Find Academic Supervisors

Javier Alonso-Mora is a Professor in the Department of Mechanical Engineering at Delft University of Technology, specializing in Learning & Autonomous Control. His research focuses on autonomous systems, robotics, motion planning, and transportation logistics, with applications in mobile manipulation, dynamic environments, and urban mobility. He leads key projects such as INTERACT (Intuitive Interaction for Robots among Humans) and ACT (Perceptive Acting Under Uncertainty), exploring human-robot interaction, autonomous vehicles, and healthcare robotics. Notable achievements include an ERC Starting Grant (2022) and a Veni Grant (2017). His work addresses challenges in robot navigation, control systems, and fleet optimization, with contributions to both theoretical advancements and practical implementations. Projects like TRiLOGy focus on sustainable water transportation, while HARMONY advances assistive robotics in healthcare. Alonso-Mora’s research leverages geometric fabrics for motion planning, probabilistic modeling for dynamic environments, and multi-agent coordination. He collaborates internationally and contributes to open-source frameworks for robotics. His recent publications emphasize safety-aware control, instance-aware semantic mapping, and adaptive systems for cluttered environments.

• Autonomous Systems
• Robotics
• Motion Planning
• +3 more

Thomas Bäck is a Professor at the Leiden Institute of Advanced Computer Science (LIACS) , Leiden University , Netherlands, and a member of the interdisciplinary programme Society, Artificial Intelligence and Life Sciences (SAILS) . His academic career spans roles at Leiden University (1996–present) and leadership positions at the Center for Applied Systems Analysis in Dortmund (1994–2000). Education : Diplom-Informatiker (Computer Science), Technische Universität Dortmund (1990) Dr. rer. nat. (Computer Science), Technische Universität Dortmund (1994) Research Interests : Dr. Bäck specializes in evolutionary computation , machine learning , and their applications in sustainable smart industry and healthcare . Recent work focuses on integrating large language models (LLMs) and quantum computing into optimization frameworks, with projects like CIMPLO (predictive maintenance), ECOLE (experience-based optimization), and SAPPAO (airline operations optimization). Scientific Contributions : His 526+ publications cover evolutionary algorithms, quantum optimization, and LLM-driven design, with recent trends including: Quantum computing (e.g., quantum approximate optimization, quantum advantage challenges) LLM integration (e.g., hyperparameter tuning, mutation control, code evolution graphs) Healthcare and industry (e.g., predictive maintenance, anomaly detection, melt quality prediction) Algorithm benchmarking (e.g., IOHprofiler, MA-BBOB, explainable benchmarking) Scientific Awards : IEEE Fellow (2022) Royal Netherlands Academy of Arts and Sciences (KNAW) member (2021) Academia Europaea member (2022) IEEE Computational Intelligence Society Evolutionary Computation Pioneer Award (2015) Fellow, International Society of Genetic and Evolutionary Computation (2003) Best Ph.D. thesis award, German Society of Computer Science (GI) (1995) Advising and Grants : He supervises Ph.D. candidates in evolutionary computation and machine learning and has secured 7 major grants from organizations like the Dutch Research Council , European Commission , and The Research Council of Norway . His editorial roles include Editor-in-Chief of the Evolutionary Computation Journal and associate editorships in leading AI journals.

• Evolutionary Computation
• Machine Learning
• Natural Computing
• +5 more

Dr. Prasanth Venugopal is an Associate Professor specializing in Power Electronics with a focus on advanced energy transfer systems and battery technology. His research spans wireless power transfer, electric vehicle charging, and electrochemical impedance spectroscopy for battery diagnostics. Primary research areas: Wireless Power Transfer (100%), Harmonics (88%), Inductive Power Transfer (87%), Battery Engineering (48%) Recent publications demonstrate expertise in transformerless converter designs, multi-level architectures, and AI-driven battery capacity estimation. He has pioneered meander coil topologies for harmonic mitigation and developed computation-light models for battery aging analysis. His work includes collaborations on Li-ion battery degradation, onboard chargers for electric vehicles, and hybrid power systems for electric aircraft. Despite significant output in IEEE Transactions, no explicit awards or student mentoring data appears in the provided texts.

• Wireless Power Transfer
• Harmonics
• Inductive Power Transfer
• +5 more

Thiago Batista Soeiro serves as a Full Professor with exceptional scholarly impact, evidenced by over 200 research publications and an h-index of 27. His work fundamentally advances power electronics applications in transportation and energy systems, particularly through innovations in electric vehicle infrastructure and sustainable power conversion technologies. Despite the absence of explicit institutional affiliation in source materials, his research permeates critical IEEE journals and conferences. Professor Soeiro's research portfolio centers on: Power converter design for electric vehicle charging systems AI-driven battery health estimation using electrochemical impedance spectroscopy Wireless power transfer optimization for automotive applications High-efficiency topologies for more electric aircraft Hydrogen energy system integration Advanced semiconductor utilization in grid-connected systems Analysis of his 2023-2025 publications reveals accelerating innovation in wide-voltage-range converters, predictive battery management, and fault-tolerant power systems. His work increasingly bridges machine learning with power electronics, notably through computation-light AI models for battery diagnostics, while maintaining strong focus on practical implementation challenges in EV charging and aircraft electrification. No scientific awards or honors were documented in the available materials. Similarly, information regarding student supervision, research grants, laboratory facilities, or collaborative teams was not provided in the source texts.

• Power Electronics
• Electric Vehicle Systems
• Battery Management Systems
• +3 more

Paul Boersma is a Professor of Phonetic Sciences at the University of Amsterdam within the Faculty of Humanities. His research explores how phonetic, phonological, and morphological phenomena emerge through computational modeling using artificial neural networks and Optimality Theory, with a focus on multi-level constraint interactions and distributional learning. University of Amsterdam Faculty of Humanities Phonetic Sciences Key research areas include: Computational Modeling : Simulations of phonological category emergence from phonetic data Optimality Theory : Gradual Learning Algorithm applications BiPhon Framework : Parallel bidirectional phonology/phonetics models Statistical Learning : Cross-situational and distributional learning mechanisms Recent publications emphasize: 2025: Inclusive speech recognition systems using Whisper model 2025: F0 ratio analysis for creaky voice diagnostics 2024: Prosodic clitics in child speech and checked tones in Shanghai Chinese 2023: Distributional learning in developmental language disorder contexts 2022: Substance-free phonological features and ghost segment phenomena He has also contributed extensively to the Praat software for phonetic analysis, with continuous updates since 1993.

• Phonetics
• Phonology
• Computational Linguistics
• +7 more

Serge Fehr is a Senior Researcher in the Cryptology Group at CWI (Centrum Wiskunde & Informatica) in Amsterdam and a part-time Professor at the Mathematical Institute of Leiden University. His research focuses on foundational aspects of cryptology, including post-quantum cryptography, information-theoretic security, zero-knowledge proofs, and secure multiparty computation. He participates in AMSec (Amsterdam Cyber Security Center) and leads work packages in the NWO-funded HAPKIDO consortium. Education: M.Sc. in Mathematics from ETH Zürich (1998) Ph.D. in Cryptography from ETH Zürich and University of Aarhus (2003) Postdoc at Macquarie University (2003-2004) Research Interests: Post-quantum cryptographic primitives (e.g., digital signatures, lattice-based schemes) Quantum-resistant protocols (e.g., non-resignable signatures, Fiat-Shamir transforms) Foundational security proofs in the quantum random oracle model (QROM) Secure multiparty computation and privacy-preserving healthcare systems Key Activities: Editorial Board Member: Journal of Cryptology , IEEE Transactions on Information Theory Program Committee Co-Chair: EUROCRYPT 2025 Steering Committee Member: Beyond IID Information Theory, QCrypt Co-organizer: Symposium Series on Post-Quantum Cryptography Grants/Awards: NWO Cybersecurity consortium grant (HAPKIDO - 2021) NWO Veni Grant (2005) NWO Open & Free Competition Grants (2008, 2013) Students/Advising: Supervised or served on committees for over 15 Ph.D. students, including work on post-quantum signatures, MPC applications, and lattice-based cryptography. Labs/Teams: Active in CWI’s Cryptology Group and Leiden’s Mathematical Institute, collaborating with industry partners (e.g., KPN, Microsoft) in cybersecurity initiatives.

• Cryptology
• Post-Quantum Cryptography
• Information-Theoretic Cryptography
• +5 more

Matthias Schlottbom is an Associate Professor specializing in Mathematics of Computational Science, with a focus on numerical methods and their applications in physics, biology, and engineering. His research integrates advanced computational techniques with interdisciplinary problems, including radiative transfer, photonic crystals, and chemotaxis modeling. Research Interests: Schlottbom’s work spans numerical analysis, finite element methods, and machine learning. He develops high-order discretization schemes, iterative solvers for anisotropic transport, and mathematical frameworks for biological network formation. Publications: Recent articles highlight his contributions to accelerating radiative transfer simulations, extending component mode synthesis for Helmholtz equations, and analyzing diffusion limits in kinetic models. His work often bridges computational mathematics with practical applications in photonics and multiscale systems. Collaborations: He actively collaborates on datasets for optical simulations, radiative transfer algorithms, and photonic crystal modeling, contributing to open-access repositories like 4TU.Centre and Zenodo. Activities: Schlottbom has organized workshops such as the Kinetic Theory Workshop in the Netherlands and delivered keynotes on residual minimization and data-driven methods for transport equations. Scientific Awards: No specific awards or fellowships are mentioned in the provided materials. Advising & Grants: Details about students, advising roles, or grant funding are not included in the available data.

• Numerical Analysis
• Computational Mathematics
• Radiative Transfer
• +4 more

Dr. Martin Rohde is a Professor and Group Leader at the Radiation Science & Technology department within the Faculty of Applied Sciences at Delft University of Technology (TU Delft) in the Netherlands. He leads the Transport Phenomena & Nuclear Applications research group, focusing on advanced nuclear reactor technologies, particularly molten salt reactors, and their associated transport phenomena. Professor Rohde's research interests span across several critical areas in nuclear engineering and fluid dynamics. His work primarily focuses on understanding transport phenomena in nuclear applications, with particular emphasis on molten salt reactors for sustainable and safe nuclear power generation, innovative production techniques of medical isotopes, and advanced energy storage systems like flow batteries. His research group actively investigates complex physical phenomena occurring under extreme conditions such as high pressures, high temperatures, and interactions with radioactive processes. His publication record demonstrates a strong focus on computational methods for nuclear applications, particularly the Lattice Boltzmann Method (LBM), which is used to model fluid flow, heat transfer, and phase change phenomena in nuclear systems. Recent work has concentrated on freezing and melting processes in molten salt reactors, microfluidic separation techniques for medical isotopes, and advanced modeling of flow batteries. His research shows a clear progression toward increasingly sophisticated numerical methods applied to real-world nuclear engineering challenges. Professor Rohde has secured significant funding through multiple European Commission projects including ENDURANCE, MIMOSA, and ReZilient, demonstrating the international recognition of his research. He has supervised numerous PhD and MSc students, many of whom have gone on to complete theses on topics related to molten salt reactors, microfluidics, and flow battery technology. His research group includes several technicians, post-doctoral researchers, and PhD candidates working collaboratively on cutting-edge nuclear technology. The Transport Phenomena & Nuclear Applications laboratory operates several specialized facilities including the ESPRESSO facility for measuring melting and solidification under convective boundaries, and experimental setups for studying molten salt behavior, microfluidic purification, and flow battery technology. The group maintains strong collaborations with international partners including TRIUMF (Canada), NRG, and URENCO (The Netherlands).

• Nuclear Reactor Technology
• Molten Salt Reactors
• Transport Phenomena
• +5 more

Geoffrey Nelissen is an Assistant Professor in the Department of Mathematics and Computer Science at Eindhoven University of Technology. He holds additional roles as a Research Scientist and Investigador Auxiliar at INESC TEC (Portugal), contributing to interdisciplinary research in real-time systems. His work focuses on schedulability analysis, parallel task execution, and real-time communication protocols, with applications in embedded systems and multicore architectures. Research Interests: Real-Time Scheduling Response Time Analysis Multi-core and Parallel Systems Time-Sensitive Networking (TSN) Formal Verification Recent work emphasizes schedule abstraction frameworks, memory contention analysis, and deterministic communication protocols. His research has received recognition through multiple best paper awards including ICESS 2021 and RTAS 2022. He actively contributes to conferences like RTSS and ECRTS while teaching courses on Real-Time Systems and Operating Systems. Collaborations span European institutions with focus on embedded systems, autonomous driving, and safety-critical applications. Current projects explore holistic approaches to WCRT analysis and resilient real-time communication architectures.

• Real-Time Systems
• Schedulability Analysis
• Multicore Platforms
• +3 more

Nicolas Cambier is a Visiting Professor at Vrije Universiteit Amsterdam, affiliated with the Faculty of Science's Artificial Intelligence department and the Network Institute. His research focuses on swarm robotics, collective behavior, and evolutionary systems. He explores topics like emergent communication, modular robotics, and prosociality in robotic swarms. Key areas include task-driven language evolution, adaptive decision-making, and environmental interaction in constrained environments. His work bridges theoretical models with practical implementations, emphasizing self-organization and cultural evolution in synthetic systems. Recent contributions address challenges in heterogeneous swarms, skill acquisition in modular robots, and decision-making without prior knowledge. He collaborates widely, with publications in IEEE Robotics and Automation Letters, Nature Communications, and top conferences like GECCO and Distributed Autonomous Robotic Systems. Research interests span robotics, artificial intelligence, and evolutionary computation, with applications to both theoretical frameworks and real-world robotic systems. His studies often involve agent-based simulations and embodied evolution approaches to understand complex collective phenomena.

• Swarm Robotics
• Artificial Intelligence
• Collective Decision Making
• +5 more

Dr. Gualbert Oude Essink is an Associate Professor at the Faculty of Geosciences , Department of Physical Geography at Utrecht University . He is also a senior hydrogeologist at Deltares, specializing in coastal groundwater systems, saltwater intrusion, and delta sustainability. His work focuses on improving freshwater availability in vulnerable coastal zones under climate change, subsidence, and anthropogenic pressures. Dr. Essink holds a PhD in Civil Engineering from Delft University of Technology and has extensive experience in applied hydrogeology. He leads research projects globally, including in the Netherlands, Egypt (Nile Delta), Bangladesh (Kulna region), Singapore, and Vietnam (Mekong Delta). Key initiatives include GO-FRESH (Aquifer Storage and Recovery) and Rise and Fall (Mekong Delta subsidence strategies). His research integrates modeling, geophysical surveys, and stakeholder engagement. Notable areas include variable-density groundwater flow, airborne electromagnetic surveys for salinity mapping, and sustainable groundwater management. He supervises multiple PhD candidates and teaches at IHE Delft, contributing to global water education. Dr. Essink has authored over 100 peer-reviewed articles, focusing on delta sustainability, climate impacts, and groundwater systems. His work bridges academia and practice, addressing freshwater security challenges in coastal regions through innovative solutions like managed aquifer recharge and saltwater intrusion mitigation.

• Hydrogeology
• Coastal Groundwater Systems
• Saltwater Intrusion
• +6 more

Dr. Dierck Hillmann is an Associate Professor at the Faculty of Science, Department of Biophotonics and Medical Imaging, Vrije Universiteit Amsterdam. He holds a PhD in Holoscopy from Luebeck University (2013). His research focuses on advanced optical imaging techniques, particularly Optical Coherence Tomography (OCT), with applications in retinal imaging, functional signal analysis, and computational imaging. He is affiliated with the LaserLaB - Biophotonics and Microscopy research group. Key research areas include improving OCT resolution through holographic methods, functional imaging of retinal neurons and photoreceptors, and developing computational adaptive optics to enhance imaging quality. His work addresses challenges like speckle reduction, aberration correction, and real-time data processing in biomedical imaging. Dr. Hillmann’s contributions span over 37 publications, including innovations in full-field OCT, optoretinography, and phase-sensitive measurements. He teaches courses such as Computational Optical Imaging and Light-Tissue Interaction. His current project explores imaging individual retinal cells and their functions using advanced techniques. No scientific awards are explicitly listed, but his extensive publication record reflects significant academic impact. Students advised are not specified in the provided materials.

• Optical Coherence Tomography
• Biophotonics
• Medical Imaging
• +3 more

Anuj Pathania serves as an Assistant Professor in the Parallel Computing Systems (PCS) group within the Informatics Institute at the University of Amsterdam's Faculty of Science. His research pioneers sustainable computing systems operating under severe power, thermal, and reliability constraints, with significant contributions to energy-efficient hardware design and embedded systems. Education: PhD in Computer Science (2018), Karlsruhe Institute of Technology MSc in Computer Science (2012), National University of Singapore B.Tech in Computer Science (2009), Maharaja Agrasen Institute of Technology Pathania's research centers on low-power design and sustainable systems for constrained environments, with particular expertise in thermal management of 3D-stacked architectures and energy-efficient machine learning inference . His work bridges electronic design automation with real-world reliability challenges, developing novel power budgeting techniques like T-TSP that incorporate transient temperature effects ignored by conventional methods. Current projects include EU-funded initiatives on energy labeling for digital services, addressing ecological impacts through technological, behavioral, and legal frameworks. His publication trajectory reveals a strategic evolution toward zero-waste computing , with recent work (2023-2025) focusing on hardware-software co-design for edge AI, energy modeling across computing continua, and parameter-efficient neural adaptation. Key themes include thermal-aware scheduling for S-NUCA many-cores, cooperative processor utilization in heterogeneous systems, and sustainability metrics for digital services. Scientific Recognition: Best Paper Award Nomination at IEEE Computer Society Annual Symposium on VLSI 2023 for 3D-TTP power budgeting technique Pathania actively mentors 4 PhD students (Ehsan Aghapour, Saeedeh Baneshi, Sudam Wasala, Yixian Shen) and has successfully supervised 5 Master's theses (including Cum Laude defenses by Joris op ten Berg and Jurre Wolff). His research is supported by major grants including Energy Labels for Ecologically Sustainable Digital Services (2023-2024) and Towards Zero-Waste Computing (2021-2025), developing simulation frameworks like HotSniper and CoMeT for thermal analysis. The PCS group maintains strong industry collaborations with ARM and NVIDIA, particularly through tools like ARM-CO-UP for heterogeneous processor utilization.

• Low Power Design
• Sustainable Systems
• Electronic Design Automation
• +4 more

Jaco van de Pol is a Full Professor of Computer Science at Aarhus University, holding dual roles in the Digital Society Institute and Formal Methods and Tools. He earned his PhD from Utrecht University in 1996, specializing in Termination of Higher-order Rewrite Systems, and a Master's in Computer Science (Term Rewriting) in 1992. His research focuses on model checking, formal methods, algorithms, and automated verification, contributing to UN Sustainable Development Goals related to innovation and education. Education: PhD, Termination of Higher-order Rewrite Systems, Utrecht University (1996) Master's in Computer Science (Term Rewriting), Utrecht University (1992) Research Interests: His work spans model checking, formal verification, parallel algorithms, and their applications in software engineering and bioengineering. He emphasizes practical formal methods, such as SCC algorithms and timed automata analysis, to solve complex computational challenges. Awards: Best Paper Award SPIN 2017 (2017) Best Student Paper Award (2018) Advising & Grants: Supervised 12 students and contributed to collaborative projects in formal methods and computational biology. His research has been applied to areas like cartilage phenotype modeling and parallel algorithm design. Labs/Teams: Engages with interdisciplinary teams, including computational biology and distributed systems groups, to advance formal methods in practical contexts.

• Model Checking
• Formal Methods
• Computer Science
• +3 more

Rianne Conijn is an assistant professor in the Human-Technology Interaction group at Eindhoven University of Technology (TU/e), Netherlands. Her research bridges data-driven methodologies (machine learning, statistical modeling) with human-centered design to enhance learning analytics, explainable AI, and writing process analysis. She holds a joint PhD (cum laude) from Antwerp University and Tilburg University, and an MSc (cum laude) in Human-Technology Interaction from TU/e. Academic Background: MSc (2015, TU/e, cum laude), PhD (2020, Antwerp University & Tilburg University, cum laude). Research Focus: Learning analytics, keystroke logging, explainable AI for education, data dashboards, and self-regulated learning dynamics. Teaching: Courses in Advanced Research Methods, Human-AI Interaction, Behavioral Research Methods, and AI ethics in education. Her recent publications explore parallel language planning in writing, longitudinal self-regulated learning strategies, and generalizability of academic performance prediction models. She leads an NWO Veni project on Human-Centered AI in education, emphasizing tailored explanations for student-AI collaboration. Scientific awards include cum laude distinctions for her MSc and PhD, and the NWO Veni grant. Collaborative work spans institutions in the Netherlands, Norway, and the U.S., with applications in intelligent tutoring systems and ethical AI deployment in exams. Key trends across her work: integration of machine learning with educational theory, leveraging keystroke data for cognitive process insights, and prioritizing actionable, explainable AI systems for student support. Publications span journals like the Journal of Experimental Psychology: General , Computers and Education , and IEEE Transactions on Learning Technologies . Scientific Awards: NWO Veni grant for Human-Centered AI in education Cum laude for MSc and PhD Grants & Collaborations: National Science Foundation grants (2016868, 2302644) for biometric feedback in writing UK Research and Innovation grant (ES/W011832/1) for real-time AI scaffolding TU/e Boost! Program grant for self-regulated learning analysis Labs & Teams: EAISI Foundational (Eindhoven AI Systems Institute) Human Technology Interaction group at TU/e Collaboration with Norwegian Reading National Center (University of Stavanger) Project teams for Waterproof ITS and ProWrite grants

• Learning Analytics
• Human-Technology Interaction
• Artificial Intelligence
• +6 more