Find Academic Supervisors

Gaurav ChoudharyView profile

Associate Professor

• Cybersecurity
• Machine Learning
• Internet of Things
• +8 more

Gaurav Choudhary is an Associate Professor at the Technical University of Denmark (DTU), affiliated with the College of Engineering and the Cybersecurity Engineering department. His research focuses on emerging threats in digital systems, with a particular emphasis on Malware Detection, Internet of Things (IoT) security, and Machine Learning applications in cybersecurity. His recent work explores intrusion detection in IoT ecosystems, drone identification using RF signals, and the analysis of virtual private networks and proxies. These studies span subfields like Tiny Machine Learning, Scalable Deployment, and Tor Browser forensics, reflecting his interest in both theoretical and applied security challenges. The majority of his publications from 2025 revolve around enhancing cybersecurity frameworks through innovative machine learning techniques, user-centric authentication methods, and forensic investigation tools. Despite his active research contributions, no specific scientific awards or students he has advised are mentioned in the provided data.

• Machine Learning
• Data Systems
• Resource-Aware Computing
• +5 more

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.

Morten Opprud JakobsenView profile

Associate Professor

• Embedded Systems
• Tiny Machine Learning
• Condition Monitoring
• +2 more

Morten Opprud Jakobsen is an Associate Professor at the Department of Electrical and Computer Engineering , Aarhus University. His research focuses on condition monitoring of rotating machinery , with special emphasis on lubrication condition detection using acoustic signals and implementation of Embedded Machine Learning for predictive maintenance. Primary Research Areas: Embedded Systems, Tiny Machine Learning, Machine Diagnostics Key Techniques: MEMS sensors, Acoustic Emission Analysis, LoRa/Bluetooth Low Energy communication Recent work demonstrates technical expertise in developing low-cost condition monitoring systems that combine vibration and acoustic analysis for early failure detection in mechanical components. Publications in Mechanical Systems and Signal Processing and Tribology International highlight interdisciplinary applications of machine learning in mechanical engineering. Contact: morten@ece.au.dk | +45 21 36 70 53

Maria PapaioannouView profile

Researcher

• Cybersecurity
• Machine Learning
• Internet of Things
• +3 more

Maria Papaioannou is a Postdoctoral Researcher at the Department of Applied Mathematics and Computer Science (DTU Compute) at the Technical University of Denmark, specializing in cybersecurity engineering with contributions to UN Sustainable Development Goals through secure technology development. Her research focuses on Machine Learning applications for Internet of Things security, including intrusion detection systems, honeypot optimization, and user-centric authentication. She investigates technical implementations of tiny machine learning for resource-constrained devices while addressing human factors in security adoption, particularly for emerging technologies like passkeys. Recent publications reveal strong thematic convergence across machine learning-driven security solutions for IoT ecosystems, emphasizing both algorithmic innovation and usability considerations. Her review articles systematically analyze research gaps in intrusion detection scalability, passkey adoption barriers, and adaptive deception technologies, highlighting interdisciplinary connections between cybersecurity, human factors, and embedded systems engineering.

Titus Theodorus RobroekView profile

Researcher

• Machine Learning
• Deep Learning
• Resource-aware Computing
• +4 more

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.