Find Academic Supervisors

Lukas Paul Achatius GalkeView profile

Assistant Professor

• Machine Learning
• Natural Language Processing
• Deep Learning
• +10 more

Lukas Paul Achatius Galke is an Assistant Professor in the Department of Mathematics and Computer Science at the University of Southern Denmark (SDU), where he conducts research in machine learning, natural language processing, and cognitive modeling. He is affiliated with the IMADA research group and holds an ORCID identifier (0000-0001-6124-1092), indicating active academic engagement. His research interests lie at the intersection of deep learning and linguistic cognition, with a focus on emergent communication, language modeling, and graph neural networks. Key areas include compositional language structure, quantization-aware training, and the generalization abilities of large language models across languages. His work combines computational methods with insights from cognitive science and neuroscience. The recent publications (2024–2025) highlight a strong trend in analyzing how neural networks mimic human language learning, assess scientific convergence through bibliographic data, and optimize model efficiency. His work spans both theoretical and applied domains, including automated analysis of research fields and cognitive modeling of language acquisition. Lukas collaborates with a diverse group of researchers and co-authors, indicating active supervision and mentorship. While no formal awards are listed, his publications in high-impact journals like Nature Communications and IEEE Transactions on Engineering Management reflect significant scholarly contributions. He is involved in research teams focusing on data science and AI, particularly within the IMADA group. His work on graph-structured metadata and neural network behavior suggests ongoing projects in scientific intelligence and cognitive AI systems.

Mart LubbersView profile

Assistant Professor

• Task-Oriented Programming
• Internet of Things (IoT)
• Embedded Domain-Specific Languages
• +4 more

Mart Lubbers is an Assistant Professor at Radboud University , focusing on Computer Science . He transitioned from a PhD candidate (2018–2023) and Researcher roles to his current faculty position, with a research emphasis on Task-Oriented Programming and IoT systems . Academic affiliations: Radboud University (current), Netherlands Defence Academy (2017), Max Planck Institute for Psycholinguistics (2013–2015) Teaching: Compiler Construction , New Devices Lab , and Sustainable IoT workshops in SusTrainable summer schools His research bridges Embedded Systems , Functional Programming , and Green Computing , particularly through the development of the mTask framework for IoT orchestration. Publications highlight innovations in DSL design , low-power computing , and tierless language architectures . Current supervision includes PhD candidates Niek Janssen and Benedikt Rips , alongside BSc advisees. He actively contributes to conferences as PC member/chair (TFP, IFL, CompSys).

Partsinevelos PanagiotisView profile

Professor

• Space Informatics
• Geoinformatics
• Satellite Remote Sensing
• +12 more

Professor Partsinevelos Panagiotis is affiliated with the Technical University of Crete's School of Mineral Resources Engineering, where he works in the Mineral Resources Detection and Identification Department. He leads the Laboratory of Geodesy and Geoscience Informatics (SenseLab) and teaches courses including Geographic Information Systems, Technical Geodesy, and Environmental Remote Sensing. His office is located in rooms M3.305 and M1.212, with contact number +30 28210 37628. Research Focus Space Informatics Unmanned Systems (drones, airborne, ground, and sea applications) Machine Learning and Neural Networks Geometric Documentation of Monuments Smart Cities and Precision Agriculture His recent publications demonstrate expertise in UAV-based spatial analysis, environmental monitoring, and geoinformatics applications. Research projects include EU-funded initiatives like 'Space Innovation Analysis' and 'AID4PV PV Plant Diagnosis', alongside national projects such as 'SandMap' and 'Drones 2 GNSS.' Notable Projects Partner PI in EU-funded 'Space Innovation Analysis' (2023) Co-PI in 'AID4PV' UAV-based PV diagnostics (2021-present) Coordinator of 'SandMap' (2020-present) PI for 'UAV Precision Measurements' (2018-2023) Coordinator of Crete-GIS architecture (2017-2019) Leader in ERANETMED 'DESiRES' project Professor Partsinevelos contributes to geospatial innovation through field robotics, satellite technologies, and data science applications, with emphasis on environmental and urban challenges.

Jean Pierre DavidView profile

Professor

• Digital Signal Processing
• Distributed and Concurrent Processing
• Computer Architecture and Design
• +6 more

Jean Pierre David is a Full Professor in the Department of Electrical Engineering at Polytechnique Montréal. He has been with the institution since January 2006, was promoted to Associate Professor in June 2013, and became a Full Professor in June 2021. His research focuses on digital systems design, reconfigurable systems, and hardware implementations of artificial intelligence applications. David received his Electrical Engineering degree (specializing in electronics) from the University of Liège (Belgium) in 1995. He completed his Ph.D. in June 2002 at the Catholic University of Louvain, with research focused on reconfigurable systems (FPGAs). Before joining Polytechnique Montréal, he was a professor at the University of Montreal from August 2002 to January 2006. Jean Pierre David's research spans several key areas in electrical engineering and computer science. His primary focus is on digital systems design, configuration, and programming, with particular expertise in reconfigurable systems such as FPGAs and microcontrollers. He has made significant contributions to Hardware Description Languages (HDL), developing methodologies for fast, safe, and simple design of digital architectures. His work extends to Hardware-in-the-Loop (HIL) simulation, Deep Packet Inspection (DPI) for high-speed communications (10GBE, 40GBE, 100GBE), and applications of digital systems in artificial intelligence, particularly neural network implementations. David's recent research has increasingly focused on energy-efficient AI hardware, RISC-V processor design for neural network acceleration, and specialized architectures for low-precision computation. His publication record shows a clear evolution from foundational work in digital system design and FPGA implementation toward increasingly sophisticated applications in artificial intelligence and neural network acceleration. The most recent publications demonstrate expertise in creating specialized hardware for efficient AI computation, with a strong emphasis on low-precision and binary neural networks that can run efficiently on resource-constrained devices. His work bridges computer architecture, electrical engineering, and artificial intelligence, creating practical hardware solutions for emerging computational challenges. David is affiliated with several important research groups and institutions including the Strategic Microsystems Group of Quebec (ReSMiQ), the Institute of Electrical and Electronics Engineers (IEEE), and the Institute for Data Valorization (IVADO). His work has been recognized through numerous publications in high-impact journals and conferences, with a total of 108 publications to his name. Professor David has supervised an impressive number of graduate students throughout his career, mentoring 9 Ph.D. students and 24 Master's students to completion. His students have worked on diverse topics including FPGA-based neural network acceleration, hardware implementations of deep learning algorithms, energy harvesting systems for IoT devices, and specialized architectures for low-precision computation. His lab appears to maintain strong connections with industry through various research projects and collaborations with researchers like Yves Savaria. His research laboratory focuses on the intersection of hardware design and artificial intelligence, with particular emphasis on creating efficient implementations of neural networks on specialized hardware platforms. The lab maintains strong connections with industry partners and collaborates extensively on projects related to network processing, AI acceleration, and energy-efficient computing systems.

Çağatay EdemenView profile

Assistant Professor

• Communication Theory
• Information Theory
• Visible Light Communication
• +5 more

Çağatay Edemen is an Assistant Professor in the Department of Electrical and Electronics Engineering at Özyeğin University's Faculty of Engineering since 2013. His research spans communication theory, optical wireless systems, and next-generation mobile networks with significant contributions to international standards. Education: PhD in Electrical Engineering, Işık University (2014) Master's in Electrical Engineering, Işık University (2006) Bachelor's in Electrical Engineering, Marmara University (2003) Dr. Edemen's research focuses on information-theoretic aspects of multi-user cooperative communication and visible light communication (VLC) applications . His work bridges theoretical analysis with practical implementations, particularly in optical wireless systems for underwater, terrestrial, and vehicular environments. Recent projects explore reconfigurable intelligent surfaces (RIS) for underwater acoustic communication and VLC for electric vehicle charging infrastructure. His publication portfolio shows strong emphasis on optical wireless communication (35%), multi-user cooperative systems (30%), and 5G/6G mobile technologies (25%), with growing interest in underwater and vehicular applications. The most cited works include his IEEE WCNC 2008 Best Paper Award-winning research on three-user cooperative channels. Awards: IEEE WCNC Best Paper Award (2008) IEEE SIU Communication Society Best Paper Award (2024) IEEE Senior Member (2022) MEVICO Silver Award (2013) Dr. Edemen has secured multiple TÜBİTAK and European research grants totaling over $2M, including projects on UAV-based optical wireless backhaul and underwater VLC networks. He currently supervises 7 graduate students across PhD and MS programs, with research focusing on RIS performance, optical channel modeling, and vehicular networking. As executive director of OKATEM (Centre of Excellence in Optical Wireless Communication Technologies), he leads an international research team developing next-generation communication systems for terrestrial, non-terrestrial, and underwater environments. His CT&T Research Group actively collaborates with NYU Abu Dhabi and industry partners on optical wireless communication standards development, with recent patents pending in electric vehicle communication systems and accident notification technologies.

Barry O'SullivanView profile

Professor

• Artificial Intelligence
• Machine Learning
• Constraint Satisfaction
• +3 more

Professor Barry O'Sullivan is a leading academic and industry expert in artificial intelligence at University College Cork , where he serves as a full professor in the School of Computer Science & IT . He directs the Insight Centre for Data Analytics and the SFI Centre for Research Training in AI, and holds influential advisory roles at Europol's GRACE project, Leuven.ai institute, and the Computational Sustainability Network. His €300m+ R&D funding achievements and 15+ recent publications highlight his technical and societal impact. Co-founder and Chief AI Officer, Stimul.ai Member of University College Cork Governing Body Vice Chair, European Commission High-Level Expert Group on AI Former President, European AI Association His research spans AI ethics , machine learning , and computational sustainability , with recent work focusing on explainable AI for medical diagnostics, energy-efficient scheduling in manufacturing, and human-AI collaboration mechanisms. His publications reveal expertise in constraint programming, anomaly detection, and multilingual reasoning systems. Notable awards include fellowships from AAAI, EurAI, and the Royal Irish Academy, alongside leadership roles in AI policy committees for the European Commission and Irish health research ethics. He leads a team of PhD researchers at UCC, including Marco Dalla and Sharmi Dev Gupta, while directing major European AI initiatives like the ALTAI assessment framework.

Yves WeinandView profile

Professor

• Timber Construction
• Computational Geometry Processing
• Robotic Fabrication
• +7 more

Yves Weinand serves as Full Professor of Timber Construction at École Polytechnique Fédérale de Lausanne (EPFL), where he directs the transdisciplinary Wood Construction Laboratory (IBOIS) within the School of Architecture, Civil and Environmental Engineering. His appointment spans the Institute of Civil and Environmental Engineering (IIC) and teaching units in Architecture (SAR) and Civil Engineering (SGC), reflecting his interdisciplinary focus at the intersection of structural engineering and architectural design. His research expertise spans computational geometry processing, robotic timber construction, and experimental joinery techniques, with particular emphasis on digital fabrication workflows for complex wood structures. Weinand's work integrates augmented reality systems with traditional craftsmanship and develops novel computational methods for timber plate structures and through-tenon connections. Analysis of his recent publications reveals a strong trend toward hybrid digital-physical construction workflows, with increasing focus on AI-assisted fabrication, real-time quality control, and sustainable material utilization. His research bridges fundamental structural mechanics with practical building applications through technology transfer projects. Lignum Prize - Western Region Mention (2018) French Academy of Architecture silver medal (2017) Grand Prix d'architecture de Wallonie for Vidy theater (2019) Wood Distinction 2019 for Vaud Parliament building (2019) Medal for Research and Technique from French Academy of Architecture (2017) Weinand supervises numerous doctoral candidates while teaching ten courses across bachelor to postgraduate levels, including computational architecture and digital timber construction. His laboratory collaborates with industry partners through technology transfer contracts for projects like the Annen Vault, Rossinière Centre, and Brussels Sportstower, implementing EPFL-developed computational methods in real-world construction. The IBOIS laboratory operates as a transdisciplinary research hub connecting architectural design exploration with structural engineering innovation, maintaining active partnerships with timber industry stakeholders and architectural practices across Europe.

• Information Security
• Cybersecurity
• Biometrics
• +3 more

Bian Yang is a Professor at the Department of Information Security and Communication Technology , Norwegian University of Science and Technology (NTNU), Faculty of Information Technology and Electrical Engineering. His research focuses on biometric data protection , privacy-enhancing technologies , and healthcare cybersecurity since joining NTNU in 2008. Education: Ph.D. (2006), M.S. (2002), B.S. (2000) Research Interests: Secure health data management, privacy-preserving computing, human factors in security, secure biometrics, and multimedia security His work includes establishing the eHealth and Welfare Security (eHWS) group under NTNU's Center for Cyber and Information Security (CCIS) in 2016, aiming to bridge cybersecurity and healthcare sectors. Recent publications emphasize privacy-preserving continuous authentication , generative AI in biometrics , and consent management in IoT . Articles span journals like Pattern Recognition , IEEE Access , and Journal of Personalized Medicine , reflecting interdisciplinary applications of machine learning , GANs , and policy frameworks to healthcare security. Scientific Contributions: Over 15 recent publications address topics such as AI-driven stress detection , security for wearable devices , and cybersecurity in low-income healthcare , with a focus on privacy barriers , adaptive protocols , and multi-party data governance .

Luke GesslerView profile

Assistant Professor

• Low-resource NLP
• Language Resource Development
• Endangered Language Documentation
• +10 more

Luke Gessler is an Assistant Professor in the Department of Linguistics at Indiana University, with adjunct appointments in Computer Science, Cognitive Science, and Middle Eastern Languages and Cultures. His research bridges computational linguistics and endangered language documentation, focusing on developing tools and methodologies for low-resource natural language processing (NLP). He previously held a postdoctoral position with the NALA Group at the University of Colorado Boulder and earned his Ph.D. in computational linguistics from Georgetown University, where he collaborated with the Corpling Lab and NERT. His primary research interests include: Low-resource NLP Language resource development NLP-capable language documentation systems Multilingual and cross-lingual modeling Efficient training of language models for under-resourced languages His recent publications (2019–2025) show a strong focus on addressing the performance gap between high- and low-resource languages through innovative algorithmic and infrastructural solutions. Key themes include domain adaptation in machine translation, morphological segmentation with translation assistance, multilingual evaluation frameworks (e.g., PrOnto), and the development of shared software infrastructures to integrate documentary linguistics with NLP. His work often involves multilayer annotation, human-in-the-loop systems, and model efficiency improvements such as in MicroBERT. Luke Gessler is also the webmaster of langdoc.net , a discussion forum for language documentation and technology, underscoring his commitment to community engagement and open scholarship. He actively contributes to major NLP venues including ACL, COLING, LREC, and CoNLL. Scientific Contributions and Collaborations: Developed MicroBERT for efficient training of monolingual BERTs in low-resource settings. Co-created Xposition , a multilingual database of adpositional semantics. Contributed to AMALGUM , a balanced, multilayer English web corpus. Active collaborator with researchers such as Amir Zeldes, Nathan Schneider, and Katharina von der Wense. He advises no listed students in the provided data, but his work has clear implications for training and mentoring in interdisciplinary computational linguistics. He has not received any explicitly mentioned scientific awards, but his consistent publication record in top venues reflects significant scholarly impact. His lab or research team is not explicitly named, but his affiliations with the Corpling Lab, NALA Group, and langdoc.net suggest participation in collaborative, community-driven research environments.

Daniel SchlörView profile

Researcher

• Machine Learning
• Anomaly Detection
• Natural Language Processing
• +5 more

Dr. Daniel Schlör is a researcher at the Chair of Data Science (Informatics X) at the University of Würzburg, with additional affiliations to the CLiGS (Computational Literary Genre Stylistics) research group in Digital Humanities. His work focuses on machine learning for cybersecurity, fraud detection, and explainable AI, with recent projects exploring synthetic data generation, knowledge graph integration, and deep learning for imbalanced datasets. Research interests include Explainable AI (XAI) for anomaly detection Deep learning architectures for domain-specific relationships Multi-agent simulations for fraud scenario modeling Computational stylistics in digital humanities Article trends show expertise in Developing novel neural units (e.g., ModeConv) for structural anomaly differentiation Advancing XAI methods with generative inpainting techniques Creating open ERP datasets for occupational fraud research Applying graph neural networks to water distribution leakage detection Labs & collaborations include the Data Science Chair’s AI Institute at Hubland Nord campus and CLiGS research group for computational literary analysis.

Jun LuoView profile

Associate Professor

• Mobile and Pervasive Computing
• Wireless Networking
• Machine Learning
• +12 more

Jun Luo is an Associate Professor in the School of Computer Science and Engineering at Nanyang Technological University (NTU), Singapore. He earned his PhD in Computer Science from EPFL under the supervision of Prof. Jean-Pierre Hubaux and completed postdoctoral research at the University of Waterloo. He joined NTU in 2008 as an Assistant Professor and was promoted to Associate Professor in 2014. He served as Deputy Director of the Centre for Multimedia and Network Technology from 2010 to 2013. Education: PhD in Computer Science, Swiss Federal Institute of Technology in Lausanne (EPFL), 2006 MS in Electrical Engineering, Tsinghua University, 2000 BS in Electrical Engineering, Tsinghua University, 1997 Research Interests: Jun Luo's research focuses on mobile and pervasive computing, wireless networking, machine learning, and applied operations research. His primary research thrusts include: Contact-free Sensing Driven by Deep Learning : Leveraging RF, acoustic, and visible light signals for human activity recognition, respiration monitoring, and localization without wearable devices. Visible Light Communication and Sensing : Exploring LED-camera systems for data transmission, occupancy inference, and indoor broadcasting. Indoor and Outdoor Localization and Tracking : Developing systems using WiFi, geomagnetism, and crowdsourced data for precise positioning. Machine Learning for Mobile Networking : Applying deep learning and optimization to improve wireless network performance, mobile crowdsensing, and resource allocation. Publication Trends: His recent publications (2021–2023) demonstrate a strong focus on deep learning-enhanced sensing using RF and acoustic signals, particularly for health monitoring (e.g., respiration, heartbeat), multi-person tracking, and privacy-preserving techniques. He frequently collaborates with researchers in signal processing, computer vision, and networking, publishing in top venues like IEEE Transactions on Mobile Computing, MobiCom, and INFOCOM. His work emphasizes practical deployment on commodity devices and integration of sensing with communication systems. Scientific Recognition: IEEE Fellow Advising and Grants: Dr. Luo has advised numerous PhD and Master's students, as evidenced by the extensive list of student co-authors across his publications. He has led significant research projects in wireless sensor networks, mobile computing, and IoT systems, likely supported by competitive grants from Singaporean and international funding agencies. His role as Deputy Director of a research center indicates leadership in managing research teams and collaborative efforts. Labs and Teams: He leads a research group focused on mobile and distributed computing, deep learning, and computer vision. His team actively publishes in top-tier conferences and journals, working on projects involving RF sensing, acoustic platforms, visible light communication, and privacy-aware systems. The group collaborates with researchers both within NTU and internationally, particularly in Canada and China.

Josef van GenabithView profile

Professor

• Language Technology
• Multilingualism
• Machine Translation
• +3 more

Josef van Genabith is a Scientific Director at the German Research Center for Artificial Intelligence (DFKI) and holds the Chair for Translation-Oriented Language Technology at Saarland University . His work focuses on multilingual technologies, machine translation, and natural language processing. Director of Multilingual Technologies at DFKI (2014–present) Full Professor at Saarland University (2014–present) Advisory roles: LINDAT-CLARIN (2010–present), CTYI (2012), NSF Hindi-Urdu Treebank Project (2009) Research Interests include: Development of integrative language processing systems for cross-lingual applications Deep learning for end-to-end language technology (DEEPLEE project) Quality translation frameworks (QT21 project) EU Council Presidency Translator (EUC PT) initiatives Sign language data acquisition via vision-language models His recent projects address challenges in machine translation evaluation, educational technology enhancements, and multilingual data curation.

• Statistical Modelling
• Machine Learning
• AI Evaluation
• +5 more

Line H. Clemmensen is a Professor in the Department of Mathematical Sciences at the University of Copenhagen's Faculty of Science, with additional roles as Co-founder and Chief Scientific Officer at Interhuman AI. Her work bridges academic research and industry applications in statistical modeling and machine learning. Her research centers on statistical modeling and machine learning with emphases on low-resource domains, explainable AI, and fair modeling—particularly within health and life science contexts. Key focus areas include computational statistics, machine learning fairness, and rigorous statistical evaluation of AI systems, addressing critical challenges in data-scarce and high-stakes environments. Analysis of her 2024-2025 publications reveals strong interdisciplinary trends: bioinformatics applications (gene co-expression networks in single-cell genomics), computational psychiatry (OCD and oxytocin interactions), agricultural science (crop health via remote sensing), and critical AI evaluation (facial recognition fairness, LLM biases in STEM education). Her work consistently integrates statistical rigor with machine learning to solve domain-specific problems while prioritizing ethical considerations like fairness and explainability.

• Distributed Systems
• Internet of Things (IoT)
• Edge Computing
• +8 more

Rahim Rahmani is a full Professor at Stockholm University's Department of Computer and Systems Sciences, where he leads the laboratory for Distributed Immersive Participation . His work bridges Distributed Systems , Internet of Things (IoT) , and Healthcare Technology , with a focus on security, edge computing, and immersive systems for societal challenges. Research Interests : Distributed Intelligence, Edge Computing, Blockchain, Extended Reality (XR), Adversarial Machine Learning, and Cognitive Controllers for 5G/6G networks. Teaching : Program Director for the Master's Programme in Computer and Systems Sciences, teaching courses on IoT, Network Security, and Computer Architecture. Publication Trends : His recent work emphasizes secure data sharing in IoV using Blockchain, Federated Learning for healthcare diagnostics (e.g., sepsis and COVID-19 detection), and XR platforms for autism support. Key subfields include Context-Aware Systems , Decentralized Identity Management , and Edge-Cloud Collaboration .

William WangView profile

Professor

• Artificial Intelligence
• Machine Learning
• Natural Language Processing
• +3 more

William Wang is a Professor of Computer Science at the University of California, Santa Barbara (UCSB), holding the Mellichamp Chair in Artificial Intelligence. He leads the UCSB NLP Group, directs the Center for Responsible Machine Learning, and co-directs the Mind and Machine Intelligence Initiative. His research focuses on machine learning, natural language processing, and interdisciplinary data science, emphasizing scalable algorithms for complex datasets. He earned his PhD from Carnegie Mellon University and has mentored numerous students in AI and NLP. Affiliations: Director of UCSB NLP Group, UCSB Center for Responsible Machine Learning, and Mind and Machine Intelligence Initiative. Education: PhD in Computer Science from Carnegie Mellon University, MS from Columbia University. Research interests include statistical relational learning, knowledge representation, and ethical AI. Notable awards include the NSF CAREER Award, IEEE AI's 10 to Watch, and the Karen Sparck Jones Award. His work bridges theoretical foundations and practical applications in AI, with contributions to vision-language models, multimodal reasoning, and generative AI. Key publications span top venues like NeurIPS, ICLR, and CVPR, addressing challenges in LLM reasoning, video generation, and ethical AI systems. He has advised over 15 PhD students and postdocs, many now in academia and industry leadership roles. Labs/Teams: UCSB NLP Group, Center for Responsible Machine Learning, and collaborations on multimodal AI and safety.