جست‌وجوی استاد و پژوهشگر

• Software Engineering
• Deep Learning
• Autonomous Systems
• +۳ مورد دیگر

Prof. Dr. Andrea Stocco is a Professor at the Technische Universität München (TUM), affiliated with the TUM School of Computation, Information and Technology. His research focuses on the intersection of software engineering and deep learning, particularly addressing the robustness and reliability of data-intensive systems. Key areas include autonomous vehicles, web application testing, and automated functional oracles for deep learning systems. He leads initiatives such as the Lehrstuhl für Software und Systems Engineering , collaborating on projects like CrESt and SUPPRA – Algorand Center of Excellence . Research interests encompass monitoring techniques for AI-driven systems, test suite maintainability, and scenario-based testing of cyber-physical systems (CPS). His work emphasizes practical applications, such as improving testing frameworks for evolving web applications and enhancing interoperability in autonomous driving systems (ADS). Recent efforts include leveraging large language models (LLMs) for secure code assessment and benchmarking generative AI for test input generation. No scientific awards are explicitly mentioned in the provided texts. His publications reflect a strong focus on testing methodologies, with over 40 articles since 2013, covering domains like web test automation, dependency-aware testing, and safety-critical failure prediction in autonomous systems. Advising and grants details are not detailed in the current data, but his lab contributes to TUM's broader efforts in software engineering and systems reliability.

Anders Rantzerمشاهده پروفایل

استاد

• Control Systems Theory
• Scalable Control
• Adaptive Control
• +۷ مورد دیگر

Anders Rantzer is a Professor of Automatic Control at the Department of Control Engineering, Faculty of Engineering, Lund University, Sweden. He has held visiting positions at Caltech (2004–2005) and the University of Minnesota (2015–2016) as the Taylor Family Distinguished Visiting Professor. His academic journey began with a PhD from KTH Stockholm in 1991, followed by a postdoc at the Institute for Mathematics and its Applications (IMA), University of Minnesota. His research interests center on modeling, analysis, and synthesis of control systems , with a strong focus on scalability, adaptation, and applications in energy networks . He is particularly known for foundational work in positive systems and integral quadratic constraints (IQCs) . These theoretical frameworks are critical in analyzing stability and robustness of large-scale interconnected systems. His work bridges mathematical rigor with practical engineering applications, especially in sustainable energy and networked systems. The recent publications and lecture materials reflect a consistent trajectory in scalable and robust control, optimization, and distributed systems. Themes such as large-scale convex optimization , nonlinear and stochastic control , and network dynamics dominate his scholarly output, indicating a sustained commitment to advancing control theory for complex, real-world systems. Scientific honors include: Fellow of IEEE Member of the Royal Swedish Academy of Engineering Sciences (IVA) Chairman of the Swedish Scientific Council for Natural and Engineering Sciences Chairman of the Royal Physiographic Society of Lund Rantzer has supervised numerous students and contributed extensively to academic leadership and education. He has been involved in major national and international research initiatives such as WASP (Wallenberg AI, Autonomous Systems and Software Program) and ELLIIT. His work includes developing educational tools and courses in control, optimization, and machine learning. He leads and contributes to research projects on autonomous systems, cloud control, and smart energy networks. He is affiliated with the Control Lab at LTH and participates in collaborative efforts such as the Nordic University Hub on Industrial Internet of Things (HI2OT). His work integrates theoretical advances with practical implementations in robotics, biomedical systems, and industrial automation.

Gerd Bruderمشاهده پروفایل

دانشیار

• Human-Computer Interaction
• Virtual Reality
• Augmented Reality
• +۷ مورد دیگر

Gerd Bruder is an Associate Professor at the University of Central Florida , affiliated with the Department of Computer Science . He previously held positions as a Research Associate Professor at UCF's Institute for Simulation and Training (2016-2022), Post-Doc at University of Hamburg (2014-2016), and Post-Doc at University of Würzburg (2011-2014). Habilitation : Computer Science, University of Hamburg (2017) Ph.D. : Computer Science, University of Münster (2011) M.Sc. : Computer Science (minor in Mathematics), University of Münster (2009) Research Interests : Gerd Bruder's work focuses on Extended Reality (XR) systems, spanning Virtual Reality (VR) , Augmented Reality (AR) , and Mixed Reality (MR) . Key areas include human factors , digital twins , 3D user interfaces , and perceptual modeling , with applications in healthcare, military operations, architecture, and smart environments. Recent Publications highlight innovations in XR locomotion , trust calibration with autonomous agents, spatial cognition , and haptic-visual integration . His 2025 articles address task-switching efficiency in XR and collaborative mixed reality design. Scientific Awards : Best Paper at ACM VRST 2023 2021 TechConnect Innovation Award Best Demo at ACM SUI 2021 Multiple Best Paper Awards (2016-2023) Best Poster & Demo Awards (2015-2020) Patents include systems for medical simulation , smart environment interruption management , and adaptive AR interfaces . His work bridges academic research and practical applications across VR, AR, and MR domains.

• Machine Learning
• Dynamical Systems
• Optimization
• +۳ مورد دیگر

Michael Muehlebach leads the independent Learning and Dynamical Systems research group at the Max Planck Institute for Intelligent Systems in Tuebingen, Germany. His interdisciplinary work bridges machine learning, dynamical systems theory, and control engineering to develop algorithms for cyber-physical systems with theoretical guarantees and practical implementations. Dr. Muehlebach received his B.Sc. and M.Sc. in Mechanical Engineering from ETH Zurich in 2010 and 2013, specializing in robotics and control systems. He completed his Ph.D. at ETH's Institute for Dynamic Systems and Control under Prof. R. D'Andrea in 2018, followed by postdoctoral research with Prof. Michael I. Jordan at UC Berkeley. His research focuses on constrained optimization, reinforcement learning, and control theory with applications in robotics. He pioneered approaches that express constraints in terms of velocities rather than positions, enabling more efficient optimization algorithms. His work spans theoretical foundations to physical implementations, including the One-Wheel Cubli balancing robot and electromagnetic navigation systems. Recent publications reveal a strong trend toward physics-informed machine learning, particularly for robotics applications requiring real-time performance and safety guarantees. Dr. Muehlebach has received numerous prestigious awards: Outstanding D-MAVT Bachelor Award Willi-Studer prize for best Master's degree ETH Medal and HILTI prize for doctoral thesis Branco Weiss Fellowship (2018) Emmy Noether Fellowship (2020) Amazon Fellowship (2024) He actively mentors doctoral researchers including Hao Ma, Melis Ilayda Bal, and Onno Eberhard, with research supported by multiple grants. His group maintains strong collaborations with Bernhard Schölkopf's Empirical Inference group at the Max Planck Institute. The Learning and Dynamical Systems group develops innovative hardware and software platforms, including Floaty (a wind-harnessing flying robot), advanced electromagnetic navigation systems, and data-efficient learning methods for robotic table tennis. Their approach combines rigorous theoretical analysis with practical validation on physical systems, emphasizing the integration of known physical structure into machine learning algorithms to improve sample efficiency and ensure generalization.

• Machine Learning
• Reinforcement Learning
• Robotics
• +۳ مورد دیگر

Georg Martius is a Full Professor in the Department of Computer Science at the University of Tübingen's Faculty of Science and a Max Planck Research Group Leader at the MPI for Intelligent Systems. Since April 2023, he has been a core member of the DFG-funded Cluster of Excellence 'Machine Learning: New Perspectives for Science,' which received extended funding through 2032 for its mission to integrate machine learning into fundamental scientific discovery processes. His academic foundation includes a PhD from the University of Göttingen and Bernstein Center for Computational Neuroscience (2005), a Diploma in Computer Science from the University of Leipzig (2003), and a visiting research period at the University of Edinburgh's Division of Informatics. Postdoctoral positions followed at the Max Planck Institutes for Dynamics and Self-Organization (Göttingen, 2009), Mathematics in the Sciences (Leipzig, 2010), and IST Austria (2015). Professor Martius's research pioneers the intersection of reinforcement learning, robotics, and tactile sensing, with emphasis on developing autonomous systems capable of natural locomotion, dexterous manipulation, and physical-world understanding. His work bridges theoretical machine learning with practical hardware applications, particularly in creating differentiable simulators, superresolution tactile sensors, and biologically plausible learning frameworks for robotic control. Analysis of his 2024-2025 publications reveals dominant trends in offline reinforcement learning (especially goal-conditioned and diversity-maximization techniques), object-centric representation learning for video understanding, and tactile sensing innovations. A strong thread connects foundation models to world model construction, while his work on differentiable physics engines enables precise collision handling and contact dynamics for real-world robotic control. His leadership roles include directing the Distributed Intelligence research team at Tübingen and contributing to major collaborative initiatives like the Real Robot Challenge and Myochallenge 2022. The Cluster of Excellence appointment represents recognition of his contributions to transforming scientific methodology through machine learning, particularly in automating hypothesis generation and experimental design. Current projects focus on integrating large-scale machine learning with embodied intelligence, advancing tactile perception systems like the Minsight vision-based sensor, and developing neuroplasticity-inspired approaches for robust out-of-distribution detection. His work directly impacts fields requiring physical interaction intelligence, from autonomous navigation to medical robotics, with emphasis on sample-efficient learning from limited real-world data.

Alexander Pretschnerمشاهده پروفایل

استاد

• Software Engineering
• Testing
• Information Security
• +۵ مورد دیگر

Prof. Alexander Pretschner is a Professor of Software & Systems Engineering at the Technical University of Munich (TUM) and Founding Director of the Bavarian Research Institute for Digital Transformation (bidt). He also serves as Scientific Director of fortiss, a Bavarian research institute for software-intensive systems. His research focuses on software engineering, testing, information security, and ethical software development. Pretschner holds a PhD from TUM and has held academic positions at Karlsruhe Institute of Technology (KIT) and TU Kaiserslautern. He is a co-editor of several prestigious journals, including IEEE Transactions on Reliability and the Journal of Software Testing, Verification and Reliability. Education: PhD in Computer Science, Technical University of Munich MSc in Computer Science, University of Kansas (on Fulbright Scholarship) Diplom in Computer Science, RWTH Aachen University Research Interests: His work spans testing methodologies, secure software design, and ethical considerations in agile development. Notable contributions include frameworks for metamorphic testing, distributed data usage control, and accountability mechanisms for cyber-physical systems. Awards: IBM Faculty Award (2012, 2013) Google Focused Research Award (2011, 2012) EARTO Innovation Prize (2014) 2nd Platz Supervisory Award (2020) Advising & Grants: Pretschner has supervised numerous PhD and Master’s students, contributing to over 200 publications. He leads projects like EDAP (Ethical Deliberation in Agile Processes) and collaborates with industry partners on cybersecurity and AI ethics initiatives. Labs & Teams: His work is anchored in bidt, fortiss, and TUM’s Chair of Software & Systems Engineering, focusing on societal impacts of digitalization and trustworthy AI systems.

• Machine Learning
• Dynamical Systems
• Control Theory
• +۴ مورد دیگر

Michael Mühlebach is a Research Group Leader at the Max Planck Institute for Intelligent Systems in Tübingen, Germany, leading the independent Learning and Dynamical Systems group. His academic journey began at ETH Zurich where he earned his B.Sc. (2010) and M.Sc. (2013) in mechanical engineering, specializing in robotics, systems, and control. He completed his Ph.D. at ETH Zurich in 2018 under Prof. R. D'Andrea, followed by postdoctoral research at UC Berkeley with Prof. Michael I. Jordan. Dr. Mühlebach's research spans machine learning, dynamical systems, control theory, and optimization . His work bridges theoretical foundations with practical applications in robotics, developing methods that incorporate physical constraints and system dynamics into learning frameworks. His group focuses on online learning, physics-informed machine learning, and large-scale optimization for cyber-physical systems, with applications in electromagnetic navigation, robotic table tennis, and energy-efficient flight systems like the shape-changing robot Floaty . His publication record shows a strong focus on constrained optimization, with recent work exploring decision-dependent stochastic optimization, nonlinear feedback, and the theoretical foundations of reinforcement learning. His research integrates perspectives from control theory, dynamical systems, and optimization to develop algorithms with strong theoretical guarantees and practical performance. Outstanding D-MAVT Bachelor Award Willi-Studer prize for best Master's degree ETH Medal and HILTI prize for doctoral thesis Branco Weiss Fellow (2018) Emmy Noether Fellowship (2020) Amazon Fellowship (2024) Dr. Mühlebach actively mentors doctoral researchers and is seeking talented students for PhD and Master's projects. His research group has received funding from multiple prestigious fellowships and maintains collaborations across institutions including ETH Zurich, UC Berkeley, and various Max Planck research units. The group's work spans theoretical developments to practical implementations on robotic systems, demonstrating strong connections between mathematical theory and physical realization.

Kai Arrasمشاهده پروفایل

استاد

• Robotics
• Machine Learning
• Human-Robot Interaction
• +۶ مورد دیگر

Kai Arras is a Professor at the Institute for Artificial Intelligence (KI) at the University of Stuttgart and heads the Socially Intelligent Robotics (SIR) Lab. His research lies at the intersection of robotics, machine learning, and human-robot interaction (HRI), with a focus on open-world embodied intelligence, sustainably engaging interactive robots, and AI-based industrial robotics. He is also a Cyber Valley Endowed Chair for Autonomous Systems and leads collaborations with institutions like the Fraunhofer Society and the University of Bonn.

• Computer Architecture
• Non-Volatile Memory Systems
• Deep Learning Systems
• +۳ مورد دیگر

Jishen Zhao is an Assistant Professor in the Department of Computer Science and Engineering at the University of California, San Diego (Jacobs School of Engineering). His research focuses on computer architecture, non-volatile memory systems, and deep learning acceleration. Dr. Zhao has published extensively in top venues including ISCA, MICRO, ASPLOS, and IEEE Transactions. He collaborates with researchers at UCSD and beyond to advance systems for emerging applications in AI and autonomous vehicles. Dr. Zhao's primary research areas include persistent memory systems, hardware/software co-design for deep learning, and safety-critical computing. He develops techniques for crash consistency, memory disaggregation, and efficient neural network deployment. His work on autonomous vehicles addresses scenario generation and perception-aware system design. Recent projects explore LLM applications for software engineering and hardware verification. Analysis of Dr. Zhao's 2024-2025 publications reveals a strong shift toward AI-integrated systems research. He applies large language models to tasks like RTL verification and software issue localization while continuing to innovate in memory systems for serverless computing. There is growing emphasis on safety-critical systems for autonomous vehicles and energy-efficient neural network training using novel hardware architectures. Information about Dr. Zhao's scientific awards, advising activities, grants, and laboratory facilities was not available in the provided documentation.

Jihyun Leeمشاهده پروفایل

استادیار

• Manufacturing Automation
• Mechatronics
• Robotics
• +۷ مورد دیگر

Jihyun Lee is an Assistant Professor in the Department of Mechanical and Manufacturing Engineering at the Schulich School of Engineering, University of Calgary. She was awarded the Anna Boyksen Fellowship by the Technical University of Munich Institute for Advanced Study (TUM-IAS) in 2021, hosted by Prof. Michael Zäh. Doctorate in Mechanical Engineering from University of Michigan-Ann Arbor (2016) Prior post at Korea Institute of Machinery and Materials (2016-2019) Her research focuses on mechatronics, robotics, manufacturing automation, and control systems , with applications in machine tools, additive manufacturing, and precision measurement. She integrates artificial intelligence and optimization to enhance industrial automation. Recent publications highlight work on vibration control , sensor fusion , and flexible manufacturing systems . Her team explores dynamic modeling , nanocomposite sensors , and human-in-the-loop robotics for industrial and marine applications. 2020 Remote Teaching Award, Schulich Engineering 2020 Early Achievement Award, Association of Korean-Canadian Scientists and Engineers 2018 Best Achievement Award, KIMM She supervises doctoral and master’s students at the University of Calgary, emphasizing hands-on experience and MATLAB/Python simulation skills in her lab. Her work bridges quantum logic and industrial robotics through interdisciplinary collaborations.

• Technology Acceptance
• Artificial Intelligence
• Sustainable Transportation
• +۴ مورد دیگر

Magnus Liebherr is a Professor at the University of Duisburg-Essen , focusing on Technology Acceptance , Artificial Intelligence , and Sustainable Transportation . His work bridges Business Administration with Human-Computer Interaction , exploring how users adapt to emerging technologies like autonomous vehicles and AI systems. Key research areas include: Acceptance of AI applications in mobility Business model development for climate-neutral transportation Cognitive and psychological factors in technology interaction Digital media effects on adolescent well-being Trust calibration in automated systems His recent publications examine Large Language Model dependency , gamified language learning , and mental workload metrics for autonomous vehicles. While no formal awards are listed, his interdisciplinary work spans psychology , engineering , and business strategy .

• LiDAR-based Perception
• Autonomous Vehicles
• Agricultural Robotics
• +۴ مورد دیگر

Jens Behley is a Lecturer (Privatdozent) and postdoctoral researcher at the Department of Photogrammetry, University of Bonn. He completed his habilitation in 2023 with a thesis on LiDAR-based spatio-temporal scene understanding for autonomous vehicles and earned his PhD in 2014 under Prof. Armin Cremers. His research focuses on LiDAR perception, agricultural robotics, and 3D scene understanding. Behley is an Associate Editor at IEEE Robotics and Automation Letters (RA-L) and has authored influential datasets like SemanticKITTI and BonnBeetClouds3D. Education: PhD in Computer Science, University of Bonn, 2014 Habilitation in Photogrammetry, University of Bonn, 2023 Research Interests: LiDAR-based perception in urban and agricultural environments, machine learning for robotics, semantic mapping, SLAM algorithms, and 3D reconstruction. His work bridges computer vision and robotics, with applications in autonomous vehicles and precision agriculture. Awards: Best Agri-Robotics Paper Award (IROS 2024) Outstanding Reviewer Awards (ECCV, CVPR, ICRA) Faculty Award for Geodesy (2021) Advisees & Grants: Behley collaborates extensively with the PRBonn lab and researchers like Cyrill Stachniss, focusing on projects funded by EU Horizon and industry partners. His team develops open-source tools for LiDAR processing (e.g., KISS-ICP, VDBFusion). Labs/Teams: Part of the Photogrammetry and Robotics Institute (IGG) at the University of Bonn, contributing to the PRBonn research group.

• Neuromorphic Computing
• Hardware Accelerators
• VLSI Circuits
• +۵ مورد دیگر

Prof. Dr. Tobias Gemmeke is a University Professor at RWTH Aachen University's Faculty of Electrical Engineering and Information Technology, leading the Chair of Integrated Digital Systems and Circuit Design. His work focuses on neuromorphic computing, hardware accelerators, and energy-efficient electronics. He has pioneered advancements in FPGA-based computational neuroscience simulators, neuromorphic processor architectures, and sensor integration for industrial and medical applications. Research interests include time-domain computing, ReRAM reliability, and co-optimization of neural networks with hardware. Notable contributions include the neuroAIx framework for accelerated neuroscience simulations and energy-efficient ASIC designs for post-quantum cryptography. He actively explores memristive devices and domain generalization techniques for edge computing. Recent publications highlight innovations in spiking neural networks, sensor systems for plain bearings, and time-domain compute-in-memory engines. His work bridges theoretical neuroscience with practical hardware implementations, emphasizing scalability and real-time performance.

• Formalized Mathematics
• Proof Assistants
• Homotopy Type Theory
• +۳ مورد دیگر

Floris van Doorn is a Professor at the Mathematical Institute of the University of Bonn where he leads the Formalized Mathematics group. His research focuses on making it viable to formalize research mathematics in proof assistants that can check the correctness of such proofs. He primarily works with the Lean Theorem Prover and is a maintainer of its mathematical library (mathlib). University of Bonn: Professor (2023-present) University of Paris-Saclay: Postdoc with Patrick Massot (2021-2023) University of Pittsburgh: Postdoc with Tom Hales (2018-2021) Carnegie Mellon University: PhD under Jeremy Avigad and Steve Awodey (2013-2018) Van Doorn's research interests center on formalized mathematics, tools and automation for formalization, and homotopy type theory. He has made significant contributions to several major formalization projects including the Carleson project (proving Carleson's theorem), the sphere eversion project (formalizing Gromov's h-principle), the Flypitch project (formalizing the independence of the continuum hypothesis), and the Spectral sequences project. His work demonstrates that proof assistants can handle complex areas of mathematics beyond algebra, including differential topology and analysis. His recent publications show a consistent focus on advancing formalized mathematics, with his most recent work formalizing the Gagliardo-Nirenberg-Sobolev inequality and continuing the Carleson project. His publications span theoretical foundations of type theory, practical applications of formalization, and educational resources for learning proof assistants. Skolem award (2025) for the paper 'The Lean Theorem Prover (System Description)' Van Doorn actively mentors students and collaborators, with Maria, Michael, and Arend recently joining his formalization group in Bonn. He has taught various courses on formalized mathematics and proof assistants at the University of Bonn, University of Pittsburgh, and Carnegie Mellon University. His educational efforts include developing learning resources such as the Natural Number Game and the online book 'Mathematics in Lean.' He also maintains an active presence in the Lean community through the Formalized Mathematics group and collaborative projects like the Carleson project, which invites participation from those familiar with Lean.

Zhongliang Jiangمشاهده پروفایل

پژوهشگر

• Medical Robotics
• Image Processing
• Robotic Learning
• +۳ مورد دیگر

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