جستجوی استاد راهنمای دانشگاهی

Roland Büchiمشاهده پروفایل

استاد

• Control Systems Engineering
• Robotics
• Artificial Intelligence
• +۴ مورد دیگر

Prof. Dr. Roland Büchi is a Professor at the School of Engineering, Zurich University of Applied Sciences (ZHAW), specializing in control systems engineering and applied AI. His affiliation includes leadership roles in research projects such as the ongoing 'Digital Bridge to Computer Science' initiative. With a career spanning decades, he maintains active research output and industrial collaborations. Research Interests: Büchi focuses on control systems optimization , particularly PID controller tuning using AI methods, system identification, and applications in robotics and drone technology. His work bridges theoretical control theory with practical implementations in mechatronics. Recent efforts explore machine learning for hysteresis modeling and swarm optimization for control systems. Publication Trends: His 15 most recent works (2018-2024) emphasize AI-driven control optimization , drone technology advancements, and engineering education. Dominant themes include PID parameter tuning for time-delayed systems, drone telemetry, and adaptive learning algorithms. A notable shift toward AI/ML applications in control engineering emerged post-2020. Labs and Teams: Büchi collaborates with researchers like Lukas Gruber and has historical ties to ETH Zurich’s robotics projects. His work involves experimental validation at ZHAW’s engineering facilities, with patents in turbocharger magnetic bearing systems.

Colin Jonesمشاهده پروفایل

دانشیار

• Optimal Control
• Model Predictive Control
• Optimization
• +۶ مورد دیگر

Colin Jones is an Associate Professor at the École Polytechnique Fédérale de Lausanne (EPFL) in the Automatic Control Laboratory, School of Engineering. He earned his BASc and MASc in Electrical Engineering and Mathematics from the University of British Columbia (1994-2002) and a PhD in Control Theory from the University of Cambridge (2002-2005). Prior to EPFL, he was an assistant professor there and a senior researcher at ETH Zürich. Current role: Director of the Robotics, Control, and Intelligent Systems Doctoral Program at EPFL Research focus: Optimization-based and model predictive control (MPC) for renewable energy systems, green energy management, and data-driven control methods His recent work (2023-2025) spans high-speed predictive control , smart grid optimization , and physically consistent neural networks , with applications to buildings, hovercrafts, and power systems. He has secured an ERC Starting Grant for his research on optimal control of building networks. Publications include over 200 papers in journals like Automatica , IEEE Transactions , and Energy and Buildings . Notable article trends include distributed optimization , data privacy in energy systems , and nonlinear MPC for autonomous vehicles . Scientific Awards : ERC Starting Grant for optimal control of building networks Advising : Supervises 10 current PhD students and has advised 19 past PhD students, including Alessandretti Andrea and Diwale Sanket Sanjay. Grants and projects emphasize smart energy systems , predictive demand response , and nonlinear control .

Melanie Zeilingerمشاهده پروفایل

دانشیار

• Learning-based Control
• Distributed Control and Optimization
• Robotics
• +۴ مورد دیگر

Prof. Dr. Melanie Zeilinger is an Associate Professor at the Department of Mechanical and Process Engineering at ETH Zurich, leading the Intelligent Control Systems group at the Institute for Dynamic Systems and Control. She holds a diploma in Engineering Cybernetics from the University of Stuttgart (2006) and a Ph.D. in Electrical Engineering from ETH Zurich (2011). Her postdoctoral research included stints at EPFL (2011–2012), a Marie Curie fellowship at UC Berkeley and the Max Planck Institute (2012–2015), and a professorship at the University of Freiburg (2018–2019). Her research focuses on learning-based control, distributed control systems, and robotics , with applications to medical devices (e.g., hydrocephalus shunts) and human-in-the-loop systems. She organizes the Conference on Learning for Dynamics and Control (L4DC) and contributes to initiatives like the "Algorithm on My Team" project. Her awards include the ETH Medal for her PhD thesis, a Marie-Curie IO Fellowship , and an SNF Assistant Professorship grant . She serves as an Associate Editor for IEEE Control Systems Letters and actively reviews for top journals/conferences like IEEE TAC, Automatica, and NeurIPS. Key projects include: VIEshunt: A smart ventricular shunt for hydrocephalus treatment, combining control systems and medical engineering. Autonomous Racing: Contextual tuning and safety-certified learning-based MPC for real-time obstacle avoidance. Data-Driven Control: Integrating Gaussian processes and state-space models into MPC frameworks for uncertain systems. Her work bridges control theory, machine learning, and robotics, addressing societal challenges such as healthcare and energy efficiency.

Matthieu Cordمشاهده پروفایل

استاد

• Computer Vision
• Machine Learning
• Deep Learning
• +۷ مورد دیگر

Matthieu Cord is a Professor at Sorbonne University and Scientific Director of valeo.ai, leading research in computer vision, deep learning, and computational cooking. He heads the MLIA team at ISIR Lab, focusing on multimodal models, transformers, and efficient architectures. Research areas include computer vision, large language models with vision, and AI-driven food analytics. Key projects: VISA-DEEP AI chair, Foundation VaViM models, and SmolVLA collaboration with Hugging Face. His recent work examines scalable multimodal models , trajectory prediction , and diffusion-based segmentation , with studies on in-context learning and biased shortcut learning in visual question answering. Articles highlight DeiT variants , fishr for OoD generalization , and STEEX for counterfactual explanations . Scientific awards include IUF Honorary Membership (2009), BMVC 2017 Best Paper, and ICIP 2018 Best Paper. As an advisor, he supervised PhD theses on topics like GAN editing , semantic segmentation , and multimodal retrieval . Current roles involve mentoring the 'Research Band' at MLIA and leading EU-funded initiatives like SCAPE. His work bridges theoretical AI exploration with practical applications in autonomous driving and food technology.

Siyu Tangمشاهده پروفایل

استادیار

• Computer Vision
• Machine Learning
• Digital Humans
• +۵ مورد دیگر

Siyu Tang is an Assistant Professor in the Department of Computer Science at ETH Zürich, where she leads the Computer Vision and Learning Group (VLG) at the Institute of Visual Computing. Her research focuses on computational models for human perception and digitalization through computer vision and machine learning. Her educational background includes: PhD in Computer Science, Max Planck Institute for Informatics (2017), supervised by Prof. Bernt Schiele Master of Science in Media Informatics, RWTH Aachen University Bachelor of Science in Computer Science, Zhejiang University, China Dr. Tang specializes in human-centric computer vision, developing statistical models for motion analysis, pose estimation, and digital human creation. Her work integrates machine learning with optimization techniques to enable machines to interpret human activities from visual data, with applications spanning virtual reality, healthcare, and human-computer interaction. Key research thrusts include generative models for content creation, egocentric vision, and human motion synthesis. Her recent publications (2024-2025) demonstrate intense focus on 3D human modeling and neural rendering, with Gaussian splatting emerging as a dominant technique for efficient avatar creation and scene reconstruction. Significant themes include text-driven motion synthesis using diffusion models, relightable avatars, surgical training applications, and egocentric multimodal pretraining. This work bridges computer vision, graphics, and machine learning to advance human digitalization. No scientific awards were mentioned in the provided text. Dr. Tang leads the VLG research group at ETH Zürich, mentoring PhD and Master's students in human-centric AI. She previously secured an early career research grant from the Max Planck Institute for Intelligent Systems to establish her independent research program. Her group actively pursues funding for projects in human motion analysis, 3D reconstruction, and generative modeling, with strong industry and clinical collaborations. The Computer Vision and Learning Group (VLG) operates within ETH's Institute of Visual Computing, maintaining dedicated facilities for motion capture, 3D scanning, and high-performance computing. The team collaborates internationally with institutions like the Max Planck Society and focuses on scalable solutions for real-world human digitalization challenges, including surgical training systems and immersive virtual environments.

Harald C. Gallمشاهده پروفایل

استاد

• Software Engineering
• Software Evolution
• Software Architecture
• +۳ مورد دیگر

Harald C. Gall is a Professor of Software Engineering and Dean of the Faculty of Business, Economics, and Informatics at the University of Zurich (UZH). He leads the Software Evolution and Architecture Lab, focusing on software evolution analysis, mining software repositories, and cloud-based software engineering. His research emphasizes improving software development productivity through data-driven insights. He has held visiting positions at Microsoft Research and the University of Washington. Education: PhD (Dr. techn.) and Master's (Dipl.-Ing.) in Informatics from TU Vienna Research Interests: Software evolution, mining software archives, cloud-based tools, developer productivity, and empirical software engineering. Notable contributions include the Evolizer , ChangeDistiller , and SOFAS systems. Key Contributions: Established the Mining Software Repositories (MSR) research area, program chair for ICSE 2011 and ESEC/FSE 2005, associate editor of leading journals like Empirical Software Engineering and IEEE Software. Awards: Most Influential Paper Award, Test of Time Award, and multiple Best Paper Awards. Recognized for contributions to SE research methodologies and tool development. Professional Activities: ACM SIGSOFT awards chair, board member of Informatics Europe, and executive committee member of CHOOSE (Swiss SIG for OO Systems). Labs/Teams: Director of the Software Evolution and Architecture Lab at UZH, leading projects like SURF-MobileAppsData (SNSF-funded) and DevCloud (Hasler Foundation).

Renata Borovica-Gajicمشاهده پروفایل

دانشیار

• Database Systems
• Machine Learning
• Artificial Intelligence
• +۱۲ مورد دیگر

Renata Borovica-Gajic is an Associate Professor in Data Analytics and an ARC DECRA Fellow at the School of Computing and Information Systems (CIS), University of Melbourne. She also serves as Associate Dean (Diversity and Inclusion) for the Faculty of Engineering and IT, demonstrating leadership in both research and academic community development. Her research lies at the intersection of database systems, machine learning, and artificial intelligence, with a vision of creating adaptive, self-driving database engines that optimize query execution in real-time. Her work spans learned indexes, query optimization, data quality, and data-driven traffic optimization, aiming to reduce costs and improve performance in data analytics. The recent publications reflect a strong trend toward integrating machine learning into core database operations—particularly through learned indexes, bandit-based tuning, and reinforcement learning for traffic systems. These works emphasize automation, provable guarantees, and real-time adaptation, showcasing a cohesive research agenda focused on intelligent, self-optimizing data systems. Her scientific excellence is recognized by numerous awards, including: L'Oréal-UNESCO for Women in Science Fellowship (2023) Victorian Young Tall Poppy (2024) Test of Time Award at SIGMOD 2022 Multiple Research and Teaching Excellence Awards from the University of Melbourne Google Research Inclusion Award (2021) She actively mentors PhD students and leads significant research projects funded by the Australian Research Council, Google, and Telstra. Her service includes roles as Associate Editor for SIGMOD Record, conference organization (e.g., aiDM, ADC, VLDB), and leadership in diversity and inclusion initiatives. She has also contributed to influential publications such as a chapter in the 7th edition of Database System Concepts . Her research lab focuses on AI-powered databases, traffic optimization via reinforcement learning, and self-healing data systems, positioning her at the forefront of next-generation data management.

Johannes Gräffمشاهده پروفایل

دانشیار

• Neuroscience
• Bioengineering
• Control Systems
• +۳ مورد دیگر

Johannes Gräff is an Associate Professor at EPFL, affiliated with the Bioengineering Institute (BMI) under the School of Life Sciences (SV). He also serves as Director of the Neuroscience Doctoral Program (EDNE) and holds roles in the Synapsy Research Center (SRC). His research focuses on interdisciplinary applications of control systems, robotics, and data-driven optimization in bioengineering and manufacturing. Gräff leads the Prof. Gräff Unit (UPGRAEFF) and teaches courses on neuroscience and general biology. He advises multiple doctoral students and contributes to academic governance through roles in doctoral commissions and program management. His expertise spans adaptive control, Bayesian optimization, and closed-loop systems, with applications in precision engineering, additive manufacturing, and neuroscientific instrumentation. Gräff’s work bridges theoretical control methodologies with practical industrial and biomedical challenges, emphasizing safety and efficiency in automation. He oversees the Neuroscience Doctoral Program, guiding interdisciplinary research training, and maintains administrative responsibilities in EPFL’s academic and research structures. His lab (graefflab.epfl.ch) focuses on advancing technologies for autonomous systems and precision control in dynamic environments.

Miguel Ramirez Gonzalezمشاهده پروفایل

پژوهشگر

• Power Systems
• Renewable Energy Integration
• Machine Learning
• +۵ مورد دیگر

Dr. Miguel Ramirez Gonzalez is a Researcher at the Zurich University of Applied Sciences (ZHAW) , affiliated with the School of Engineering and the IEFE Electric Power Systems and Smart Grids department. His research focuses on modern power system challenges, including renewable integration, stability enhancement, and machine learning applications for grid security. Research Focus Dr. Gonzalez's work spans: Power System Dynamics : Inertia quantification, stability assessment, and oscillation damping. Renewable Integration : Grid-forming converters, HVDC links, and solar/wind integration. Computational Methods : Machine learning (CNNs, transfer learning) for security assessment and optimization. Real-time Simulation : Hardware emulation and co-simulation of transmission-distribution networks. Project Leadership He leads key initiatives: Deputy Project Leader for inertia measurement to support renewables (ongoing). Project Leader for expanding the CE-Nordic dynamic grid model using OPAL-RT (completed). Team member in Europe-North Africa interconnection studies (completed). Publication Trends His 15 most recent works (2020-2025) emphasize machine learning-driven solutions for power system stability, real-time simulation validation, and converter-based grid support. Dominant themes include CNN architectures for security assessment, spatio-temporal data analytics, and hardware-in-the-loop testing for low-inertia systems. Laboratory & Collaboration He contributes to the IEFE laboratory, specializing in dynamic hardware emulation and real-time grid simulation. Collaborations include work with international utilities and conferences like IEEE PowerTech and CIGRE.

Andrea Carronمشاهده پروفایل

مدرس ارشد

• Model Predictive Control
• Learning-based Control
• Coverage Control
• +۷ مورد دیگر

Dr. Andrea Carron is a Senior Lecturer at ETH Zürich, affiliated with the Intelligent Control Systems group under Professor M. Zeilinger at the Department of Mechanical and Process Engineering. He holds a PhD in Information Technology from the University of Padova (2016) and was a Postdoc at ETH Zurich from 2016 to 2020. Education: B.S. and M.Sc. in Control Engineering (University of Padova, 2010 and 2012) Professional Roles: Senior Lecturer (ETH Zurich, 2022–present), Postdoc Fellow (ETH Zurich, 2016–2020) Research Interests: Andrea Carron's work focuses on Model Predictive Control (MPC) and Learning-based Control with safety guarantees. His research addresses challenges in Distributed Safe Learning , Coverage Control , and Autonomous Racing , utilizing Gaussian Processes and Stochastic Control frameworks. He has developed safety filters for racing vehicles, scalable MPC for mobility-on-demand systems, and Kalman-filter-enhanced GP regression techniques. Article Trends: Recent publications emphasize Autonomous Racing (ForzaETH Race Stack), Safe Learning for distributed systems, Gaussian Process applications in control, and Robust MPC under uncertainty. His work bridges machine learning and classical control theory, with applications in robotics and real-time systems. Teaching Activities: He has taught courses such as Signals and Systems and Advanced Model Predictive Control at ETH Zurich and Ashesi University since 2017. Course content includes discrete-time signal processing, system identification, and control algorithms.

Bartolomeo Stellatoمشاهده پروفایل

استادیار

• Mathematical Optimization
• Machine Learning
• Optimal Control
• +۳ مورد دیگر

Bartolomeo Stellato is an Assistant Professor at Princeton University's Department of Operations Research and Financial Engineering. He is associated faculty in Electrical and Computer Engineering and Computer Science, and affiliated with Princeton's AI, Robotics, and Center for Statistics and Machine Learning initiatives. His research bridges mathematical optimization, machine learning, and optimal control for real-time decision-making in uncertain environments. PhD advisors: Georgina Hall, Brandon Amos Key collaborators: Bart Van Parys (MIT), Jaime Fernandez Fisac (UPenn), Adrien Taylor (INRIA) His work on Mean Robust Optimization combines robust and distributionally robust optimization through data clustering, achieving 3+ orders of magnitude speedups in portfolio optimization. He also develops learned optimizers (e.g., L2WS, GeNIOS) and OSQP , which won the Beale–Orchard-Hays Prize. Recent grants include NSF CAREER (2023) and ONR Young Investigator (2025) awards. Article Trends : His research spans optimization verification (3/7 of 2025 articles), data-driven methods (5/7 of 2024-2025 articles), control applications (4/15 articles), and equitable resource allocation (2/15 articles). Key subfields include algorithm verification, stochastic optimization, multi-agent systems, and hardware acceleration. Scientific Awards : ONR Young Investigator (2025), Howard B. Wentz Award (2024), NSF CAREER (2023), ISSNAF Strazzabosco (2022) Students : Mentored 12+ graduate students/postdocs including Irina Wang (Wallace Fellowship), Rajiv Sambharya (Best Paper Award at JMLR), and Vinit Ranjan (co-author Mathematical Programming paper) He leads the Princeton Optimization, Learning, and Control Workshop and collaborates with institutions like MIT, INRIA, and Los Alamos National Laboratory. His group develops open-source tools (OSQP, L2WS) with over 300,000 downloads.

Alisa Rupenyan-Vasilevaمشاهده پروفایل

استاد

• Industrial AI
• Automation of manufacturing systems
• Robotics
• +۴ مورد دیگر

Alisa Rupenyan-Vasileva is a Professor of Industrial AI at the ZHAW School of Engineering, Zurich University for Applied Sciences (ZHAW), where she leads the Industrial AI group at the ZHAW Center for AI. Her research focuses on integrating machine learning and control theory to advance manufacturing systems, robotics, and process optimization. She holds a PhD in Physics from Vrije Universiteit Amsterdam (2009), an MSc in Laser Physics and Optics from Sofia University (2005), and a BSc in Engineering Physics (2003), also from Sofia University. Her professional experience includes roles as Senior Scientist at ETH Zurich’s Automatic Control Laboratory, Group Leader at inspire AG (ETH Zurich’s technology transfer organization), and Head of Application Development at Qualysense AG. She has led projects such as 'Continuous optimization and control for advanced manufacturing' and 'Intelligent planning for robot-based manufacturing.' Research interests span industrial control systems, neurosymbolic AI, and data-driven optimization with applications in additive manufacturing and robotics. Notable contributions include Bayesian Optimization for controller tuning and melt pool control in laser powder bed fusion. She is an IEEE member and contributes to networks like the International Federation for Automatic Control and NCCR Automation. Her work emphasizes safe and efficient learning algorithms, with applications in centrifugal compressors, gas turbines, and precision motion systems. Recent talks include keynotes at the European Control Conference (2024) and the Benelux meeting on Systems and Control (2024).

Alireza Karimiمشاهده پروفایل

استاد

• Robust Control
• System Identification
• Data-driven Controller Tuning
• +۳ مورد دیگر

Alireza Karimi is a Full Professor at École Polytechnique Fédérale de Lausanne (EPFL) within the School of Engineering, Institute of Mechanical Engineering. He leads the Data-Driven Modelling and Control (DDMAC) research group and serves on the Doctoral Program Commission for Robotics, Control, and Intelligent Systems. His educational background includes: B.Sc. and M.Sc. in Electrical Engineering from Amir Kabir University (Tehran Polytechnic), 1987 and 1990 DEA and Ph.D. in Automatic Control from Institut National Polytechnique de Grenoble (INPG), 1994 and 1997 Karimi's research focuses on data-driven controller tuning and robust control methodologies, with significant applications in mechatronic systems and electrical grids. His work bridges theoretical control concepts with practical implementations in power systems, robotics, and adaptive optics. Recent publications demonstrate strong emphasis on frequency-domain methods, Koopman operator applications, and robustness against nonlinearities. Analysis of his 15 most recent publications (2023-2024) reveals dominant research themes: data-driven frequency-domain control (appearing in 70% of works), power system applications (40%), and advanced robotics/optics implementations (30%). His group consistently develops methods for fixed-structure controllers validated through industrial case studies. Regarding academic leadership, Karimi supervises 6 active PhD students while having directed 11 completed theses. His teaching portfolio includes courses in Automatic Control, System Identification, and Advanced Control Systems. He maintains the DDMAC laboratory focused on experimental validation of control algorithms, with notable projects in adaptive optics for astronomy, grid-forming inverters for renewable integration, and precision motion control systems.

Dominik Zumbühlمشاهده پروفایل

استاد

• Quantum Physics
• Condensed Matter Physics
• Nanotechnology
• +۳ مورد دیگر

Dominik Zumbühl is a Professor of Experimental Physics at the University of Basel and Director of the Swiss National Center of Competence in Research (NCCR) SPIN. His research focuses on quantum transport experiments in semiconductor nanostructures for quantum computing, particularly electron and hole spin qubits in GaAs, Ge/Si nanowires, and graphene. Department of Physics Swiss Nanoscience Institute (SNI) Basel QC2 Center for Quantum Computing Georg H. Endress Postdoc Cluster Zumbühl's work spans ultralow-temperature physics (below 1 mK), spin-orbit coupling in quantum wells/wires, machine learning for quantum device optimization , and exotic quantum phases like Majorana fermions and helical nuclear spin order. His group has achieved milestones in quantum dot thermometry and microkelvin cooling , including the Cold Chip World Record (224 µK) . Recent publications highlight quantum transport in Luttinger liquids , chiral superconductivity in graphene , and AI-driven quantum device calibration . Awards include the ERC Starting Grant (2008) and APS Fellowship (2023) . He leads a team of 15+ researchers including postdocs and PhD students, with alumni in prestigious institutions like IBM, ETH Zurich, and Princeton. Collaborations include MIT, Harvard, and the European Microkelvin Platform.

Corsin Battagliaمشاهده پروفایل

استاد مدعو

• Sustainable Energy Storage
• Lithium-Ion Batteries
• Sodium-Ion Batteries
• +۴ مورد دیگر

Corsin Battaglia is an Adjunct Professor at ETH Zurich (Department of Information Technology and Electrical Engineering) and EPFL (School of Engineering, Institute of Materials), while directing the Materials for Energy Conversion laboratory at Empa. His research focuses on sustainable battery technologies including next-generation lithium-ion, sodium-ion, and post-lithium systems, as well as CO₂ electrochemical conversion to synthetic fuels. Education: Ph.D. in Physics from Université de Neuchâtel, followed by postdoctoral research at EPFL, UC Berkeley, and Lawrence Berkeley National Labs. Joined Empa in 2014. Research Interests: Advanced battery materials (electrolytes, electrodes) CO₂-to-fuel electrochemical processes Scalable battery manufacturing technologies Operando characterization techniques Publications: Over 200 peer-reviewed articles and 8 patents, focusing on electrolyte design, electrode fabrication, and electrochemical reaction mechanisms. Recent work emphasizes robotic automation in materials discovery and high-throughput experimentation. Leadership Roles: President of Swiss Battery Association (iBAT), member of Alistore-ERI, Swiss representative in Battery2030+, and founding member of Battery European Partnership Association. Advising: Current PhD students include Claudia Bissattini and Julia Lorenzetti; past advisees include Daniel Landmann and David Reber. Teaches courses on charge transport in energy systems at EPFL. Infrastructure: Empa lab specializes in advanced characterization tools like muon-induced X-ray emission (MIXE) and operando synchrotron X-ray scattering.