Find Academic Supervisors

• Medical Image Analysis
• Computer Vision
• Machine Learning
• +4 more

Vajira Thambawita is a researcher at the University of Oslo's Department of Informatics, specializing in medical image analysis and AI-driven healthcare solutions. With over 90 publications since 2019, their work spans gastrointestinal endoscopy, reproductive medicine technology, and multimedia systems for clinical applications. Research focuses on medical image segmentation (polyp detection, sperm tracking), anomaly detection in time-series data, and multimodal analysis for clinical decision support. Key projects include the Medico Multimedia Task at MediaEval (sperm tracking), VISEM-Tracking dataset, and ImageCLEFmedical challenges for GI tract analysis. Their work bridges computer vision with clinical practice through collaborations with Oslo University Hospital and Simula Research Laboratory. Recent publications demonstrate strong trends in generative AI for medical data augmentation (SinGAN-Seg, PolypConnect), explainable AI for clinical validation , and multimodal integration (SoccerNet-Echoes). The research consistently targets real-world clinical problems with emphasis on transparency and robust evaluation. Thambawita actively contributes to major benchmark challenges including ImageCLEF, Medico, and Medical AI competitions, serving as organizer and participant in advancing evaluation standards for medical AI systems.

• Machine Learning
• Causal Modeling
• Statistical Learning Theory
• +9 more

Christian Reimers is a Project Group Leader and PostDoc at the Department Biogeochemical Integration (BGI) of the Max Planck Institute for Biogeochemistry in Jena, Germany. He leads the Project Group Adapting Machine Learning for Earth Systems and contributes to the Research group Atmosphere-Biosphere Coupling, Climate and Causality , focusing on integrating machine learning with Earth systems research. Education : M.Sc. and B.Sc. in Mathematics from Georg-August-University, Göttingen (2013-2017, 2011-2013) His research lies at the intersection of machine learning , causal modeling , and Earth system science . Recent work explores hybrid physics-AI models for evapotranspiration and carbon cycle dynamics, spatiotemporal deep learning for climate teleconnections, and bias mitigation in neural network classifiers. Publications span climate informatics , phenology modeling , and neural network interpretability . Key trends in his 15 most recent articles (2018-2025) include: deep learning architectures for Earth observation; causal inference in climate-vegetation interactions; hybrid modeling combining physics and data-driven approaches; and bias detection in medical imaging applications. His work often addresses interpretability challenges in black-box models. Current affiliations include: Max Planck Institute for Biogeochemistry (since 2021) Project Group EarthNet Research group Atmosphere-Biosphere Coupling, Climate and Causality

• Computer Vision
• Pattern Recognition
• Biometrics
• +3 more

Zhen Lei is a researcher at the National Laboratory of Pattern Recognition , Chinese Academy of Sciences, Beijing, China. His work focuses on Computer Vision , Biometrics , and Pattern Recognition . Research trends from his publications include Face Recognition , Anti-Spoofing , Person Re-Identification , 3D Face Reconstruction , and Vision Transformers . He has contributed to Information Fusion , Neural Networks , and Image Processing journals. Recent awards or affiliations with honorifics were not explicitly mentioned. Collaborative efforts are evident through co-authorship with experts in Face Detection , Domain Adaptation , and Medical Imaging .

• Wireless Communications
• Polarization Techniques
• Knowledge Graph Completion
• +7 more

Fangfang Liu is an active researcher with an extensive publication record spanning from 2005 to 2025, demonstrating significant contributions across multiple domains in computer science and engineering. Their work shows consistent collaboration with researchers including Weimin Li, Caili Guo, Zhimin Zeng, and Chunyan Feng, suggesting strong institutional ties within their research community. Fangfang Liu's research spans several key areas including wireless communications, knowledge graph completion, semantic communications, and fake news detection. Their work demonstrates expertise in applying machine learning techniques to solve complex problems in network security, IoT systems, and multimedia analysis. The research portfolio shows a progression from foundational work in wireless communications and polarization techniques toward more recent applications in AI-driven security and knowledge representation. The publication trends reveal a strategic expansion from traditional communications engineering into cutting-edge AI applications. Early work focused on polarization techniques and wireless channel modeling, while recent publications emphasize knowledge graphs, multimodal fake news detection, and semantic communications. This evolution demonstrates adaptability to emerging research frontiers while maintaining technical depth in signal processing and network analysis. Fangfang Liu has made substantial contributions through numerous publications in prestigious venues including IEEE Transactions, Expert Systems with Applications, and Knowledge-Based Systems. Their collaborative approach is evident through extensive co-author networks across academia and research institutions. While specific advising information isn't detailed in the publication record, Fangfang Liu appears to lead research groups working on precision assembly, knowledge graph applications, and IoT security. The research program demonstrates strong connections between theoretical foundations and practical implementations in communication systems and AI applications.

• Underwater Image Enhancement
• Deep Learning
• Computer Vision
• +3 more

Alzayat Saleh is an active academic researcher specializing in computer vision and deep learning applications for underwater and agricultural domains. He has co-authored over 28 publications from 2015-2025, focusing on self-supervised learning, image enhancement, and environmental impact mitigation. Key collaborators: Mostafa Rahimi Azghadi, Marcus Sheaves, Dean R. Jerry, Alex Olsen Research Focus: His work addresses critical challenges in underwater visual analysis and precision agriculture , including: Fish segmentation and tracking without human annotations Weed detection in long-tailed datasets Environmental monitoring through adaptive deep learning Herbicide reduction in robotic spot-spraying systems The 2024-2025 publications reveal a trend toward self-supervised and semi-supervised approaches for ecological applications, with recent work on 3D underwater reconstruction and real-time shadow removal.

• Artificial Intelligence
• Machine Learning
• Data Analytics
• +7 more

Leo Mrsic is a researcher with extensive contributions to Artificial Intelligence, Machine Learning, and Data Analytics. He has published numerous high-impact articles in journals like Artificial Intelligence Review and Quantum Machine Intelligence , covering topics such as quantum computing applications, blockchain in education, and neurotechnological frameworks. His work also extends to environmental engineering and biomedical research. Research Interests : AI, quantum computing, blockchain, sentiment analysis, educational technology, genomics Collaborators : Siddhartha Bhattacharyya, Mislav Balkovic, Mateo Sokac, Sovan Samanta Recent Articles (2025-2022) explore quantum-inspired metaheuristics, fog computing frameworks, and EEG-based image generation. No awards, students, or institutional affiliations are explicitly mentioned in the provided data.

Tronje KemenaView profile

Researcher

• Ocean Modeling
• Climate Dynamics
• Earth System Modeling
• +5 more

Tronje Kemena is a researcher at GEOMAR Helmholtz Centre for Ocean Research Kiel, focusing on ocean modeling, data analytics, and machine learning applications in climate science. His work primarily involves support and operation of the FOCI model for the BM Group, with emphasis on understanding Earth system dynamics and climate change impacts. Dr. Kemena completed his PhD in 2017 and Master's degree in 2014, both at Christian-Albrechts-Universität Kiel. His doctoral research focused on 'Assessment of Feedbacks in the Earth System under Anthropogenic Forcing: Two Case Studies,' while his Master's thesis examined 'Impact of the carbon cycle on the development of a Snowball Earth.' His research interests span ocean modeling, climate dynamics, Earth system science, and data-intensive approaches to understanding biogeochemical cycles. Kemena specializes in analyzing carbon cycle feedbacks, ocean deoxygenation processes, and the application of machine learning to climate and oceanographic data. His work often bridges theoretical modeling with practical data analysis techniques. Kemena's publication record shows a strong focus on Earth system modeling, with particular expertise in ocean biogeochemistry and climate dynamics. His recent work has increasingly incorporated data analytics and machine learning approaches, reflecting the growing importance of these techniques in climate science. He has made significant contributions to understanding carbon cycle feedbacks, ocean deoxygenation, and the development of ocean alkalinity enhancement techniques as potential climate intervention strategies. Dr. Kemena is actively involved in several research projects related to ocean-based climate solutions, including the OceanNETs project which focuses on developing and assessing ocean-based negative emission technologies. His work combines theoretical modeling with practical applications to address pressing climate challenges.

Stephan GünnemannView profile

Professor

• Machine Learning
• Data Analysis
• Graph Neural Networks
• +4 more

Prof. Stephan Günnemann is a Professor of Data Analytics and Machine Learning at the Technical University of Munich (TUM), affiliated with the TUM School of Computation, Information and Technology. He leads the Munich Data Science Institute as Executive Director and directs the Konrad Zuse School of Excellence in Reliable AI. His research focuses on enhancing the reliability of machine learning systems, particularly in graph-based and temporal data analysis. Prof. Günnemann holds a PhD from RWTH Aachen University (2012) and has held postdoctoral and senior research positions at Carnegie Mellon University (USA), Simon Fraser University (Canada), and Siemens AG. He founded the Emmy Noether Research Group at TUM in 2015 and has been recognized with prestigious awards including the Heinz Maier-Leibnitz Medal (2022) and the ACM SIGKDD Best Paper Award (2018). His research interests span adversarial robustness, graph neural networks, and molecular data analysis. Recent work emphasizes certifiable AI safety, efficient data pruning, and uncertainty estimation in heterogeneous systems. He has contributed to over 150 peer-reviewed publications, with a focus on foundational ML challenges and real-world applications. Key Awards: Heinz Maier-Leibnitz Medal (2022), Google Faculty Award (2020), DFG Emmy Noether Programme (2015) Leadership Roles: Executive Director of Munich Data Science Institute, Director of Konrad Zuse School Past Roles: Postdoctoral Fellow at CMU, Researcher at Siemens His lab actively explores cutting-edge AI topics such as graph representation learning, quantum chemistry simulations, and trustworthy ML systems. Current projects include developing certifiable defense mechanisms against adversarial attacks and scalable molecular generation frameworks.

Fabian J. TheisView profile

Professor

• Systems Biology
• Mathematical Modeling
• Single-Cell Analysis
• +3 more

Prof. Dr. Dr. Fabian J. Theis is a full Professor of Biomathematics at Technische Universität München (TUM) since 2013 and Director of the Institute of Computational Biology at the Helmholtz Center Munich. He leads the Theis Lab, focusing on machine learning approaches for single-cell genomics and systems biology. His academic journey includes a doctorate in biophysics (2002) and computer science (2003), followed by postdoctoral work in Regensburg, Tokyo, and Tallahassee. Research areas include mathematical modeling of biological processes, single-cell analysis, and integrating omics data into systems medicine. He has advised numerous PhD candidates and leads projects on computational health, including the Lung Cell Atlas and drug discovery. Notable awards include the Gottfried Wilhelm Leibniz Prize (2023) and ERC Advanced Grant (2022). His lab hosts over 50 researchers, including postdocs, PhD candidates, and engineers, working on topics like spatial transcriptomics and multi-modal data integration. Collaborations with global institutions drive advancements in computational biology and medical discovery.

• Biomedical Engineering
• Medical Diagnostics
• Microfluidics
• +7 more

Prof. Hayden Oliver holds the Heinz Nixdorf Chair of Biomedical Electronics at the Technical University of Munich (TUM), within the TUM School of Computation, Information and Technology. His research focuses on interdisciplinary biomedical engineering, combining electronics, optics, microfluidics, and materials science to develop novel in-vitro diagnostic methods. Current work emphasizes magnetic and optical methods for functional diagnostics of blood cells, with applications in point-of-care testing and quantitative phase imaging. Education: Studied biochemistry at the University of Vienna (PhD, 1999). Postdoctoral research at Harvard University, followed by habilitation in Analytical Chemistry at the University of Vienna. Prior to TUM, he worked at IBM Research Zurich (2005–2007) and Siemens Corporate Technology/Siemens Healthcare (2007–2017) on organic electronics for medical imaging and diagnostics. Appointed to TUM in 2017. Research Themes: Digital holographic microscopy, magnetic flow cytometry, label-free cell analysis, AI-driven diagnostics, and microfluidic device development. His work bridges fundamental science (e.g., cell mechanics) with clinical applications (e.g., rapid disease diagnostics). Awards: European Inventor Award (2017), AMA Innovation Award (2016), Siemens NTF Award (2013), Young Investigator Award (2002). Lab/Teams: Leads the Biomedical Electronics research group at TUM, collaborating with industry partners (e.g., Siemens) and academic institutions globally. Grants/Funding: Not explicitly listed in text, but implied through high-profile publications and awards.

• Machine Learning
• Natural Language Processing
• Cybersecurity
• +5 more

Cécile Paris is a researcher at CSIRO , focusing on interdisciplinary applications at the intersection of Artificial Intelligence , Cybersecurity , and Human-AI Collaboration . Her work spans domains such as biomedical informatics, social media analysis, and robotics. Research Interests: Developing collaborative frameworks for human-AI teams Explainable AI in cybersecurity Trust modeling in social networks Fake news detection via temporal and graph-based networks Image captioning with multimodal learning Stance classification across domains Article Trends: Recent publications emphasize adaptive alert prioritization (2025), systematic reviews on cybersecurity challenges (2024), and foundational work in NLP for epidemic intelligence (2018-2020). Key methodologies include graph neural networks, transformer models, and adversarial training.

Patrick van der SmagtView profile

Professor

• Machine Learning
• Probabilistic Inference
• Neural Networks
• +1 more

Patrick van der Smagt is a Professor at the Technical University of Munich in the Department of Computer Science. He serves as the Director of the Foundation Robotics Labs and AI Research Group, focusing on machine learning and robotics research. His work spans probabilistic inference, neural networks, and sim-to-real transfer for robotics. Research Interests : Machine Learning Probabilistic Inference Neural Networks Robotics Publications : His recent work includes advancements in robotic action space design, sim-to-real transfer, and human reflex adaptation studies, alongside foundational contributions to convolutional networks for optical flow and neural-controlled prosthetics. Labs & Teams : Leads the Foundation Robotics Labs and AI Research Group, fostering interdisciplinary research at the intersection of machine learning and robotics.

Michael MumaView profile

Professor

• Robust Data Science
• Signal Processing
• Machine Learning
• +5 more

Michael Muma is a Professor in the Department of Electrical Engineering and Information Technology at Technische Universität Darmstadt. His research focuses on robust data science theory and methods applied to signal processing and machine learning in biomedicine and engineering. He leads the ERC Starting Grant ScReeningData project, developing methods for reproducible information discovery in biomedical databases, and is a Principal Investigator in the LOEWE center emergenCITY and BMBF cluster curATime. Prior roles include Independent Junior Research Group Leader (Athene Young Investigator) and Lecturer at TU Darmstadt from 2017 to 2022, and Research Associate (Post-Doc since 2014) from 2009 to 2017. His research interests span robust statistical methods, high-dimensional data analysis, emergency response systems, and biomedical signal processing. Notable projects include FDR-controlled portfolio optimization, ECG delineation algorithms, and radar-based vital sign estimation. Muma has contributed to distributed sensor networks, robust clustering, and sparse regression techniques. His work addresses challenges in multi-source detection, financial data analysis, and genomics through interdisciplinary approaches combining signal processing, machine learning, and robust statistics. Recent publications emphasize scalable solutions for high-dimensional problems, including applications in robotics, cardiology, and financial index tracking.

• Control Systems
• Mechatronics
• Vehicle Dynamics
• +5 more

Prof. Dr.-Ing. Ulrich Königorski is a Professor of Control Systems and Mechatronics at the Technical University of Darmstadt's Institute of Automatic Control and Mechatronics. He holds a doctorate from TU Karlsruhe and has extensive industry experience at Robert Bosch GmbH and Thyssen Henschel. His research focuses on modeling nonlinear systems, mechatronic systems control, vehicle dynamics, and microgrid control. Königorski leads the Control Systems and Mechatronics Laboratory and has contributed to projects like Proreta 3 (driver assistance systems) and FLORIDyn (wind turbine modeling). His work bridges theoretical control methods with practical applications in automotive, robotics, and renewable energy sectors. Education: Electrical Engineering (1977-1983, TU Karlsruhe), PhD (1987, TU Karlsruhe) on state feedback design. Professional roles include Dean of the Faculty of Electrical Engineering and spokesperson roles in academic committees. Research interests emphasize system modeling, robust control, and mechatronics applications. Notable areas include automated driving trajectory planning, sensorless motor control, and inverter-based microgrid synchronization. His recent work addresses challenges in unsupervised scenario extraction for autonomous systems and gait phase estimation for prosthetics. Labs/Teams: Head of the Control Systems and Mechatronics Laboratory. Collaborations include industry partnerships for automotive and renewable energy projects. Ongoing research focuses on enhancing safety in nonlinear systems, optimizing energy grids, and advancing human-centric automation technologies.

Gullal Singh CheemaView profile

Researcher

• Multimodal Learning
• Natural Language Processing
• Computer Vision
• +3 more

Gullal Singh Cheema is a Researcher at the Leibniz Information Centre for Science and Technology (TIB) within the Visual Analytics research group (Program Area C - Research and Development). His work bridges computer vision and natural language processing to advance multimodal analysis in digital media contexts. His research spans multimodal learning, natural language processing, and computer vision with emphasis on fact-checking systems, claim detection, and social media analytics. Key contributions include the MM-Claims dataset for multimodal verification and leadership in the CLEF CheckThat! lab series addressing political bias and news source authority. His work integrates visual and textual evidence for applications in news analysis, event-centric knowledge graphs, and wildlife conservation. Recent publications (2021-2023) reveal a concentrated focus on multimodal news verification, with significant output in CLEF and SemEval evaluation campaigns. His research demonstrates consistent innovation in fusing transformer architectures with syntactic features while expanding into multilingual sentiment analysis and misogynistic meme detection. Cheema actively shapes the research community through workshop organization (MUWS'22) and dataset development. His Visual Analytics group at TIB drives methodological advances in multimodal understanding with direct applications for social media monitoring and digital humanities.