Find Academic Supervisors

• Astroparticle Physics
• High-Energy Gamma-Ray Astronomy
• Very-High-Energy Gamma Rays
• +10 more

Dr. Markus Holler is a Scientific Staff member at the Institute for Astro- and Particle Physics, Faculty of Mathematics, Computer Science and Physics, University of Innsbruck, Austria. He is a key contributor to the H.E.S.S. (High Energy Stereoscopic System) collaboration, focusing on very-high-energy gamma-ray astronomy and astroparticle physics. His research spans a wide range of high-energy astrophysical phenomena, including pulsars, supernova remnants, active galactic nuclei, gamma-ray binaries, and transient events such as gamma-ray bursts and neutron star mergers. His research interests are centered on high-energy gamma-ray astronomy , astroparticle physics , and multi-messenger astronomy . He specializes in the morphology and spectral analysis of extended gamma-ray sources, diffuse emission studies, and the development of advanced data analysis and simulation techniques for Imaging Atmospheric Cherenkov Telescopes. His work often involves large-scale international collaborations such as H.E.S.S., LIGO/Virgo, IceCube, and the Event Horizon Telescope (EHT). He has contributed to landmark discoveries, including the detection of 100 TeV gamma rays from the Crab Nebula, the study of particle acceleration in SS 433, and multi-messenger follow-ups of neutron star mergers. The trends in his recent publications highlight a strong focus on TeV emission from extreme astrophysical environments , morphological studies of nebulae and halos , multi-wavelength and multi-messenger campaigns , and methodological advancements in gamma-ray data analysis . His work frequently bridges observational results with theoretical models of particle acceleration, cosmic-ray propagation, and emission mechanisms. Dr. Holler actively participates in public outreach, delivering popular science lectures on topics such as Cherenkov telescopes and cosmic messengers. He has also held organizational roles within the H.E.S.S. collaboration, including chairing sessions and leading task forces related to diffuse emission and analysis frameworks. There are no indications of formal teaching roles or academic advising in the provided data.

Erich SchikutaView profile

Associate Professor

• Cloud Computing
• Information and Database Systems
• Parallel and Distributed Computing
• +3 more

Erich Schikuta is an associate professor at the University of Vienna's Faculty of Computer Science and heads the Research Group Workflow Systems and Technology. He has held multiple administrative roles, including Vice Dean and Curricular Commission Head. His research focuses on Cloud Computing, Parallel/Distributed Systems, Neural Networks, and Database Systems, with over 200 peer-reviewed publications. Education: Studied Computer Science, Business Informatics, and Mathematics at the University of Technology Vienna and University of Vienna. Holds titles including Dipl.-Ing., Dr., and Berufstitel Universitätsprofessor (2009). Research interests emphasize Utility Computing, Cloud Resource Optimization, and Neural Network Simulation. Notable contributions include ranking 25th globally in Cloud Computing productivity and developing frameworks like N2Sky for neural network-as-a-service. Key awards include the 2011 Best Paper Award and 2010 Best Teacher Award. Active in international collaborations, he serves as a European Commission evaluator (FP5-FP7/H2020) and chairs program committees. Labs/Teams: Leads the Research Group Workflow Systems and Technology. Involved in projects like BIG-Business (Grid Infrastructure) and 'Änderung in kollab.Prozessen' (2011-2016).

• 3D Modeling
• Spatial Data Analysis
• Bayesian Models
• +5 more

Hai Huang is a researcher affiliated with the Institute of Cartography and Geoinformatics at Leibniz University of Hannover's Faculty of Civil Engineering and Geodesy. He holds the academic rank of Researcher and has conducted postdoctoral research in DFG-funded projects focusing on 3D city model generation using statistical methods. His habilitation thesis (2018) explored Bayesian models for pattern recognition in spatial data. His research interests span 3D modeling, spatial data analysis, Bayesian statistics, geoinformatics, remote sensing, and image processing. Key areas include LiDAR data processing, object-level segmentation of RGBD data, and generative statistical approaches for urban and environmental modeling. His publications reflect expertise in 3D reconstruction of buildings and vegetation, trajectory analysis, and geospatial data integration. Notable works include Bayesian-based methods for anomaly detection in trajectory data and hybrid approaches for building footprint extraction from LiDAR datasets. His work bridges computational statistics with geospatial applications, emphasizing practical solutions for urban modeling and environmental monitoring. No academic awards or students are explicitly listed in the provided text.

Christian MertesView profile

Researcher

• Computational Genomics
• Bioinformatics
• Genetic Diagnosis
• +5 more

Christian Mertes is a researcher affiliated with the Chair of Computational Molecular Medicine at Technische Universität München (TUM). He holds a PhD (2021) and works as Chief Technical Officer at OmicsDiscoveries GmbH since 2024. Previously, he was a visitor at Stanford University (2020, 2017–2018) and a system administrator/research assistant at TUM. His education includes a B.Sc. (2011) and M.Sc. (2015) in Bioinformatics from TUM/LMU. His research focuses on computational genomics, aberrant splicing detection, and multi-omics integration for Mendelian disease diagnostics. Key contributions include developing FRASER (for splicing analysis) and OUTRIDER (for gene expression outlier detection). His work bridges bioinformatics, clinical genetics, and algorithm development. Publications highlight advancements in RNA-Seq analysis, proteogenomics, and diagnostic pipelines. He has collaborated with groups at Stanford, LMU Munich, and other institutions. Teaching involvement includes courses on data analysis in R and statistical methods for systems genetics. Mertes’ lab (Chair of Computational Molecular Medicine at TUM) emphasizes translational bioinformatics, with projects addressing rare disease mechanisms and precision medicine applications. His work has implications for improving diagnostic rates in genetic disorders through integrated omics approaches.

Xue LiuView profile

Professor

• Intelligent Computing and Communications Systems
• Sustainable Computing
• AI and Machine Learning Applications
• +3 more

Xue Liu is a Professor and William Dawson Scholar at McGill University's School of Computer Science, affiliated with the Department of Mathematics and Statistics (courtesy appointment) and the Department of Electrical and Computer Engineering. He holds positions as Vice President R&D and Chief Scientist at Samsung AI Center Montreal, and serves as Chair (2021-2023) of ACM SIGBED. His research focuses on intelligent computing, cyber-physical systems, sustainable computing, AI/ML applications, IoT, and blockchain technologies. Dr. Liu has received multiple Best Paper Awards from conferences such as IEEE GLOBECOM, IEEE HPCC, and IEEE/ACM IWQoS. He is a Fellow of the Canadian Academy of Engineering (FCAE) and IEEE (FIEEE). His work bridges academia and industry, with entrepreneurial ventures including advisory roles at Infinity Stones Inc., TandemLaunch, and Aerial Technologies. He co-edits the Journal of Blockchain Research and serves on editorial boards of major journals like ACM Transactions on Cyber-Physical Systems and IEEE Transactions on Networking. Key research contributions include innovations in blockchain infrastructure, smart grid optimization, and AI-driven systems. His labs, including the Cyber-Physical Intelligence Lab and MILA affiliation, advance interdisciplinary research in machine learning, robotics, and networked systems. Dr. Liu mentors startups and advises initiatives like Learnable Inc., integrating AI into education and healthcare. Education Affiliations: McGill University School of Computer Science (prestige rankings include top 25 globally per QS 2023) Awards: Multiple Best Paper Awards, FCAE & FIEEE Fellowships Labs: Cyber-Physical Intelligence Lab, MILA, CIM, SYTACom Publications: Over 150 peer-reviewed articles, including books on cloud computing, smart grids, and cyber-physical systems

• Wireless Communications
• IoT
• Machine Learning
• +7 more

Professor Yonghui Li is a faculty member at the University of Sydney, holding the position of Professor and Director of the Wireless Engineering Laboratory within the School of Electrical and Information Engineering. His research focuses on cutting-edge areas such as wireless communications, IoT, 5G/6G networks, machine learning, and signal processing. He has authored over 400 publications, including a book on full-duplex communications, and secured over $8 million in competitive research funding. Notably, he was awarded the Australian Research Council (ARC) Future Fellowship (2012) and IEEE Fellow (2015). Education: PhD in Telecommunications (2002) Roles: Editor for IEEE Transactions on Communications, Specialty Chief Editor of Frontiers Signal Processing Journal Key Projects: Multi-gigabit mm-wave cellular networks, machine learning for wireless spectrum management, 5G ultra-reliable low-latency systems His research has led to high-impact outcomes, including ISI high-cited papers and ESI Web of Science recognition. He also pioneered the IoT undergraduate program at the University of Sydney. Awards: IEEE Best Paper Awards (ICC 2014, PIMRC 2017), ARC Fellowships Grants: $500M national 'Smart Grid Smart City' project participation Current projects explore 3D beamforming, non-orthogonal multiple access, and physical-layer security in 5G systems. His work bridges theory and practice, with prototypes developed for mm-wave networks and AI-driven spectrum management.

David HunterView profile

Lecturer

• Computer Vision
• Image Processing
• 3D Shape Retrieval
• +3 more

David Hunter is a Lecturer in Computer Science at the Department of Computer Science, Aberystwyth University. His research spans computer vision, binocular vision, and educational identity studies. He has contributed to projects involving 3D shape retrieval, natural image statistics, and protein domain analysis through initiatives like SHREC challenges. Research Interests: Computer Vision: Focusing on binocular disparity, depth perception, and natural image processing. 3D Shape Retrieval: Developing novel algorithms for nonrigid and protein surface comparisons. Educational Studies: Exploring student and graduate identity transitions in higher education. His publications highlight contributions to journals like Royal Society Open Science and conferences such as CSCI. He manages the Tobii Pro Eye Tracking Glasses facility, aiding visual research. Supervised graduate students include Dave Davies (MPhil) and Muhammad Iftikhar (PhD).

Nour El Houda YELLASView profile

Assistant Professor

• Edge Computing
• Federated Learning
• Network Optimization
• +3 more

Nour El Houda YELLAS is an Assistant Professor at Telecom SudParis, affiliated with SAMOVAR research center and the NeSS department. Her work focuses on optimizing resource allocation in edge computing and telecommunications networks, with emphasis on federated learning, network slicing, and blockchain oracles. She holds a PhD in Networking and Internet Architecture from Hesam University and Cnam Paris (2023). Research interests include multi-criteria optimization for edge computing systems, robust network design, and machine learning applications in telecommunications. Recent work addresses latency-sensitive applications, federated learning pipelines, and anomaly detection in 5G/6G infrastructures. Her publications span conferences such as CNSM, NOMS, and IWCMC, with contributions to IEEE Transactions and Computer Networks journals. Current projects involve sustainable edge resource allocation and in-network learning architectures.

Anna PetrasovaView profile

Researcher

• Geospatial Analytics
• Environmental Modeling
• Open Source Software Development
• +3 more

Anna Petrasova is a Geospatial Software Engineer at North Carolina State University (NCSU), specializing in dynamic spatio-temporal modeling of urbanization, pest/pathogen spread, and geospatial simulation interfaces. She holds a Ph.D. in Geospatial Analytics and is a core contributor to the open-source GRASS GIS project. Her work focuses on participatory modeling, cloud-based geospatial platforms, and intuitive GUI design for collaborative decision-making in environmental and urban planning contexts. As a member of the OSGeo Foundation and GRASS GIS Steering Committee, she advocates for open-source software in research and education. Research interests span geospatial analytics, environmental modeling, and participatory approaches to addressing climate change and invasive species threats. She has developed tools like Rapid-DEM for topographic updates and GRASS_FUTURES for urban growth simulation. Her 15 most recent articles (2021–2024) emphasize open-source platforms, stakeholder engagement in modeling, and applications of GIS in flood risk reduction, invasive species forecasting, and landscape dynamics. Contributions include integrating FUTURES and PoPS models into GRASS, as well as Jupyter Notebook workflows for geospatial education. Anna collaborates with institutions like the Tangible Landscape Lab and has led over 900 code contributions across repositories, including core GRASS GIS development and educational tutorials.

• AI for Networking
• Edge Computing
• Federated Learning
• +4 more

Prof. Xiaoming Fu is a Professor at the Institute of Computer Science, University of Göttingen, and Head of the Computer Networks Group. He also holds a secondary membership in the Center for Statistics. His research focuses on AI-driven networking, edge computing, federated learning, and video analytics. He teaches courses such as 'AI-Empowered Networking and Mobile Communications' and oversees multiple seminars and internships on topics like smart cities and network optimization. His work spans technical innovations in multi-agent systems, low-latency services, and resource-efficient machine learning. Recent articles highlight contributions to neural-optimized video streaming, privacy-preserving MARL, and cross-modal social media analysis. He collaborates on interdisciplinary projects involving mobility data, socioeconomic analysis, and quantum networks. Prof. Fu’s research integrates reinforcement learning, network architecture design, and distributed systems to address challenges in 5G/6G, IoT, and smart cities. His lab develops solutions for edge-cloud orchestration, video quality adaptation, and resilient resource allocation, with applications in both academia and industry.

• Machine Learning
• Medical Imaging
• Healthcare Analytics
• +5 more

Dr. Santosh Tirunagari is a Research Fellow in Machine Learning at Middlesex University , focusing on Computer Science with applications in medical imaging , healthcare analytics , and biometric security . His research bridges machine learning with clinical data to improve diagnostic accuracy and patient outcomes. His work spans medical imaging analysis , explainable AI , and biometric anti-spoofing . Recent publications highlight latent diffusion models for visual attribution and convex segmentation algorithms in biomedical imaging, alongside systematic reviews on pediatric AI applications and chronic disease prediction . Key technical contributions include dynamic mode decomposition for motion correction in MRI, adversarial latent transformations for medical image analysis, and self-organizing maps for patient clustering. His collaborations with researchers like David Windridge and Noriko Poh demonstrate interdisciplinary teamwork.

Emmett J. IentilucciView profile

Associate Professor

• Remote Sensing
• Spectral Image Processing
• Hyperspectral Imaging
• +7 more

Dr. Emmett J. Ientilucci is an Associate Professor in the Chester F. Carlson Center for Imaging Science at Rochester Institute of Technology (RIT), where he holds the Gerald W. Harris Endowed Professorship and serves as Graduate Admissions Chair. His work focuses on hyperspectral imaging, remote sensing, and sensor calibration. Research interests include: Remote Sensing Spectral Image Processing Hyperspectral Target Detection Atmospheric Compensation Radiative Transfer Modeling Recent publications highlight applications in drone-based hyperspectral systems, biomass estimation via LiDAR, airborne landmine detection, and multi-platform data calibration. His work bridges theoretical modeling with real-world sensor design. Awards include: 2020-21 Eisenhart Provost’s Award for Teaching IEEE-Eta Kappa Nu induction (2024) Senior Member of SPIE and IEEE Member of Optica and ISEE He teaches graduate courses in radiometry, remote sensing, and imaging science, while supervising 94 students (33 lead, 60 committee) and refereeing for 18 journals.

• Control Theory
• Nonlinear Systems
• Complex Networks
• +7 more

Jinsheng Sun is a prolific researcher with active contributions to Control Theory , Complex Networks , and Image Processing , often collaborating with scholars from institutions like the University of Melbourne and Harbin Institute of Technology . His work spans theoretical advancements and applied methodologies. Key Affiliations : Co-authored papers with researchers from diverse domains, indicating interdisciplinary collaborations. Research Themes : Focus on control systems for nonlinear dynamics, synchronization in complex networks, and forensic analysis of digital media. Research Interests revolve around: Designing adaptive control mechanisms for nonlinear systems and vibration systems , as seen in his 2025 work on transient performance design. Advancing complex network analysis through novel algorithms for identifying vital spreaders and secure synchronization protocols. Developing digital forensics techniques for JPEG compression and watermarking, with applications in information security . Recent Article Trends highlight his focus on control theory (e.g., neural network-based tracking control, H∞ fault detection) and network science (e.g., pinning synchronization, spreader identification). His 2024 work also includes robotics (object-driven navigation) and point cloud segmentation via interactive frameworks. Advising and Grants are not explicitly mentioned, but his extensive co-authorship network suggests mentorship roles. Funding details remain absent in the provided data. Labs and Teams : Collaborated with teams working on TCP/AQM systems , image encryption , and metabolic network reconstruction (e.g., 2018 work on Eriocheir sinensis).

• Human-Centered Artificial Intelligence
• Explainable Artificial Intelligence
• Interactive Machine Learning
• +7 more

Andreas Holzinger is a University Professor for Digital Transformation for Smart Agriculture and Forestry at the Institute of Forest Engineering, Department of Agricultural Sciences, University of Natural Resources and Life Sciences Vienna (BOKU). He pioneered interactive machine learning with human-in-the-loop frameworks to advance trustworthy AI, focusing on explainability, robustness, and ethical alignment with human values. His work bridges artificial intelligence with forestry technology, ensuring safety, privacy, and sustainability. 2022: University Professor at BOKU 2021: IFIP Fellow 2020: ELLIS Member 2019: Visiting Professor at University of Alberta 2019: Academia Europaea Full Member His research interests span Human-Centered AI , Explainable AI , Digital Transformation in Forestry , and Trustworthy AI . He develops frameworks where humans collaborate with AI systems to enhance decision-making in forest operations, focusing on transparency and controllability. His recent publications emphasize Interactive Machine Learning and Explainable AI in forestry contexts, including cable corridor planning, danger zone detection, and sensor-based data interpretation. Articles explore 3D point cloud analysis , human-robot collaboration , and contextual counterfactuals to improve AI systems' reliability. Scientific Awards: Most influential Paper Award (2025) Fellow of IFIP (2021) Member of ELLIS (2020) Full Member, Academia Europaea (2019) He supervises theses on Forestry Technology , Plant Development , and Stress Resilience , mentoring students working on smart sensors, genetic analysis, and AI applications in agricultural systems. His collaborations span institutions in Austria, Canada, Finland, Italy, New Zealand, and the USA.

Rüdiger LehmannView profile

Professor

• Geodesy
• Surveying Engineering
• Geodetic Statistics
• +5 more

Prof. Dr.-Ing. Rüdiger Lehmann is a Professor of Surveying Engineering at the Faculty of Spatial Information, Dresden University of Applied Sciences. His academic career focuses on advancing geodetic measurement techniques and statistical analysis methods. He serves on the editorial board of the Journal of Surveying Engineering and leads research initiatives in geodetic statistics and cloud computing applications for geospatial data analysis. His work bridges theoretical advancements with practical applications in surveying engineering and geodetic science. Professor Lehmann's primary research interests center on developing innovative estimation techniques for complex geodetic observation models. His work spans outlier detection , robust estimation methods , advanced hypothesis testing , and model selection in geodetic adjustment . He has made significant contributions to Monte Carlo techniques for geodetic data analysis and has pioneered approaches for enhancing the accuracy, reliability, and computational efficiency of geodetic measurements. His research addresses fundamental challenges in processing increasingly complex geospatial data while maintaining statistical rigor. An analysis of Professor Lehmann's publication record reveals a strong focus on statistical foundations of geodesy. His work consistently addresses core challenges in coordinate transformations, network reliability, and error modeling. A notable trend is his investigation of alternative approaches to traditional statistical methods, including comparative studies of mean shift versus variance inflation for outlier detection. His research bridges theoretical statistics with practical surveying applications, with increasing emphasis on computational efficiency and cloud-based solutions as evidenced by his IN DUBIO PRO GEO project. Professor Lehmann serves on the editorial board of the Journal of Surveying Engineering, contributing to the scholarly discourse in his field. His leadership in this capacity demonstrates recognition of his expertise by the international geodetic community. Professor Lehmann leads the research project "Advanced methods of Geodetic Statistics" and has developed "IN DUBIO PRO GEO - Geodetic Cloud Computing," a universal geodetic software solution. These initiatives reflect his commitment to advancing both theoretical frameworks and practical tools for the geodetic community. His work supports the next generation of geospatial professionals through innovative computational approaches. Professor Lehmann's laboratory work focuses on computational geodesy, particularly through the IN DUBIO PRO GEO platform, which provides cloud-based solutions for geodetic computations. This environment supports advanced statistical analysis of geodetic networks and promotes accessibility of sophisticated computation methods to practitioners worldwide.