جست‌وجوی استاد و پژوهشگر

• Distributed Systems
• Networks
• Data Stream Analysis
• +۹ مورد دیگر

Yann Busnel is a Full Professor at IMT Atlantique in France, where he serves as Head of the Network Systems, Cyber Security and Digital Law (SRCD) department at the Rennes Campus since January 2017. He also leads the D2-NTS department (Networks, Telecommunications and Services) within UMR IRISA. His academic journey includes positions as Assistant Professor at Université de Nantes (2009-2014), Associate Professor and Head of Computer Science Department at ENSAI (2014-2016), and postdoctoral work at Università "La Sapienza" in Rome. Busnel's research focuses on models for large-scale distributed systems and networks, with particular expertise in data stream analysis over massive datasets. His work spans cybersecurity, dependability, medical data analysis (including pharmacovigilance and genomic sequence analysis), and the self-organized coordination of drone fleets for disaster management. He has published over 100 peer-reviewed papers and coordinated numerous national and international research projects. His recent scholarly output demonstrates a clear progression toward applied cybersecurity solutions, particularly in federated learning for intrusion detection, blockchain analysis, and wireless network security. The research shows strong interdisciplinary connections between distributed systems theory and practical security applications, with increasing emphasis on medical data analysis and drone coordination systems. His work bridges theoretical computer science with real-world security and networking challenges. Busnel actively mentors PhD students across multiple institutions, with current advisees working on topics including AI in cybersecurity using game theory, threat intelligence tools for IoT, and trust in distributed network services. He has coordinated substantial research funding through projects like CMA TCE (France 2030), EDIH Bretagne (Horizon Europe), and various ANR-funded initiatives. He leads several research chairs including OM&AI2MD (Operations Models and Artificial Intelligence to Manage Disasters), PRACom (Pôle de Recherche Avancée en Communications), and Cyber CNI (Cyber Security of Critical Infrastructures), demonstrating his leadership in both academic research and industry collaboration. His work with these chairs focuses on practical applications of his theoretical research in disaster management, communications technology, and critical infrastructure protection.

Zhenzhang Yeمشاهده پروفایل

پژوهشگر

• Optimization
• Photometry-Based Reconstruction
• Visual SLAM
• +۴ مورد دیگر

Zhenzhang Ye is a researcher affiliated with the Computer Vision Group at the Technical University of Munich (TUM) , part of the TUM School of Computation, Information and Technology. His work focuses on Photometry-Based Reconstruction , Optimization , and Geometry Processing , with additional interests in Visual SLAM , Deep Learning , and Biomedicine . Research Interests : Optimization techniques, Photometry-Based Reconstruction, and geometric processing for computer vision tasks. Publications : Active contributor to conferences like CVPR, AISTATS, AAAI, and ICCV, with recent works on 3D human motion prediction, hypergradient estimation, and photometric stereo. Contact: yez@in.tum.de

• Gamification in Health Behavior Change
• Digital Health Interventions
• Human-Computer Interaction
• +۴ مورد دیگر

Dr. Manuel Schmidt-Kraepelin is a postdoctoral researcher at the Information Infrastructures group (Technical University of Munich), focusing on gamification for health behavior change and distributed ledger technology applications in healthcare. With dual master's (2016) and bachelor's (2013) degrees in Information Systems from the University of Cologne, he brings extensive experience from KIT, University of Kassel, and University of Cologne research roles. His work combines behavioral science with technical systems design. As a reviewer for major IS journals and conferences (CAIS, ECRA, HICSS, ICIS), he contributes to academic quality assurance. Recipient of multiple best reviewer awards and the prestigious Dean's Award at Cologne, he was honored with the 2014-2016 Deutschlandstipendium and a 2020 DAAD travel grant. Key research areas: gamification mechanics, health behavior change, blockchain in healthcare, narrative transportation theory Recent publications examine gamified procrastination mitigation, expert intuition in medical annotation, and privacy calculus in genetic data sharing Developed taxonomy for gamification concepts in health apps and identified gamification user archetypes Active in design science research and literature synthesis methodologies Awarded best paper recognitions at ICIS and HICSS, with 15+ recent publications across top venues including JMIR, CHI, and IEEE Transactions. Currently teaches Master's seminars on gamified information systems at TUM's Heilbronn campus.

Michelangelo Ceciمشاهده پروفایل

استاد

• Data Mining
• Machine Learning
• Relational Data Mining
• +۵ مورد دیگر

Michelangelo Ceci is a full professor at the Department of Computer Science, University of Bari, Italy. His academic career spans over two decades with significant contributions to data mining and machine learning research. He has established himself as a leading figure in the European data mining community through his extensive publication record and leadership roles in major conferences. Dr. Ceci's primary research interests focus on data mining and machine learning, with particular emphasis on multi-relational data mining, text mining, spatial data mining, spatio-temporal data mining, and semi-supervised/transductive learning. His work bridges theoretical foundations with practical applications across diverse domains including social network analysis, financial technology, healthcare informatics, and environmental monitoring. His research demonstrates a consistent trajectory toward increasingly complex data structures and more sophisticated modeling techniques. The most recent publications reveal several key trends: a growing focus on explainable AI systems, increased application of graph-based methods for spatio-temporal data, expansion into biomedical applications particularly in microbiome analysis, and development of robust methods for anomaly detection in cryptocurrency and social networks. His work increasingly integrates multiple data sources and perspectives through multi-view learning approaches. Dr. Ceci has served on the program committees of major international conferences including IEEE ICDM, IJCAI, ECMLPKDD, SIAM SDM, ECAI, ISMIS, PAKDD, DEXA, and ACM SAC. He has held editorial positions with journals such as IJSNM, IJDATS, and Journal on Advances in Intelligent Systems, and has been actively involved in organizing conference tracks and workshops. His research has been supported through significant projects including serving as national coordinator of FP7612944 MAESTRA and coordinator of a research unit in the PONREC project Vi-POC. He has participated in numerous national (PRIN-COFIN 2001, 2009) and international research projects (IST-1999-20882: COLLATE). Dr. Ceci has also contributed to the academic community through mentoring PhD students and early-career researchers, though specific names of advisees are not documented in the available information.

Saso Dzeroskiمشاهده پروفایل

استاد

• Machine Learning
• Data Mining
• Computational Scientific Discovery
• +۷ مورد دیگر

Sašo Džeroski is a Professor at the Department of Knowledge Technologies, Jožef Stefan International Postgraduate School, and a Scientific Councillor at the Jožef Stefan Institute in Slovenia. He leads a research group of ~17 members, focusing on constraint-based data mining, structured output prediction, and automated modeling of dynamic systems, with applications in systems biology, ecology, and environmental sciences. His research interests span data mining, machine learning, computational scientific discovery, and their applications in life sciences and medicine. He has coordinated major projects like MAESTRA (FP7) and LANDMARK (H2020), and contributed to EU initiatives in systems biology ( E.E.T. Pipeline , PHAGOSYS ). He developed methodologies for predictive clustering trees and hierarchical multi-label classification, with software tools ProBMoT and CLUS . The 15 most recent articles highlight his work on ensemble learning for multi-output systems, structured output prediction in medical imaging, ecological modeling using machine learning, and ontologies for data mining. These publications reflect interdisciplinary trends combining computational methods with applications in fungal biology, endocytosis, and agricultural drainage. Scientific Awards: Foreign Member, Macedonian Academy of Sciences and Arts Advising includes mentoring PhD students (e.g., Nikola Simidjievski, Jovan Tanevski) and MSc students. Grants and projects emphasize EU funding (H2020, FP7) and national initiatives in knowledge technologies. He has served on editorial boards of journals like Machine Learning and Ecological Informatics , and organized key conferences (e.g., ECML PKDD-2015 ).

• Machine Learning
• Bioinformatics
• Drug Discovery
• +۵ مورد دیگر

Sandor Szedmak serves as a Research Fellow in the Department of Computer Science at Aalto University, Finland, and is affiliated with the Helsinki Institute for Information Technology (HIIT). He operates within Professor Juho Rousu's research group, focusing on interdisciplinary machine learning applications with strong ties to computational biology and bioinformatics. His institutional presence is active, as evidenced by current contact details and ongoing publication output. Dr. Szedmak's research specializes in developing advanced machine learning frameworks for complex biological and educational challenges. His primary interests include drug combination effect prediction, protein function annotation through multi-view learning, strain design optimization using reinforcement learning, and personalized educational systems for computer science students. He pioneers methods in latent tensor reconstruction, scalable variable selection, and kernel generalization, targeting high-dimensional data problems in pharmacology and computational biology. His publication trajectory from 2020-2025 reveals a concentrated effort in bioinformatics applications, with landmark papers in Nature Communications and Bioinformatics on drug combination prediction. The work consistently leverages tensor factorization and multi-view learning to model biological systems, while recent expansions include educational data mining for programming behavior analysis. This demonstrates both methodological consistency in machine learning innovation and strategic domain diversification. No scientific awards or fellowships are documented in the provided materials. While no student advisees or grant details are explicitly listed, Dr. Szedmak's collaborative publication pattern—particularly within the Rousu research group and HIIT—suggests active participation in team-based research initiatives. His work appears funded through institutional channels given the consistent output in computational biology. He operates within the Helsinki Institute for Information Technology (HIIT), a joint research institute of Aalto University and the University of Helsinki, and is embedded in Professor Juho Rousu's research group. This environment facilitates cross-disciplinary collaboration between computer science and life sciences, with infrastructure supporting high-performance computing for large-scale biological data analysis.

• Bioinformatics
• Machine Learning
• Computational Biology
• +۳ مورد دیگر

Robert Ebo Armah-Sekum is a Doctoral Researcher at Aalto University's Department of Computer Science, specializing in bioinformatics and machine learning. His research focuses on applying tensor-based multi-view multi-label learning techniques to computational biology problems. His work bridges data science methodologies with biological applications, particularly in protein function prediction. Current collaborations include Sandor Szedmak and Juho Rousu in the development of latent tensor reconstruction approaches for biomolecular analysis.

• Machine Learning
• Natural Language Processing
• Data Mining
• +۳ مورد دیگر

Jinbin Zhang is a Doctoral Researcher at the Department of Computer Science, Aalto University. Their work intersects machine learning and computational linguistics, focusing on extreme multi-label classification and text analysis. Research Interests : Large Language Models and Zero-shot Learning Extreme Multi-label Classification Historical Text Analysis and Genre Detection Publications : 2025: LLM-based zero-shot tagging 2024: Calibration in extreme multi-label classification 2022: Sequential genre change detection in historical texts

• large scale learning
• extreme multi-label classification
• deep learning
• +۲ مورد دیگر

Rohit Babbar serves as an Assistant Professor in the Department of Computer Science at Aalto University, Finland, leading a research group dedicated to advancing large-scale machine learning methodologies. His team specializes in tackling computational challenges inherent in extreme classification problems with massive output spaces while ensuring model robustness. His primary research domains encompass large-scale learning systems, extreme multi-label classification architectures, deep learning integration, sequential data processing, and robustness engineering. This work directly addresses industry pain points like computational inefficiency in massive label spaces and model vulnerability to distribution shifts, with applications spanning natural language processing, information retrieval, and recommendation systems. Publication trends reveal a strategic focus on algorithmic innovation for extreme classification, featuring breakthroughs in dynamic sparsity techniques, large language model integration for zero-shot scenarios, and calibration of extreme classifiers. Recent work demonstrates consistent emphasis on computational efficiency through optimized negative sampling, lightweight frameworks like InceptionXML, and specialized metrics for long-tail performance evaluation. Scientific recognition includes: Outstanding Reviewer Award at ACL 2021 Conference (July 2021) for exceptional contributions to computer science peer review As research group leader, Babbar directs collaborative efforts on next-generation classification systems while mentoring emerging scholars in machine learning. His team maintains active partnerships with industry leaders in search and recommendation technologies. The research group operates at the intersection of theoretical machine learning and practical deployment, developing frameworks that balance computational feasibility with predictive accuracy in extreme-scale environments. Current initiatives focus on integrating foundation models with specialized classification architectures while addressing real-world challenges like data sparsity and concept drift.

Dan Xuمشاهده پروفایل

پژوهشگر

• Computer Vision
• Machine Learning
• Deep Learning
• +۸ مورد دیگر

Dr. Dan Xu is a Postdoctoral Researcher at the Visual Geometry Group (VGG) within the Department of Engineering Science at the University of Oxford. His research focuses on computer vision, machine learning, and deep learning, particularly for 2D/3D scene understanding tasks including depth estimation, object detection, and image generation. Ph.D. in Computer Science (2018), University of Trento Research Assistant, Chinese University of Hong Kong Dr. Xu's research spans computer vision and deep learning , with specific interests in scene depth prediction , visual SLAM , object contour detection , and generative adversarial networks . Recent work explores 3D Gaussian splatting , diffusion models , and multi-task learning . Key research trends include 3D scene reconstruction , controllable video generation , and multi-modal alignment . His publications emphasize neural radiance fields , attention mechanisms , and generative models for advanced visual tasks. Best Paper Award Nominee at ACM Multimedia 2018 Best Scientific Paper Award at ICPR 2016 Student Travel Grant (SIGMM/ACM Multimedia 2016) Dr. Xu contributes to open-source projects and provides training/testing code for his research. He actively reviews for premier journals and conferences including CVPR , NeurIPS , and TPAMI .

• Audio Signal Processing
• Explainable AI
• Speaker Localization
• +۳ مورد دیگر

Théo Mariotte is an Assistant Professor at Le Mans University, affiliated with the LST team within the LIUM laboratory. His research focuses on lightweight and explainable audio processing systems, particularly for speaker localization and segmentation in multi-microphone environments. His primary research interests include Audio Signal Processing , Explainable AI , Speaker Localization , and Machine Learning for audio applications. Mariotte develops efficient algorithms for speaker segmentation, speech separation, and overlapped speech detection using multimicrophone arrays, with emphasis on interpretability and real-world applicability in meeting scenarios. Mariotte's publication record shows a strong focus on multimicrophone audio processing, with recent work spanning speaker segmentation (2025), multiple choice learning for speech separation (2025), explainable audio segmentation (2024), and beamformer selection for speaker diarization (2024). His research demonstrates consistent innovation in handling overlapped speech and developing lightweight, interpretable models for real-world audio applications. No scientific awards were mentioned in the source material. Mariotte actively contributes to open-source audio processing tools, including repositories for spherical acoustic holography, speaker localization algorithms, and MATLAB audio processing tools. His work bridges theoretical machine learning with practical audio engineering applications. He operates within the LIUM laboratory's LST (likely Speech and Text) team, focusing on developing robust audio processing solutions for meeting environments using distributed microphone systems.

Adam L Meyersمشاهده پروفایل

عضو هیئت علمی

• Computational Linguistics
• Natural Language Processing
• Machine Translation
• +۷ مورد دیگر

Adam L Meyers is a Visiting Clinical Associate Professor at New York University 's Rory Meyers College of Nursing , where he contributes to academic excellence and clinical education. Despite his title in a health-focused institution, his extensive publication record reveals a strong background in Computational Linguistics and Natural Language Processing through affiliations with NYU's Linguistic Annotation and Machine Translation initiatives.

• Machine Learning
• Medical Imaging
• Artificial Intelligence
• +۴ مورد دیگر

Vanya Valindria is an Assistant Professor at Monash University specializing in Artificial Intelligence applications for medical diagnostics, with active supervision of PhD students in machine learning for medical image analysis. Her academic foundation includes a Doctoral degree in Artificial Intelligence from Imperial College London (2015–2019), where her thesis investigated machine learning approaches for whole-body scan analysis. Her research centers on AI-driven medical image interpretation , particularly tuberculosis detection from chest radiographs using advanced neural architectures. She extends this work into generative AI for marketing applications and critically examines ethical frameworks for global healthcare AI , with strong emphasis on feminist perspectives and data politics in algorithmic systems. Publication trends reveal a strategic shift from pure technical innovation toward socio-technical integration, combining hybrid deep learning models (Vision Transformers with CNNs) with ethical implementation studies in resource-limited settings like Indonesia. She actively mentors PhD candidates while contributing extensively to academic service through peer review for Medical Image Computing conferences and leadership in the 'Advancing AI Capacity in Indonesian Universities' initiative, which trains faculty across 15+ institutions in AI curriculum development.

François HUمشاهده پروفایل

مدرس

• Machine Learning
• Insurance
• Finance
• +۷ مورد دیگر

François HU is a Lead AI Researcher and Lecturer in Machine Learning and Computational Statistics at institutions including Conservatoire national des arts et métiers (Cnam), Institut Polytechnique de Paris (ENSAE), EPITA, and Institut des Actuaires. Since 2024, he has led the R&D AI Lab at Milliman France, focusing on Generative AI and Trustworthy AI for insurance and finance applications. PhD in Machine Learning and Insurance (2019-2022), Institut Polytechnique de Paris (CREST-ENSAE) Postdoctoral Researcher (2022-2024), Université de Montréal (Department of Mathematics and Statistics), affiliated with MILA and Algora Lab His research spans algorithmic fairness, semi-supervised learning, NLP, and GenAI, with applications in insurance, biostatistics, and finance. He developed an Early Warning System for Infectious Diseases under the Mathematics for Public Health initiative and contributed to ESG concept identification in Canadian companies via Algora Lab. Recent publications focus on fairness in multi-class classification, Wasserstein barycenters for bias mitigation, and trustworthy AI frameworks. Scientific awards include the 2022 French best actuarial science thesis award. He has taught Python programming, numerical algorithms, Bayesian machine learning, and optimization at EPITA and Institut Polytechnique de Paris, and developed educational materials including notebooks and datasets for practical workshops.

• Neurology
• Proteomics
• Neurodegenerative Disorders
• +۴ مورد دیگر

Prateek Kumar serves as an Associate Research Scientist in the Department of Neurology at Yale School of Medicine, where he is a core member of the Rangaraju Lab. His work bridges advanced proteomic methodologies with neurological disease mechanisms, focusing on translational applications for neurodegenerative disorders and stroke. Dr. Kumar's research centers on cell-type specific proteomics in neurodegenerative contexts, particularly leveraging TurboID proximity labeling to map molecular alterations in Alzheimer's disease and stroke models. His investigations target parvalbumin interneurons , astrocytes , and microglia , revealing metabolic vulnerabilities and neuroinflammatory pathways. Current projects emphasize multi-omics integration and machine learning for biomarker discovery, with direct implications for diagnostic tools and therapeutic targeting. Analysis of his recent publication trajectory (2024-2025) shows concentrated innovation in plasma proteomics for stroke diagnostics , cellular subcompartment proteomics , and neuroimmune interactions . Key themes include distinguishing stroke mimics from transient ischemic attacks, identifying atrial fibrillation biomarkers in stroke patients, and characterizing Kv1.3 channel roles in neuroinflammation—demonstrating methodological rigor across in vivo models and clinical datasets. Within the Rangaraju Lab, Dr. Kumar contributes to Yale's collaborative neuroscience ecosystem through the Yale Center for Experimental Neuroimaging and Clinical Neurosciences Imaging Center , focusing on proteomic signatures of early Alzheimer's pathology and ischemic injury. His work directly supports the department's mission in neurodegenerative disease research and clinical translation.