Find Academic Supervisors

Steven Xiaotian DaiView profile

Lecturer

• Real-Time Systems
• Cyber-Physical Systems (CPS)
• Robotics
• +6 more

Dr. Steven Xiaotian Dai is a Lecturer in Cyber-Physical Systems at the Department of Computer Science, University of York, UK. He leads the ReFLEX Lab and is affiliated with the Real-Time and Distributed Systems Group. His research focuses on real-time scheduling, digital twins, and safety assurance in autonomous systems, with applications in aerospace, robotics, and avionics. He holds a PhD from the University of York (2019, Best Thesis Award) and has contributed to prestigious projects like SCHEME (Innovate UK) and MOCHA (Huawei). Dr. Dai’s academic qualifications include a PhD in Real-Time Systems (York), MSc in Control Systems (Sheffield), and BSc in Automatic Control (Nanjing). His research grants include £2.2M from Innovate UK for SCHEME and £985K from Huawei for MOCHA. He actively serves on program committees for leading conferences like RTSS and RTAS. His research interests encompass real-time scheduling on multi-cores, timing assurance for CPS, and hardware-software co-design. Collaborations span industries and academia globally, addressing challenges in safety-critical systems. Dr. Dai supervises multiple PhD and MSc students, emphasizing adaptive scheduling, digital twins, and robotics. He is a member of IEEE CS, IEEE RAS, and ACM SIGBED, and his work has been published in over 30 peer-reviewed articles. The ReFLEX Lab explores cutting-edge topics like routerless network-on-chip optimization and ROS 2 executor timing analysis.

• Machine Learning
• Artificial Intelligence
• Radio Astronomy
• +4 more

Amir Aghabiglou is a Researcher at Heriot-Watt University, affiliated with the School of Engineering & Physical Sciences and the Institute of Sensors, Signals & Systems. His work focuses on advancing deep learning techniques for high-dynamic range imaging in radio astronomy and medical imaging. He specializes in developing neural network architectures like the R2D2 series, which address challenges in large-scale data processing, uncertainty quantification, and real-time image reconstruction. Key research contributions include scalable MRI reconstruction methods, robust radio-interferometric imaging frameworks, and spherical extensions of neural networks for wide-field astronomical observations. His collaborations span institutions like the European Southern Observatory and involve datasets such as the VLA and Cygnus A reconstructions. Aghabiglou’s research integrates signal processing, machine learning, and domain-specific knowledge to solve interdisciplinary problems. He has developed open-source tools and frameworks for assessing scientific productivity metrics, bridging data science with bibliometrics. His work has been published in leading journals like the Astrophysical Journal and presented at conferences such as EUSIPCO. He actively contributes to both theoretical advancements and practical implementations in imaging sciences.

• Human-Computer Interaction
• Interaction Design
• Educational Technology
• +4 more

Robb Mitchell is an Associate Professor at the University of Southern Denmark, affiliated with the Centre for Learning Computational Thinking and the Department of Design and Communication . His research focuses on the intersection of technology, design, and social interaction, emphasizing embodied learning, collaborative interfaces, and the ethical implications of digital tools. Mitchell explores how tangible and digital systems can enhance education, social connectivity, and creative expression through experimental designs. His work spans areas such as Human-Computer Interaction , Embodied Learning , and Collaborative Technologies , with a particular interest in fostering convivial interactions and addressing interpersonal gaps through design. Mitchell’s publications reflect a commitment to innovative methodologies, including participatory design, prototyping, and empirical studies of user behavior. Key themes in his research include: Designing tools for educational and social contexts Exploring the role of technology in everyday interactions Developing frameworks for sustainable and ethical technology integration While no explicit awards or grants are listed, Mitchell’s extensive publication record and involvement in interdisciplinary research initiatives highlight his contributions to the field. His work often bridges academic research and practical applications, aiming to create meaningful technological interventions in real-world settings.

Mads Brath JensenView profile

Assistant Professor

• Computational Architecture
• Robotic Fabrication
• Human-Robot Interaction
• +7 more

Mads Brath Jensen is an Assistant Professor at the Department of Architecture, Design and Media Technology within the Technical Faculty of IT and Design at Aalborg University, Denmark. His academic and research work is centered on the integration of robotics, computational design, and material behavior in architectural processes. His research interests focus on computational architecture , robotic fabrication , and interactive human-robot co-creation . He investigates how real-time feedback between humans, materials, and robots influences cognitive design decisions. His work spans parametric design , clay 3D printing , acoustic tectonics , and sustainable material systems , often manifesting in physical research pavilions and robotic installations. The trends in his recent publications highlight a consistent trajectory toward interactive design frameworks , material-aware robotic processes , and acoustic-architectural integration . His work bridges theoretical design exploration with practical, buildable outcomes, emphasizing real-time human intervention in digital fabrication. Scientific Awards: No formal scientific awards listed in the provided text. Advising and Grants: Mads Brath Jensen has supervised and co-supervised multiple research projects, often as Principal Investigator (PI), including Whispering Wall , Acoustic Tectonics Studio , and Utzon(x) Research Pavilion . These projects were research-based and involved collaboration with external partners such as Boligfonden Kuben. While specific grant amounts are not listed, the sustained project activity indicates successful funding acquisition. He has also advised on educational frameworks and design studios, contributing to research-based teaching at BSc and MSc levels. Labs and Teams: He is affiliated with the Create Integrated Architecture group and the Centre for Sustainable and Digital Transformation at Aalborg University. He has collaborated extensively with Professor Iben Worre Foged and others in the Utzon(x) research and teaching initiatives, organizing summer schools and workshops. His work involves interdisciplinary teams integrating architecture, robotics, and computer vision.

• Explainable AI
• Interpretable Machine Learning
• Deep Learning
• +13 more

Wojciech Samek is a Professor in the Department of Electrical Engineering and Computer Science at the Technical University of Berlin and Head of the AI Department at Fraunhofer Heinrich Hertz Institute (HHI), Germany. He holds a joint appointment, bridging academic research and industrial application in artificial intelligence. He is a Fellow at BIFOLD and ELLIS Unit Berlin, and a Principal Investigator in several DFG projects including DeSBi and BIOQIC. PhD (Dr. rer. nat.) with distinction, Technical University of Berlin, 2014 Studies in Computer Science, Humboldt University, Heriot-Watt University, University of Edinburgh His research centers on Explainable AI (XAI) , Trustworthy Deep Learning , and Efficient AI . He pioneered Layer-wise Relevance Propagation (LRP), a foundational method for interpreting deep neural networks. His work spans model interpretation, robustness against adversarial attacks, neural network compression, federated learning, and applications in healthcare, communications, and multimedia. The recent articles highlight a strong trend toward extending explainability beyond deep models , with research on concept-level explanations, unsupervised learning interpretability, and XAI-driven model improvement. There is a consistent focus on robustness, privacy, and efficiency in distributed learning settings, particularly for federated and edge AI. Applications in medical AI and neuroscience are prominent, emphasizing safety and interpretability in high-stakes domains. Best Paper Award, Pattern Recognition (2020) Digital Signal Processing Best Paper Prize (2022) Fellow, BIFOLD - Berlin Institute for the Foundation of Learning and Data Member, Germany's Platform for Artificial Intelligence Senior Editor, IEEE TNNLS; Associate Editor, Pattern Recognition, Digital Signal Processing, PLoS ONE Area Chair, NeurIPS, ICML, NAACL Program Chair, IEEE MLSP 2023 Contributor to ISO/IEC MPEG-17 NNC standard Prof. Samek advises a large group of PhD students and leads a vibrant research team at Fraunhofer HHI and TU Berlin. His group has secured significant funding through DFG, BIFOLD, and industrial collaborations. He has co-authored over 200 peer-reviewed papers, many of which are ESI Hot or Highly Cited. He is deeply involved in organizing workshops and tutorials on XAI, federated learning, and neural compression at top venues like NeurIPS, ICML, CVPR, and IEEE conferences. His lab develops open-source tools such as the LRP Toolbox , Keras Explanation Toolbox , DeepCABAC , and Quantus , promoting reproducibility and adoption of interpretable and efficient AI methods. The team is actively working on next-generation AI that is not only accurate but also transparent, robust, and trustworthy.

Tara MehtaView profile

Associate Clinical Professor

• Clinical Psychology
• Child and Youth Mental Health
• Disparities in Mental Health Access
• +5 more

Dr. Tara Mehta is an Associate Professor of Clinical Psychology and Director of Psychology Training at the Institute for Juvenile Research, affiliated with the Department of Psychiatry at the University of Illinois Chicago (UIC). She leads the ACCESS for Mental Health research lab, focusing on reducing ethnic minority disparities in child mental health access through community-based collaborative research and workforce development. Her work emphasizes leveraging natural resources in schools and after-school settings, activating paraprofessionals, and enhancing organizational factors to sustain effective practices. She collaborates with Chicago Public Schools, Chicago Park District, and community mental health centers. Associate Professor of Clinical Psychology, UIC Director of Psychology Training, Institute for Juvenile Research Core faculty, UIC Center for Clinical and Translational Science Her research interests center on ecological public health models, mental health services research, and Asian American mental health. She has secured significant grants, including a 5-year NIMH R01, and leads projects like Building multi-level support for recreational staff and Patient Navigators for Children's Mental Health . Key themes in her recent publications include implementation science, ADHD intervention through physical activity, and trauma-informed care in hospitals. Her awards include Fellow status in APA and SCCAP. She is actively recruiting doctoral students for Fall 2024 admissions. Community-Based Research Dissemination & Implementation Science Workforce Development Paraprofessional-Led Services Scientific awards include: Fellow, American Psychological Association (2024) Fellow, Society of Clinical Child and Adolescent Psychology (SCCAP) (2024) Dr. Mehta’s lab, ACCESS for Mental Health, partners with Urban Initiatives, Chicago Park District, and social service agencies to implement programs like Leaders @ Play and Project BUILT. Her grants focus on NIMH-funded studies examining mental health navigation and virtual professional learning communities.

Jaron Skovsted GundersenView profile

Academic

• Privacy-Preserving Computation
• Secure Multi-Party Computation
• Federated Learning
• +15 more

Jaron Skovsted Gundersen is a Research Assistant at the Department of Electronic Systems, within The Technical Faculty of IT and Design at Aalborg University, Denmark. He is actively involved in the Automation & Control group and the Learning and Decisions Lab, focusing on privacy-preserving distributed systems, quantum coding, and decentralized control for infrastructure resilience. His research centers on advanced topics in secure computation and machine learning, including privacy-preserving distributed consensus , secure multi-party computation using Shamir secret sharing , federated learning , and quantum stabilizer codes . His work integrates theoretical foundations with practical applications in critical systems such as water and power distribution networks. The trend in his publications shows a strong emphasis on data privacy in distributed machine learning , leveraging techniques like subspace perturbation and differential quantization. His recent articles span high-impact journals such as IEEE Transactions on Information Forensics and Security and IEEE Journal on Selected Areas in Information Theory, reflecting contributions to both theoretical and applied aspects of information security and control systems. He has been a project participant in the SWIFT research initiative (2019–2024), which investigates decentralized control solutions for electric and water distribution systems. His activities include multiple conference presentations, participation in academic workshops, and public engagement through events like the PDJF Grundfos Prize 'The Stars of Tomorrow' EXPO. He also delivered a lecture on technological solutions in water technology at a national climate meeting in 2022. PhD graduate (March 2021) Active researcher in privacy-preserving machine learning and quantum coding Contributor to resilient infrastructure control systems Regular participant in international conferences and workshops Gundersen is affiliated with the Learning and Decisions Lab at Aalborg University, where he collaborates on cutting-edge research in distributed intelligence, secure computation, and adaptive control systems. The lab fosters interdisciplinary work combining control theory, information theory, and machine learning for real-world applications.

• Aerodynamics
• Turbulent Flow Analysis
• Particle Image Velocimetry (PIV)
• +9 more

Stefano Discetti is a Full Professor in the Department of Aerospace Engineering at Universidad Carlos III de Madrid, leading the Aerospace Engineering Research Group. His work bridges experimental fluid dynamics, machine learning, and flow control. Key research themes include turbulence characterization, heat transfer enhancement, and advanced measurement techniques like PIV/PTV. He has developed data-driven methods for flow estimation, sensor placement optimization, and physics-informed neural networks. Research Trends from his publications show a focus on: Machine learning for turbulent flow reconstruction (GANs, CNNs, KNN) Non-intrusive sensing from wall measurements Manifold learning and reduced-order modeling Heat transfer control via plasma actuators and passive structures Time-resolved diagnostics using hybrid experimental/numerical approaches Projects include principal roles in EU and national grants like NEXTFLOW (2021-2026) and EXCALIBUR (2023-2026), with industry collaborations at Airbus and TU Delft.

Bo BernhardssonView profile

Professor

• Automatic Control
• Control Theory
• Modeling and Control of Uncertain Systems
• +17 more

Bo Bernhardsson is a Professor in Automatic Control at the Department of Automatic Control, Faculty of Engineering (LTH), Lund University. He has been a full-time professor at Lund since 2010, following a decade (2001–2010) as an Expert in Mobile System Design and Optimization at Ericsson. He is affiliated with major research initiatives including ELLIIT (Excellence Center in Information Technology), LCCC (Lund Center for Control of Complex Engineering Systems), and WASP-AS (Wallenberg AI, Autonomous Systems and Software research school), where he has played a leadership role since 2016. His research focuses on modeling and control of uncertain and large-scale systems, with applications spanning industrial automation, mobile communications, particle accelerators, biomedical systems, and navigation technologies. He integrates theoretical control methods with practical implementations, particularly under constraints such as communication limitations, noise, and delays. His recent publications reveal a strong trend in networked control, communication-constrained estimation, and optimization-based control design. The works span theoretical advances in signal estimation under SNR constraints, event-based and stochastic control, and practical applications like IMU-radio fusion for navigation and RF field control in particle accelerators. Keywords include Control Theory, Communication Systems, Optimization, Signal Processing, and Networked Control , with subfields such as encoder-decoder co-design, virtual antenna arrays, and dynamic programming for time-delay systems. PhD in Control, Lund University, 1992 Professor in Automatic Control, Lund University, since 1999 Expert, Mobile Systems, Ericsson, 2001–2010 Bo Bernhardsson has supervised over 20 PhD and licentiate students, including Jacob Bergstedt (immune system modeling), Anders Mannesson (navigation and radio), and Erik Johannesson (control under communication constraints). His research has been funded by major entities such as the European Spallation Source and the Wallenberg Foundation. He teaches advanced courses in Linear Systems, Convex Optimization, and Robust Control, and has contributed significantly to both academic and industrial advancements in control engineering. He leads and collaborates on interdisciplinary projects involving real-time control, autonomous systems, and machine learning, often in partnership with industry and international research centers. His work in the RobotLab at LTH and on cloud-based control systems highlights his engagement with emerging technologies.

• Numerical Analysis
• Stochastic Differential Equations
• Runge-Kutta Methods
• +4 more

Anne Kværnø is a Professor in the Department of Mathematical Sciences at NTNU. Her research focuses on numerical analysis, particularly stochastic differential equations, Runge-Kutta methods, and geometric integration. She has made significant contributions to the development of high-order numerical schemes for stochastic systems, including exponential integrators and Lawson schemes. Her work emphasizes preserving invariants and stability properties in stochastic and deterministic systems. Key research areas include multirate methods for coupled systems, differential-algebraic equations, and applications in energy storage modeling. She has collaborated extensively on projects involving aquifer thermal energy storage (ATES) systems and groundwater flow analysis. Her publications span prestigious journals like BIT Numerical Mathematics, SIAM Journal on Numerical Analysis, and Journal of Computational Physics. She has presented at major conferences such as ICIAM, SciCADE, and ENUMATH, focusing on stochastic numerical methods and their applications. Her research bridges theoretical developments with practical computational challenges in engineering and environmental modeling.

Peter Ruijten-DodoiuView profile

Assistant Professor

• Education Technologies
• Student Growth and Development
• Process-Based Assessment
• +3 more

Dr. Peter Ruijten-Dodoiu is an Assistant Professor in the Human-Technology Interaction research group at Eindhoven University of Technology (TU/e), within the Department of Industrial Engineering and Innovation Sciences. His work bridges social psychology, educational technology, and human-computer interaction, focusing on how innovative technologies can enhance education and societal well-being. He holds a PhD in Social Psychology (2015) from TU/e, where he explored human responses to artificial agents. Before becoming an assistant professor in 2017, he served as a lecturer in the same department. Academic Background : BEng in Electrical Engineering (2005) – Avans University of Applied Sciences MSc in Human-Technology Interaction (2011) – TU/e PhD in Social Psychology (2015) – TU/e, thesis: Responses to Human-Like Artificial Agents Research Focus : Peter’s research emphasizes process-based learning , leveraging technology to cultivate student self-awareness and growth mindsets. He investigates: Educational technologies for iterative learning Sustainable behavior change via persuasive systems Human-robot interaction in social contexts His projects often involve collaborations across disciplines, including robotics, environmental psychology, and engineering education. Teaching Activities : He instructs courses such as Social and Environmental Psychology , Behavioral Research Methods , and interdisciplinary projects like the HTI Challenge-Based Learning initiative. These activities reflect his commitment to connecting theory with real-world societal challenges. Labs & Affiliations : Peter is affiliated with the EAISI Foundational and EAISI Mobility research groups at TU/e, focusing on foundational AI and mobility-related technological innovations.

Hans ButlerView profile

Professor

• Control Systems
• Lithographic Scanner Control
• Dynamics and Vibration Isolation
• +3 more

Hans Butler is a part-time Full Professor in the Control Systems group of the Department of Electrical Engineering at Eindhoven University of Technology. He holds an MSc (1986) and PhD (1990) from Delft University of Technology. As an ASML Fellow, he specializes in dynamics and control systems with a focus on lithographic scanner control, particularly sub-nanometer positioning accuracy challenges. His work integrates physics-guided neural networks and advanced control strategies for mechatronic systems. Research interests include feedforward control design, vibration isolation, opto-mechatronics, and precision engineering applications. His contributions bridge academic research and industrial practice through collaborations with ASML, focusing on high-precision mechatronic systems used in semiconductor manufacturing. Butler has led teams in actuators development, system dynamics, and damping solutions at ASML since 1991. His recent work emphasizes data-driven methods and neural network implementations in control systems, supported by grants like NWO project 17973. He co-developed a MATLAB toolbox for physics-guided neural networks, fostering practical tooling for control engineers.

Martijn AnthonissenView profile

Assistant Professor

• Computational Illumination Optics
• Numerical Mathematics
• Scientific Computing
• +5 more

Martijn Anthonissen is an Assistant Professor in the Department of Mathematics and Computer Science at Eindhoven University of Technology (TU/e). He is a core member of the Computational Illumination Optics group, focusing on numerical methods for optical system design, freeform optics, and inverse problems in illumination engineering. His work combines advanced mathematical models with computational techniques to develop efficient optical systems for applications like LED lighting, automotive headlights, and laser technology. Education: Martijn earned his mathematics degree at TU/e and participated in the Japan Prizewinners Program, working at Hitachi in Tokyo. He completed his PhD on numerical combustion before transitioning to computational optics. He has held visiting positions at institutions worldwide, including Yale University and the Weierstrass Institute. Research: His research emphasizes solving Monge-Ampère equations and generated Jacobian equations to design freeform optical surfaces. Key areas include hyperbolic PDEs, Liouville's equation in geometrical optics, and Lie algebraic methods for aberration analysis. Collaborations with industry partners aim to translate theoretical advances into practical optical solutions. Teaching: He teaches courses in numerical linear algebra, mathematical modeling, and scientific computing, contributing to TU/e's teacher training programs. His educational materials, including YouTube videos on numerical methods, are widely used. Publications: Over 50 peer-reviewed articles since 2013, including high-impact work in Journal of the Optical Society of America , Journal of Computational Physics , and Optics Express . Current projects include neural network approaches for PDEs and optimization of gradient-index lenses. Labs/Teams: Leads the Computational Illumination Optics group, which has active PhD and industrial research programs. The group's interdisciplinary approach bridges mathematics, physics, and engineering.

• Mathematical Optimization
• Algorithmic Game Theory
• Multi-Agent Systems
• +3 more

Max Klimm is an Assistant Professor for Discrete Optimization at Technische Universität Berlin, affiliated with the Department of Mathematics within Faculty II – Mathematics and Natural Sciences. He holds a PhD in Mathematics from TU Berlin (2012) and previously served as an Assistant Professor for Operations Research at Humboldt-Universität zu Berlin (2014–2016) and led the Junior Research Group for Optimization under Uncertainty at the Einstein-Center for Mathematics (2014–2016). His research focuses on algorithmic game theory, mathematical optimization of multi-agent systems in traffic, telecommunications, and economics. Key areas include network flows, congestion games, mechanism design, and infrastructure networks. He actively contributes to academic activities as an associate editor for journals like International Journal of Game Theory and Operations Research Forum , and has served on program committees for conferences such as AAAI, EC, and WINE. Recent work includes studies on evolutionary road network reconstruction, robustness of potential-based flows, and algorithmic solutions for knapsack and assignment problems. His research bridges theoretical foundations with practical applications in transportation and resource allocation.

Nuutti HyvönenView profile

Professor

• Inverse Problems
• Electrical Impedance Tomography
• Numerical Analysis
• +3 more

Nuutti Hyvönen is a Professor and Head of Department at the Department of Mathematics and Systems Analysis, Aalto University, School of Science. His research focuses on inverse problems, electrical impedance tomography, and numerical analysis with applications in biomedical imaging and engineering. He holds a Doctor of Science (Technology) degree and has extensive experience in developing mathematical methods for imaging and reconstruction algorithms. His work emphasizes improving the accuracy and robustness of tomographic techniques, particularly in handling modeling errors and electrode configurations. Key research areas include: Inverse problems for partial differential equations Electrical impedance tomography (EIT) and its biomedical applications Bayesian experimental design and uncertainty quantification Numerical methods for nonlinear imaging problems Recent publications highlight advancements in: Optimal electrode positioning Series reversion methods in Calderón problems Feasibility of EIT for monitoring intracerebral hemorrhages Edge-enhancing reconstruction algorithms He has contributed to over 79 peer-reviewed articles, with notable work on monotonicity-based reconstruction methods and stochastic Galerkin finite element approaches. His research bridges mathematical theory and practical applications in medical imaging, engineering, and computational science.