جست‌وجوی استاد و پژوهشگر

• Reinforcement Learning
• Machine Learning
• Artificial Intelligence
• +۳ مورد دیگر

Marco Bagatella is a doctoral researcher at the Institute of Machine Learning (ETH Zürich), focusing on advanced machine learning and artificial intelligence research. His work spans reinforcement learning, behavioral cloning, and causal inference, often addressing challenges in generalization, exploration, and policy adaptation. Contact: marco.bagatella@inf.ethz.ch Specializes in Reinforcement Learning (including offline and multi-task settings) Expertise in causal modeling and counterfactual data augmentation Investigates graph neural networks for biological systems Active contributor to AI/ML publications (15+ recent works) His research explores problem space transformations, optimal transport for zero-shot imitation learning, and temporal logic-based exploration. Current projects focus on improving policy robustness and adaptability across diverse domains. Scientific awards: No formal honors listed yet. Collaborates with ETH Zurich's machine learning teams on cutting-edge algorithm development and theoretical analysis.

• Robotics
• Computer Graphics
• Control Systems
• +۳ مورد دیگر

Prof. Stelian Coros is an Associate Professor at the Department of Computer Science, ETH Zürich, and Head of the Institute for Intelligent Interactive Systems. His research focuses on robotics, computational design, and control systems, with applications in robotic manipulation, simulation, and autonomous systems. His work integrates principles from computer science, mechanical engineering, and artificial intelligence to advance the capabilities of robots in real-world environments.

Mohamed Abdalmoatyمشاهده پروفایل

پژوهشگر

• Data-Driven Modelling
• Control Systems
• Signal Processing
• +۵ مورد دیگر

Dr. Mohamed Abdalmoaty is a Researcher at ETH Zürich's Institut für Automatik, specializing in the Department of Automatic Control. His work focuses on Data-Driven Modelling and Control , with expertise in system identification, stochastic systems, and optimal control. He holds an affiliation within the Professorship for Complex Systems Control. His research interests emphasize data-driven approaches for predictive control, frequency-domain identification, and nonlinear dynamical systems. He has contributed to advancements in Kalman filter formulations, stochastic Wiener models, and cybersecurity in control systems, particularly in medical applications like the artificial pancreas. Abdalmoaty’s recent publications (2022–2024) highlight innovations in time-varying normalizing flows, privacy-preserving network control, and robust parameter estimation under uncertainty. His work bridges theoretical control systems with practical applications in machine learning and biomedical engineering. He has developed software tools for system identification and simulation, including implementations for frequency-domain analysis and nonparametric closed-loop identification. His research aligns with emerging trends in hybrid machine learning-control frameworks and resilient cyber-physical systems.

• Computational Robotics
• Artificial Intelligence
• Path Planning
• +۳ مورد دیگر

Valentin Hartmann is affiliated with the Computational Robotics department at ETH Zürich. His research focuses on advanced robotics, motion planning, and multi-agent systems. He develops algorithms for optimal path planning, task allocation, and autonomous decision-making in complex robotic environments. His work addresses challenges in multi-robot coordination, manipulation, and scalable solutions for construction and industrial automation. Key contributions include benchmark datasets for multi-agent planning and novel methods for efficient motion and task scheduling. Recent research highlights include advancements in kinodynamic motion planning (e.g., iDb-A*, iDb-RRT), optimization of multi-arm systems, and robotic construction applications. His projects emphasize reproducibility through dedicated planning tools (Planner Developer Tools) and leverage material integration for collective building tasks.

• Control Systems
• Data-Driven Control
• Optimization Algorithms
• +۳ مورد دیگر

Jaap Eising is a Lecturer at the Department of Information Technology and Electrical Engineering at ETH Zürich. His research focuses on control systems, data-driven control methodologies, optimization algorithms, and system identification. He explores topics such as affine time-invariant systems, robust control of linear systems, and adaptive control strategies for uncertain systems. His work emphasizes theoretical foundations and practical applications in control theory, leveraging data-driven approaches to enhance system analysis and stabilization. Recent publications highlight advancements in data-driven techniques for mode detection, parameter variation decoupling, and cautious optimization under noisy data. His research also addresses challenges in online adaptive control and stabilization of switched systems. Key themes include informativity frameworks, quadratic matrix inequalities, and the integration of statistical modeling with control theory. No scientific awards or grants are explicitly mentioned in the provided texts. His academic contributions are centered on advancing control systems through rigorous mathematical analysis and innovative algorithmic designs.

• High-Voltage Engineering
• Condition Monitoring
• Fault Detection
• +۵ مورد دیگر

Chi-Ching Hsu is a researcher at ETH Zurich's High Voltage Engineering department under the Power Systems and High Voltage Lab. Their work focuses on condition monitoring of high-voltage electrical equipment using advanced sensing and machine learning techniques. Key areas include fault diagnostics for circuit breakers, predictive maintenance strategies, and development of edge-sensing platforms for grid infrastructure. Hsu has contributed to experimental studies using vibration and acoustic signals to assess equipment degradation, emphasizing unsupervised learning methods and explainability in AI-driven diagnostics. Education: Doctorate in D-ITET program at ETH Zurich Research interests span electrical grid systems, fault detection methodologies, and the integration of IoT-based solutions for industrial equipment monitoring. Their work bridges mechanical, electrical, and data science disciplines to improve reliability of power transmission components. Publications highlight innovative approaches to transform fault detection into proactive health management systems, with applications in both laboratory setups and real-world grid environments.

• Game Technology
• Human-Computer Interaction
• Augmented Reality
• +۲ مورد دیگر

Dr. Henning Metzmacher serves as a Lecturer at ETH Zürich's Department of Computer Science, affiliated with the Game Technology Center (GTC). His research spans game technology, augmented reality applications for education, and personalized thermal comfort systems. With expertise in human-computer interaction and control systems, he develops innovative solutions ranging from scent-enabled gaming interfaces to AI-driven climatization technologies. His research interests focus on immersive technologies for education and human-centered environmental systems. Recent work explores sensory enhancement in gaming through olfactory feedback systems and augmented reality tools for AI education. Additionally, he investigates personalized thermal management using advanced control algorithms, sensor fusion, and real-time monitoring techniques. Publications demonstrate consistent focus on human-centered technology applications. Early work centered on thermal comfort modeling and building energy systems. Recent publications show strong emphasis on game technologies, augmented reality interfaces, and advanced control systems for personalized environments. This progression reflects expanding research into interactive systems and human-technology interfaces.

• Computer Vision
• Machine Learning
• Medical Imaging
• +۳ مورد دیگر

Dr. Sergey Prokudin is a Lecturer at the Department of Computer Science, ETH Zürich, affiliated with the Professur für Computer Vision. His research focuses on computer vision, machine learning, medical imaging, and robotics. Key areas include 3D reconstruction, neural fields, and uncertainty quantification in biomedical applications. His work bridges theoretical advancements in neural networks with practical applications in healthcare and robotics. Research interests span dynamic scene understanding, spatiotemporal modeling, and medical imaging processing. Notable projects include digital twin creation for spinal surgery, robust MRI parameter estimation, and avatar generation from single images. He has published extensively on topics like Gaussian splatting, residual neural fields, and point cloud alignment. No scientific awards have been explicitly listed. His advising and grants focus on interdisciplinary projects combining computer vision with medical engineering. He is part of ETH's Computer Vision group and contributes to labs exploring 3D modeling and medical visualization technologies.

• Additive Manufacturing
• Agile Hardware Development
• Product Development
• +۴ مورد دیگر

Dr. Daniel Omidvarkarjan is a Lecturer at the Department of Mechanical and Process Engineering at ETH Zürich, affiliated with the Chair of Product Development and Engineering Design. His research focuses on additive manufacturing (AM), agile hardware development, and their industrial applications. He emphasizes bridging academic research with practical industry challenges, particularly in scaling AM technologies and optimizing design processes. His educational contributions include developing open-source simulators like Bender and designing curricula for agile methodologies. His work spans material science (e.g., vacuum compatibility of AM materials), automation in design configurations, and crisis-driven prototyping strategies during the pandemic. Key themes in his publications include adoption strategies for AM, neural network-driven quality control, and business model innovations for manufacturing firms. His research often involves collaboration with industry partners to validate tools and methodologies in real-world settings. No scientific awards or grants are explicitly mentioned in the provided information. His contact details include an ETH Zürich email address, and he is based in Zurich, Switzerland.

• Artificial Intelligence
• Machine Learning
• Neural Networks
• +۳ مورد دیگر

Dr. Rik Ubaghs is a Researcher at ETH Zürich, affiliated with the Neuronal Learning and Intelligent Systems Department. His work focuses on advancing artificial intelligence and computational models of neural systems. Contact: Winterthurerstrasse 190, 8057 Zürich, Switzerland | Email

• Deep learning
• Reinforcement learning
• Computer vision
• +۳ مورد دیگر

Dr. Jan Petrik is a full-time faculty member at ETH Zürich, affiliated with the Professorship for Advanced Manufacturing. His research focuses on integrating artificial intelligence with manufacturing processes, particularly in deep learning, reinforcement learning, and computer vision applications for metal forming and additive manufacturing systems. Current position: Professor, Advanced Manufacturing, ETH Zürich Research interests: AI-driven manufacturing optimization, microstructural control, and process modeling Recent work: Development of AI frameworks like DeepForge, RLTube, and CrystalMind for metal forming and additive manufacturing

• Artificial Intelligence Ethics
• Computational Neuroscience
• Machine Learning
• +۳ مورد دیگر

Sander de Haan is a Research Fellow and PhD student at the University of Lucerne's Institute for Social Ethics (ISE), concurrently pursuing a computational neuroscience PhD at ETH Zurich under Prof. Benjamin F. Grewe and Prof. Peter G. Kirchschlaeger. His research integrates ethical analysis with artificial intelligence and biological learning systems. Education: Master's in Computer Science Engineering from EPFL, with projects in theoretical machine learning, medical diagnostics AI, and deep learning for pose estimation/cell segmentation. Prior experience includes AI internships at Logitech (brain-machine interfaces), medical psychedelic data science, and teaching roles at EPFL/ETHZ. Research focuses on ethical dimensions of AI, principles of learning systems, and interdisciplinary connections between neuroscience and ethics. No specific awards or grants are listed in the provided text. Labs/teams: Affiliated with the Institute of Neuroinformatics (ETH Zurich) and the Lucerne Graduate School in Ethics (LGSE).

Martin Gjoreskiمشاهده پروفایل

پژوهشگر

• Artificial Intelligence
• Machine Learning
• Federated Learning
• +۴ مورد دیگر

Martin Gjoreski is an Ambizione Fellow funded by the Swiss National Science Foundation (SNSF) and holds the position of Researcher at the Faculty of Informatics, Università della Svizzera italiana (USI) in Switzerland. He also serves as a lecturer, teaching courses like Mobile and Wearable Computing during the 2024/25 academic year. His research focuses on Artificial Intelligence, particularly machine learning, federated learning, and explainable AI (XAI), applied to wearable computing, affective computing, and digital healthcare. Education: PhD in Computer Science (2016-20) from the Jožef Stefan International Postgraduate School, Slovenia. Thesis: 'A fusion of classical and deep machine learning for mobile health and behavior monitoring with wearable sensors.' Notable achievements include the 'Jožef Stefan golden emblem' for an outstanding PhD thesis and inclusion in the 'top 2% scientists in the world' (2021). Research Interests: AI-driven healthcare solutions Federated learning for privacy preservation Explainable AI in pervasive systems Wearable sensor data fusion Grants: Leading the 'XAI-PAC' project (SNSF, 2024-2028) and contributing to 'SmartCHANGE' (Horizon Europe, 2024-2028) and 'TRUST-ME' (SNSF, 2024-2027). These focus on AI-based health monitoring and privacy-aware solutions. Labs/Teams: Member of the People-Centered Computing Lab at USI, led by Professors Marc Langheinrich and Silvia Santini. Collaborates on projects involving smart glasses, affective computing datasets, and federated learning frameworks.

Luca Di Graziaمشاهده پروفایل

پژوهشگر

• Software Engineering
• Generative AI
• Deep Learning
• +۷ مورد دیگر

Luca Di Grazia is a Researcher (Postdoctoral) in the STAR group at the University of Lugano (USI), Switzerland, supervised by Prof. Mauro Pezzè. He holds a PhD (summa cum laude) in Software Engineering from the University of Stuttgart, advised by Prof. Michael Pradel. Previously, he completed his Bachelor's and Master's degrees in Computer Engineering at the Polytechnic of Turin, Italy, with a minor in Embedded Systems. His research focuses on Generative AI, Program Repair, Software Evolution, and Code Search techniques. Education: Bachelor's and Master's in Computer Engineering, Polytechnic of Turin (Italy), with a thesis on "Protein classification using geometrical features for 3D face analysis". PhD in Computer Science (summa cum laude) from University of Stuttgart (Germany), thesis: "Supporting Software Evolution via Search and Prediction". Postdoctoral Researcher at USI, Switzerland. Research Interests: Generative AI for software testing and bug fixing (e.g., winning an Uber competition with a GenAI tool). Program Repair techniques, such as PyTy for Python type errors. Code Search and Change Retrieval (e.g., DiffSearch engine). Software Evolution and Type Annotation studies in Python. Achievements: ACM SIGSOFT Distinguished Paper Award at ESEC/FSE 2022 for work on Python type annotations. Second prize at ACM Student Research Competition at ICSE 2022. Won GenAI Uber competition (2023) with a project to boost developer productivity, beating 103 teams. Summa cum laude PhD (2024). Recipient of national scholarships during his studies at Polytechnic of Turin. Advising: Supervised seven students on projects including automated error repair and testing frameworks. Labs/Teams: STAR group at USI, collaborating with JetBrains and Uber.

Anupama Unnikrishnanمشاهده پروفایل

پژوهشگر

• Quantum Computing
• Quantum Information Science
• Network Security
• +۳ مورد دیگر

Anupama Unnikrishnan is a researcher at ETH Zurich, affiliated with the ETHCQST+ initiative focused on quantum science and technology. Her work intersects quantum computing and computer security, with a particular emphasis on probabilistic data structures and quantum network verification. Research Interests: Quantum computing and cryptography Security analysis of probabilistic data structures Verification protocols in quantum networks Recent Article Trends: Her publications explore adversarial contexts in data structures, authentication mechanisms for quantum teleportation, and anonymity in quantum networks, demonstrating a strong focus on security and integrity in emerging quantum technologies.