Recent work focuses on lightweight monocular solutions, sensor fusion techniques, and computational efficiency optimization for autonomous vehicles. Analysis of his 2024-2025 publications reveals strong trends in collective perception infrastructure, NeRF/Gaussian Splatting integration for SLAM, and multi-sensor dataset development. His research consistently benchmarks computational costs against accuracy improvements while creating valuable resources like the OPNV public transportation dataset and Rover multi-season SLAM corpus. As Director of the Institute for Intelligent Systems, Enzweiler leads initiatives advancing autonomous driving technologies through practical implementations and industry-relevant research frameworks.

• Energy Applications
• Photo-catalysis
• Electro-catalysis
• +6 more

Dr. Jang Mee Lee is a Research Fellow in the Department of Research & Innovation at RMIT University, Australia. Her research focuses on inorganic nanohybrids for energy applications, including photo/electro-catalysis, secondary batteries, CO2 capture, and mineral processing for critical metals. She is actively involved in developing advanced materials for sustainable energy systems and has collaborated on projects addressing challenges in clean energy and resource efficiency. Her work spans interdisciplinary areas such as nanomaterials synthesis, electrochemical systems, and environmental applications. Key projects include optimizing noble-metal-free nanosheets for catalytic hybridization matrices, studying multifunctional nanostructures for energy conversion, and exploring bio-inspired carbon nitride nanoarchitectures for water splitting. Dr. Lee’s research also addresses practical applications like CO2 capture and lithium-ion battery performance using hierarchical carbon structures derived from unconventional sources like soft drinks and Victorian brown coal. Dr. Lee is open to supervising Masters and PhD students in energy-related fields. Her contributions align with UN Sustainable Development Goals 7 (Affordable and Clean Energy) and 9 (Industry, Innovation, and Infrastructure).

Sarahjasmin FinkelmeyerView profile

Researcher

• Organic Thin Films
• Electrochemistry
• Catalysis
• +7 more

Dr. Sarahjasmin Finkelmeyer is a Researcher at the Leibniz Institute of Photonic Technology (Leibniz-IPHT) , affiliated with the Work group Organic Thin Films and Interfaces under the Research Department Photonics and Quantum Detection . Her work focuses on the interfacial engineering of organic thin films and nanomaterials for optoelectronic and energy-related applications. Her research interests involve Electrochemical behavior of catalyst-coordinated graphene nanoribbons Stabilization of organic solar cells through amphiphilic additives Tuning optical properties via intermolecular interactions Defect-free Langmuir-Blodgett film fabrication Micro-optical devices from hybrid glasses Controlling frontier orbital energies in layered thin films Recent studies highlight her contributions to Hydrogen evolution reaction (HER) catalyst immobilization Morphology stabilization in polymer solar cells Thermal imprinting of ZIF-62 hybrid glasses Quantifying surface-to-bulk interactions in organic semiconductors She collaborates with researchers such as Martin Presselt, Ksenija Glusac, and Jordi Cabana across interdisciplinary projects bridging materials science, electrochemistry, and optical engineering. Her workgroup specializes in Langmuir-Blodgett techniques, spectroscopic analysis, and atomic force microscopy for thin film characterization.

Martin MagnussonView profile

Professor

• Robotics
• Artificial Intelligence
• Autonomous Systems
• +5 more

Martin Magnusson is a Professor at the Department of Natural Sciences and Technology, Örebro University, leading the Center for Applied Autonomous Sensor Systems (AASS) and the Robot Navigation and Perception Lab . His research focuses on robotics and artificial intelligence , particularly 3D mapping, localization, radar-based navigation, and human-robot interaction . Email: martin.magnusson@oru.se Phone: +46 19 303870 Location: Room T1222 His work addresses fundamental challenges in achieving robust autonomy through innovations like the 3D Normal Distributions Transform (3D-NDT) and methods for scan registration in dynamic environments. Recent research extends to radar-based navigation and heterogeneous map data integration , with ethical implications regarding military applications of autonomous systems. Key research themes include: Autonomous Perception: Radar and lidar sensor fusion for localization Dynamic Mapping: Flow-aware and quality-assessed environmental models Human-Aware Robotics: Predictive modeling for safe shared-space navigation Professor Magnusson teaches Computer Graphics , connecting academic principles (e.g., ray tracing, light scattering) to applied research in radar simulation models and neural rendering . His research projects span DARKO (agile production robots), NiCE (changing environment navigation), and Radarize (underground autonomous vehicles).

Karsten DanzmannView profile

Professor

• Gravitational Wave Astronomy
• Laser Interferometry
• Quantum Measurement
• +2 more

Karsten Danzmann is a Professor at Leibniz Universität Hannover and Director of the Max Planck Institute for Gravitational Physics (Albert Einstein Institute) since 2002. He leads the Laser Interferometry and Gravitational Wave Astronomy department, focusing on advanced technologies for gravitational wave detection. Education : Diploma in Physics (1977), Universität Hannover PhD in Atomic and Molecular Physics (1980), Universität Hannover His research interests span gravitational wave astronomy, laser interferometry, quantum measurement, and space-based detector technology. He pioneered key innovations at the GEO600 detector, including squeezed light implementation and high-power lasers, now used in LIGO, Virgo, and KAGRA. He also leads the LISA space mission consortium for low-frequency gravitational wave detection in space. Scientific awards : Honorary Doctorate (RWTH Aachen, 2025) Edison Volta Prize (2018) Princess of Asturias Award (2017) Gruber Prize (2016) Hall of Fame der deutschen Forschung (2019)

Patrick ForréView profile

Assistant Professor

• Causal Inference
• Machine Learning Theory
• Information Theory
• +4 more

Patrick Forré is an Assistant Professor and Lab Manager of the AI4Science Lab at the Informatics Institute, Faculty of Science, University of Amsterdam. His work bridges theoretical machine learning and scientific applications, fostering interdisciplinary collaboration across informatics, mathematics, ecology, chemistry, physics, biology, and astrophysics. His research centers on mathematical foundations of machine learning including causal inference, graphical models, information theory, conditional independence structures, and geometric deep learning. He specializes in applying these techniques to scientific data problems, particularly in electro-catalysis and nitrogen fixation, where machine learning enhances molecular simulations and quantum chemical modeling. His theoretical work addresses non-linear structural causal models with cycles and latent confounders. The AI4Science Lab under his management focuses on detecting hidden patterns in scientific data through projects like electrode-electrolyte interface modeling, nitrogen-fixing coordination complexes analysis, and classical DFT neural approximations. Located in LAB42 Building at Amsterdam Science Park, the lab connects diverse scientific disciplines through machine learning innovation while organizing colloquia, workshops, and PhD defenses.

Michael C. LuView profile

Professor

• Maternal and Child Health
• Public Health
• Health Equity
• +3 more

Dr. Michael C. Lu is currently the Dean of the School of Public Health at the University of California, Berkeley. Previously, he served as Director of the federal Maternal and Child Health Bureau under the Obama Administration, where he received the U.S. Department of Health and Human Services’ Hubert H. Humphrey Service to America Award in 2013. His academic career includes roles as a professor of obstetrics-gynecology and public health at UCLA, with research focused on racial-ethnic disparities in birth outcomes. As an obstetrician, he attended over 1,000 births and was repeatedly named a Best Doctor in America since 2005. He has contributed to National Academy of Medicine committees and co-authored the report Vibrant and Healthy Kids: Aligning Science, Practice, and Policy to Advance Health Equity . His education includes degrees from Stanford University, UC Berkeley, and UCSF. Research interests emphasize improving maternal and child health equity through life-course perspectives and advancing medical imaging technologies. He has pioneered innovations in MRI motion sensing, wireless implant communication, and computational imaging algorithms. His work bridges clinical practice, public health policy, and biomedical engineering. Awards include teaching recognition and federal service accolades. He collaborates on interdisciplinary teams to address global health challenges, leveraging both clinical expertise and technological advancements. Publications span maternal health equity frameworks and cutting-edge MRI methodologies, reflecting his dual focus on societal health disparities and medical technology. Current efforts at UC Berkeley aim to integrate public health strategies with emerging imaging and data science tools. Despite no listed advisees, his mentorship is evident through residency program leadership and training grants in maternal and child health.

• Algorithmic Sequential Decision-Making
• Robust AI
• Safe AI
• +11 more

Ilija Bogunovic is an Assistant Professor (UK Lecturer) in the Department of Electronic and Electrical Engineering at University College London (UCL), Faculty of Engineering Sciences. His research focuses on algorithmic sequential decision-making for robust, reliable, and safe artificial intelligence, with applications in large language model alignment, reinforcement learning, and human feedback integration. His research interests lie at the intersection of machine learning, optimization, and decision theory. He investigates robustness in Bayesian optimization, bandits, and reinforcement learning under adversarial attacks, model misspecification, and distributional shifts. His work spans both theoretical foundations and real-world applications in energy systems, mobility, and AI safety. A central theme is developing algorithms that are provably robust and efficient in uncertain and potentially corrupted environments. The recent publications highlight a strong trend in robust decision-making, particularly in reinforcement learning and preference optimization. Key themes include adversarial robustness in large models, distributional robustness in RL, safe multi-agent systems, and robust Bayesian optimization. The work frequently involves theoretical analysis of regret and sample complexity, combined with practical algorithm design for high-impact applications. Scientific Awards: Google Research Scholar Program Award (Machine Learning & Data Mining), 2023 EPSRC New Investigator Award, 2023 Ilija Bogunovic actively supervises PhD and MSc students, with several of his advisees leading papers accepted to top venues like NeurIPS and ICLR. He has secured competitive research grants, including the EPSRC New Investigator Award, to support his work on robust decision-making. He is building a research team focused on robust and safe AI. He leads a research team and has initiated an online reading group on modern adaptive experimental design and active learning, fostering a collaborative research environment.

Barbara WhiteView profile

Professor

• Contemporary Composition
• Interdisciplinary Collaboration
• Music and Embodiment
• +10 more

Barbara White is Professor of Music at Princeton University, where she has been teaching since 1998. She is a distinguished composer with decades of experience in concert music and interdisciplinary collaboration, and an accomplished clarinetist with special interest in shakuhachi (Japanese bamboo flute) and Cape Breton musical traditions. Her research interests span contemporary composition, the relationship between music and everyday experience, gender studies in music, traditional music practices, and the intersection of art with text, movement, and cultural context. She has developed innovative courses on topics including 'outsider music,' interculturalism and exoticism, autobiography and masquerade, and the relationship between specialized art-making and everyday experience. White's scholarly work explores the coordination between sound and movement, the relationship between creative activity and everyday life, and the impact of gender, listening, and spirituality on music. Her compositions range from intimate solo and chamber works to larger projects like the opera 'Weakness,' which premiered in 2012 and examines themes of female sovereignty and bystander complicity through a Celtic myth adaptation. Fromm Music Foundation Commission recipient (2017) Extensive work with shakuhachi performer Riley Lee Duo collaborations including 'Farewell to Music' and 'Fork & Spoon' Development of unique performance practices integrating contemplative and creative elements As a dedicated educator, White serves as composition teacher and dissertation advisor, cultivating both open-mindedness and rigor in her students. Her teaching philosophy emphasizes receptive listening to young composers' ideas while developing their technical and conceptual skills. She has hosted visiting artists like Riley Lee in her seminars, culminating in full concerts of student works.

Theo ScholtesView profile

Researcher

• Quantum Magnetometry
• Dark Matter Detection
• Atomic Physics
• +3 more

Dr. Theo Scholtes is a researcher in the Quantum Systems department at the Leibniz Institute for Photonics (Leibniz-IPHT), specializing in quantum magnetometry and dark matter detection. He leads the Quantum Magnetometry working group and has developed advanced optically pumped magnetometer (OPM) systems for ultrasensitive magnetic field measurements. Key research areas: Quantum magnetometry, dark matter detection, sensor technology Notable collaborations: Global Network of Optical Magnetometers for Exotic physics searches (GNOME) His work focuses on overcoming technical limitations in OPMs, including dead zones, heading errors, and transient phase responses. He has pioneered innovations like the light-shift dispersed Mz mode, omnidirectional magnetic field sensitivity, and passivated gold mirror integration in alkali vapor cells. Recent publications (2023-2025) address vector magnetometry in Earth's magnetic field, dead-zone-free sensors, and domain wall detection for dark matter. Earlier work (2016-2020) includes laser stabilization techniques and magnetorelaxometry in biomedical contexts.

Amelia Carolina SparavignaView profile

Assistant Professor

• Image Processing
• Liquid Crystals
• Solid State Physics
• +11 more

Amelia Carolina Sparavigna is a Tenured Assistant Professor at the Department of Applied Science and Technology (DISAT), Politecnico di Torino . Her research spans image processing , liquid crystal physics , and thermal conductivity in solids, with interdisciplinary applications in archaeology , cultural heritage protection , and environmental studies . She leads the Image Processing for Physics and Environmental Studies research unit, applying fractional calculus and variational methods to satellite imagery analysis. Research Themes : Mesophase transitions in liquid crystals, entropy-based image analysis, thermal modeling for energy/environmental applications (phase change materials, solar radiation simulation), and satellite archaeology (geoglyphs, desert kites, ancient settlements). Academic Contributions : Over 15 years of publications on thermal conductivity of diamond, nematic-smectic phase transitions, and electromagnetic shielding textiles. Recent work focuses on κ-statistics for epidemiology and entropy in image analysis. Labs & Collaborations : Member of the Institute of Fundamental Physics and Materials for Nanotechnology and Generalized Statistical Mechanics of Complex Systems research group at DISAT. Teaching : Delivers Fisica II (Physics II) for Computer Engineering at Politecnico di Torino.

Dieter De WitteView profile

Professor

• Artificial Intelligence
• Data Science
• FAIR Data
• +3 more

Prof. Dieter De Witte serves as a Professor at Ghent University, dedicating 50% of his time to the Internet Technology and Data Science Lab (IDLab) while simultaneously contributing 50% to the Royal Museums of Fine Arts Belgium (RMFAB) in Brussels through a FED-tWIN mandate from Belspo. At RMFAB, he spearheads the strategic overhaul of digital infrastructure toward FAIR-compliant and data-driven systems, while at Ghent University he collaborates with Prof. Steven Verstockt on applied AI projects across heritage, mental healthcare, and education domains. His academic foundation includes a Master's in Engineering Physics from Ghent University (2008) followed by doctoral research on Big Data technologies and FAIR data for life sciences. Prior to returning to academia in 2021, he gained industry experience as an AI consultant and team lead at Telenet and Ordina. De Witte's research centers on AI-driven transformation of cultural heritage through FAIR data publication , collection enrichment , and intuitive querying interfaces . His technical expertise spans multimodal algorithms, image segmentation, pose estimation, large language models (LLMs), and semantic technologies including SPARQL and IIIF. Current projects focus on human-in-the-loop AI systems that combine diverse AI building blocks for practical heritage applications. Analysis of his 15 most recent publications reveals a clear trajectory from early bioinformatics work (2007-2018) on genomic motif discovery and life sciences data infrastructure toward contemporary cultural heritage applications (2023-2024). Recent outputs demonstrate innovative fusion of pose estimation, linked data frameworks, and multimodal AI for museum contexts, highlighting increasing specialization in AI enrichment of digital collections while maintaining core expertise in FAIR data principles. His FED-tWIN grant enables critical knowledge transfer between academic research and cultural heritage institutions, supporting development of next-generation digital infrastructure at RMFAB. Current projects involve creating AI tools for intuitive collection exploration and systematic enrichment of heritage assets through advanced computational methods. De Witte operates within Ghent University's Internet Technology and Data Science Lab (IDLab), participating in interdisciplinary teams developing applied AI solutions. His work bridges technical innovation with practical implementation in cultural institutions, focusing on sustainable, interoperable systems that enhance public access to digital heritage collections through cutting-edge AI interaction paradigms.

Markus TatzgernView profile

Professor

• Augmented Reality
• Virtual Reality
• Mixed Reality
• +3 more

Dr. Markus Tatzgern serves as Professor for Mixed Reality and Game Development and Head of Research at the Department for Creative Technologies, Salzburg University of Applied Sciences. He leads the Digital Realities Lab, a multi-disciplinary research group focused on human-centered solutions at the intersection of software, design, and creative engineering for technology interaction. His academic foundation includes a Master of Science and Doctorate with highest distinction from Graz University of Technology. He furthered his expertise through post-doctoral research at the Christian Doppler Laboratory for Handheld Augmented Reality, recognized as a world-leading facility in mobile AR. Tatzgern's research spans Augmented Reality, Virtual Reality, Mixed Reality, and Human-Computer Interaction with emphasis on practical applications. His work addresses critical challenges in view management, haptic feedback systems, industrial procedure visualization, medical imaging interfaces, and game development. Key contributions include novel techniques for eye-perspective rendering, breathing-based VR interaction, and latency compensation in medical robotics. His publication trajectory reveals evolving focus from foundational AR visualization (2010-2016) toward applied XR solutions in healthcare, industrial training, and user experience optimization (2017-2024). Recent work demonstrates increasing integration of physiological inputs, real-world safety applications, and enterprise-grade XR prototyping. Scientific recognition includes: Honorable Mention for Best Paper at CHI 2022 for 'AirRes Mask' breathing interface Honorable Mention for Best Paper at IEEE 3DUI 2017 for adaptive rendering techniques As Head of Research, Tatzgern actively shapes the field through program committee roles and peer review for top venues including ACM CHI and IEEE VR. His four patents in AR visualization demonstrate translational impact from academic research to practical applications. The Digital Realities Lab operates as an innovation hub where software engineers, designers, and creative technologists collaborate on next-generation interaction paradigms. Current projects emphasize medical XR applications, industrial training systems, and accessibility-focused AR solutions for vulnerable populations.