Find Academic Supervisors

Farshid AmirabdollahianView profile

Professor

• Human-Robot Interaction
• Rehabilitation Robotics
• Assistive Technology
• +6 more

Farshid Amirabdollahian is a Professor of Human-Robot Interaction at the University of Hertfordshire's School of Engineering and Computer Science. He leads the Robotics Research Group and directs the Robot House and Kaspar projects. His academic roles include Theme Champion in Information and Security. He has held prior positions at the University of Salford (THINKlab) and Newcastle University (CREST). His research focuses on adaptive human-robot interaction for rehabilitation, assistive technology, and AI applications. He actively contributes to ISO standards for robot safety and ethics. Research interests include: rehabilitation robotics, assistive technology, deep learning for HRI, human performance assessment, and robot ethics. He has led EU-funded projects like Accompany and SCRIPT, and current projects include SWAG (wearable assistive garments) and Hospital@home virtual ward initiatives. His work spans over 115 publications and 16 research projects. Teaching includes robotics and AI courses at undergraduate and postgraduate levels. Collaborations involve multidisciplinary teams in healthcare tech, AI, and robotics engineering. Current projects emphasize soft wearable robotics, ambient assisted living, and humanoid robots in special education.

Vicent Girbés JuanView profile

Associate Professor

• Robotics
• Human-Robot Interaction
• Intelligent Vehicles
• +12 more

Vicent Girbés Juan is an Associate Professor in the Department of Electronic Engineering at the School of Engineering, Universitat de València. His research is centered in the HRI Human-Robot Interaction Group, where he contributes to advanced robotics, intelligent vehicles, and human-centered automation systems. He earned his PhD from Universitat Politècnica de València in 2016 with a thesis on clothoid-based planning and control in autonomous and manual-assisted driving systems, supervised by Dr. Josep Tornero Montserrat and Dr. Leopoldo Armesto Ángel. His research interests span robotics, control systems, path planning for UAVs, visible light communication, haptic feedback in teleoperation, and educational innovation in engineering. He has published extensively on smooth trajectory generation, dual-arm robot control, sensor fusion, and V2V communications. His recent work shows a growing emphasis on integrating pedagogical innovation with engineering education, including flipped evaluation, peer assessment, and hackathon-based programming learning. His publications from 2021 to 2024 reveal a dual focus: advancing industrial robotics and intelligent transportation systems, while simultaneously innovating in teaching methodologies and student engagement in higher education. Key technical areas include clothoid-based 3D path planning, cautious Bayesian optimization, VLC positioning, and haptic-assisted teleoperation. He actively collaborates on interdisciplinary projects involving human-robot cooperation, industrial automation, and educational technology, reflecting a commitment to both technological advancement and pedagogical excellence. His email is vicent.girbes@uv.es .

• Formal Verification
• Automated Reasoning
• SAT and SMT Solving
• +7 more

Bernd Becker is a Professor in the Department of Computer Science at the Faculty of Engineering, University of Freiburg, Germany. He has been actively publishing in the fields of formal methods, hardware verification, and automated reasoning since the 1980s, with a sustained record of high-impact publications up to 2025. His research interests span formal verification, SAT and SMT solving, hardware testing, probabilistic systems, and embedded systems security. His work bridges theoretical advances in automated reasoning with practical applications in processor design, RISC-V verification, and safety-critical systems. The recent articles highlight a strong focus on applying formal methods to emerging challenges in hardware and AI, including POMDPs for robot planning, RISC-V security, GPU verification, and energy-efficient monitoring. There is a clear trend toward integrating symbolic computation with machine learning and probabilistic reasoning. Bernd Becker has collaborated extensively with researchers such as Ralf Wimmer, Ilia Polian, and Matthias Sauer, indicating leadership in a large, interdisciplinary research group. His work has been supported by numerous grants, though specific details are not listed in the source text. He has contributed to major conferences such as DATE, FMCAD, and SAT, and has mentored several students and researchers, though specific names are not provided. He is involved in projects related to secure scan networks, physical unclonable functions, and smart home systems.

Sebastian BaderView profile

Researcher

• Neuro-Symbolic Artificial Intelligence
• Human Activity Recognition
• Assistive Systems for Dementia
• +7 more

Sebastian Bader is a research scientist at the Institute for Visual & Analytic Computing at the University of Rostock, where he contributes to the Chair of Mobile Multimedia Information Systems. His work bridges artificial intelligence, human-computer interaction, and healthcare applications, particularly in assistive technologies. Doctoral Degree: Dr. rer. nat. in Neural Symbolic Integration, TU Dresden, 2009 (summa cum laude) His research interests focus on neuro-symbolic AI, human activity recognition, and intelligent assistive systems for dementia care and neurorehabilitation. He investigates how AI can support clinical interventions, improve patient monitoring, and enhance explainability in medical contexts. His work emphasizes ethical considerations, real-world usability, and integration with sensor and robotic systems. The recent publications highlight a strong trend in applying AI to healthcare, particularly in modeling patient behavior, evaluating robotic assistance, and developing explainable and ethical AI systems. Topics span from underwater robotics to Alzheimer’s diagnostics, showcasing interdisciplinary expertise. He supervises Bachelor's and Master's theses on topics such as visualizing uncertainty in ML, semi-automatic test generation using LLMs, and building probabilistic digital shadows of the Port of Rostock. Sebastian Bader actively collaborates with medical researchers and industry partners, and his projects include "SAMi" (sensor-based activity management for dementia) and "E-BRAiN" (robot-assisted neurorehabilitation). He contributes to both technical AI development and real-world deployment in healthcare environments.

Xin ChenView profile

Assistant Professor

• Cyber-Physical Systems
• Formal Methods
• Safety and Security of Machine Learning Systems
• +7 more

Xin Chen is an Assistant Professor in the Department of Computer Science at the University of New Mexico , where he conducts research at the intersection of cyber-physical systems, formal methods, and machine learning. His work focuses on safety and security verification for learning-enabled systems. Research Interests: Safety and security of Cyber-Physical Systems (CPS) with machine learning components Formal verification and reachability analysis of hybrid and neural network-controlled systems Real-time monitoring and attack recovery in CPS Development of verification tools for safety-critical autonomous systems Publication Trends: His recent publications (2020–2025) emphasize formal methods for neural network verification, attack recovery in CPS, and reachability analysis. Key themes include the use of polynomial arithmetic, linear approximations, and abstraction techniques to ensure safety in systems using deep learning and reinforcement learning. Scientific Service: Program Committee Member: DAC 2025, VMCAI 2025, RTSS, AAAI, CAV, HSCC, ARCH, and others Grants and Advising: Dr. Chen is the Principal Investigator (PI) on multiple grants, including an NSF-funded project on safety-directed abstraction and correction of learning-enabled CPS. While no formal students are listed, he collaborates extensively with researchers and students on funded projects and tool development. He leads research in formal verification and has mentored work on tools like Flow* and POLAR. Labs and Tools: Flow* : A tool for reachability analysis and safety verification of nonlinear hybrid systems, with a reengineered high-performance version available on GitHub. POLAR : A framework for formal verification of neural network-controlled systems.

• Probabilistic Verification
• Formal Methods
• Artificial Intelligence Safety
• +7 more

Dr. Sebastian Junges is an Assistant Professor at the Software Science Group of Radboud University (Nijmegen, Netherlands) since September 2021. He previously held postdoctoral and research assistant positions at UC Berkeley and RWTH Aachen University respectively, working under prominent researchers like Sanjit Seshia and Joost-Pieter Katoen . Education: Ph.D. in Computer Science, RWTH Aachen University (2020) M.Sc. in Computer Science, RWTH Aachen University (2015) B.Sc. in Computer Science, RWTH Aachen University (2012) His research focuses on trustworthy algorithms for system dependability , particularly in safety-critical domains like autonomous systems and infrastructure automation. Key technical contributions include: Verification algorithms for Markov decision processes (MDPs) Parameter synthesis in probabilistic models Finite state controller generation Runtime monitoring with imprecise sensors Recent work (2022-2024) has explored multi-objective verification , neuro-symbolic reasoning , and scalable analysis of probabilistic models . His methodological approach combines formal methods with machine learning and automated reasoning techniques, often implemented in the state-of-the-art Storm probabilistic model checking framework. Selected scientific contributions include: Keynote speaker at MODEM 2024 Invited tutorial presenter at Dagstuhl 2024 Co-organizer of multiple VeriProp workshops (2021-2024) Program committee member for TACAS 2026, CONCUR 2024, and CAV 2024 He actively supervises Bachelor and Master student projects in algorithm development for probabilistic systems, including topics on: MDP model checking acceleration Runtime monitoring frameworks Syntax-guided controller synthesis State estimation for fault trees Probabilistic inference transpilation His academic service includes: Radboud Science Faculty Diversitee Committee Steering Committee of ETAPS (2025-2026) Program committee roles across 15+ top venues

Meysam GoodarziView profile

Lecturer

• Artificial Intelligence
• Data Science
• Economics
• +7 more

Meysam Goodarzi is a Lecturer at the Hertie School in Berlin, Germany, specializing in Data Structures & Algorithms within the Master of Data Science for Public Policy program. His research bridges computer science, economics, and probabilistic modeling, focusing on Bayesian inference, probabilistic graphical models, and optimization techniques. PhD in Computer Science (Humboldt University of Berlin) M.Sc. in Economics (Panthéon-Sorbonne University) His work emphasizes applications in data science and economics, particularly within AI-driven network optimization and 5G technology. He has contributed to EU Horizon 2020 projects, advancing synchronization, localization, and machine learning algorithms in telecommunications. Recent publications highlight his expertise in 5G networks , autonomous systems , and probabilistic modeling , with a focus on synchronization, localization, and communication-sensing integration. Articles span from 2024 to 2019, reflecting sustained contributions to 5G/6G research and AI applications. At Hertie School, he contributes to the Data Science Lab , integrating advanced computational techniques into public policy education. His interdisciplinary approach connects technical advancements in telecommunications with economic analysis and public sector applications.

• Algorithmic Game Theory
• Stochastic Games
• Distributed Systems
• +5 more

Hugo Gimbert is a Researcher at the LaBRI (Laboratoire Bordelais de Recherche en Informatique) within the Méthodes Formelles Team . His work bridges computational theory and practical applications, focusing on algorithmic game theory, stochastic processes, and formal methods in computer science. Research interests include: Decidability problems in distributed and stochastic games Partial observation in multi-player systems Cooperation and competition in bridging scenarios Social choice theory implementation Precision farming automation Probabilistic logics and humanoid robot control via MDPs Advising roles: PhD advisor for Soumyajit Paul, Edon Kelmendi, Quentin Rouxel, and Ludovic Hofer PhD co-advisor for Simon Mauras Former advisor for Youssouf Oualhadj Key projects: Stamina - Stabilisation monoids and regular language analysis Marmotte - CNRS committee digitalization platform Rhoban System - Real-time robotics for agriculture and soccer Parcoursup - Algorithmic college admission system for French government

• Machine Learning
• Artificial Intelligence
• Anomaly Detection
• +3 more

Alessio Mascolini serves as a Research Fellow at the Interuniversity Department of Territorial Sciences, Planning and Policies (DIST) and as an External Lecturer at the Department of Control and Computer Science (DAUIN) at Polytechnic University of Turin. His academic work bridges theoretical machine learning research with practical industrial applications, particularly in manufacturing and engineering contexts. His research interests focus on Machine Learning applications in data-limited scenarios , with specialization in anomaly detection systems , edge computing implementations , and generative modeling approaches . His work demonstrates strong connections between theoretical AI advances and real-world industrial challenges, particularly in semiconductor manufacturing and robotic production lines. Analysis of his recent publications reveals a clear trajectory toward developing resource-efficient AI systems for industrial environments, with increasing sophistication in combining neuro-symbolic approaches with diffusion models. His research consistently addresses the challenge of implementing robust machine learning solutions in resource-constrained edge computing environments. As an educator, Mascolini has served as Course Collaborator for multiple programs since 2020/21, demonstrating consistent engagement with student instruction across various engineering disciplines. His teaching portfolio shows strong alignment with his research interests, particularly in machine learning applications.

Alice M. AgoginoView profile

Professor

• intelligent learning systems
• information retrieval and data mining
• multiobjective and strategic product design
• +11 more

Alice M. Agogino is a Professor of the Graduate School in the Department of Mechanical Engineering at the University of California, Berkeley's College of Engineering. She founded and heads the Product Design Concentration for the MEng Program and held the Roscoe and Elizabeth Hughes Chair until 2022. Her research spans: Intelligent learning systems and AI Design theory, databases, and multiobjective product design Probabilistic modeling and nonlinear optimization Sensor validation, wireless networks, and robotics Gender equity in engineering Her NASA-derived work spawned Squishy Robotics, developing deployable tensegrity robots for disaster response and wildfire/methane detection that survive 600+ foot drops. Scientific Awards: None listed in source materials. As MEng Product Design Concentration Head Advisor, she mentors graduate students through Berkeley's Best Lab. Her Squishy Robotics startup translates research into societal impact, with robots deployed via helicopter for hazardous environment monitoring despite NSF funding challenges to related initiatives like the Native FEWS Alliance.

Harold TRANNOISView profile

Lecturer

• Machine Learning
• Artificial Intelligence
• Multi-agent Systems
• +3 more

Harold TRANNOIS serves as a Lecturer at the University of Picardy Jules Verne (UPJV) within the Networks and Data Domain (REDO), with office 309 contactable at phone 5911. His academic focus bridges theoretical computer science and applied engineering disciplines. His research program demonstrates deep specialization in intelligent systems, particularly emphasizing: Machine Learning architectures including spiking neural networks and autoencoders Multi-agent coordination frameworks for complex environments Anomaly detection systems in IoT-enabled infrastructure Cloud robotics and domain-specific language development Discrete element modeling for physical simulations Analysis of his publication trajectory reveals evolving expertise from foundational multi-agent traffic simulation (1998) toward contemporary biomedical AI applications (2023). Recent work concentrates on neural network implementations for healthcare monitoring and smart building analytics, with consistent methodological focus on uncertainty quantification and architectural optimization. Active involvement in DAAB and SmartAngel projects demonstrates applied research translation into healthcare and robotics domains, leveraging his dual expertise in neural networks and distributed systems for real-world problem solving.

Szegletes LucaView profile

Senior Lecturer

• Machine Learning
• Neuroscience
• Biofeedback Systems
• +4 more

Szegletes Luca is a Senior Lecturer at the Budapest University of Technology and Economics, affiliated with the Department of Automation and Applied Informatics as part of the Applied Mobile Research Group (AMORG). Her research spans multiple interdisciplinary domains, focusing primarily on integrating machine learning techniques with biofeedback systems. Her work demonstrates expertise in diffusion probabilistic models for EEG signal processing, deep learning applications in embedded systems, and reinforcement learning frameworks. She has pioneered methods for physiological data analysis, including blood pressure measurement and face anonymization in low-power environments. Notable research contributions include adaptive educational game frameworks, cognitive profiling systems, and biofeedback-driven difficulty control mechanisms. Her publications reflect a strong focus on bridging computational methods with cognitive science and biomedical applications.

Stefan LüdtkeView profile

Assistant Professor

• Neuro-symbolic machine learning
• Machine learning for heterogeneous data
• Machine learning for marine ecology
• +5 more

Stefan Lüdtke is an Assistant Professor (Juniorprofessor) for Marine Data Science at the University of Rostock (since July 2023) and concurrently serves as a junior research group leader at ScaDS.AI Leipzig . Previously, he was a postdoctoral researcher at the Institute for Enterprise Systems, University of Mannheim (2021-2023) and completed his PhD at the University of Rostock (2016-2021). Education & Career Timeline: 2023 – present: Juniorprofessor (Assistant Professor) for Marine Data Science, University of Rostock 2023 – present: Junior research group leader, ScaDS.AI Leipzig 2021 – 2023: Postdoc, Institute for Enterprise Systems, University of Mannheim 2016 – 2021: PhD studies, University of Rostock Research Interests: Dr. Lüdtke’s research integrates neuro-symbolic machine learning with practical applications spanning heterogeneous tabular data , marine ecology , and underwater technology . His work bridges symbolic reasoning and modern gradient-based learning to tackle complex real-world problems such as hyperspectral imaging for environmental monitoring, knowledge-graph completion, and robust human-activity recognition. Key focus areas include: Design of memory-augmented decision-tree ensembles and gradient-based tree learning. Data-centric evaluation and quality assessment of machine-learning models on tabular and sensor data. Domain adaptation and self-training techniques for activity recognition in changing environments. Application of AI to marine robotics and glacier-dynamics mapping. Publication Trends: Across 40+ peer-reviewed works (2017-2025), Lüdtke demonstrates a steady shift from foundational probabilistic-filtering and lifted-inference methods toward cutting-edge neural-symbolic hybrids and tabular-data-centric learning. Recent high-impact venues show contributions in machine-learning theory , computer vision , and environmental informatics , with a notable uptick in interdisciplinary projects combining AI and marine science. Scientific Awards & Honors: No specific awards are listed in the provided material. Advising & Funding: No named students or explicit grant details are disclosed in the text. Laboratories & Teams: He leads the Marine Data Science junior research group at the University of Rostock and the ScaDS.AI Leipzig junior research group, fostering cross-institutional collaboration in AI and data science.

• Battery Management Systems
• Electrochemical Impedance Spectroscopy
• Machine Learning Applications
• +7 more

Alessio De Angelis is a researcher affiliated with the University of Perugia, focusing on advanced measurement systems, battery technology, and localization techniques. His work spans disciplines such as electrical engineering, machine learning, and IoT applications. University: University of Perugia Email: alessio.deangelis@unipg.it His research interests include: Battery management and state-of-charge estimation Uncertainty quantification in AI-based systems Ultra-wideband (UWB) and magnetic localization Wireless sensor networks for environmental monitoring Signal processing for quantized and constrained data IoT applications in structural and health monitoring Recent publications highlight his expertise in battery diagnostics, localization algorithms, and AI-driven measurement systems. Key trends involve integrating machine learning with electrochemical impedance spectroscopy (EIS) and optimizing low-complexity sensor architectures.

Diego Borro YagüezView profile

Professor

• Computer Graphics
• Virtual Reality
• Augmented Reality
• +6 more

Diego Borro is a Full Professor (Catedrático) in Computer Science and Artificial Intelligence at TECNUN, Technological Campus of the University of Navarra , where he has been part of the faculty since 2004. He is a leading researcher at CEIT since 2003, focusing on Robotics, Virtual/Augmented Reality, Computer Vision, and Artificial Intelligence. His academic credentials include a PhD in Computer Science (2003) and an MS in Computer Science (2000) from the University of Navarra and University of Basque Country respectively. His research spans from 3D tracking and haptics to industry 4.0 applications and medical robotics, with over 34 journal papers and 65 conference articles. He has supervised 14 doctoral theses and participated in 55+ research projects. His leadership roles include heading CEIT's Simulation Unit (2012-2016) and Vision and Robotics (V&R) research line (2016-2022), currently serving as main researcher at Intelligent Systems for Industry 4.0 group (SS4I4). Accredited as Full Professor (Catedrático) by ANECA (3 sexenios) Member of IEEE, ACM, and Eurographics societies Key projects: STEPbySTEP exoskeleton benchmark, WARM AR maintenance systems, and inner ear drug delivery research