جست‌وجوی استاد و پژوهشگر

• Machine Learning
• Data Systems
• Resource-Aware Computing
• +۵ مورد دیگر

Pinar Tözün is an Associate Professor and Head of the Data, Systems, and Robotics department at the IT University of Copenhagen. She leads multiple research groups, including the Resource-Aware Data Systems and Data-intensive Systems and Applications. Her roles also include academic responsibility for DASYA and involvement in the Center for Climate IT. Her research focuses on resource-aware computing, machine learning systems, heterogeneous hardware optimization, and sustainable data management. Key areas include workload characterization, GPU utilization, and benchmarking frameworks for edge devices and cloud systems. Dr. Tözün has led significant projects such as MOTH (Machine Learning on Tiny Hardware), RAD+ (Resource-Aware Data Science), and DAPHNE (Integrated Data Analysis Pipelines), funded by institutions like the Novo Nordisk Foundation and the European Commission. She has published extensively in top venues, including Proc. ACM Manag. Data and Dagstuhl Reports, with a focus on efficient machine learning pipelines and hardware-aware systems. Her work has been highlighted in media discussions on sustainable hardware and software practices. Tözün also actively participates in academic governance, serving on hiring committees and shaping future faculty recruitment in data-intensive systems.

Birger Andersenمشاهده پروفایل

استاد

• Internet of Things
• Cybersecurity
• Wireless Communication
• +۴ مورد دیگر

Birger Andersen is a Professor in the Department of Engineering Technology and Didactics at the Technical University of Denmark (DTU), specializing in Energy Technology and Computer Science. His research actively contributes to UN Sustainable Development Goals through IoT security, wireless communication, and machine learning applications. He leads multiple projects including Machine Learning for Beehives Monitoring and Towards The 6G Authentication Protocol. Andersen's research spans post-quantum cryptography for IoT devices, security vulnerabilities in autonomous vehicle infrastructures, and wireless protocol analysis (LoRaWAN/NB-IoT). His work integrates machine learning for environmental monitoring and addresses jamming attacks on critical communication systems. Key focus areas include cryptographic algorithm implementation on microcontrollers and securing next-generation 6G networks. Recent publications reveal strong trends toward hybrid post-quantum cryptography solutions for resource-constrained IoT devices and 6G authentication systems. His research emphasizes practical security implementations in wireless communication protocols, with significant attention to LoRa-based systems and autonomous vehicle infrastructures. The work consistently bridges theoretical cryptography with real-world hardware constraints. Andersen supervises PhD student Turnip, T. N. (6G Authentication Protocol) and Master's student You, L. (Nature Assessment via Remote Sensing). His research is funded through projects like Machine Learning for Beehives Monitoring (2023-2026) and Jamming Against Critical Wireless Communication (2022-2023), with industry collaborations through InnoTech - TaskForce and F2D2: The Community for Dynamic Data. He is integral to DTU's Cybersecurity Labs and contributes to the Summer School in Cyber Security. His F2D2 project builds smart city cybersecurity frameworks through the Community for Dynamic Data initiative, addressing urban security challenges while advancing green transition technologies.

Bin Yangمشاهده پروفایل

استاد

• Computer Science
• Data Management
• Machine Learning
• +۹ مورد دیگر

Bin Yang is a Professor at the Department of Computer Science, Aalborg University, affiliated with The Technical Faculty of IT and Design. His research focuses on data engineering, artificial intelligence, and machine learning, with emphasis on spatiotemporal data analysis, representation learning, and time series forecasting. He leads projects like aSTEP (spatio-temporal data analytics) and Light-AI for Cognitive Power Electronics. Key contributions include advancements in trajectory data processing, anomaly detection, and lightweight neural architectures. Research interests span machine learning, data mining, intelligent transport systems, and material science applications. Notable awards include the IJCAI 2019 Distinguished PC Member and Sapere Aude Research Leader (2018). He has supervised six PhD students and contributed to over 119 publications. His work is supported by grants from Villum Foundation and EU initiatives. Yang is involved in interdisciplinary collaborations, including data-driven decision-making frameworks and environmental monitoring systems. Labs and teams include the Daisy Center for Data-intensive Systems and AI for the People initiatives. Projects emphasize real-world applications in smart cities, traffic forecasting, and sustainable technologies. His research bridges theoretical foundations with practical implementations in domains like oceanography and crowdsourcing systems.

Titus Theodorus Robroekمشاهده پروفایل

پژوهشگر

• Machine Learning
• Deep Learning
• Resource-aware Computing
• +۴ مورد دیگر

Titus Theodorus Robroek is a Postdoctoral Researcher at IT University of Copenhagen, working within the Data, Systems, and Robotics section. His research focuses on resource-aware data systems and data-intensive systems and applications, with affiliations to both the Resource-Aware Data Systems group and the Center for Climate IT. Dr. Robroek's research interests span multiple areas in machine learning systems, including: Resource-aware Machine Learning Deep Learning Training Optimization GPU Computing and Collocation ML Benchmarking Frameworks Data Management for Machine Learning Scientific Visualization for Training Systems His recent publications demonstrate a strong focus on optimizing machine learning workflows, particularly in resource-constrained environments. His work addresses critical challenges in data selection, pipeline orchestration, and efficient utilization of hardware resources for deep learning training. The research shows a progression from foundational work on data management to more comprehensive frameworks for resource-aware machine learning. Dr. Robroek has received research funding through two major projects: MOTH: Machine Learning on Tiny Hardware (Novo Nordisk Foundation, 2023-2026) RAD: Extremely Parallel and Incredibly Diverse Data Processing on Many Heterogeneous Cores (Independent Research Foundation of Denmark, 2021-2025) His PhD thesis "Resourceful Learning: Training More Models with Fewer Resources" (2024) represents a significant contribution to the field of efficient machine learning systems.

• Machine Learning
• Natural Language Processing
• Sustainable Artificial Intelligence
• +۳ مورد دیگر

Casper Dorph-Jensen serves as a Lecturer in the Department of Computer Science at the University of Copenhagen, where he is an active member of the Machine Learning section. His academic work bridges theoretical machine learning foundations with practical applications across diverse domains, contributing to both research and educational initiatives within Denmark's premier computing institution. His research spans multiple critical frontiers in artificial intelligence. Key interests include: Natural Language Processing with emphasis on emotion-aware dialogue systems and cross-cultural adaptation frameworks Sustainable AI development addressing environmental impacts of large models Quantum machine learning applications for biomolecular simulations Medical image analysis techniques for clinical diagnostics Fairness-aware information retrieval systems and recommender algorithms Analysis of his 2024-2025 publications reveals strong interdisciplinary trends combining machine learning with quantum physics, healthcare informatics, and sustainability science. Notable patterns include the application of large language models to clinical contexts (particularly nursing values evaluation), energy efficiency concerns in AI infrastructure, and novel quantum-classical hybrid approaches for scientific computing. His work frequently addresses real-world implementation challenges in noisy environments and resource-constrained settings. No scientific awards were documented in available sources. Information regarding student supervision or specific research grants remains unavailable in current public records, though his Machine Learning section affiliation suggests participation in broader departmental initiatives like the SCIENCE AI Centre and TreeSense remote sensing project. He operates within the Department of Computer Science's Machine Learning section, which maintains dedicated high-performance computing resources and participates in the university-wide SCIENCE AI Centre. This section focuses on both theoretical ML foundations and applications in medical imaging, biological data modeling, and sustainability-focused computing, with recent projects including quantum computing initiatives and environmental monitoring systems.

• Resource-Aware Machine Learning
• Data Processing on Resource-Constrained Hardware
• Storage Systems
• +۵ مورد دیگر

Pınar Tözün is an Associate Professor at the IT University of Copenhagen (ITU) since 2018, where she leads the Data, Systems, and Robotics section and the Resource-Aware Data Systems (RAD) team. Her research focuses on optimizing modern hardware (processors and storage) for data-intensive systems, with recent emphasis on resource-aware machine learning, hardware-constrained data processing, and advancements in SSD/CXL technologies. Before ITU, she was a research staff member at IBM Almaden Research Center (2015–2018), contributing to the IBM Db2 Event Store development. She holds a PhD from École Polytechnique Fédérale de Lausanne (EPFL, 2014), where she worked on the Shore-MT storage manager under Prof. Anastasia Ailamaki. She earned her bachelor’s degree in Computer Engineering from Koç University (Turkey, 2009), advised by Prof. Serdar Taşıran. Her research interests span Resource-aware machine learning, Data management systems, Edge computing, High-performance storage, and sustainable AI practices. Her work bridges hardware and software optimization, addressing challenges in distributed systems and IoT environments. She actively contributes to academic communities through workshops like DBTest and diversity initiatives in database conferences.

Peter Schneider-Kampمشاهده پروفایل

استاد

• Data Science
• Artificial Intelligence
• Machine Learning
• +۵ مورد دیگر

Peter Schneider-Kamp is a Professor of Data Science at the Department of Mathematics and Computer Science, University of Southern Denmark. His research focuses on Artificial Intelligence, Machine Learning, Privacy-Preserving Techniques, and Algorithms. He has led projects such as the Danish Foundation Models initiative and the PREPARE cardiovascular disease project. His work spans synthetic data frameworks (e.g., Syntheval), quantization-aware neural networks (BitNet), and autonomous drone systems (Drones4Safety). He has been honored with awards including the Researcher Award 2014 and the Friedrich-Wilhelm-Preis 2009. Key research interests include optimizing sorting networks, termination analysis, and UAV-based infrastructure inspection. He has contributed to over 112 publications and actively participates in academic activities like organizing the 13th ACM SIGPLAN Symposium on Principles and Practice of Declarative Programming. Schneider-Kamp also engages in educational roles, teaching courses such as DS806 and DM564 on database systems. His projects highlight interdisciplinary impact, including cardiovascular disease risk estimation (PREPARE) and AI-driven health advice analysis. He collaborates internationally, with recent work featured in venues like the International Conference on Agents and Artificial Intelligence.