Find Academic Supervisors

Zhongliang JiangView profile

Researcher

• Medical Robotics
• Image Processing
• Robotic Learning
• +3 more

Dr. Zhongliang Jiang is a senior research scientist and leader of the Robotics and Ultrasound team (RobUSt) at the Chair of Computer Aided Medical Procedures (CAMP) at Technische Universität München. He holds a Ph.D. in computer sciences (summa cum laude) and has authored/co-authored over 40 top-tier publications in robotics and medical imaging. His research focuses on robotic ultrasound systems, medical image processing, and robotic learning. Education: Ph.D. in Computer Sciences, TUM (2022, summa cum laude) M.Eng. in Harbin Institute of Technology (2017) Research Assistant at SIAT (2017-2018) Research Interests: Medical Robotics: autonomous robotic ultrasound systems Image Processing: RGB-D/ultrasound segmentation, registration Robotic Learning: reinforcement/imitation learning Robotic Control: MPC, shared control, human-robot interaction Professional Contributions: Associate Editor for ICRA 2024/2025 Guest Editor for IEEE TRO special issue on Robot-Assisted Medical Imaging Main organizer of RAMI workshops at ICRA (2023-2025) Awards: MICCAI 2023 Best Paper Runner-up Gold Medal for Master's Thesis (2017) Lab & Teaching: Leading the RobUSt team developing advanced robotic ultrasound solutions Teaching courses like Computer Aided Medical Procedures and Medical Augmented Reality Supervised over 15 Master/PhD projects in robotic ultrasound and medical imaging

• Artificial Intelligence
• Machine Learning
• Data Mining
• +5 more

Ling Zhao is a distinguished Professor at the School of Management, Huazhong University of Science and Technology, China, with extensive research contributions spanning artificial intelligence, machine learning, information systems, and biomedical applications. With over 150 publications since 2008, Dr. Zhao has established herself as a leading researcher in multiple interdisciplinary domains, particularly in applying computational methods to solve complex real-world problems. Dr. Zhao's research interests encompass a broad spectrum of cutting-edge topics including artificial intelligence, machine learning, data mining, control systems, and information systems. Her work demonstrates exceptional versatility, bridging theoretical computer science with practical applications in healthcare, transportation, cybersecurity, and business management. Notably, she has made significant contributions to sentiment analysis, medical image processing, algorithmic management, and privacy-preserving data analysis. Her research methodology often combines deep learning approaches with domain-specific knowledge to develop innovative solutions. Analysis of Dr. Zhao's recent publications (2023-2025) reveals a strong focus on interdisciplinary applications of AI, with particular emphasis on healthcare informatics (medical image analysis, disease diagnosis), human-computer interaction (algorithmic management effects), and advanced machine learning techniques (graph neural networks, multimodal learning). Her work shows a consistent trend toward increasingly complex and integrated systems that address real-world challenges across multiple domains. Dr. Zhao has made substantial contributions to academic advising and research mentorship, though specific student names aren't detailed in the available publications. Her research has been supported by various grants enabling work in AI applications, biomedical engineering, and information systems. Dr. Zhao maintains active collaborations with researchers across China and internationally, as evidenced by her co-authorship patterns. While specific laboratory information isn't explicitly mentioned in the publication records, Dr. Zhao appears to lead or be significantly involved in research groups focusing on AI applications in management and healthcare. Her work on medical imaging, sentiment analysis, and control systems suggests involvement in multiple specialized research teams addressing different application domains through computational approaches.

• Control Theory
• Distributed Parameter Systems
• Model Predictive Control
• +3 more

Lorenz Dörschel is an Adjunct Professor (Lehrbeauftragter) at the Institute of Automatic Control at RWTH Aachen University. He holds the academic title PD Dr.-Ing. habil, signifying post-doctoral research qualifications. His position is part-time, focusing on advanced control theory and applications. His primary research interests include: Control of distributed parameter systems (e.g., fluid dynamics, thermal processes) Model predictive control for industrial and automotive systems Parameter space methods for robust controller design Model reduction techniques for complex nonlinear systems Dörschel's recent publications (2018-2024) demonstrate broad applications across biomedical engineering, renewable energy, automotive systems, and industrial automation. His work consistently integrates mathematical rigor with practical implementations, emphasizing advanced control methodologies like nonlinear MPC, Lyapunov-based design, and Bayesian optimization. A recurring theme is the development of computationally efficient control strategies for distributed parameter systems. No scientific awards, student advising relationships, or research grants are documented in the available information.

Michael FinkView profile

Researcher

• Control Theory
• Automation and Robotics
• Agricultural Engineering
• +4 more

Michael Fink is a researcher at the Chair of Automatic Control Engineering , Technical University of Munich . He holds an M.Sc. in Electrical Engineering and Information Technology (2020) and a B.Eng. in the same field from Technical University Munich and University of Applied Sciences Landshut (2018), respectively. Research Interests : Model Predictive Control (MPC) with focus on stochastic and robust variants Optimal control strategies for autonomous driving and vertical farming Constraint violation probability minimization in dynamic systems Publications span topics in: Time-optimal MPC for linear systems Stochastic and robust MPC frameworks Learning-based control for greenhouse climate systems Vertical farming optimization Contact: michael.fink@tum.de

• Power Systems
• Renewable Energy
• Electric Vehicles
• +2 more

Haitham Abu-Rub is a Professor at Texas A&M University at Qatar specializing in power systems, renewable energy integration, and power electronics. His research focuses on developing innovative solutions for grid stability, EV charging infrastructure, and intelligent control systems. He has published extensively in IEEE journals and conferences, addressing challenges in smart grids and sustainable energy systems. His work spans power converter design, fault diagnosis, and AI applications in energy management. Recent projects include decentralized PV trading systems, resilient inverter networks, and physics-informed neural networks for insulation diagnostics. Dr. Abu-Rub collaborates internationally on projects involving grid-interactive buildings, digital twins for power converters, and adaptive control techniques for electric vehicle charging.

Rolf FindeisenView profile

Professor

• Model Predictive Control (MPC)
• Cyber-Physical Systems
• Battery Systems Optimization
• +5 more

Prof. Rolf Findeisen is a Professor in the Department of Control and Cyber-Physical Systems (CCPS) at Technische Universität Darmstadt. His work focuses on advancing control theory and its applications in cyber-physical systems, autonomous systems, and energy storage systems. Key areas include model predictive control (MPC), battery management systems, machine learning integration into control frameworks, and optimization of crystallization processes. He leads research on safety-critical systems, data-driven control methods, and interdisciplinary applications in robotics and biotechnology. His research spans theoretical advancements in MPC stability, stochastic control, and Gaussian process modeling, alongside practical implementations in autonomous vehicles, lithium-ion battery systems, and bioprocess optimization. Notable contributions include frameworks like HILO-MPC for integrating machine learning with control systems, and methodologies for safe exploration in autonomous navigation. Prof. Findeisen's publications emphasize energy-efficient trajectory planning, fault detection in battery systems, and real-time optimization of manufacturing processes. His work bridges academic theory with industrial applications, addressing challenges in scalability, safety, and computational efficiency. His lab collaborates on national projects like IN-Fly-Tec and INFLIGHT, focusing on innovative flight control systems and sensor technologies. Current research trends include hybrid intelligent optimization, cybergenetic control of microbial systems, and safe reinforcement learning for control systems.

• Control Systems
• Model Predictive Control
• Data-Driven Methods
• +3 more

Shuhao Yan is a Researcher and Post Doc Researcher at the University of Stuttgart, affiliated with the Department of Mathematics/IMNG within Faculty 08. His research focuses on advanced control systems, particularly in data-driven methods for uncertain systems, stochastic model predictive control (MPC), and grid-forming control in power electronics. He leads the SimTech Project Network 4-3 (II) titled 'Data-Integrated Control Systems Design with Guarantees,' emphasizing robustness and real-world applicability. His research interests revolve around integrating data with traditional control theory to address challenges in uncertain dynamic systems. Key areas include robust MPC, stochastic optimization, and the application of machine learning in control systems. He has contributed to advancements in grid stability analysis, voltage source converter control, and adaptive control strategies under intermittent data conditions. Recent publications highlight trends in learning-based control (e.g., homothetic tube MPC), stochastic MPC with probabilistic constraints, and power grid stability analysis for renewable energy integration. His work bridges theoretical foundations with practical implementations in energy systems and automation. Shuhao Yan holds a PhD and currently conducts research within the Institute of Mathematical Methods in Engineering, Numerical Analysis and Geometric Modeling. His projects emphasize interdisciplinary collaboration between control theory, applied mathematics, and electrical engineering.

Armin NurkanovićView profile

Professor

• Optimal Control
• Numerical Methods
• Hybrid Dynamical Systems
• +4 more

Dr. Armin Nurkanović is an interim professor at the Technical University of Braunschweig's Department of Mathematical Optimization, where he teaches courses on dynamic optimization and numerical methods. Previously, he completed his PhD at the University of Freiburg under Prof. Moritz Diehl, focusing on optimal control of nonsmooth dynamical systems. His research emphasizes numerical methods for hybrid systems, real-time optimization, and applications in robotics and renewable energy systems. He has received the IEEE Control Systems Letters Outstanding Paper Award (2022) and was a finalist for the 2024 European Systems & Control PhD Thesis Award. Education: Bachelor's in Electrical Engineering (University of Tuzla, 2015) Master's in Electrical Engineering and Information Technology (Technical University of Munich, 2018) PhD in Control (University of Freiburg, 2023) Research Interests: Optimal control of hybrid and nonsmooth systems (e.g., Filippov systems, switched systems) Real-time optimization for model predictive control (MPC) Robust control theory and stochastic optimization Applications in robotics and renewable energy systems Teaching & Software: Developed open-source tools nosnoc and nosnoc_py for optimal control Teaching courses on numerical optimization and optimal control at TU Braunschweig Collaborations & Students: Open to academic and industry collaborations Supervises Bachelor's/Master's theses in mathematics, engineering, and computer science

• Electrochemical Energy Storage
• System Design Methodologies
• Energy Systems Optimization
• +5 more

Prof. Dr. Walter Commerell serves as Professor at Technische Hochschule Ulm (THU) , holding the role of International Representative for the Faculty of Production Engineering and Production Management. He is the Spokesperson for ASIM (Simulation of Technical Systems Division) and Head of Steinbeis Transfer Center System Design , while also being a member of the International Energy Competence Center (IECN) and Institutes for Energy and Drive Technology/Vehicle Systems Technology.

Mohammadhossein MalmirView profile

Researcher

• Reinforcement Learning
• Robotic Manipulation
• Sim2Real Transfer
• +3 more

Mohammadhossein Malmir is a Researcher at the Chair of Robotics, Artificial Intelligence and Real-time Systems at Technische Universität München (TUM). He joined the chair in November 2019 and currently contributes to the A-IQ Ready project (successor of AI4DI ), focusing on learning algorithms for industrial manipulators and mobile robots in manufacturing and intralogistics tasks. Education B.Sc. in Electrical Engineering (Control Systems) from Amirkabir University of Technology (Tehran Polytechnic) (2014) M.Sc. in Automation and Control Engineering from Politecnico di Milano (2018) His research addresses Sim2Real Transfer , Reinforcement Learning , Robust Control , and Model Predictive Control for autonomous robotic systems. He has published extensively on sim2real policy transfer, domain randomization, and control algorithms for manipulation and navigation tasks. Recent publications highlight his work in dexterous grasping , continual domain adaptation , and deep reinforcement learning for trajectory optimization . His advising roles include supervising 13 master's students and interdisciplinary projects, with co-advisorship on topics like vision-based robotic grasping and socially compliant path planning . He teaches courses on Cognitive Systems and Cloud-Based/Simulation-Based Machine Learning in Robotics , emphasizing reinforcement learning and sim2real methodologies.

• Sustainable Energy Systems
• Numerical Optimization
• Deep Learning
• +3 more

Dr. Manuel Dahmen serves as Head of Department at the Institute of Climate and Energy Systems (ICE-1) within the Research Center Jülich. His research focuses on designing sustainable and cost-efficient energy systems through numerical optimization and deep learning techniques. He leads efforts in advancing energy system technology, particularly in optimizing renewable energy integration, process network analysis, and the co-design of fuels and engines. His work emphasizes innovative applications of machine learning, such as physics-informed neural networks and reinforcement learning, to address challenges in energy system design and operation. Key areas include reducing greenhouse gas emissions in industrial processes, optimizing energy networks, and developing data-driven models for dynamic process control. Dr. Dahmen’s contributions span algorithm development (e.g., MUSE-BB decomposition algorithms), energy system scenario generation, and robust design methodologies under uncertainty. His research bridges computational methods with practical energy solutions, aiming to achieve decarbonization in industries like copper production and transportation. Publications highlight advancements in renewable energy integration, fuel design for spark-ignition engines, and the application of graph neural networks for molecular property prediction. His interdisciplinary approach fosters collaboration across chemical engineering, computer science, and energy economics to tackle global sustainability challenges.

Katherine J. KuchenbeckerView profile

Professor

• Haptics
• Robotics
• Human-Computer Interaction
• +3 more

Katherine J. Kuchenbecker is the Director of the Haptic Intelligence Department at the Max Planck Institute for Intelligent Systems in Stuttgart, Germany, and an Honorary Professor at the University of Stuttgart. She previously held a tenured position as an Associate Professor at the University of Pennsylvania. Her research focuses on haptic interfaces and sensing systems, enabling users to interact with virtual and distant objects through touch. She earned her Ph.D. in Mechanical Engineering from Stanford University and completed postdoctoral research at Johns Hopkins University. Her academic journey includes leadership roles such as co-chair of the IEEE Technical Committee on Haptics and associate editorships for major conferences. She has received numerous awards, including the NSF CAREER Award (2009), IEEE Academic Early Career Award (2012), and elevation to IEEE Fellow (2021). Her work spans applications in medical robotics, teleoperation, and human-robot interaction. Kuchenbecker’s research emphasizes translating haptic technology into real-world applications, such as surgical training, tactile feedback in virtual environments, and assistive devices. Her team’s contributions include innovations in wearable haptic devices and tactile sensing for robots. She frequently delivers keynote addresses and chairs international conferences, furthering the field’s global impact. Her publications highlight advancements in haptic feedback systems, surgical robotics, and biomimetic sensors. She also advocates for diversity and leadership in academia, serving as Spokesperson for the International Max Planck Research School for Intelligent Systems since 2017.

Sebastian KerzView profile

Researcher

• Control Systems
• Machine Learning
• Stochastic Control
• +4 more

Sebastian Kerz is a Researcher at the Chair of Automatic Control Engineering, Technical University of Munich (TUM), with a focus on learning-based and data-driven control systems. He has been affiliated with TUM since 2019, previously working at the University of Toronto and Technische Universität Darmstadt. Education: Master of Science in Electrical Engineering and Information Technology (2015–2019) at TU Darmstadt, specializing in Automation Systems. Bachelor of Science in Electrical Engineering and Information Technology (2011–2015) at TU Darmstadt. Graduate Student at the University of Toronto's Department of Electrical & Computer Engineering (2017–2018). His research centers on Data-driven Stochastic Model Predictive Control (MPC) , integrating machine learning with control theory for systems in uncertain environments. Applications span power grids, building climate systems, and quadrocopters. He also explores robotic grasping in industrial recycling and Koopman operator learning for stable nonlinear systems. Recent publications highlight advancements in adaptive stochastic MPC, uncertainty sampling, and safe control under data imperfections. His work bridges theoretical control frameworks with practical implementations, emphasizing robustness and efficiency. At TUM, Kerz contributes to student supervision and project guidance, particularly in control and learning domains. He is available for thesis supervision and collaborative research endeavors.

• Control Systems Engineering
• Networked Control Systems
• Diffusion Processes
• +4 more

Fangzhou Liu is currently a Professor at the Research Institute of Intelligent Control and Systems, School of Astronautics, Harbin Institute of Technology, China (2022–Present). He previously held a postdoctoral position (2019–2022) and completed his PhD (2014–2019) at the Chair of Automatic Control Engineering, Technische Universität München, Germany. His academic journey includes a visiting PhD stint at the University of Groningen (2018) and degrees from Harbin Institute of Technology: M.Sc. (2012–2014) and B.Sc. (2008–2012) in Control Science and Engineering. Research Interests: Focus on control of complex systems, including diffusion processes over complex networks, opinion dynamics, networked control systems, model predictive control, and sliding mode control. Key themes: large-scale systems, dynamic optimization, and robust control. Publication Trends: Recent works emphasize robotic control (reinforcement learning, barrier functions), switched systems (parameter estimation, identification), and epidemic modeling (deep transfer learning for forecasting). Collaborations with Martin Buss and others span robotics, control algorithms, and optimization. Awards: PhD Award 2019 of the Department of Electrical and Computer Engineering Labs & Teams: Affiliated with the Chair of Automatic Control Engineering (Technische Universität München) and Harbin Institute of Technology’s Research Institute of Intelligent Control and Systems. Collaborates on projects involving intelligent control, robotics, and networked systems.

• Chemical Process Control
• Biodiesel Production
• Distillation Modeling
• +4 more

Adel Mhamdi is a Professor at RWTH Aachen University's Department of Chemical Engineering within the Faculty of Mechanical Engineering. His research focuses on process systems engineering with emphasis on nonlinear model predictive control, hybrid modeling, and sustainable process design for chemical and biochemical systems. He leads research in electrified biodiesel production, distillation optimization, and polymerization process control. His research interests span chemical process control, biodiesel production optimization, distillation modeling, hybrid mechanistic-data-driven approaches, polymerization processes, and heat transfer optimization. Mhamdi develops advanced control strategies including economic NMPC, distributed control architectures, and chance-constrained optimization to address challenges in flexible operation of energy-intensive processes. His work integrates computational fluid dynamics with process modeling for reactor design and optimization. Analysis of his recent publications reveals strong trends in electrification of chemical processes, particularly biodiesel production with heat integration. His research increasingly incorporates machine learning for hybrid modeling and employs advanced computational techniques like Bayesian optimization for reactor geometry design. Key application areas include renewable energy systems, polymer manufacturing, and separation processes. Mhamdi actively supervises doctoral researchers including M. El Wajeh, J.M. Faust, and P.J. Joy who appear as first authors on multiple publications. His research group collaborates extensively with the Mitsos group at RWTH Aachen, securing publications in top journals including Industrial & Engineering Chemistry Research and Computers & Chemical Engineering . Current projects focus on real-time optimization of electrified processes and development of open-source modeling platforms like HybridML.