Find Academic Supervisors

• Distributed Systems
• Cloud Computing
• Edge Computing
• +7 more

Nishant Saurabh is a tenured Assistant Professor at the Department of Information and Computing Sciences , Utrecht University , Netherlands. His research focuses on resource and data management , application scheduling , performance modeling , and optimization in large-scale hybrid distributed systems including Cloud, Edge, and Quantum computing. Recently, he has expanded into ML workload scheduling and causal performance attribution across the Cloud-Edge continuum. Research Expertise : Distributed Systems and Cloud Computing Quantum-HPC Middleware Systems Performance Modeling and Optimization ML/AI Workload Scheduling Cloud-Edge Continuum Architectures Observability Frameworks Scientific Leadership : Associate Editor, Springer's Journal of Cloud Computing Editorial Board, Frontiers of AI book series IBM HPC-Quantum Working Committee member Workshop Chair, IEEE IPDPS 2025 Supervision : He has supervised 16+ Master/Bachelor students on projects including container migration, reinforcement learning for serverless computing, and quantum middleware evaluation. Current PhD students include Diogo Landau (Performance Observability), Nathan Keyaerts (LLM Methods), and Negar Alizadeh (Energy Efficiency).

Arjen JansenView profile

Lecturer

• Sports Engineering
• Drug Delivery Systems
• Sustainable Design
• +3 more

Dr. Arjen Jansen is a Lecturer in Industrial Design Engineering at Delft University of Technology, affiliated with the Emerging Materials and Sustainable Design Engineering programs. His research spans sports engineering, biomedical applications (e.g., 3D drug printing), and human-powered energy systems. Notable projects include developing vibro-tactile feedback systems for skating and protective wetsuits for Olympic sailors. He teaches courses like the Design Engineering Contest and leads the Erasmus+ project A4SEE to enhance sports engineering education across Europe. Jansen's work bridges innovation theory with practical applications, emphasizing sustainability and human-centric design. Education: Doctor of Industrial Design Engineering (Dr.ir.) Research Interests: Sports technology, biomedical engineering, sustainable materials, and educational methodologies in design. His work often integrates ergonomics, environmental science, and digital tools to create impactful solutions. Grants & Collaborations: Led international projects like A4SEE (2022) and contributed to EU-funded initiatives. His research has been published in journals like Journal of Pharmaceutical Innovation and presented at conferences such as the Engineering of Sport series. Labs/Teams: Emerging Materials Program, Sports Engineering Lab.

• Control Theory
• Agricultural Engineering
• Machine Learning
• +3 more

Sjoerd Boersma is a Researcher at Biometris, Wageningen University & Research (WUR), specializing in the integration of advanced control systems with agricultural technology. His work focuses on developing predictive control models, observability analysis frameworks, and digital twin solutions to optimize greenhouse production, wind farm efficiency, and water resource management. His research spans Control Theory , Agricultural Engineering , and Machine Learning , with specific expertise in predictive control for systems with parametric uncertainty, sensor configuration optimization, and Bayesian neural methods for crop modeling. Current projects address autonomous greenhouse control using reinforcement learning and virtual crop modeling for tomato production. Recent publications reveal a strong trend toward cross-disciplinary solutions where digital twin technology bridges agricultural engineering and control systems. His work consistently targets sustainability challenges, from wind farm load reduction to precision water management and greenhouse climate optimization, demonstrating methodological innovation in applying control theory to biological systems. Boersma actively mentors PhD candidates, serving as co-promotor for van Laatum, B.'s research on autonomous greenhouse control systems. His current projects include the active Autonomous Production Control of Greenhouse Farming System with Reinforcement Learning and the recently completed Virtual Tomato Crops initiative funded by WUR. As a core member of the Biometris research group, he contributes to WUR's mission through collaborative projects like the VIOLA potato stress classification model and the digital twin framework for water management, emphasizing real-world implementation of control systems in agricultural environments.

• Intensive Care
• Critical Care Medicine
• Artificial Intelligence in Healthcare
• +5 more

Dr. Michel van Genderen serves as a Researcher in the Department of Intensive Care at Erasmus MC's Erasmus School of Medicine, Rotterdam. His work focuses on advancing critical care through innovative research in mechanical ventilation protocols, AI integration, and sustainable ICU practices. With 69 research outputs including 50 articles and 7 reviews, he actively contributes to European critical care initiatives. His research spans Intensive Care Medicine , AI applications in critical care , and environmental sustainability . Key interests include developing standardized data models for ICU networks, optimizing mechanical ventilation strategies, and implementing large language models for clinical decision support. Recent work examines green ICU initiatives from multiple stakeholder perspectives and leverages GPT-4 to improve patient understanding of radiology reports. Analysis of his 15 most recent publications reveals strong emphasis on data standardization for European ICU networks , AI-driven clinical decision tools , and sustainable critical care practices . His work consistently bridges technical innovation with practical clinical implementation, particularly in mechanical ventilation optimization and pandemic response protocols. Dr. van Genderen has supervised at least two graduate students and participates in multi-center collaborations across Europe. His research receives significant attention with news mentions and Mendeley readership. Current projects include the SWITCH study on ventilation strategies and development of the Common Data Model for Intensive Medicine Data Space in Europe. He contributes to critical care innovation through participation in European research networks focused on data interoperability and sustainable healthcare infrastructure. His work on "The green ICU" examines environmental sustainability from healthcare professional, procurement specialist, and regulatory perspectives.

Karine da Silva Miras de AraujoView profile

Assistant Professor

• Robotics
• Artificial Intelligence
• Evolutionary Computing
• +5 more

Karine da Silva Miras de Araujo is an Assistant Professor at Vrije Universiteit Amsterdam's Faculty of Science, affiliated with the Artificial Intelligence department and the Network Institute. Her research focuses on evolutionary robotics, digital organisms, and adaptive systems, emphasizing phenotypic plasticity, modular designs, and benchmarking frameworks. She teaches courses like Evolutionary Computing and Introduction to Artificial Intelligence. Her work explores topics such as controller architectures for arbitrary robot morphologies, lamarckian principles in robot evolution, and human-in-the-loop agent prototyping. Key contributions include the AMaze benchmark generator and studies on phenotypic plasticity costs in digital organisms. No scientific awards are explicitly listed. She has supervised one PhD thesis but specific student names are not provided. Her courses emphasize foundational AI concepts and evolutionary methodologies, reflecting her dual focus on theoretical advancements and practical applications in robotics.

Ilja CorneliszView profile

Associate Professor

• Educational Policy
• Mental Health
• Digital Health
• +6 more

Dr. Ilja Cornelisz is an Associate Professor at the Faculty of Behavioural and Movement Sciences, Department of Educational Sciences, Vrije Universiteit Amsterdam. He also holds an appointment at the LEARN! - Learning sciences institute and serves as an Associate/Statistical Editor for Nature PJ - Science of Learning since 2024. His research focuses on educational policy, mental health interventions, digital health applications, and student outcomes in inclusive education settings. His work contributes to UN Sustainable Development Goals, particularly in quality education and good health. Key research areas include analyzing labor market outcomes for disabled individuals, effects of special needs students on peers, and optimizing digital mental health interventions. Cornelisz has co-authored over 60 publications and led projects like 'Big Data Analysis for Mental Health' and 'House of Skills.' Research Interests: Education policy, digital therapeutics, inclusive education, and labor market dynamics Notable Projects: Big data analysis for youth mental health (2023–2028) Visual in Beeld research (2021–2023) Cornelisz has received awards such as the 2023 FGB Teacher Award and co-authored the 2018 Best Paper Award-winning research. His articles often bridge theory and practice, addressing real-world educational challenges through data-driven solutions.

• Cryptographic Hardware
• Symmetric Cryptographic Schemes
• Hardware Implementations of Cryptographic Algorithms
• +5 more

Silvia Mella is an Assistant Professor in the Digital Security (DiS) group at Radboud University, Netherlands, and a member of the Cryptographic Engineering & Side-Channel Analysis (CESCA) Lab. Her research focuses on cryptographic hardware for embedded devices, including secure implementations of cryptographic algorithms and analysis of side-channel and fault attacks. She holds a PhD from the University of Milan (2018) and previously worked at STMicroelectronics (2015–2022), developing hardware accelerators for public-key cryptography. She has supervised numerous PhD and Master’s students, co-authored over 30 publications, and contributed to cryptographic standards like ASCON and Xoodoo. Her work emphasizes bridging theoretical cryptography with practical hardware security solutions. Education: Bachelor and M.Sc. in Mathematics, University of Milan, Italy PhD in Cryptography (2018), University of Milan, Italy Research Interests: Design/analysis of symmetric cryptographic schemes Hardware implementation security Side-channel and fault attack countermeasures Post-quantum cryptography Lightweight cryptographic primitives Recent Research Trends: Recent work focuses on novel code-based cryptographic constructions (ChiLow/ChiChi), low-latency pseudorandom functions (Koala), and SCA-resistant implementations of algorithms like Xoodoo and Xoodyak. Her research bridges theoretical cryptographic analysis with practical hardware vulnerabilities, often leveraging machine learning techniques for attack detection. Advising & Collaboration: Co-supervised 10+ PhD/Master students on topics like cryptographic permutations and post-quantum schemes Active in industry-academia collaborations through STMicroelectronics and PROACT projects Involved in open-source cryptographic library development Labs & Teams: Leading research in the CESCA Lab at Radboud University, focusing on cryptographic engineering and embedded system security. Collaborates with the Politecnico di Milano and STMicroelectronics on hardware security projects.

Minh-Quan TranView profile

Researcher

• Distribution System Engineering
• Distributed Energy Resource Engineering
• State Estimation
• +5 more

Minh-Quan Tran is a University Researcher at Eindhoven University of Technology (TU/e) within the Department of Electrical Energy Systems, Faculty of Electrical Engineering. His work focuses on modern power distribution systems, renewable energy integration, and grid stability solutions, contributing to UN Sustainable Development Goals in sustainable energy. He maintains an ORCID profile (0000-0001-6430-7333) and has accumulated 99 Scopus citations for his 20 research outputs, with publication activity increasing from 2 works in 2019 to 7 in 2023. His key research interests include: Distribution System Engineering (100% fingerprint strength) Distributed Energy Resource Engineering (71%) State Estimation (64%) Voltage Stability (51%) Microgrid Control (40%) System State Monitoring (47%) Recent publications (2022-2024) reveal strong trends in AI-driven grid management, particularly Model Predictive Control and Graph Neural Networks for voltage stability and state estimation. Real-world case studies feature campus networks (e.g., Chalmers University), emphasizing practical validation of control strategies for low-voltage systems with high renewable penetration. Scientific Awards: No awards or honors are documented in the provided materials Advising and Grants: Tran lists no formal PhD/Master's students in the repository. His research involves IoT platforms, digital twins, and real-life demonstrations for flexibility optimization but lacks explicit grant details or funding acknowledgments in the extracted text. Labs and Teams: He operates within TU/e's Electrical Energy Systems research group, specializing in active distribution network monitoring, microgrid stability, and distributed resource control. Collaborations span international institutions as visualized in the repository's network map, with recent joint work involving Vietnamese and European universities.

Maryam AlimardaniView profile

Researcher

• Brain-Computer Interfaces (BCI)
• Virtual Reality (VR)
• Human-Robot Interaction
• +5 more

Maryam Alimardani is a researcher at Tilburg University's School of Humanities and Digital Sciences, Department of Cognitive Science and AI. She holds a Hospitality Contract position focusing on cutting-edge neurotechnology research. Her work contributes to UN Sustainable Development Goals related to innovation and infrastructure. Research Interests: Brain-Computer Interfaces (BCI) for human-machine interaction Virtual Reality (VR) applications in training and neurocognitive assessment EEG-based neural pattern analysis Passive BCI systems for workload prediction Gender differences in neurophysiological responses Recent Work Trends: Recent publications (2021-2024) emphasize BCI integration with VR environments, particularly in aviation training contexts. She explores gamification principles for optimizing BCI training protocols and investigates neurophysiological markers like mu rhythm suppression to improve BCI performance. Grant/Project Leadership: Principal Investigator on the Assessment and Enhancement of Pilot Training in VR using Neuro-cognitive Indicators of Learning project. Labs/Teams: Collaborates with multidisciplinary teams in aviation human factors and neuroergonomics, contributing to datasets like Psychological and Cognitive Factors in Motor Imagery BCI and Neural responses with EEG to climate change images .

• Computer Science
• Mathematical Morphology
• Data Visualization
• +4 more

Jos Roerdink is a Professor at the Bernoulli Institute within the Faculty of Science and Engineering at the University of Groningen , Netherlands. His research spans multiple disciplines including computer science, mathematical morphology, data visualization, and neuroscience, with a focus on algorithm development and applications in medical imaging and astronomical data analysis.

L. PeternelView profile

Researcher

• Human-Robot Interaction
• Robotics
• Rehabilitation Robotics
• +4 more

L. Peternel is a Robotics researcher at Delft University of Technology's Faculty of Mechanical, Maritime and Materials Engineering, specializing in Human-Robot Interaction and Rehabilitation Robotics. With 56 research outputs including conference contributions, journal articles, and patents, Peternel leads cutting-edge work in impedance control, teleoperation, and collaborative robotics applications spanning medical rehabilitation, off-Earth habitat construction, and service robotics. Research focuses on developing advanced control frameworks enabling natural human-robot collaboration. Key areas include impedance-based teleoperation systems, human motor control-inspired co-manipulation strategies, and biomechanics-aware robotic physiotherapy. Recent work explores quadruped navigation, supermarket service robots with LLM interfaces, and off-Earth habitat construction systems that integrate computer vision with human-robot teamwork. Publications demonstrate strong emphasis on practical implementations with real-world impact across medical, industrial, and extraterrestrial environments. Article trends reveal consistent focus on physical human-robot interaction mechanisms, particularly impedance control methodologies and safety-critical applications. The research portfolio spans rehabilitation robotics (shoulder therapy systems), collaborative construction (off-Earth habitats), and service robotics (supermarket assistants), with increasing integration of AI and computer vision components in recent years. Best Paper Award at FICTA 2024 for Computer Vision- and Human-Robot Interaction-Supported Assembly Finalist Best Interactive Paper Award at Humanoids 2023 for robotic shoulder rehabilitation work Peternel leads the Rhizome project developing autonomous systems for off-Earth habitat construction and collaborates extensively across disciplines including biomechanics, computer science, and aerospace engineering. Media coverage including the Dutch press feature "Een robot als collega" highlights real-world impact of this research. Supervised work includes PhD candidates in robotics, with ongoing projects focusing on patient-centered rehabilitation systems and autonomous construction robotics for space applications.

Femke van BeekView profile

Assistant Professor

• Soft Robotics
• Haptic Perception
• Human-Computer Interaction
• +5 more

Femke van Beek is an Assistant Professor in the Robotics section of the Department of Mechanical Engineering at Eindhoven University of Technology. Her research focuses on integrating soft robotics with haptic perception to develop autonomous robots with tactile sensing capabilities, particularly for agricultural applications within the 4TU Green Sensors project . She employs VR/AR experiments to study human haptic perception and design bio-inspired sensors. Academic Background : PhD in Psychonomics and Cognitive Psychology (Vrije Universiteit Amsterdam), MSc in Sensory Biology and Biomechanics (Wageningen University), and BSc in Biology (Wageningen University). Her research spans soft robotics , haptic feedback , and human-in-the-loop engineering , emphasizing practical sensor design and robotic movement optimization. Recent work includes open-source platforms for real-time control of soft robots and studies on vibrotactile vs. auditory feedback in tele-operation. Scientific Awards : Eurohaptics Society PhD Award (2017)

E.C. SlobView profile

Professor

• Applied Geophysics
• Petrophysics
• Electromagnetic Induction
• +7 more

E.C. Slob is a Professor at the Department of Applied Geophysics and Petrophysics , School of Civil Engineering and Geosciences , Delft University of Technology. His research focuses on electromagnetic induction, ground-penetrating radar, and subsurface modeling, with applications in geothermal energy, environmental monitoring, and civil infrastructure diagnostics. Active in electromagnetic subsurface characterization Pioneer in data assimilation for geothermal systems Develops advanced radar and machine learning methodologies Recent research trends emphasize: Integration of multi-physics geophysical methods Optimization of sensor placement in borehole systems Deep learning applications for tunnel inspection Monitoring of groundwater dynamics in polder systems Scientific recognition includes: Reginald Fessenden Award (2020) for electromagnetic geoscience contributions Professional engagement: Chair of 81st EAGE Annual Conference (2019) Program committee member, Near Surface Geoscience Conference (2019) Editorial board member, Geophysics (Journal) (2018) Keynote speaker, Virtual ground-penetrating radar technology (2017-2018)

• Computer Science
• Applied Mathematics
• Machine Learning
• +7 more

R.T. Wiersma is a researcher affiliated with the Faculty of Electrical Engineering, Mathematics and Computer Science at Delft University of Technology , specializing in Computer Graphics and Visualisation . Their work bridges computer science, applied mathematics, and computational geometry. Research interests include: Geometric deep learning 3D surface analysis Computer vision for cultural heritage High-performance computing algorithms Neural network adaptation for geometric data Recent publications demonstrate expertise in: Intrinsic learning on curved surfaces Geometric multigrid methods Art restoration through computational imaging Vanishing point detection in computer vision Active collaborations with researchers like E. Eisemann and K. Hildebrandt highlight their contributions to algorithm development and computational art analysis.

• Biomedical Engineering
• Optical Spectroscopy
• Medical Instrumentation
• +3 more

B.H.W. Hendriks is a Professor in the Department of Mechanical Engineering at Delft University of Technology, specializing in Medical Instruments & Bio-Inspired Technology. His research develops optical and signal processing solutions for medical applications, particularly in surgical environments and tissue analysis. His primary research domains include: Biomedical Engineering Optical Spectroscopy (Diffuse Reflectance) Medical Device Development Surgical Technology Cardiac Signal Processing Tissue Characterization Analysis of his 55+ publications reveals dual expertise: (1) Intraoperative optical sensing (e.g., fiber-optic tissue identification during spine surgery and electrosurgery), and (2) Advanced signal processing for cardiac diagnostics (atrial fibrillation mapping via electrograms/ECG). His work bridges mechanical engineering with clinical practice through real-time surgical workflow analysis and tissue-mimicking phantoms. Hendriks actively supervises research students and has generated 3 significant datasets for tissue characterization. His fingerprint shows dominant activity in spectroscopy (100%), surgery (93%), and tissue analysis (91%), with emerging work in human pose tracking for cardiac catheterization laboratories.