جستجوی استاد راهنمای دانشگاهی

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
• +۳ مورد دیگر

Prof. Ronald Meester is a Full Professor of Mathematics at the Faculty of Science, Vrije Universiteit Amsterdam. He specializes in mathematical statistics, probability theory, and their applications in legal and environmental contexts. His current positions include director of Meester Advies (Leiden) and expert for Landelijke Deskundigheidsmakelaar Politie (Apeldoorn). He has supervised 14 PhD theses and contributes to interdisciplinary research bridging statistics with law, epidemiology, and environmental policy. Research focuses on Bayesian reasoning, likelihood ratio analysis, and statistical methodologies for legal evidence evaluation. Recent work addresses nitrogen deposition policy critiques and epidemiological study design limitations. His ancillary activities include authorship (since 2003) and teaching roles at SSR Utrecht. Media engagements include commentaries on scientific integrity and environmental policy. Teaching includes the course 'Mathematical Modelling of Stochastic Systems' (2024-2025 academic year). Active in international collaborations and has produced 111 research outputs spanning articles, books, and encyclopedia entries. His work contributes to UN SDGs related to sustainable development through environmental statistical analysis.

• Mathematical Statistics
• Probability Theory
• Bayesian Reasoning
• +۳ مورد دیگر

Dr. Peter J.F. Lucas is a Full Professor specializing in Datamanagement & Biometrics with over 35 years of experience in artificial intelligence, probabilistic graphical models, and clinical decision support systems. His research spans intelligent systems, machine learning, and eHealth, with a focus on applying Bayesian networks and probabilistic logic to medical and non-medical domains.

• Artificial Intelligence
• Bayesian Networks
• Machine Learning
• +۶ مورد دیگر

Nezihe Merve Gürel is an Assistant Professor in Computer Science at Delft University of Technology (TU Delft), affiliated with the Pattern Recognition & Bioinformatics Group within the Intelligent Systems Department of the Faculty of Electrical Engineering, Mathematics and Computer Science. Her research focuses on developing robust, reliable, and efficient machine learning methods with enhanced reasoning capabilities, bridging theoretical rigor and practical applications. She emphasizes data-centric approaches to improve ML systems. Education: PhD in Computer Science from ETH Zurich, MSc from EPFL (Switzerland). Research Interests: ML robustness, reliability, reasoning, data-centric ML, federated learning, and explainable AI. Her recent work includes certified robustness for retrieval-augmented models and time-efficient learning algorithms. She has contributed to the Journal of Data-centric Machine Learning Research as an executive editor and served as a reviewer for top ML conferences (NeurIPS, ICML, ICLR). She previously held roles at IBM Research, Stanford University's Human-Centered AI Lab, and Westlake Institute for Advanced Study. Her awards include the Generation Google Scholarship and Cisco Research Funding . Scientific Awards : Generation Google Scholarship (2021) Cisco Research Center University Funding Labs & Teams : She leads research in the Pattern Recognition Laboratory at TU Delft and collaborates with international institutions like Stanford and Westlake Institute for Advanced Study.

• Machine Learning
• Computer Science
• Pattern Recognition
• +۴ مورد دیگر

Chigo M. Okonkwo is a Full Professor and Chair of Secured Ultra High Capacity Transmission at the Department of Electrical Engineering, Eindhoven University of Technology (TU/e). He leads the high-capacity optical transmission laboratory within the Electro-Optical Communications Group at the Institute for Photonics Integration, focusing on next-generation photonic networks. Education: PhD in Optical Signal Processing, University of Essex (2009) Appointments: Since 2014 (tenured), 2010 as Post-Doctoral Researcher Key Research Areas: Space Division Multiplexing, Quantum Secure Communications, Advanced Modulation Formats His work spans optical transmission systems, including probabilistic signal shaping , low-complexity digital signal processing , and multi-mode/multi-core fiber components . Recent publications highlight breakthroughs in Petabit/s transmission and quantum cryptography use cases . Scientific recognition includes: ACP 2018 Best Paper Award ECOC 2018 Student Paper Award Optica 2022 Student Paper Awards Corning Outstanding Student Paper Competition Finalist 2025 He actively contributes to standardization efforts as a Technical Program Committee member for ECOC and serves as Senior Member of IEEE. Current projects include FIQCS (Fieldlab Quantum Cryptography Solutions) and NGF-ECO1 (Netherlands Quantum Flagship), advancing quantum-secure protocols and petabit/s capacity scaling.

• Optical Transmission Systems
• Space Division Multiplexing (SDM)
• Quantum Secure Communications
• +۴ مورد دیگر

Nathan van de Wouw is a Full Professor at the Mechanical Engineering Department of Eindhoven University of Technology (TU/e), affiliated with ICMS, EAISI Mobility, EAISI High Tech Systems, EAISI Foundational, and EIRES. He also holds an adjunct Full Professor position at the University of Minnesota and a part-time Full Professorship at Delft University of Technology. His research focuses on dynamics and control of mechanical systems, including mechatronics, robotics, smart manufacturing, energy systems, and networked control. He has supervised over 150 students and led numerous projects funded by industry partners like ASML, Philips, and Shell. Education: M.Sc. (with Honors) in Mechanical Engineering, TU/e (1994) Ph.D. in Mechanical Engineering, TU/e (1999) Research Interests: Nonlinear systems and control Model reduction and complexity analysis Data-driven and networked control strategies Applications in high-tech systems, autonomous vehicles, and energy systems Awards: IEEE Control Systems Technology Award (2015) for variable-gain control in motion systems Grants & Projects: Lead projects on mechatronic design, lithography systems, and thermodynamic optimization Collaborations with TNO, ASML, and industrial partners Labs & Teams: Member of TU/e’s Dynamics and Control group Affiliated with EAISI (Eindhoven AI Systems Institute)

• Dynamics and Control
• Mechatronics and Robotics
• Smart Manufacturing
• +۶ مورد دیگر

Prof. Sander Bohte is a part-time full professor of Computational Neuroscience at the University of Amsterdam (Swammerdam Institute of Life Sciences) and an honorary full professor of Bio-inspired Neural Networks at the University of Groningen. He serves as a Scientific Staff Member and Group Leader in the Machine Learning department at CWI, Amsterdam. His work bridges computational neuroscience and machine learning with a focus on continuous-time information processing. Key research interests include: Spiking Neural Networks with predictive coding and multi-compartment models Biologically Plausible Learning in recurrent and deep architectures Working Memory modeling via reinforcement learning Neuromorphic Computing for real-time systems and GPU acceleration His recent publications highlight trends in neural adaptation , predictive coding , and SNN hardware-software co-design . Awards include the Veni Innovational Research Grant (2004) and ERCIM grant (2013) . He actively supervises MSc theses and leads grants like the NWO KIC project 'Selfhealing Neuromorphic Systems' (2024).

• Computational Neuroscience
• Machine Learning
• Neural Networks
• +۵ مورد دیگر

Dr. Assela Pathirana is a Professor at the IHE Delft Institute for Water Education, specializing in water infrastructure asset management (WIAM), climate resilience of Small Island Developing States (SIDS), and sustainable urban water systems. His work bridges academia, policy, and practice, focusing on digitalization, data-driven decision-making, and nature-based solutions. Key roles include Chief Technical Advisor for the Maldives’ water systems and leadership of a MOOC on SIDS climate adaptation. He holds a BSc (First Class Honours) from the University of Peradeniya and advanced degrees from the University of Tokyo, specializing in hydrology and water resources engineering. Education: Bachelor of Science in Civil Engineering (First Class Honours), University of Peradeniya, Sri Lanka Master’s and Doctoral Degrees in Civil Engineering (Hydrology and Water Resources), University of Tokyo, Japan Research Interests: Dr. Pathirana’s work emphasizes climate resilience, SIDS adaptation, urban flood risk management, and sustainable infrastructure. He develops decision-support tools for flood forecasting, evaluates land-use changes in Jakarta, and promotes equity in water rationing systems. His interdisciplinary approach integrates hydrological modeling, open-source software development, and policy analysis. Advising & Capacity Building: He leads training-of-trainers programs and capacity-building initiatives, enhancing postgraduate education in technical disciplines. His efforts focus on didactics and pedagogy, ensuring graduates gain both theoretical knowledge and practical expertise. Key Projects: Developed the WIAM curriculum at IHE Delft MOOC on SIDS climate adaptation and water security Consultancy for the Maldives’ Ministry of Environment and UNDP Labs & Collaborations: Engages in cross-disciplinary teams addressing urban water challenges, including sponge cities in China and flexible adaptation planning in Melbourne and Pune. His work with UNESCO-ICHARM and UNU underscores global water security and disaster risk reduction.

• Water Infrastructure Asset Management
• Climate Resilience of Small Island Developing States (SIDS)
• Hydrological Modeling
• +۵ مورد دیگر

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
• +۵ مورد دیگر

Jan Draisma is a full professor of Mathematics at the University of Bern and a part-time full professor of Applied Algebra and Geometry at Eindhoven University of Technology (TU/e), where he is affiliated with the Department of Mathematics and Computer Science, specifically in Discrete Algebra and Geometry and Coding Theory and Cryptology. He obtained his Master's and Ph.D. degrees from TU/e cum laude and held a postdoctoral position at the University of Basel (2002–2005). He returned to TU/e as an assistant professor, later advancing to associate professor (2011–2016), and served as a part-time full professor at VU Amsterdam (2015–2016). His research focuses on the interplay between combinatorics, statistics, and algebraic geometry. Key areas include tropical geometry, algebraic statistics, symmetric systems of polynomial equations in infinitely many variables, and representation stability. His work often explores the structure of infinite-dimensional algebraic objects and their finite approximations. The most recent publications highlight trends in polynomial functors, topological Noetherianity, amoebas of linear spaces, and the geometry of tensor representations. These works reflect a deep integration of algebraic geometry with combinatorics and category theory, emphasizing stabilization phenomena and symmetry in algebraic structures. NWO Vici Award: Stabilisation in Algebra and Geometry (2015) NWO Vidi Award: Finite thanks to symmetry (2010) Draisma has received significant research funding, including the NWO Vidi and Vici grants, and two NWO Free Competition grants (2008, 2012). He has supervised 16 students and is actively involved in the academic community as an associate editor for Experimental Mathematics, SIAM Journal on Applied Algebra and Geometry, and Linear and Multilinear Algebra. He has held leadership roles in major conferences such as MEGA 2015 and SIAM AG 19. He is affiliated with the research groups in Discrete Algebra and Geometry and Coding Theory and Cryptology at TU/e and leads research activities centered on algebraic methods in discrete mathematics and statistics.

• Algebraic Geometry
• Combinatorics
• Algebraic Statistics
• +۸ مورد دیگر

Dr. Tarek Alskaif is an Associate Professor of Energy Informatics at Wageningen University & Research, specializing in the intersection of information technology and energy systems. He leads research on smart energy systems, focusing on electricity markets, distributed energy resources, and AI-driven solutions. His work integrates modeling, optimization, and big data analytics to advance the sustainable energy transition. Education: PhD in Energy Informatics (2012–2016, Cum Laude) from Universitat Politècnica de Catalunya, Spain. Postdoc at Utrecht University’s Copernicus Institute (2016–2020). Current roles include coordinating the BSc Data Science Minor and teaching Python and Big Data courses. Research interests emphasize leveraging digitalization for energy systems, including smart grids, electric mobility, and battery storage. Notable projects include HighLO Energy Markets (EU-funded, using particle physics and AI for market transparency) and MESSM (coordinated via TKI Urban Energy). He also leads the AI ELSA Lab (NWO-funded). Editorial roles include Associate Editor for IEEE Transactions on Smart Grid and IEEE Power Engineering Letters . Member of IEEE, the Netherlands Institute for Research on ICT (4TU.NIRICT), and the Technical Program Committee for IEEE SmartGridComm and PSCC 2026. Has supervised over 50 students (MSc/BSc) and 7 PhDs. Projects address challenges like grid congestion, EV charging optimization, and decentralized energy trading. His work bridges academic research with industry collaborations, including partnerships with CERN and ACER.

• Energy Informatics
• Smart Grids
• Artificial Intelligence
• +۹ مورد دیگر

Ivana Nikoloska is an Assistant Professor at the Department of Electrical Engineering, Eindhoven University of Technology (TU/e). She is affiliated with the Center for Quantum Materials and Technology Eindhoven and BIASlab (Bayesian Intelligence and Stochastic Agents Lab). Her academic career includes prior roles as a Research Associate at King’s College London and a Visiting Researcher at Aalborg University. PhD: Monash University, Australia (2023) MSc & Dipl.-Ing.: University of Ss. Cyril and Methodius, North Macedonia Research Interests span foundational and applied machine learning, quantum computing, and information/communication engineering. Her work focuses on integrating Bayesian inference, variational methods, and quantum technologies for tasks like signal processing, channel estimation, and power control optimization. Quantum Machine Learning Bayesian Simulation-Based Inference Meta-learning for Wireless Systems Hybrid Quantum-Classical Architectures Stochastic Signal Processing Quantum Sensing & Metrology Notable Trends in Publications include quantum recurrent neural networks with adaptive gating, Bayesian frameworks for quantum sensing, and meta-learning applications in communication systems. She explores variational inference for planning and robust algorithms for channel estimation under non-ideal conditions.

• Machine Learning
• Quantum Computing
• Communication Engineering
• +۷ مورد دیگر

Sofie Haesaert is an Assistant Professor in the Control Systems group at the Department of Electrical Engineering, Eindhoven University of Technology. Her work focuses on formal verification and control synthesis methods for cyber-physical systems, particularly through stochastic simulation relations and temporal logic specifications. Education: BSc (cum laude) and MSc (cum laude) in Mechanical Engineering and Systems & Control from Delft University of Technology; PhD from Eindhoven University of Technology (2017) Experience: Postdoctoral researcher at Caltech (2017-2018), then returned to TU/e as Assistant Professor Her research interests include: Cyber-physical systems verification Stochastic control methods Temporal logic specification Markov decision processes Formal methods in control engineering Model abstractions and simulation relations Recent publications show strong focus on: Stochastic temporal logic control Robust and risk-aware control Multi-agent system verification Formal synthesis via simulation relations AI integration in control systems Software tools for formal control Scientific achievements: Veni Grant recipient (2020) Co-developer of the SySCoRe toolset for stochastic control synthesis Contributor to formal verification benchmarks through ARCH-COMP reports She contributes to education through courses on: Control principles for engineered systems Control challenges in autonomous racing Supervisory control of cyber-physical systems Haesaert collaborates across disciplines including computer science, applied mathematics, and robotics, with over 750 citations and significant contributions to formal control theory for stochastic systems. Her work bridges theoretical developments with practical applications in autonomous systems and complex control architectures.

• Cyber-Physical Systems
• Control Theory
• Formal Verification
• +۳ مورد دیگر

Rodrigo González is an Assistant Professor at the Department of Mechanical Engineering, Eindhoven University of Technology, since 2022. His research focuses on data-driven modeling, estimation, and control methods for high-tech precision systems, with applications in motion control and continuous-time system identification. Education: Ph.D. in Electrical Engineering (KTH Royal Institute of Technology, 2022) M.Sc. in Electronic Engineering (Universidad Técnica Federico Santa María, 2016) His work emphasizes continuous-time system identification, state-space modeling, and Bayesian estimation techniques. Key research themes include motion control tuning, multivariable systems, and noise/disturbance modeling in precision engineering applications. Rodrigo has received the Best Electronic Engineering Student Award (2016) and Best Thesis Award from Universidad Técnica Federico Santa María. He has active collaborations with institutions like Universidad Técnica Federico Santa María through visiting researcher appointments. Scientific awards include: Best Electronic Engineering Student Award (2016) Best Thesis Award (Universidad Técnica Federico Santa María)

• Data-driven modeling
• System identification
• Control systems engineering
• +۴ مورد دیگر

Réka Szabó is an Assistant Professor at the Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence, University of Groningen, Netherlands, since October 2022. Her academic trajectory includes a postdoctoral fellowship at Université Paris-Dauphine (2019–2022) and a Ph.D. in Mathematics (2015–2019) at the University of Groningen under Daniel Valesin. Education: B.Sc. in Mathematics, Budapest University of Technology and Economics (2008–2011) M.Sc. in Applied Mathematics, Budapest University of Technology and Economics (2011–2015) M.Sc. in Computer Science, University of Trento and Eötvös Lóránd University (2012–2014) Ph.D. in Mathematics, University of Groningen (2015–2019) Réka’s research focuses on Interacting Particle Systems and Percolation Theory , with significant contributions to understanding phase transitions, critical behavior, and stochastic dynamics in models like the Contact Process, Oriented Percolation, and Bootstrap Percolation. Her work combines rigorous mathematical analysis with applications to statistical physics and complex networks. She is actively involved in academic service, including organizing the Workshop On Randomness and Discrete Structures (WORDS 2025) and co-organizing the Probability and Statistics Seminar in Groningen. Réka teaches advanced courses such as Stochastic Processes, Interacting Particle Systems, and Mathematics and its Environment.

• Probability Theory
• Interacting Particle Systems
• Percolation Theory
• +۳ مورد دیگر