جستجوی استاد راهنمای دانشگاهی

Emilio Frazzoliمشاهده پروفایل

استاد

• Autonomous Robotics
• Control Systems
• Game Theory
• +۴ مورد دیگر

Emilio Frazzoli is a Full Professor at ETH Zurich’s Department of Mechanical and Process Engineering. He leads the Institute for Dynamic Systems and Control and the Center for Sustainable Future Mobility, focusing on autonomous systems, robotics, and socio-technical control frameworks. Current affiliations: ETH Zurich (Dynamic Systems and Control, Sustainable Future Mobility) Research: Autonomous mobility-on-demand, game theory for resource allocation, and safety verification in multi-agent systems His work bridges robotics, control theory, and economics, with projects like the open-source AMoDeus simulation framework for autonomous taxis and karma-based resource allocation systems. Recent publications emphasize trustworthy AI, reproducibility in autonomous vehicle control, and human-robot interaction challenges. Notable projects include nuReality (VR-based pedestrian interaction studies) and CARSI II (context-driven vehicle interfaces).

Ulrik Brandesمشاهده پروفایل

استاد

• Social Network Analysis
• Graph Theory
• Network Science
• +۳ مورد دیگر

Ulrik Brandes serves as Full Professor and Head of the Department of Humanities, Social and Political Sciences at ETH Zurich, holding the Professorship for Social Networks. He actively teaches courses including Network Analysis and Applied Network Science: Sports Networks for Fall semester 2025, with office location at WEP J 14, Weinbergstr. 109, Zurich. His research spans Social Network Analysis, Graph Theory, and Network Science, with significant applications in sociology, sports analytics (particularly soccer and Australian Football League), and archaeological networks. As a longstanding member of the International Network for Social Network Analysis (since 2001) and the Academy of Sociology (since 2017), he bridges theoretical graph algorithms with practical interdisciplinary applications, recently expanding into sports analytics through the Football Scouting Association (2023). Analysis of his recent publications reveals strong thematic continuity in network centrality, temporal dynamics, and robustness, with increasing application diversity across sports analytics, archaeological trade networks, and decentralized social media platforms. His work consistently emphasizes efficient computational methods for real-world network problems. Honors: Simmel Award (2024) Prof. Brandes maintains active research leadership through departmental oversight and course development, though specific grant details and student advisement records are not publicly documented in available sources. His departmental role indicates substantial administrative responsibilities alongside research and teaching commitments.

David Steurerمشاهده پروفایل

دانشیار

• Theoretical Computer Science
• Algorithms
• Sum-of-Squares Method
• +۵ مورد دیگر

David Steurer is an Associate Professor in the Department of Computer Science at ETH Zurich, where he leads the Institute for Theoretical Computer Science. His research bridges theoretical computer science, mathematics, and practical applications in machine learning and statistics. Steurer has made significant contributions to the understanding of sum-of-squares methods, semidefinite programming, and high-dimensional estimation problems. Steurer earned his B.Sc. & M.Sc. in Computer Science from Saarland University in 2006, followed by a Ph.D. in Computer Science from Princeton University in 2010 under the supervision of Sanjeev Arora. His doctoral dissertation, "On the Complexity of Unique Games and Graph Expansion," received an Honorable Mention for the ACM Doctoral Dissertation Award in 2011. Steurer's research focuses on algorithm design through mathematical programming relaxations, particularly semidefinite programming and the sum-of-squares method. His work addresses computational complexity of high-dimensional estimation problems including tensor decomposition, clustering, Gaussian mixture models, and stochastic block models. He also investigates algorithmic aspects of robustness and differential privacy, especially for estimation tasks. His theoretical contributions have practical implications for machine learning and data analysis in the presence of noise and adversarial conditions. Analysis of Steurer's recent publications reveals a consistent trajectory in advancing the sum-of-squares framework for high-dimensional estimation and robust statistics. His work demonstrates how sophisticated mathematical programming techniques can provide certifiable guarantees for challenging problems in machine learning. The research spans theoretical foundations while maintaining relevance to practical applications, particularly in scenarios involving corrupted or noisy data. A notable trend is the extension of sum-of-squares methods to handle increasingly complex statistical models while providing rigorous performance guarantees. ERC Consolidator Grant (2019) Michael and Sheila Held prize (2018, with Raghavendra) Invited speaker at International Congress of Mathematicians (2018) STOC Best Paper Award (2015) Alfred P. Sloan Research Fellowship (2014) NSF CAREER Award (2014) Microsoft Research Faculty Fellowship (2014) FOCS Best Paper Award (2010) Steurer has advised numerous PhD students and postdocs who have gone on to prominent positions, including Sam Hopkins (now faculty at MIT), Jonathan Shi, and Aaron Potechin. His research is supported by multiple prestigious grants, including an ERC Consolidator Grant, an NSF CAREER Award, and a Microsoft Research Faculty Fellowship. He has served on numerous program committees for top theoretical computer science conferences and has been recognized for his service to the community through roles such as internal member of the Scientific Advisory Committee of the Institute for Theoretical Studies at ETH Zurich. As head of the Institute for Theoretical Computer Science at ETH Zurich, Steurer leads a research group focused on advancing the theoretical foundations of computer science with particular emphasis on mathematical optimization methods. His team explores the boundaries of what can be efficiently computed in high-dimensional settings, with applications spanning machine learning, statistics, and data science. The group maintains strong connections with both theoretical and applied researchers across ETH and the broader academic community.

• Robotics
• Artificial Intelligence
• Computer Vision
• +۳ مورد دیگر

Björn Jensen is a Professor and Co-Head of the AI Robotics Research Lab at Lucerne University of Applied Sciences and Arts (HSLU), specifically within the Lucerne School of Computer Science and Information Technology. He also teaches medical robotics at the University of Bern's Biomedical Engineering Program. His professional background includes roles at the Autonomous Systems Lab at EPFL, Switzerland, and founding the startup Singleton 3D focusing on 3D laser measurement technology. Educational background: MSc in Electrical Engineering (Automation & Image Processing) from TU Darmstadt (1998), followed by a Master's in Industrial Management from the same institution. PhD in human-robot interaction from EPFL (2005), with research stints at Tokyo University (2005) and involvement in projects like Robox@Expo.02 and Smarter-Elrob. Research interests span robotics, human-robot interaction, autonomous systems, medical robotics, and sensor-based navigation. Notable projects include the 'Smart Ennoblement Factory', 'NaviMow' autonomous lawnmower, and 'Bagger Assistenzsysteme'. His work emphasizes real-world applications of robotics in dynamic environments and human-centric systems. Lab leadership includes co-directing the AI Robotics Research Lab, focusing on advancing robotics technologies for practical scenarios. No scientific awards explicitly listed, but contributions to industry-academia collaborations are highlighted through startup ventures and applied research projects.

Berno Büchelمشاهده پروفایل

استاد

• Social Networks
• Game Theory
• Microeconomics
• +۴ مورد دیگر

Berno Büchel is a Professor in the Department of Economics at the University of Fribourg, Switzerland. He holds a Chair in Microeconomics and is affiliated with the Faculty of Management, Economics and Social Sciences. His research focuses on social networks, game theory, and microeconomic systems, with particular emphasis on strategic behavior, market dynamics, and information sharing. Education and Career: PhD (Dr. rer. pol.) from Bielefeld University (2009) Habilitation in Economics from Hamburg University (2011–2015) Post-doctoral positions at Saarland University (2009–2011) and the University of St. Gallen (2015–2017) Visiting scholarships at Stanford University (2015), Paris 1 Panthéon-Sorbonne (2006–2007), and other institutions Research Interests: Analysis of strategic interactions in markets and networks Behavioral economics and information avoidance Labor market policies and migration impacts Trust dynamics and network structures Key Contributions: His work spans theoretical and empirical studies, including analysis of peer-to-peer platforms, law enforcement mechanisms, and social learning in teams. Recent publications address misinformation dynamics and the economic implications of migration policies. Teaching and Engagement: Leads the Chair of Microeconomics at Fribourg, offering courses on advanced economic theory. Maintains active collaborations with institutions globally, including the Liechtenstein-Institute.

• Positional games
• Discrete geometry
• Computational geometry
• +۳ مورد دیگر

Miloš Stojaković is a Full Professor at the Department of Mathematics and Informatics, Faculty of Sciences, University of Novi Sad, Serbia. He has been with the university since 1999, progressing from student to Assistant Professor (2006-2011), Associate Professor (2011-2016), and Full Professor (2016-present). He leads the Foundations of Computer Science group and serves as an editor for Discrete Mathematics & Theoretical Computer Science and the Novi Sad Journal of Mathematics. Ph.D. in Computer Science, ETH Zurich (2005), advisors: Emo Welzl and Tibor Szabó M.Sc. in Computer Science, University of Novi Sad (2001), advisor: Joviša Žunić B.Sc. in Mathematics and Computer Science, University of Novi Sad (1999) Stojaković's research centers on positional games, discrete and computational geometry, discrete random structures, combinatorial algorithms, and graph theory. His work explores theoretical aspects of combinatorial games, particularly Maker-Breaker type games on graphs and other structures. He investigates geometric properties of discrete structures and develops algorithms for geometric problems. His research has applications in theoretical computer science and combinatorics, with significant contributions to understanding game strategies, structural properties, and computational complexity in discrete settings. Stojaković's recent publications (2016-2025) primarily focus on positional games, extending classical results to new settings like posets, generalized saturation, and constructor-blocker variants. He examines computational complexity aspects and structural properties of game outcomes. His work often involves collaborations with leading researchers including Dan Hefetz, Michael Krivelevich, and Tibor Szabó, demonstrating the ongoing vitality of combinatorial game theory and its connections to other areas of discrete mathematics. "Dr Z. Đinđić Award" for the best young scientist in Vojvodina (2008) "Best Student of University of Novi Sad Award" (1998/99) Stojaković has supervised three PhD students to completion: Mirjana Mikalački (2014), Marko Savić (2018), and Jelena Stratijev (2023). His research has been supported by various grants, including the 1 million RSD grant accompanying the Dr Z. Đinđić Award. He has organized multiple workshops including the Novi Sad Workshop on Foundations of Computer Science series (2015, 2017, 2024) and co-organized the Mini-Workshop on Positional Games at Oberwolfach Research Institute (2018) and the Positional Games workshop within A Sparse (Graphs) Coalition (2021). Since 2018, Stojaković has led the Foundations of Computer Science group at the University of Novi Sad. He maintains active international collaborations, having taught courses at Eötvös Loránd University Budapest, University of Buenos Aires, University of Szeged, University of Banja Luka, University of Groningen, and Graz University of Technology. His research group focuses on theoretical aspects of combinatorial games, discrete geometry, and related computational problems, contributing significantly to these fields through publications, workshops, and academic leadership.

• Machine Learning
• Explainable Artificial Intelligence (XAI)
• Rule Extraction from Neural Networks
• +۴ مورد دیگر

Guido Bologna is an Assistant Professor at the Haute école du paysage, d'ingénierie et d'architecture de Genève (HEPIA), within the School of Technique et IT and the Department of Informatique et systèmes de communication. His research focuses on explainable artificial intelligence (XAI), rule extraction from neural networks, and their applications in healthcare and computer vision. His work emphasizes transparent decision-making in AI systems, particularly for medical diagnostics (e.g., melanoma detection from images) and radiotherapy side-effect prediction. He co-leads the HES-XPLAIN platform, an open-source initiative to democratize XAI development. Key projects include PRE-ACT (predicting radiotherapy side effects) and Fidex/FidexGlo algorithms for explaining ensemble models and SVMs. Notable contributions include rule extraction from deep neural networks using techniques like DIMLP ensembles and DCT-based feature maps. His research bridges theoretical advances in AI with practical applications, such as sensory substitution devices for visually impaired individuals and gait analysis for elderly mobility aids. Bologna collaborates internationally on interdisciplinary projects, spanning healthcare informatics, robotics, and neuroscience. His work addresses challenges in algorithmic transparency, fairness, and privacy in AI systems, with a focus on real-world deployable solutions.

Charbel Toumiehمشاهده پروفایل

پژوهشگر ارشد

• Autonomous Systems
• Robotics
• Aerial Robotics
• +۳ مورد دیگر

Charbel Toumieh is a Research Fellow at the École Polytechnique Fédérale de Lausanne (EPFL), based in the Intelligent Systems Laboratory (LIS) under the School of Engineering (STI). His research focuses on advanced robotics, particularly in aerial systems, motion planning, and autonomous systems. He holds a postdoctoral position and contributes to projects involving multi-agent coordination, high-speed navigation, and energy-efficient drone designs. Key research areas include teleoperation of aerial swarms, adaptive morphing for avian-inspired drones, and decentralized multi-agent planning. His work addresses challenges in cluttered environments, dynamic obstacle avoidance, and real-time trajectory optimization. The LIS lab, part of the Institute of Microengineering (IGM), emphasizes innovative solutions in intelligent systems and robotics. Recent publications highlight advancements in motion planning algorithms, safe corridor generation using voxel grids, and GPU-accelerated exploration techniques. His research bridges theoretical control systems with practical applications in autonomous robotics, aiming to enhance efficiency and resilience in robotic systems.

Laura Grigoriمشاهده پروفایل

استاد

• High-Performance Scientific Computing
• Numerical Linear Algebra
• Communication-Avoiding Algorithms
• +۶ مورد دیگر

Laura Grigori is a Professor of Applied and Computational Mathematics at EPFL and Head of the Laboratory for Simulation and Modeling at the Paul Scherrer Institute (PSI), Switzerland. She holds a Ph.D. in Computer Science from Université Henri Poincaré (2001) and has been a leading figure in high-performance numerical algorithms and communication-avoiding methods. Her research focuses on numerical linear algebra, randomized algorithms, and scalable scientific computing for applications in astrophysics, molecular simulations, and exascale systems. Affiliations: EPFL Chair of High-Performance Numerical Algorithms & Simulation; Head, PSI Laboratory for Simulation and Modeling. Education: Ph.D. in Computer Science, Université Henri Poincaré (2001); Postdoctoral Researcher, UC Berkeley/LBNL (2001–2003); Senior Researcher at INRIA (2004–2023). Research Interests: Communication-avoiding algorithms, randomized numerical linear algebra, parallel preconditioners, tensor computations, and exascale computing challenges. Awards: SIAM Fellow (2020), ERC Synergy Grant (2018), SIAM Supercomputing Career Prize (2024), and Best Paper Awards (2016, 2023). Grants & Leadership: PI of the ERC Synergy Project EMC² (Extreme-scale Computational Chemistry); Chair of PRACE Scientific Steering Committee (2021–2022); SIAM SIAG Supercomputing Chair (2016–2017). Labs/Teams: Alpines Group (INRIA/Sorbonne University), EMC² Collaboration (Cancès, Maday, Piquemal).

Tommaso Polonelliمشاهده پروفایل

مدرس

• Embedded systems
• IoT
• Wireless Sensor Networks
• +۷ مورد دیگر

Dr. Tommaso Polonelli is a Lecturer and Postdoctoral Researcher at ETH Zürich's Department of Information Technology and Electrical Engineering, affiliated with the Center for Project-Based Learning (PBL). He leads teaching initiatives and research in IoT systems, energy-efficient electronics, and autonomous technologies. His research focuses on energy-efficient systems, smart sensing, and ultra-low power computing, with applications in UAVs, wind turbine monitoring, and wearable devices. He also serves as a Scientific Advisor at RTDT Laboratories AG, an ETH spin-off. Polonelli holds a PhD (2020) and Master’s degree (2017) in Electronic Engineering from the University of Bologna. He has co-authored over 60 publications and received awards such as the Spark Award (2021), KITE Award finalist (2024), and the Certificate of Achievement for National Scientific Qualification (2023). He is an active IEEE member and contributes to both academia and industry through innovations in sensor technology and predictive maintenance. His work emphasizes interdisciplinary collaboration, with projects ranging from structural health monitoring to AI-enhanced systems. Recent efforts include Aerosense—a MEMS-based monitoring system for wind turbines—and ElectraSight, a smart eyewear platform with non-invasive eye tracking.

Pierluigi Caponeمشاهده پروفایل

استاد

• Flight Control Systems
• Aerospace Engineering
• Unmanned Aerial Vehicles (UAV)
• +۴ مورد دیگر

Prof. Pierluigi Capone serves as Professor and Head of the Flight Mechanics and Flight Control Systems research focus at Zurich University of Applied Sciences (ZHAW) School of Engineering. He teaches core courses including Aircraft Systems - Control Systems, Flight Mechanics and Simulation, and Safety Critical Systems while coordinating drone-related initiatives and the Specific Operations Risk Assessment (SORA) professional development program. His academic credentials include an MSc in Aerospace Engineering from Politecnico di Milano (1988-1994), complemented by a CAS in University Didactic (2022-2023), EASA Design Organisation Approval certification (2021), and leadership training from Bombardier and AgustaWestland. Industry experience spans 20+ years at major aerospace firms including AgustaWestland (Head of Control Laws Design, 2008-2013) and Bombardier Aerospace (Engineer Specialist, 2001-2004). Capone's research centers on flight control system design for fixed/rotary-wing aircraft, parameter estimation, and UAV safety. Recent work emphasizes tilt-rotor transition dynamics, real-time simulation fidelity, and collision avoidance algorithms, with significant contributions to XV-15 tilt-rotor modeling and atmospheric disturbance analysis for complex terrain operations. His team develops novel inceptor designs for compound helicopters and gimballed tilt-rotor systems. Publication trends reveal focused advancement in UAV operational reliability (HORUS project), hybrid-electric tilt-wing EMS aircraft, and human-drone communication interfaces. Key subfields include wake interference modeling, multi-fidelity eVTOL certification frameworks, and rotorcraft model uncertainty quantification. Current projects include White-box Helicopter System Identification (Project Leader), Flight Mechanics of Tilt-Wing Aircraft Under Harsh Conditions (Deputy Leader), and Swiss Drone Base Camp Lodrino (Team Member). Completed initiatives span Digital Transformation for Aviation Safety Standards, Power Reserve Calculators for General Aviation, and Novel Collision Avoidance Algorithms. Capone actively participates in the European Rotorcraft Forum (ERF) International Committee WG-112 on VTOL, Vertical Flight Society, and Swiss Association of Aeronautical Sciences, maintaining industry connections through his ORCID (0009-0006-5395-5670) and professional networks.

Aude Billardمشاهده پروفایل

استاد

• Machine Learning
• Robotics
• Mechatronics
• +۲ مورد دیگر

Aude Billard is a Full Professor at the Learning Algorithms and Systems Laboratory (LASA) within the École Polytechnique Fédérale de Lausanne (EPFL) , where she leads groundbreaking research in robotics and machine learning. She also holds teaching roles in the Microengineering and Mechanical Engineering Sections and serves as a member of the Conference of Section Directors (CDS) and the STI School Direction . PhD in Artificial Intelligence (University of Edinburgh, 1998) MSc in Knowledge-Based Systems (University of Edinburgh, 1996) BSc & MSc in Physics (EPFL, 1995) Her research focuses on machine learning techniques for robotics , particularly in dexterous manipulation , dynamical systems , and human-robot interaction . She has pioneered methods for constraint learning from demonstrations, obstacle avoidance in crowded environments, and adaptive control systems, with recent works exploring AI ethics, sustainability in robotics, and improving robotic dexterity beyond human capabilities. Awarded multiple times for her contributions, including IEEE Fellow , ERC Advanced Grant , and Swiss National Science Foundation Career Award , Billard has also received recognition for teaching and service leadership in robotics societies. Scientific Leadership : IEEE Robotics and Automation Society President Education Advocacy : Innovative Teaching Grant recipient PhD Mentorship : Supervised ~30 PhD students

Vijay Kumarمشاهده پروفایل

استاد

• Robotics
• Autonomous Systems
• Bio-inspired Algorithms
• +۲ مورد دیگر

Vijay Kumar is the Nemirovsky Family Dean of Penn Engineering with professorial appointments in Mechanical Engineering and Applied Mechanics, Computer and Information Science, and Electrical and Systems Engineering at the University of Pennsylvania. He leads research on autonomous ground/aerial robots and bio-inspired swarm algorithms at the GRASP Laboratory, where he previously served as director (1998–2004). His work spans robotics, cyberphysical systems, and AI applications in disaster response and construction. Education: B.Tech from Indian Institute of Technology Kanpur, Ph.D. from Ohio State University (1987) Research focuses on robot swarms , autonomous navigation , and bio-inspired collective behaviors . Recent publications explore connectivity optimization, semantic SLAM, and stochastic traffic modeling using robot swarms and neural networks. Applications include GPS-denied environments and decentralized edge AI infrastructure. Scientific Awards: IEEE/ASME Fellow, NAE/APS/AAAS member Entrepreneurship: Co-founded Exyn Technologies, advisor to Treeswift, board member of WeRobotics and O2Micro Labs/teams include the GRASP Laboratory and collaborations with industry partners like Rivian and Amazon. Grant activities involve AI for medicine, intelligent assistive robotics, and digital twin infrastructure.

Timothé Krauthمشاهده پروفایل

پژوهشگر

• Aerospace Engineering
• Artificial Intelligence
• Machine Learning
• +۳ مورد دیگر

Dr. Timothé Krauth is a researcher at Zurich University of Applied Sciences (ZHAW) School of Engineering within the Aviation Infrastructure department. Holding a Ph.D. in Applied Mathematics from ISAE-Supaero (2021-2024), his work focuses on applying machine learning techniques to air traffic management challenges. He actively contributes to research projects like "Achieving Human-Machine Collaboration with Artificial Situational Awareness" and "Making I-CNS A Reality" for integrated aviation systems. Ph.D. in Applied Mathematics, ISAE-Supaero (2021-2024) His research interests center on aerospace engineering and artificial intelligence, specializing in trajectory modeling, collision risk estimation, and uncertainty quantification in air traffic systems. He has developed deep learning frameworks for flight path analysis and safety prediction. Krauth's publications demonstrate expertise in: Collision risk modeling using variational autoencoders Deep generative modeling for aviation safety Large-scale trajectory dataset creation Adaptive importance sampling techniques Mid-air collision probability assessment Terminal area traffic management As a team member in ongoing projects, he applies these methods to create safer and more efficient aviation systems through integrated communication, navigation, and surveillance technologies.

Taiji Suzukiمشاهده پروفایل

دانشیار

• Machine Learning
• Statistical Learning Theory
• Deep Learning
• +۶ مورد دیگر

Prof. Taiji Suzuki is an Associate Professor at the University of Tokyo in the Department of Mathematical Informatics . He also serves as Team Leader of the "Deep Learning Theory" team at AIP-RIKEN , Japan. With a PhD in Information Science and Technology from the University of Tokyo (2009), he has held academic positions at the University of Tokyo (2009-2013) and Tokyo Institute of Technology (2013-2017) before returning to the University of Tokyo in 2017. University of Tokyo (2004: BEng in Mathematical Engineering) University of Tokyo (2006: MSc in Information Science and Technology) University of Tokyo (2009: PhD in Information Science and Technology) His research interests span statistical learning theory , deep learning , kernel methods , sparse estimation , and stochastic optimization . He investigates how neural networks adapt to function smoothness, avoid the curse of dimensionality, and achieve global optimization through mean-field dynamics. His work bridges theoretical guarantees (minimax optimality, convergence analysis) with practical implementations (transformers, graph neural networks, diffusion models). The 15 most recent articles focus on transformers' representation power, graph neural networks' limitations, diffusion models' convergence, and optimization theories for deep learning. Key themes include information-theoretic bounds , feature learning dynamics , and mean-field analysis . He explores applications in AI for medicine, federated learning, and biomedical modeling. Awards & Recognition: Outstanding Paper Award, ICLR 2021 MEXT Young Scientists’ Prize Outstanding Achievement Award, Japan Statistical Society 2017 Outstanding Achievement Award, Japan Society for Industrial and Applied Mathematics 2016 Best Paper Award, IBISML 2012 Best Paper Candidate, ICDM 2019 He has served as Area Chair for NeurIPS, ICML, ICLR, AISTATS, and as Program Chair for ACML. His scientific contributions include convergence theories for stochastic gradient methods, minimax analysis of deep learning vs kernel methods, and novel frameworks for distributional optimization in diffusion models.