Find Academic Supervisors

• Medical Physics
• Radiation Oncology
• Proton Therapy
• +8 more

Prof. Katia Parodi is a Professor at the Chair of Medical Physics within the Faculty of Physics at Ludwig Maximilian University of Munich. Her research focuses on advanced imaging techniques for particle therapy, including proton and ion beam applications. She leads studies in proton computed tomography (CT), ionoacoustic monitoring, and radiobiological assessments of high-dose-rate therapies like FLASH radiotherapy. Her work integrates cutting-edge technologies such as photon-counting CT, machine learning for dosimetry prediction, and novel contrast agents for multimodal imaging. She also explores applications of radioactive ion beams for precision oncology and has pioneered the SIRMIO small-animal irradiation platform for pre-clinical research. Key research interests include improving proton beam range verification through bremsstrahlung imaging, optimizing particle therapy with AI-driven algorithms, and understanding biological effects of ultra-high dose-rate irradiation. She collaborates on international projects, including FLUKA code development for radiation simulations and European initiatives on privacy-protected patient data analysis. Her publications highlight advancements in in-beam PET systems, molecular dynamics simulations of radiation effects, and the development of 3D-printed phantoms for proton CT. Current efforts emphasize translating AI innovations into clinical practice for personalized radiotherapy.

Volker SkwarekView profile

Professor

• Cybersecurity
• Distributed Systems Security
• IoT and Sensor Networks
• +3 more

Prof. Volker Skwarek is a Professor for Technical Informatics at the Department of Industrial Engineering and Management, Hamburg University of Applied Sciences. He leads the research group DLT³-Hamburg and heads the research and transfer center for digital business processes. His work focuses on cybersecurity in distributed systems, IoT security, and blockchain applications in industrial environments. He has held leadership roles including Deputy Head of the Department HWI and coordinates the graduate focus area 'Information Science'. Research interests include ultra-low-power IoT systems, self-sovereign identity frameworks, and secure blockchain-sensor integrations. Prof. Skwarek has authored over 30 peer-reviewed articles, 2 books, and multiple patents. He actively contributes to international standardization of blockchain technologies and serves on editorial boards for distributed ledger journals. His projects span smart grid security, decentralized energy trading systems, and forensic blockchain analysis. Notable projects include WaterGridSense 4.0 (smart grid resilience), LegalBlock (legal frameworks for smart contracts), and TrustedCamApp (secure video streaming in sensor networks). He emphasizes practical applications alongside theoretical advancements, collaborating closely with industry partners to address real-world challenges in digital security and automation. Prof. Skwarek holds national/international awards for research and education innovation. His teaching includes courses on technical informatics, programming in C, and cybersecurity fundamentals. He advocates for continuous professional development through academic-industry partnerships and standardization initiatives.

• Computational Semantics
• Multilingual Representation Learning
• Low-Resource NLP
• +4 more

Goran Glavaš is a Professor at the University of Würzburg, holding the Chair for Natural Language Processing (Informatik XII) within the Faculty of Mathematics & Computer Science, and a member of the Center for Artificial Intelligence and Data Science (CAIDAS). His research focuses on computational semantics, multilingual/low-resource representation learning, and NLP applications in social sciences/humanities. He previously held roles as Assistant Professor at the University of Mannheim and Interim Associate Professor at LMU Munich. Glavaš earned his doctorate in 2014 from the University of Zagreb under Jan Šnajder. Research Interests: Glavaš explores fair and sustainable NLP, multilingual system development, and cross-lingual adaptation. His work emphasizes resource-poor language support, ethical AI practices, and bridging NLP with humanities/social sciences. Recent studies include multilingual hallucination detection, geographic LLM adaptation, and news recommendation systems. Recent Trends in Publications: His 2024 work spans multilingual models (e.g., NLLB-LLM2Vec), cross-lingual news recommendation (MANNeR), and code analysis (IRCoder, which won ACL’s Outstanding Paper Award). Earlier 2023 contributions include multilingual dialogue systems (Multi2WOZ) and simplified neural encoders for news recommendation. Awards: 2024 ACL Outstanding Paper (IRCoder), 2024 EACL Outstanding Paper (Kardeş-NLU) Advising & Labs: Leads the WüNLP research group at CAIDAS. His lab focuses on democratizing NLP through open-source tools and cross-disciplinary collaborations. No current advisee list is provided, but past roles suggest active mentorship in multilingual NLP domains.

Kai PolstererView profile

Research Professor

• Astroinformatics
• Machine Learning
• Data Science
• +4 more

Dr. Kai Polsterer is the Scientific Director of the Heidelberg Institute for Theoretical Studies (HITS) since January 2025, succeeding Tilmann Gneiting. He has been a leading researcher at HITS since 2013, heading Europe's first astroinformatics research group. His work focuses on developing machine learning methods to handle exponential data growth in astrophysics, including projects like the ERC Synergy Grant for universe mapping and exascale computing initiatives (SPACE). He collaborates across disciplines with statisticians and physicians to advance weather forecasting and cardiological analysis. Polsterer is actively involved in international organizations such as the International Astroinformatics Association (IAIA, President since 2023), IEEE Task Force on Mining Complex Astronomical Data, and the German Physical Society's AI working group. His research spans astroinformatics, data science, and interdisciplinary applications of AI. His expertise includes photometric redshift estimation, galaxy classification, and unsupervised learning techniques. Polsterer has pioneered tools like PINK (Parallelized Kohonen Maps) and contributed to projects like the LUCIFER instrument for near-infrared spectroscopy. He emphasizes open science and collaborative frameworks, such as the HiPS ecosystem for astronomical data visualization. His current role as Scientific Director reflects his leadership in advancing theoretical studies through computational and data-driven approaches. Polsterer’s contributions include over 50 peer-reviewed articles, spanning astroinformatics, machine learning applications in astronomy, and computational methodologies. He advocates for interdisciplinary research, bridging astrophysics, computer science, and medical data analysis. His ERC Synergy Grant project exemplifies his commitment to large-scale, collaborative science addressing fundamental questions in cosmology and data science.

Patrick L. KohlView profile

Research Fellow

• Social Insect Ecology
• Animal Behavior
• Honeybee Conservation
• +3 more

Dr. Patrick L. Kohl is a Postdoctoral Research Fellow at the Department of Population Genomics in Farm Animals at the University of Hohenheim (Walter Benjamin Program, DFG). His research focuses on the ecology, evolution, and behavior of social insects, particularly honeybees (Apis spp.), with an emphasis on understanding wild-living populations' dynamics and the impacts of domestication. He completed his doctoral studies in Biology at the University of Würzburg (2019–2023), funded by the German Federal Environmental Foundation, where he investigated wild honeybee colony demographics and parasite burdens in Germany. His academic journey includes a Master's in Biology (University of Würzburg, 2015–2018), with a thesis on alpine pollination ecology, and a Bachelor's in Biosciences (University of Münster and Universidad de Granada, 2011–2014), focusing on honeybee drone production. He conducted comparative behavioral studies on Asian honeybees at India's National Centre for Biological Sciences (2015–2018). Dr. Kohl's current project, funded by the German Research Foundation (DFG), explores genomic signatures of modern beekeeping practices in Apis mellifera. This work investigates whether domestication pressures have driven functional genetic differences between wild and managed populations, impacting fitness and conservation strategies. His publications span topics like honeybee communication (waggle dance analysis), parasite ecology, and habitat preferences, with a focus on Central European forests and semi-natural landscapes. He has contributed to high-impact journals such as Proceedings of the Royal Society B , Oecologia , and Trends in Ecology & Evolution . Scientific contributions include identifying reduced parasite loads in feral colonies and demonstrating how landscape features influence overwintering survival. His work bridges evolutionary biology, applied ecology, and conservation policy, emphasizing the need for sustainable apiculture practices.

• Neurotechnology
• Cognitive Neuroscience
• Neurophysiology
• +3 more

Prof. Dr. Jacob Simon is a Neurologist and Neurophysiologist at the Technical University of Munich (TUM) , where he holds the Professorship for Translational Neurotechnology since 2019. His research focuses on the neuronal basis of cognitive functions like perception, memory, and language, bridging experimental neuroscience with clinical applications. MD from Yale University (2006) Postdoc at Tübingen's Center for Integrative Neuroscience Board-certified Neurologist Prof. Jacob's lab investigates how populations of neurons in cognitive brain centers (prefrontal cortex, parietal cortex, basal ganglia) interact to produce intelligent behavior. Using techniques like large-scale extracellular recordings , optogenetics , and invasive human neurotechnology , his team explores neuromodulation, particularly dopamine's role in cognitive circuits. Their translational approach connects findings from mice to human neurosurgical patients , aiming to improve treatments for cognitive disorders. Recent publications highlight breakthroughs in understanding striatal dopamine signals (2024), primate prefrontal cortex geometry (2023), and human microelectrode array recordings (2023). The lab has received multiple ERC grants (2017, 2024) and contributes to TUM's Innovation Network NEUROTECH (2021-2026). Scientific Recognition: ERC Consolidator Grant (2024) Early Excellence Academy Fellow (2023) ERC Starting Grant (2017) Charité Clinical Scientist Award (2014) Prof. Jacob actively teaches in TUM's Neuroengineering and Medical Life Sciences programs while leading a vibrant team of neuroscientists, engineers, and clinicians. His work appears in top journals like Nature Neuroscience , Science Advances , and Neuron , with a focus on understanding cognition's cellular basis for clinical applications.

• Cognitive Science
• Psychology
• Computational Modeling
• +5 more

Andrew Perfors is a Professor of Psychology at the University of Melbourne, leading the Complex Human Data Hub and the Computational Cognitive Science Lab within the Melbourne School of Psychological Sciences . His research focuses on applying quantitative methods to understand higher-order cognition, including concepts, language, decision-making, and misinformation dynamics. He employs computational models and experimental approaches to investigate how cognitive constraints and social environments shape human behavior at individual and group levels. Education: PhD in Brain & Cognitive Sciences (MIT, 2008), MA in Linguistics (Stanford, 2000), BSc in Symbolic Systems (Stanford, 1999). Research Themes: Cognitive modeling of decision-making, cultural evolution, language acquisition, and misinformation spread. His work bridges computational methods with psychological experimentation, exploring topics like sampling assumptions in reasoning, trust in information sources, and the cognitive basis of gender categorization. Key Projects: Active grants include Understanding Information and Trust (2018–2025), Bridging the Meaning Gap (2023–2027), and Anti-trans Disinformation Campaigns analysis. Recent work addresses misinformation mitigation strategies and the cognitive naturalness of trans-inclusive gender categories. Grants & Collaborations: Funded by ARC and NHMRC. Collaborations include work on contact-tracing technologies during the pandemic and cross-cultural studies on privacy calculus. Labs & Teams: Directs the Complex Human Data Hub, focusing on societal challenges like misinformation and cultural dynamics. Leads interdisciplinary teams in the Computational Cognitive Science Lab, integrating psychology, computer science, and linguistics.

Eamonn BellView profile

Assistant Professor

• Digital Humanities
• History of Computing
• Digital Musicology
• +4 more

Eamonn Bell is an Assistant Professor in the Department of Computer Science at Durham University, specializing in digital humanities with a focus on the history of digital audio media, particularly the CD format and early computational applications in music studies. He holds a PhD in Music Theory from Columbia University (2019), where he completed his dissertation on 'The Computational Attitude in Music Theory' under Joseph Dubiel, and previously earned a joint honors degree in Mathematics and Music from Trinity College Dublin (2013). His research interests span digital humanities, media archaeology, and the intersection of computing history with musicology. Bell has conducted significant research on the CDLink platform developed by The Voyager Company, exploring how early web technologies enabled time-synchronized interactions with audio CDs. His scholarly work examines the historical development of digital audio formats, particularly the Compact Disc, and their cultural implications. Bell's publications reveal a consistent focus on the materiality of digital media, particularly how the CD format transformed music consumption and production. His research analyzes the technical specifications of CD encoding (including interleaving and error correction), the historical context of CD adoption, and the cultural narratives surrounding digital audio. He has also written extensively about AI's relationship to musical knowledge and creativity, situating contemporary discourse within historical contexts of computational approaches to music. Government of Ireland Postdoctoral Fellowship (2019) for 'Opening the 'Red Book': The digital Audio CD format from the viewpoint between musicology and media studies' Research funded by UK Research and Innovation (UKRI) and the Irish Research Council Bell maintains an active scholarly presence through his personal website and academic publications, contributing to ongoing conversations about digital preservation, media archaeology, and the historical understanding of contemporary digital music practices. His work bridges technical understanding of media formats with critical cultural analysis.

• Programming Languages
• Program Verification
• Software Security
• +5 more

Gilles Barthe is a scientific director at the Max Planck Institute for Security and Privacy in Bochum, Germany, and a part-time research professor at the IMDEA Software Institute in Madrid, Spain. His research focuses on programming languages, program verification, cryptography, privacy, and mathematical logic, with an emphasis on applying relational verification to cryptographic systems. He leads the Foundations of Security and Privacy Group, developing techniques to ensure cryptographic software is secure against timing and side-channel attacks, and verifies implementations of post-quantum algorithms like Kyber. Barthe has pioneered tools such as EasyCrypt for formal proofs, and contributed to standards like AWS Key Management Service verification. His work bridges theory (e.g., quantum verification) and practice (e.g., compiler optimization for constant-time code). Education: Ph.D. in 1993 from University of Manchester, followed by roles at INRIA, IMDEA, and others. Research Interests: Secure compilation, cryptographic protocol verification, differential privacy, and quantum programming semantics. Recent articles explore secure compilation against Spectre variants, quantum relational logics, and formal verification of lattice-based cryptography. He has advised over 20 PhD/Master’s students, many contributing to cryptographic verification frameworks. Barthe’s teams collaborate on high-assurance implementations, masking techniques, and mitigating modern microarchitecture threats. His lab’s work includes PanCast for epidemic risk mitigation and certified cryptographic libraries.

• Computer Vision
• Control Theory
• Biomedical Image Analysis
• +5 more

Zhen Tang is an active researcher with a prolific publication record spanning from 2007 to 2025, primarily in computer science and engineering disciplines. Their work appears consistently in high-impact venues including IEEE Access, IEEE Transactions, and major conferences in computer vision and systems engineering. Research interests span computer vision, control theory, biomedical image analysis, machine learning, and multi-agent systems. Tang's work demonstrates a strong interdisciplinary approach, connecting theoretical control systems with practical applications in medical imaging, distributed computing, and emerging technologies like DNA computing. Recent publications show a growing interest in large language models, blockchain applications, and ethical considerations in technology design. The publication trends reveal an evolution from foundational work in image processing and pattern recognition (2010-2015) to more complex systems involving multi-agent control and deep learning (2016-2020), and most recently expanding into large language models, blockchain healthcare applications, and technology ethics. The research shows strong connections between theoretical control systems and practical applications across multiple domains. Zhen Tang has established long-term collaborations with researchers including Yanli Wan, Zhenjiang Miao, Wei Wang, and others, suggesting stable research group affiliations. The consistent publication output across 18 years indicates an established academic career with significant contributions to multiple subfields within computer science and engineering.

• Computer Science Education
• Culturally Responsive Computing
• Augmented Reality for Education
• +4 more

Dr. Wei Yan is an Associate Professor in the Department of Computer & Information Science & Engineering at the University of Florida's Herbert Wertheim College of Engineering. With an extensive publication record spanning computer science education, augmented reality applications, and culturally responsive computing, Dr. Yan has established a significant research presence with numerous publications in top-tier conferences and journals from 2023-2025. Dr. Yan's research interests focus on culturally responsive computing education, particularly with Indigenous communities including the Navajo Nation. Their work bridges the gap between technical computing concepts and culturally relevant pedagogy, with a special emphasis on spatial reasoning and mathematics education through augmented reality technologies. The research program has produced innovative AR classroom applications that help students understand complex spatial transformations and matrix algebra through interactive 3D visualizations. Through collaborations with researchers like Ashish Amresh, Paige Prescott, Maya Israel, and Heather Burte, Dr. Yan has developed several educational technology interventions that address inclusion in computer science education. Their publications reveal a strong commitment to broadening participation in computing, particularly among underrepresented groups, with a focus on teacher professional development and curriculum design that respects cultural contexts. Dr. Yan's technical expertise spans both educational technology development and core computer science topics, as evidenced by publications ranging from spatial reasoning in AR classrooms to advanced topics in integer coding and neural image compression. This interdisciplinary approach allows for the development of sophisticated educational tools grounded in solid computer science principles. Current research directions include AI-enhanced educational applications, culturally responsive computing curriculum development, and the integration of conversational AI with augmented reality for improved learning experiences. The work has significant implications for how computing education can be made more accessible and relevant to diverse student populations.

• Vision-Language
• Multimodal Deep Learning
• Semantic Segmentation
• +3 more

Philipp Jonas Rösch is a Researcher and PhD Candidate at Bundeswehr University Munich, holding the position of Research Head for Artificial Intelligence at the Institute for Distributed Intelligent Systems (VIS). He is affiliated with the Chair of Data Science under Prof. Michaela Geierhos at the Research Institute CODE. He holds a Master's degree in Statistics from Ludwig-Maximilians-Universität München and has industry experience prior to academia. His research focuses on Vision-Language systems, Multimodal Deep Learning, and Damage Recognition, with notable contributions to datasets like dacl10k and InpaintCOCO . He co-organizes the Machine Learning Interest Group (MLIG), a platform for researchers at UniBw M and HSU focusing on ML/AI topics. Rösch manages the GPU cluster 'Monacum One' and advises students on Bachelor's/Master's theses in Deep Learning or Vision-Language domains. His work emphasizes real-world applications in infrastructure inspection and military vehicle detection, leveraging both academic and industrial perspectives.

• Computer Vision
• Machine Learning
• Rail Transportation Systems
• +5 more

Yu Qian is a Professor in the Department of Electrical and Computer Engineering at the University of Southern California's Viterbi School of Engineering, with extensive research contributions spanning computer vision, machine learning, and transportation systems. Their work bridges theoretical advances with practical applications, particularly in railroad safety and ferroelectric computing architectures. Research interests focus on computer vision applications for transportation safety, including railroad inspection systems and pedestrian behavior analysis at grade crossings. Additional expertise includes ferroelectric computing-in-memory architectures, micro-expression recognition, and multimodal sentiment analysis. The research program demonstrates strong interdisciplinary collaboration across engineering disciplines. Recent publications (2023-2025) show a clear trend toward hardware-algorithm co-design for specialized computing, particularly using ferroelectric devices for optimization problems, alongside continued work in transportation safety applications. The research spans both theoretical contributions to neural network architectures and practical implementations for real-world problems. While specific awards aren't documented in the provided records, the extensive publication record in high-impact venues demonstrates significant scholarly contribution to the field. The research program appears well-funded, supporting numerous collaborative projects across multiple institutions.

• Computer Vision
• Machine Learning
• Signal Processing
• +6 more

Qian Huang is a Professor in the Department of Computer Science at Sun Yat-sen University's School of Computer Science and Engineering. With over 350 publications spanning from 1992 to 2025, Dr. Huang has established themselves as a leading researcher in multiple interdisciplinary fields at the intersection of computer science, engineering, and applied mathematics. Dr. Huang's research spans several critical domains in modern computing. Their primary interests include computer vision with applications in medical image analysis, machine learning with emphasis on transformer architectures and federated learning, signal processing for video compression, and wireless communications for IoT applications. Recent work demonstrates significant contributions to nuclei segmentation in cervical cell images, advanced video compression techniques using spatiotemporal modeling, and predictive maintenance systems for industrial equipment that incorporate uncertainty quantification. An analysis of Dr. Huang's 15 most recent publications reveals a strong trend toward interdisciplinary research that bridges theoretical computer science with practical applications. Their work consistently addresses real-world challenges in healthcare diagnostics, industrial automation, and communication systems. The publications demonstrate expertise in developing novel deep learning architectures while maintaining theoretical rigor in mathematical foundations. Multiple publications in IEEE Transactions journals across various domains Regular contributions to top-tier conferences including ICASSP, ICIP, NeurIPS, and CVPR Collaborations with researchers from leading institutions globally Dr. Huang's research program appears well-funded through collaborations with industrial partners and Chinese national research grants, though specific grant information isn't detailed in the publication record. Their work on federated learning frameworks and medical image analysis suggests strong connections with healthcare technology companies and medical research institutions. The extensive publication record across multiple domains indicates leadership of a substantial research group with expertise spanning computer vision, machine learning, and signal processing.

• Artificial Intelligence
• Machine Learning
• Remote Sensing
• +6 more

Xing Jin is a Researcher affiliated with the University of Health and Rehabilitation Sciences in Qingdao, China. His work spans multiple domains including Artificial Intelligence , Machine Learning , and Remote Sensing , with a focus on Facial Expression Recognition , Environmental Monitoring , and Cybersecurity . Recent research involves Transformer-based models for emotion detection under the Internet of Medical Things (IoMT) , UAV remote sensing for water quality analysis, and Q-learning applications in public goods game theory. His 2025 publications highlight deep learning architectures , generative verification , and multi-turn reinforcement learning for LLM agents. He has no explicitly stated scientific awards or advising roles in the provided data. His collaborative work includes cybersecurity mechanisms for HTML5-based mobile apps , energy-efficient cloud computing , and biomedical signal processing to study anesthesia effects. Key co-authors include Zhen Wang , Wenlei Shi , and Dan Pei . His 2025 articles address environmental informatics , generative modeling , and medical AI .