جست‌وجوی استاد و پژوهشگر

• Explainable AI
• Artificial Intelligence
• Deep Learning
• +۵ مورد دیگر

Wojciech Samek is a Professor in the Department of Electrical Engineering and Computer Science at the Technical University of Berlin and jointly heads the Department of Artificial Intelligence at Fraunhofer Heinrich Hertz Institute (HHI) in Berlin, Germany. Dr. Samek received his Dr. rer. nat. degree with distinction (summa cum laude) from the Technical University of Berlin in 2014 after studying computer science at Humboldt University of Berlin, Heriot-Watt University, and University of Edinburgh. During his studies, he received scholarships from the German Academic Scholarship Foundation and the DFG Research Training Group GRK 1589/1, and was a visiting researcher at NASA Ames Research Center. Professor Samek's research focuses on Explainable AI, where he has made significant contributions to interpreting, explaining, and visualizing deep learning models. His work also extends to neural network compression and federated learning, addressing critical challenges in transparency, efficiency, and privacy in AI systems. His research spans both theoretical foundations and practical applications of artificial intelligence. Senior editor of IEEE TNNLS Editorial board member of PLoS ONE and Pattern Recognition Elected member of the IEEE MLSP Technical Committee Member of scientific advisory board of IDEAS NCBR 2020 Pattern Recognition Best Paper Award Multiple best paper awards Part of the expert group developing the ISO/IEC MPEG-17 NNR standard Recognized for "Highly Cited Papers" (top 1%) in Engineering Professor Samek has co-authored over 150 peer-reviewed publications and serves as associated faculty at BIFOLD – Berlin Institute for the Foundation of Learning and Data and the ELLIS Unit Berlin. He has edited influential books including "Explainable AI: Interpreting, Explaining and Visualizing Deep Learning" (2019) and "xxAI – Beyond explainable AI" (2022), and regularly organizes workshops and tutorials on cutting-edge AI topics. His dual appointments bridge academic research at Technical University of Berlin with applied research at Fraunhofer HHI, positioning him at the forefront of AI innovation in both theoretical and practical domains.

• Hardware Design
• FPGA Programming
• Parallel Computing
• +۵ مورد دیگر

Satnam Singh is a Professor at Newcastle University's School of Electrical and Electronic Engineering, UK. With a research career spanning over three decades from 1989 to present, Singh has established himself as a leading expert in hardware design, FPGA programming, and parallel computing systems. His research interests focus on hardware-software co-design , reconfigurable computing , and functional programming applications for hardware design. Singh has pioneered work in using functional languages like Haskell for hardware description and verification, particularly through his contributions to the Lava hardware description language. His work bridges theoretical computer science with practical hardware implementation challenges. Analysis of his publication history reveals a clear evolution from early work on formal verification and FPGA design in the 1990s, through substantial contributions to parallel programming models in the 2000s, to more recent applications of machine learning techniques in diverse domains including cheminformatics and sensory systems. His 2022-2025 publications demonstrate continued innovation in specialized processor programming, AI applications for olfactory systems, and health hazard classification using deep learning. Singh has maintained extensive collaborations throughout his career, notably with Krishna R. Pattipati (15 joint publications), Anuradha Kodali (8 publications), and David J. Greaves (5 publications), reflecting his ability to bridge theoretical computer science with practical engineering applications. His work spans multiple prestigious venues including FPGA, FCCM, ICFP, and IEEE Transactions on Systems, Man, and Cybernetics, demonstrating both theoretical depth and practical impact across computer architecture, programming languages, and applied machine learning domains.

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +۹ مورد دیگر

Han Chen is an active academic researcher in the fields of artificial intelligence, machine learning, computer vision, and biomedical informatics, with extensive publications in top-tier venues such as CVPR, ICLR, IEEE Transactions, and Bioinformatics. The research spans diverse applications including medical image analysis, deepfake detection, federated learning, and multimodal affective computing. The research interests of Han Chen include artificial intelligence, machine learning, computer vision, medical image analysis, graph neural networks, and cybersecurity. The work emphasizes deep learning architectures, multimodal fusion, and robustness in AI systems, particularly in healthcare and security applications. Key themes include disentangled representation learning, contrastive learning, and transformer-based models. The recent articles demonstrate a strong trend toward multimodal AI, with applications in medical diagnostics (e.g., mammography, nasopharyngeal imaging), trustworthy AI (e.g., deepfake detection, backdoor defense), and efficient learning paradigms (e.g., federated learning, weak supervision). There is a consistent focus on enhancing model interpretability, robustness, and real-world applicability across domains. Scientific Awards: No awards explicitly mentioned in the provided text. Advising and Grants: There is no explicit information about students or advising. However, the high volume of collaborative research suggests active involvement in research teams and likely supervision of graduate students. Funding sources are not mentioned, but the scope of work implies support from national or institutional research grants in AI and health informatics. Labs and Teams: While no specific lab or team is named, the collaborative nature of the work—especially with researchers in medical imaging and federated learning—suggests membership in a multidisciplinary AI research group, possibly affiliated with a medical school or engineering institute.

Gjergji Kasneciمشاهده پروفایل

استاد

• Machine Learning
• Artificial Intelligence Ethics
• Algorithmic Fairness
• +۴ مورد دیگر

Prof. Dr. Gjergji Kasneci is a Professor of Responsible Data Science at the Technical University of Munich (TUM), leading the Chair of Responsible Data Science. He holds affiliations with the TUM School of Social Sciences and Technology and the TUM School of Computation, Information and Technology. His research focuses on ethical, legal, and societal aspects of AI, emphasizing transparency, fairness, and robustness in machine learning algorithms. Prof. Kasneci’s academic journey includes a PhD in Computer Science from the University of Marburg (2009), postdoctoral research at Microsoft Research Cambridge, and leadership roles at the Hasso Plattner Institute and SCHUFA Holding AG. He was an Honorary Professor at the University of Tübingen (2018–2023) and currently serves as Vice Dean and Information Officer at TUM. Key awards include the Seoul Test of Time Award (2018) and an Honorary Professorship from the University of Tübingen (2019). He leads initiatives like the AI in Finance Lab and contributes to AI policy through projects such as the EU-funded AI4POL initiative.

Ali Shojaieمشاهده پروفایل

استاد

• Biostatistics
• Statistical Learning
• Machine Learning
• +۴ مورد دیگر

Ali Shojaie is a Professor of Biostatistics & Statistics at the University of Washington , currently serving as Interim Chair of Biostatistics. His research bridges statistical learning, network analysis, and high-dimensional data modeling with applications in biological and health sciences. Research Focus: Shojaie develops advanced methodologies for causal inference, spatial statistics, and semi-supervised learning. His recent work includes network-based gene set analysis, Granger causality estimation, and regularization techniques for complex data structures. Scientific Contributions: Received the 2022 Leo Breiman Award for innovative statistical learning research Elected as Fellow of the Institute for Mathematical Statistics (IMS) and American Statistical Association (ASA) Secured major NIH grants for projects on gene-phenotype associations and explainable AI in neuroscience Advising & Leadership: His students have won multiple awards at ASA and AISTAT conferences. He co-developed the netgsa and spacejam R packages for network-based analysis and spatial modeling. Current Projects: Shojaie leads NIH-funded research on prefrontal brain stimulation and gene knockout studies, combining statistical theory with interdisciplinary applications in biology and bioengineering.

• Social Network Analysis
• Formal Concept Analysis
• Graph Mining
• +۷ مورد دیگر

Bénédicte Le Grand is a prominent computer science researcher with an extensive publication record spanning over two decades, from 1999 to 2025. Her academic work demonstrates significant contributions across multiple domains within computer science, particularly in social network analysis, formal concept analysis, and graph mining methodologies. She maintains active collaborations with numerous researchers including Marie-Aude Aufaure (16 publications), Michel Soto (13 publications), and Nourhène Ben Rabah (12 publications), indicating her integration within a strong academic network. Her research interests center around information retrieval systems, semantic web technologies, and data mining approaches, with recent work showing a notable shift toward healthcare applications of artificial intelligence. In the past five years, she has published extensively on medical diagnostic applications including Alzheimer's detection, polycystic ovary syndrome detection, and depression identification through social network analysis. Her work also extends to educational data mining, affective computing for workplace stress detection, and explainable AI for security applications. The analysis of her publication trends reveals a consistent research trajectory that has evolved from foundational work in semantic web and topic map visualization in the early 2000s toward increasingly applied machine learning and AI solutions for healthcare and education challenges. Her recent publications demonstrate a strong interdisciplinary approach, bridging computer science with medical and educational domains to develop practical AI solutions. She has been particularly active in French academic conferences such as INFORSID and has contributed to international venues including RCIS (Research Challenges in Information Systems), ICAART (International Conference on Agents and Artificial Intelligence), and AICCSA (International Conference on Computer Systems and Applications), establishing herself as a significant contributor to the European computer science research community.

• Machine Learning
• Natural Language Processing
• Cybersecurity
• +۵ مورد دیگر

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.

• Biomedical Signal Processing
• Medical Imaging
• Computer Security
• +۶ مورد دیگر

Jun Shen is a Professor at Southeast University's School of Computer Science and Engineering in Nanjing, China. With over 100 publications spanning from 1991 to 2025, Professor Shen has established a prolific research career across multiple disciplines including biomedical engineering, computer security, artificial intelligence, and wireless communications. Professor Shen's research interests focus on the intersection of computer science and practical applications, particularly in medical imaging, cybersecurity, and AI-driven solutions for healthcare and engineering problems. His recent work demonstrates significant contributions to breast cancer diagnosis through advanced medical imaging techniques, automotive cybersecurity, and novel approaches to drug discovery using deep learning. Analysis of Professor Shen's recent publications reveals a strong trend toward interdisciplinary research that combines computer vision, machine learning, and domain-specific knowledge to solve complex problems in healthcare and engineering. His work often involves collaborations across institutions and disciplines, indicating an extensive professional network. Professor Shen has made notable contributions to medical image analysis for cancer diagnosis, particularly in breast cancer metastasis detection using multi-parametric MRI and dual-energy CT. His cybersecurity research focuses on automotive systems and cyber-physical systems security. In AI applications, he has developed innovative approaches for drug-target binding prediction and vision-language models for medical applications. Professor Shen's publication record demonstrates consistent productivity with 13 papers in 2024 and 9 papers already published or accepted in 2025, indicating active and ongoing research programs across multiple domains.

• Probability Theory
• Stochastic Processes
• Mathematical Physics
• +۳ مورد دیگر

Lisa Hartung is a Full Professor (W3) at Johannes Gutenberg University Mainz's Department of Mathematics since 2025, leading research in stochastic processes. She previously held Associate (2019-2025) and Assistant Professor (2019) positions at the same institution and served as a Courant Instructor at NYU's Courant Institute (2016-2018). Her educational background includes a PhD from Bonn University (2016) under Prof. Anton Bovier in Probability Theory. Key research interests span branching Brownian motion , Gaussian free fields , random energy models , and stochastic processes on evolving networks , with applications in statistical mechanics and mathematical physics. Her work frequently addresses extremal processes, phase transitions, and complex temperature phenomena. Analysis of her recent publications reveals dominant trends in log-correlated random fields, variable-speed branching processes, and hard-wall boundary conditions. Articles consistently explore asymptotic behavior, maximum processes, and structural properties of stochastic systems, with increasing interdisciplinary connections to computer science through data stream modeling. Scientific recognition includes: Förderpreis der Fachgruppe Stochastik (2018) Hartung secures substantial research funding as PI for multiple high-impact projects: the ANR-DFG grant Random Energy Models (2020), DFG's SPP 2265 Random Geometric Systems (2020), TRR146 Multiscale Simulation Methods (2020), and Carl-Zeiss project TOPML (2022). She also serves on the executive board of DMV Fachgruppe Stochastik (since 2023) and coordinates Jugend trainiert Mathematik for grades 8/9. Her research group participates in the DFG-funded network Stochastic Processes on Evolving Networks and collaborates internationally through workshops on interacting particle systems and stochastic population models.

• Clinical Natural Language Processing (NLP)
• Medical Informatics
• Machine Learning
• +۳ مورد دیگر

Jens-Michalis Papaioannou is a prominent Researcher in clinical natural language processing (NLP) and medical informatics, with extensive publications in top-tier venues like ACL, LREC, and EMNLP. His work focuses on improving clinical decision support systems through advanced machine learning techniques. 2024 : Revisiting clinical outcome prediction for MIMIC-IV with biomedical transformers 2023 : Developing MEDBERT.de for German medical NLP and MedAlpaca conversational AI 2022 : Introducing ProtoPatient for interpretable diagnosis prediction 2021 : Creating self-supervised knowledge integration frameworks for admission note analysis His research spans seven major themes : Clinical outcome prediction from admission notes Cross-lingual knowledge transfer in medical NLP Prototypical network applications Data drift analysis in longitudinal datasets Knowledge integration techniques Model optimization for healthcare LLM interpretability frameworks He has collaborated with Wolfgang Nejdl, Alexander Löser, and Betty van Aken on 13+ publications , with over 445 citations. Notable contributions include: Novel patient similarity modeling approaches ICD code hierarchy integration methods Multilingual clinical model strategies Adversarial robustness analysis Medical conversational AI frameworks

Eirini Ntoutsiمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Machine Learning
• Data Mining
• +۶ مورد دیگر

Eirini Ntoutsi is an Associate Professor at the Faculty of Electrical Engineering and Computer Science at Leibniz University Hannover and a member of the L3S Research Center. Her academic journey includes a post-doctoral position at LMU Munich with an Alexander von Humboldt Foundation fellowship, and she earned her PhD from the University of Piraeus, Athens under the supervision of Y. Theodoridis. She holds a diploma and M.Sc. in Computer Engineering & Informatics from the University of Patras, Greece. Her research lies at the intersection of Artificial Intelligence and Machine Learning, focusing on two main pillars: learning over complex data and data streams (covering adaptive learning, change detection, and model stability), and responsible Artificial Intelligence (covering fairness-aware learning, data quality, and proper evaluation of AI/ML methods). Her work addresses critical societal challenges related to bias in algorithmic decision-making systems. Dr. Ntoutsi's recent publications demonstrate a strong focus on drift-aware learning for imbalanced data streams and fairness in AI systems. Her research shows a clear progression from foundational work in pattern management during her PhD to cutting-edge applications addressing real-world challenges in social streams, sensor data, and recommendation systems. Alexander von Humboldt fellowship for postdocs Best-student paper award at ICBK 2018 1st place in the 2006 innovation competition in Greece Dr. Ntoutsi actively mentors PhD and master's students while leading major research initiatives including NoBIAS (EU-funded, as Network coordinator), BIAS (Volkswagen Stiftung-funded), and OSCAR (DFG-funded). Her interdisciplinary approach combines technical AI solutions with philosophical and legal considerations to develop more equitable algorithmic systems.

• Empirical Software Engineering
• Code Quality Analysis
• Software Maintenance
• +۷ مورد دیگر

Rocco Oliveto is a Professor in the Department of Computer Science at the University of Salerno, Italy, with a distinguished research career spanning over two decades in empirical software engineering. His work bridges theoretical software engineering principles with practical applications, with recent expansion into healthcare informatics and machine learning applications. His research interests focus on code quality assessment, software maintenance practices, developer behavior analysis, and empirical studies of software engineering phenomena. He has made significant contributions to understanding code smells, bug prediction, API compatibility issues, and more recently, container technologies and smart contract analysis. His recent work demonstrates a strategic expansion into healthcare applications, leveraging software engineering techniques for medical diagnostics and rehabilitation systems. Oliveto's publication pattern shows consistent productivity with multiple high-impact publications each year across top venues including IEEE Transactions on Software Engineering, ACM Transactions on Software Engineering and Methodology, and Empirical Software Engineering journal. His recent articles (2023-2025) reveal a growing interest in applying software engineering techniques to healthcare domains while maintaining strong contributions to core software engineering topics. The research demonstrates sophisticated methodological approaches combining empirical studies with machine learning techniques. His collaborative network includes prominent researchers such as Simone Scalabrino, Gabriele Bavota, and Andrea De Lucia, with whom he has co-authored numerous high-impact publications. This collaboration spans both traditional software engineering topics and emerging interdisciplinary applications in healthcare.

Rezaul Karimمشاهده پروفایل

پژوهشگر

• Machine Learning
• Explainable AI
• Natural Language Processing
• +۶ مورد دیگر

Rezaul Karim is a Sr. Data Scientist at ALDI SÜD - International Data & Analytics and Guest Researcher at RWTH Aachen University's Faculty of Computer Science, Department of Computer Science 5. He completed his PhD in Explainable AI at RWTH Aachen University with distinction (Summa Cum Laude) under the supervision of Prof. Dr. Stefan Decker. His research focuses on machine learning, explainable AI, and their applications in healthcare and bioinformatics. Karim has developed methods for cancer susceptibility prediction from multiomics data, explainable knee osteoarthritis diagnosis, and privacy-preserving analytics for sensitive medical data. His work bridges the gap between theoretical machine learning and practical applications in medical diagnostics and biological data analysis. Karim's publication record shows consistent output in top venues, with recent work emphasizing interpretable models for complex biomedical problems. His research integrates machine learning with semantic web technologies to create data and knowledge-driven analytical solutions that maintain model transparency. Best paper (NLP) award at a top data science conference ICT Young Researcher Award 2020 for significant contributions to ICT-related research As an educator, Karim teaches Data Science in Medicine Seminar (WS 2025) and has previously taught courses on building Large Language Model Applications and Semantic Web technologies. His industry experience at Fraunhofer FIT and ALDI SÜD provides practical context to his academic work, ensuring his research addresses real-world challenges in data science and machine learning deployment.

Marion Büttgenمشاهده پروفایل

استاد

• Digital Transformation
• Organizational Behavior
• Service Management
• +۴ مورد دیگر

Prof. Dr. Marion Büttgen holds the Chair of Business Administration (Corporate Management) at the University of Hohenheim since March 2009. Previously, she was a professor of Service Management at Stuttgart Media University (2006–2009). She earned her Ph.D. in Economics from the University of Cologne (1999) and completed her habilitation in Business Administration there in 2006, focusing on consumer integration behavior in interactive service provision. Her research focuses on digital transformation, organizational behavior, AI-driven service innovation, and sustainable business practices. Education: 1988–1993: Business Administration studies at University of Cologne and University of St. Gallen 1987–1988: Economics studies at University of Bonn 1999: PhD in Economics (University of Cologne) 2006: Habilitation in Business Administration (University of Cologne) Research Interests: Digital leadership frameworks, AI in public services, sustainability strategies, employee-customer interaction dynamics, and technology-induced stress in service environments. Key Contributions: Pioneered frameworks for human-AI collaboration, developed models explaining customer participation stress, and explored leadership behaviors during digital transformation. Her work bridges academic theory with practical applications in service industries.

Florian Kalteneckerمشاهده پروفایل

پژوهشگر

• Food Informatics
• Artificial Intelligence
• Machine Learning
• +۳ مورد دیگر

Florian Kaltenecker is a research assistant and PhD student at the Department of Food Informatics, part of the Institute of Food Science and Biotechnology within the Faculty of Natural Sciences at the University of Hohenheim. His work focuses on developing hybrid digital twin models for spray drying processes using physics-informed machine learning, explainable AI, and transfer learning. Prior to his current role, he served as a student assistant in Plant Evolutionary Biology and Food Informatics departments. He holds a M.Sc. in Agricultural Biology (2021-2024) and a B.Sc. in Biology (2017-2021) from the University of Hohenheim. His research aligns with interdisciplinary goals in food science and sustainability, emphasizing computational methods to optimize food processing systems. He contributes to projects addressing digital transformation in food supply chains and collaborates with industry partners through the Computational Science Hub. Current efforts include enhancing model interpretability and scalability for industrial applications.