Find Academic Supervisors

• Machine Learning
• Deep Learning
• Neural Networks
• +7 more

Sung-Bae Cho is a prolific researcher in computer science with a publication record spanning over three decades from 1990 to 2025, demonstrating sustained academic productivity and research leadership. Their work primarily focuses on machine learning, neural networks, and their applications across diverse domains including cybersecurity, healthcare, and intelligent systems. Cho's research interests center around advanced machine learning techniques including deep learning architectures, Bayesian networks, and context-aware systems. Their work shows a consistent evolution from traditional neural networks to modern deep learning approaches, with recent publications emphasizing graph neural networks, federated learning, and anomaly detection systems. The research demonstrates strong methodological rigor with applications in practical domains such as traffic prediction, medical diagnosis, and security systems. The publication pattern reveals significant research productivity with multiple high-impact papers annually in reputable venues including IEEE Access, Neurocomputing, and Expert Systems with Applications. Recent work shows particular emphasis on addressing contemporary challenges in machine learning including continual learning, few-shot learning, and privacy-preserving approaches in federated settings. The research demonstrates both theoretical contributions and practical implementations across various application domains. Cho has cultivated an extensive collaborative network with numerous co-authors including Kyung-Joong Kim, Satchidananda Dehuri, Jin-Hyuk Hong, and Seok-Jun Bu, suggesting leadership in research groups and projects. The collaborative pattern indicates supervision of junior researchers and students, though specific advisee relationships aren't explicitly documented in the publication metadata. The researcher maintains active contributions to laboratory and team-based research, with recent publications indicating involvement in projects related to healthcare AI, cybersecurity systems, and intelligent transportation. Current research directions appear focused on addressing limitations in deep learning including catastrophic forgetting, data scarcity, and privacy concerns through novel architectural and training approaches.

Oliver JäkelView profile

Professor

• Medical Physics
• Radiation Oncology
• Ion Beam Therapy
• +15 more

Prof. Dr. Oliver Jäkel is a leading academic and medical physicist serving as Full Professor for Medical Physics at the Medical Faculty Heidelberg of Heidelberg University, Head of the Division of Medical Physics in Radiation Oncology at the German Cancer Research Center (DKFZ), and Medical Physics Director of the Heidelberg Ion Beam Therapy Center (HIT) at Heidelberg University Hospital (UKHD). He holds an Scopus ID: 7003530041, ORCID: 0000-0002-6056-9747, and an h-index of 57, reflecting his significant scholarly impact. His research interests are centered on medical physics in radiation oncology , particularly in particle therapy involving protons, carbon ions, and helium ions. Key areas include dosimetry , MR-guided and adaptive radiotherapy , treatment planning , in vivo monitoring using secondary fragment tracking , and AI applications in imaging and contouring . He has pioneered work in helium beam radiography, motion management, and Monte Carlo simulations for dose verification. His recent publications (2024–2025) reveal a strong focus on deep learning for MRI-to-CT conversion, intrafractional motion tracking, and quality assurance in hybrid MRI-particle therapy systems. These works span journals such as Physics in Medicine and Biology , Medical Physics , and Cancers , demonstrating a consistent trend toward integrating advanced imaging, AI, and novel particle beams into clinical radiotherapy workflows. Among his scientific honors are: Chair of ICRU Report 93 on Light Ion Beam Therapy reporting Spokesperson for HGF and DFG research groups in hadron therapy Accredited mentor for medical physics experts Member of IAEA and ICRU task groups Chair of DKFZ’s Commission for Good Scientific Practice Prof. Jäkel has supervised one habilitation and 31 dissertations, indicating active academic mentorship. He leads international Master’s programs in medical physics and has organized summer schools in Chile and Thailand. He is involved in multiple advisory boards, including the DFG, FCT Portugal, and Ontario’s Univ. Health Network. His leadership extends to large-scale research initiatives such as the Heidelberg Institute for Radiation Oncology (HIRO) and the development of postgraduate curricula in medical physics. He is actively involved in laboratory and clinical research at DKFZ and HIT, where his team develops and validates novel imaging and dosimetry systems for particle therapy, including Timepix-based detectors, FLUKA simulations, and MR-integrated workflows. Future research directions include expanding helium ion therapy, improving biological effectiveness models, and advancing AI-driven adaptive radiotherapy.

Stefan HarmelingView profile

Professor

• Machine Learning
• Deep Learning
• Reinforcement Learning
• +3 more

Prof. Dr. Stefan Harmeling is a Distinguished Professor at TU Dortmund University 's Faculty of Computer Science and leads the Harmeling Lab . Previously, he served as Professor for Machine Learning at Universität Düsseldorf (2014-2022) and held postdoctoral positions at the University of Edinburgh (2005-2007) and Max Planck Institutes in Tübingen (2007-2014). His research spans machine learning , deep learning , reinforcement learning , and causality , with applications in medical imaging , seismology , and political discourse analysis . Education : Dipl. Math. in Mathematical Logic, University of Münster (1998) M.Sc. in Computer Science, Stanford University (2000) Dr. rer. nat. in Machine Learning, University of Potsdam (2005) His scientific contributions include 15 recent publications (2021-2025) covering topics from cardiac MRI analysis to LLM applications in political deliberation . These works demonstrate expertise in generative models , self-supervised learning , and adversarial robustness . Notable awards : 2022: Helsinki Tomography Challenge winner 2012: Günter Petzow Prize 2011: DAGM Prize 2009: COSMOSTAT Best Contribution 2009: GREAT08 Challenge Top Score As an advisor, he has mentored 8 PhD students including Tobias Uelwer (2023 PhD), Sebastian Konietzny , and Maike Behrendt . His teaching portfolio includes courses on probabilistic reasoning, machine learning, and deep learning at TU Dortmund. He also serves as area chair for TinyPapers@ICLR 2024 and organizer for multiple NeurIPS/ICML workshops.

Stevan RudinacView profile

Associate Professor

• multimedia
• computer vision
• information retrieval
• +3 more

Stevan Rudinac is an Associate Professor of Artificial Intelligence for Business at the University of Amsterdam Business School, which is part of the Faculty of Economics and Business. He also serves as a guest researcher at the Informatics Institute of the University of Amsterdam. His academic career focuses on advancing multimedia analytics with applications to societal challenges. Rudinac's primary research interests include multimedia analytics, computer vision, information retrieval, machine learning, and artificial intelligence with specific applications in urban computing. His work aims at enabling large-scale multimedia analytics based on higher semantic level relevance criteria by jointly analyzing visual content with heterogeneous associated information including text, metadata, and social network data. What particularly fascinates him is the potential of artificial intelligence in addressing important societal challenges, such as liveability and security. His recent publication trends (2020-2025) demonstrate a strong focus on interactive multimedia systems, particularly the Exquisitor framework for interactive learning and search, hypergraph learning techniques, urban computing applications, and the integration of large language models with multimedia systems. His work spans both theoretical advancements in multimodal learning and practical applications addressing real-world challenges. Rudinac has been actively involved in academic service, particularly in organizing workshops and conferences in the Multimedia Modeling (MMM) series, and has contributed to advancing the field through his editorial work on special issues focused on content-based multimedia indexing. He currently advises multiple PhD students working on diverse topics including art market analysis, marketing applications of AI, urban computing, and multimedia forensics, demonstrating the breadth of his research impact across different domains.

Heike TrautmannView profile

Professor

• Data Science
• Artificial Intelligence
• Multiobjective Optimization
• +4 more

Prof. Heike Trautmann is a Professor of Data Science: Statistics and Optimization at the University of Münster, holding the Chair of Statistics and Optimization within the Department of Information Systems and Statistics, School of Business & Economics. She also serves as Vice Dean for Internationalization at the School. Her academic career includes roles such as Pascal Professor at Leiden University (2017) and a visiting position at the University of Twente (2021–2026). She earned her PhD (2004) and Habilitation (2013) in Statistics from TU Dortmund, focusing on optimization methodologies. Her research interests span Multiobjective Optimization, Evolutionary Algorithms, Automated Algorithm Selection, Data Stream Mining, and Social Media Analytics. Notable projects include the COSEAL consortium for algorithm selection, the Benchmarking Network for optimization heuristics, and the ERCIS Social Media Analytics Competence Center addressing online disinformation. She has led initiatives like MODERAT! (automated comment moderation) and Algorithmisierung und gesellschaftliche Interaktion (societal impact of algorithms). Awarded multiple best paper prizes, including at CBI 2019 and PPSN XIV 2016, she emphasizes interdisciplinary collaboration. Her work bridges technical advancements with societal implications, reflected in contributions to AI ethics and platform regulation. Active in international conferences (e.g., EMO, GECCO), she maintains roles in program committees and advisory boards such as CLAIRE and ACM SIGEVO. Funded projects include EU initiatives on algorithm configuration and DAAD collaborations on platform regulation. Her research outputs span over 50 peer-reviewed articles since 2012, focusing on algorithmic innovation and real-world applications in optimization and digital media.

• Data Deduplication
• Spatial Data Mining
• GPU Computing
• +3 more

Witold Andrzejewski is an active researcher in computer science, focusing on data deduplication pipelines, co-location pattern mining, and GPU-accelerated algorithms. His work bridges academia and industry, with publications analyzing customer record deduplication in the financial sector, performance optimization of spatial data processing, and comparative studies of statistical modeling versus machine learning approaches. 2025: Co-location pattern mining with Euclidean metrics 2024: Customer data deduplication parameter tuning 2023: Text similarity measures in financial applications

• Explainable AI
• Human-like machine learning
• Explanation Generation
• +7 more

Professor Ute Schmid is a Full Professor of Cognitive Systems at the University of Bamberg, where she has been a faculty member since September 2004. She leads the Cognitive Systems Group within the Bamberg Center of AI (BaCAI), focusing on creating AI systems that generate human-like explanations and reasoning processes. Her research bridges cognitive science and artificial intelligence to develop methods for explanation generation, inductive programming, and interactive machine learning. Professor Schmid's work emphasizes practical applications of explainable AI across diverse domains including image classification, medical diagnosis, and educational technologies. Her research on contrastive explanations, near misses, and human-AI alignment has significantly advanced the field of XAI. She has also pioneered research on AI literacy, recognizing the growing importance of basic AI understanding for responsible tool usage by non-experts. Her publication record demonstrates exceptional productivity and impact, with numerous articles in top-tier venues including Nature Machine Intelligence, IEEE Transactions on Visualization and Computer Graphics, and the Journal of Web Semantics. Her 2025 paper 'Aligning generalization between humans and machines' represents a significant theoretical contribution to understanding human-machine cognitive alignment. Professor Schmid actively contributes to gender diversity research in computer science through studies examining why women pursue PhDs in the field. She has also made important contributions to computing education, investigating how students acquire programming skills and how AI tools like code generators are integrated into learning processes. As an educator and researcher, Professor Schmid maintains strong international collaborations, with co-authors spanning multiple countries and institutions. Her interdisciplinary approach is evident in her diverse publication venues and collaborative work that bridges computer science, cognitive science, education, and application domains.