Find Academic Supervisors

• Autonomous Driving
• Computer Vision
• Robotics
• +3 more

Markus Enzweiler serves as Professor of Computer Science and Autonomous Systems at Esslingen University of Applied Sciences within the Department of Computer Science and Engineering. He concurrently holds the leadership position of Director at the Institute for Intelligent Systems, where he oversees research initiatives focused on intelligent systems development for real-world autonomous applications. His research program centers on computer vision for autonomous systems , with specialized expertise in visual-inertial SLAM, collective perception, and neural rendering techniques. Key investigation areas include environmental robustness across agricultural and urban settings, real-time processing constraints for embedded systems, sensor fusion methodologies (particularly camera-radar integration), and the application of generative models for perception enhancement. His work consistently addresses practical implementation challenges such as computational efficiency and sensor calibration in unstructured environments. Analysis of his 2023-2025 publications reveals three dominant research trajectories: (1) Advancement of lightweight perception systems through stixel-based representations and neural rendering; (2) Development of infrastructure-supported collective perception frameworks with datasets like CoopScenes and OPNV; and (3) Rigorous benchmarking of SLAM components in domain-specific contexts including agricultural robotics and multi-season navigation. His recent systematic review on LLM-based vulnerability detection also demonstrates expanding interest in software security for autonomous systems. As Director of the Institute for Intelligent Systems, Prof. Enzweiler leads a research ecosystem focused on translating theoretical advances into practical autonomous vehicle technologies. His team develops specialized datasets (Rover, OPNV) and software stacks for smart city environments, emphasizing the integration of novel perception approaches with vehicle dynamics modeling and real-time operational constraints.

Mauro PrevostiniView profile

Lecturer

• Wireless Sensor Networks
• Precision Agriculture
• Embedded Systems
• +4 more

Mauro Prevostini is the Program Manager of the Faculty of Informatics at the Università della Svizzera italiana (USI) since 2004 and holds the academic rank of Lecturer. He previously managed the creation of the Faculty of Informatics from 2001 to 2004 and has been a staff member of the ALaRI institute until 2018. His roles include coordinating academic-industry collaborations and promoting computer science education in local schools. Education: MSc in Electrical Engineering (ETH Zürich, 1994), Thesis: "On-Line Recognition of Masticatory Muscles Activity with Long-Time EMG Recorder" Secondary Education: Liceo Cantonale Lugano 1 (1984–1988) Research Interests: Focuses on Wireless Sensor Networks applied to Precision Agriculture, particularly in pest monitoring systems like PreDiVine DSS. His work integrates UML-based design methodologies for embedded systems and cyber-physical systems. Collaborations include ALaRI and Agroscope research center. Academic Contributions: Over 15 key publications from 2003–2022 emphasize system-level design, hardware/software co-design, and sensor network optimization. Recent articles address deep learning strategies for pest detection and adaptive decision support systems. Professional Activities: Co-founder of Dolphin Engineering Sagl (2012–present), a Precision Agriculture startup Member of the academic senate (2017–2019; 2023–present) Former coordinator of the Ticino branch for the informatica08 initiative (2008) Labs & Projects: Leads projects on wireless sensor networks for agricultural monitoring and collaborates with ALaRI on embedded system design tools and methodologies.

Olivier VerscheureView profile

Adjunct Professor

• Stream and Big Data Mining
• Geospatial Analysis
• Large-scale Data Management
• +4 more

Olivier Verscheure serves as the Executive Director of the Swiss Data Science Center (SDSC), a national R&D center organizationally hosted by both École Polytechnique Fédérale de Lausanne (EPFL) and ETH Zurich. He also holds multiple Adjunct Professor appointments at EPFL, specifically within the School of Computer and Communication Sciences (SIN and SSC) and the School of Engineering (SEL). His educational background includes: Ph.D. in Computer Science from École Polytechnique Fédérale de Lausanne (EPFL), June 1999 Verscheure's research focuses on the intersection of data science and real-world applications. His work centers on stream and big data mining, geospatial analysis, and large-scale data management. These technical capabilities are applied across diverse domains including personalized health and medicine, Intelligent Transportation Systems, telecommunications, smart building technologies, Smart Grid infrastructure, healthcare analytics, and waste water management systems. His approach emphasizes creating practical data science solutions that address complex challenges in these sectors while considering the constraints of real-world deployment. An analysis of his recent publication record reveals a strong focus on real-time data processing and analytics, particularly for transportation and urban systems. His work frequently addresses challenges in handling massive time series data, developing efficient architectures for low-latency analytics, and creating practical applications for smart city infrastructure. There's a clear progression from theoretical data science contributions to production-ready systems that can process billions of data points daily, demonstrating his ability to bridge research and practical implementation. His notable achievements include: Two IBM Outstanding Technical Achievement Awards Best Paper Award for his research Student Best Paper Award Verscheure has substantial experience in research leadership and mentoring. During his tenure at IBM, he managed the Exploratory Stream Analytics research group and led a technical and management team of approximately 40 people at the IBM Research lab in Ireland. He has served on PhD committees at major universities and published nearly 100 research papers that have garnered over 2,400 citations. His work has resulted in more than 40 US and international patents, demonstrating both academic and practical impact. As Executive Director of the Swiss Data Science Center, Verscheure oversees a distributed multi-disciplinary team working across domains including personalized health, transportation, earth and environmental science, social science and digital humanities, and economics. The center aims to federate data providers, data and computer scientists, and subject-matter experts around a cutting-edge analytics platform while addressing security and privacy issues. Under his leadership, the SDSC develops embedded data science support, offers end-to-end data science services, and fosters a community to share tools and knowledge in data science.

• Biomedical Engineering
• Signal Processing
• Machine Learning
• +3 more

Andrea Ronco is a Researcher affiliated with the Department of Rehabilitation Engineering at ETH Zürich. His work focuses on advancing sensor technologies and their applications in biomedical engineering, autonomous systems, and IoT. He leads research in mmWave radar systems for non-invasive vital sign monitoring, in-ear gesture recognition, and low-power embedded sensor solutions. Professional Affiliation: Staff of Professorship for Rehabilitation Engineering Research interests span radar technology optimization, real-time signal processing for healthcare, and robust sensor fusion for autonomous vehicles. His recent work emphasizes miniaturization and energy efficiency in wearable and embedded systems. Key contributions include TinyssimoRadar and In-ear-voice platforms that integrate advanced sensing with minimal power consumption. Ronco's publications address challenges in sensor robustness against environmental conditions and real-world deployment scenarios. His research outputs demonstrate expertise in interdisciplinary domains combining electrical engineering, machine learning, and biomedical applications. Current projects likely explore next-generation sensing modalities for wearable health monitoring and smart infrastructure systems.

• Embedded Systems
• AI Algorithms
• FPGA/VHDL Design
• +5 more

Jürgen Wassner is a Professor of Edge Computing and Co-Head of the Competence Center for Intelligent Sensors and Networks at the Lucerne School of Engineering and Architecture (HSLU T&A). He holds a master's degree from Technical University of Dresden and a PhD from ETH Zurich. His career spans industry roles in Silicon Valley Group Inc. and telecommunication R&D before joining academia in 2007. His research focuses on embedded systems, AI at the edge, FPGA/VHDL design, real-time systems, and powerline communication . He leads projects such as 'Visual-Servoing Testbed with AI-Hardware in the Loop' and 'Low-Cost High-Performance Intelligent Camera for Space Debris Mitigation,' collaborating with companies like Diehl Aerospace. Recent publications emphasize wire fault detection using powerline communication, AI-based odor classification systems, and hardware acceleration for CNNs. His work bridges theoretical research with practical applications in avionics, rail freight, and space debris mitigation. Wassner advises Master of Science in Engineering students and has been recognized for his media engagement including a feature in the Luzerner Zeitung. He teaches Digital Design (FPGA, VHDL) and Digital Signal Processing , emphasizing hands-on implementation of cutting-edge systems.

Christian VogtView profile

Lecturer

• IoT and Embedded Systems
• Wireless Sensor Networks
• Medical Electronics
• +3 more

Dr. Christian Vogt Dr. Christian Vogt is a Lecturer at the Department of Information Technology and Electrical Engineering within the Center for Project-Based Learning at ETH Zürich. His research focuses on IoT systems, embedded electronics, and wearable technology with applications in agriculture, healthcare, and urban computing. He specializes in energy-efficient sensor networks, flexible electronics, and real-time data transmission systems. Research Interests Vogt's work bridges hardware innovation and software optimization, emphasizing practical implementations through project-based learning. Key areas include: IoT and Wireless Sensor Networks Low-power embedded machine learning Wearable biomedical devices Flexible/stretchable electronics Ultra-wideband and radar-based sensing Publications Recent work highlights energy-efficient livestock monitoring systems using LoRa, battery-free sensor nodes powered by human body energy, and real-time gesture recognition with novel sensors. His research often involves interdisciplinary collaborations between computer science, electrical engineering, and biomedical applications. Affiliations ETH Zürich - Center for Project-Based Learning Department of Information Technology and Electrical Engineering Contact: christian.vogt@pbl.ee.ethz.ch

• Machine Learning
• Visual Analytics
• Artificial Intelligence
• +3 more

Thilo Spinner is a Researcher affiliated with the Department of Computer Science at ETH Zürich, contributing to the Professorship for Computer Science. His work focuses on visual analytics, explainable AI, and machine learning interpretability. He has developed tools like iNNspector for deep model debugging and explAIner for interactive machine learning frameworks. His research spans topics such as uncertainty-aware dimensionality reduction, language model explainability, and pandemic data visualization (e.g., Coronavis for Covid-19 tracking). Key contributions include frameworks for model interpretability, real-time parameter optimization, and emergency response analysis tools like NEAT. His articles highlight innovations in visualizing complex AI systems and addressing challenges in model transparency and pandemic management. Education details are not explicitly provided in the text. His research trends emphasize bridging technical AI systems with human-understandable insights through visualization and interactive tools. He has explored diverse applications from healthcare crisis analysis to neural network debugging.

Philippe Cudré-MaurouxView profile

Professor

• Exascale Information Management
• Big Data
• Artificial Intelligence
• +13 more

Philippe Cudré-Mauroux is a Full Professor at the University of Fribourg, Switzerland , where he leads the eXascale Infolab . He has held visiting researcher positions at MIT and Microsoft CISL , and serves on the Research Council of the Swiss National Science Foundation and the Scientific Advisory Board of the CHIST-ERA EU Research Programme . Research Interests: His work spans exascale information management , big data , AI , knowledge graphs , linked data , time series data repair , and emergent semantics . He focuses on building scalable, intelligent data systems that integrate storage, computation, and semantics. Publication Trends: His recent work emphasizes schema-aware knowledge graph completion , time series imputation and benchmarking , hardware-accelerated data systems , and large language models for data cleaning . His research bridges database systems, AI, and systems architecture, often targeting high-performance, real-world applications. Scientific Awards: ERC Consolidator Grant (2016) Google Faculty Research Award (2013) Verisign Internet Infrastructures Award (2012) Best Paper Awards at VLDB (2020), AAMAS (2019), and Swiss Data Science Conference (2020) EPFL Doctorate Award and Press Mention (2007) Best Mentor Award at ISWC (2010) Advising and Grants: He mentors a large group of researchers and students, many of whom are co-authors on his publications. He has secured significant funding, including a €2M ERC Grant and multiple Google and Amazon grants, supporting a vibrant research lab focused on next-generation data infrastructure. Labs and Teams: He leads the eXascale Infolab at the University of Fribourg, a dynamic research group actively publishing in top-tier venues and developing innovative tools for data management and AI integration.

Andrei BursucView profile

Lecturer

• Computer Vision
• Machine Learning
• Autonomous Driving
• +3 more

Andrei Bursuc is a Research Scientist and Deputy Scientific Director at Valeo.ai, where he focuses on perception for autonomous driving. Concurrently, he serves as a Research Associate at the Astra Inria project team and teaches the Deep Learning DIY course at Paris Institute of Technology (École Polytechnique) since 2019. He holds a PhD from Mines ParisTech and Telecom SudParis (2012) and a BSc from Polytechnic University of Bucharest (2009). His research spans computer vision and machine learning , with applications in autonomous driving, uncertainty quantification, self-supervised learning, and foundation models. Recent work emphasizes scalable representation learning, open-vocabulary segmentation, and generative modeling for driving scenarios. His publications (2024–2025) cluster around themes of semantic segmentation , depth estimation , and vision-language models , with innovations in unsupervised learning, cross-modal distillation, and efficiency. Trends indicate a focus on robustness, real-world generalization, and multi-sensor integration. He co-organizes workshops (e.g., Uncertainty Quantification at CVPR/ECCV) and serves as Area Chair for NeurIPS/CVPR/ECCV. He contributes to EU projects (e.g., ELLIOT) and open-source initiatives.

• Multiprocessor Systems
• Real-Time Embedded Computing
• Distributed Signal Processing
• +3 more

Prof. Dr. Matthias Rosenthal is a Professor of Multiprocessor and Real-Time Systems at the ZHAW School of Engineering, Zurich University of Applied Sciences (ZHAW), where he also serves as Head of the Research/Focus Area Realtime Platforms. He holds a PhD and MSc in Electrical Engineering from ETH Zurich (1993–1997). His research focuses on multiprocessor systems, hybrid multicore architectures, distributed signal processing, embedded GPU computing, and real-time embedded systems. Key projects include In-Flight GNSS Interference Detection, dAIrector (automated multi-camera live production), and novel AFM techniques for industrial quality control. He has led over 15 industry-focused projects, including collaborations with Innosuisse and companies like Harman International. His work emphasizes real-time systems, FPGA-GPU co-design, and embedded AI solutions. Education: PhD (ETH Zurich, 1997), MSc (ETH Zurich, 1993) Awards: CTI Startup Label (2005) Teaching: Lectures on digital systems, real-time computing, and information theory Notable contributions include advancements in embedded machine learning for food waste management, secure boot concepts for Zynq MPSoC, and low-latency wireless video systems. His research bridges theoretical computer engineering with practical industrial applications.

Jan S. HesthavenView profile

Professor

• Computational Mathematics
• Numerical Analysis
• Scientific Computing
• +6 more

Jan S. Hesthaven is a Professor and Provost at EPFL, leading academic affairs. He holds a Master's from the Technical University of Denmark (DTU) and a PhD in Numerical Analysis, followed by an honorary dr.techn degree from DTU. His research focuses on high-order computational methods for wave problems, reduced order models, and machine learning integration. He has co-authored over 175 papers and 4 monographs. Previously, he served as Dean of the School of Basic Sciences at EPFL and held roles at Brown University, including Director of the Center for Computation and Visualization. Awards include the Alfred P. Sloan Fellowship and the Philip J. Bray Award. Education: Master of Science in Computational Physics, DTU (1991) PhD in Numerical Analysis, DTU (1995) dr.techn in Computational Mathematics, DTU (2009) Research Interests: Development of high-order numerical methods, computational wave propagation, geophysical flows, and machine learning applications in scientific computing. His work bridges traditional methods with AI-driven approaches for real-time modeling and structural health monitoring. Recent Work: His 2023–2025 publications emphasize machine learning-enhanced models, reduced order methods, and seismic data analysis for environmental applications. Key techniques include physics-informed neural networks and graph-based operator learning. Awards: Alfred P. Sloan Fellowship (2000) NSF Career Award (2002) Philip J. Bray Award (2004) Dr.techn from DTU (2009) Grants & Leadership: Led the Center for Computation and Visualization (CCV) at Brown (2006–2013) and co-directed the NSF Institute ICERM (2010–2013). Current roles include Provost at EPFL and leadership in MATHICSE. Collaborates with industry and applied scientists on computational challenges. Labs & Teams: Active in the MATHICSE lab, focusing on numerical methods and high-performance computing. Involved in interdisciplinary projects combining AI with traditional computational science.

• Computer Architecture
• Computer Arithmetic
• RISC-V Systems
• +3 more

David Mallasen Quintana is a Scientist and Lecturer at the Swiss Federal Institute of Technology Lausanne (EPFL), affiliated with both the School of Engineering (STI) and the Integrated Systems Laboratory (ESL) within the Institute of Electrical and Microengineering. He also holds teaching responsibilities in the School of Computer and Communication Sciences (IC) at EPFL. Dr. Quintana completed his Ph.D. in Computer Engineering at Universidad Complutense de Madrid in 2024. His research focuses on: Computer architecture and arithmetic systems RISC-V ecosystem development and customization Energy-efficient hardware design and domain-specific accelerators Posit arithmetic implementations and optimizations Embedded systems and low-power computing solutions His recent publications demonstrate consistent focus on RISC-V extensions, posit arithmetic implementations, and hardware/software co-design approaches for efficient computing. The work spans from fundamental arithmetic units to application-specific accelerators, with evolving emphasis on scientific computing applications and energy-constrained environments. Dr. Quintana actively contributes to open-source hardware projects including PERCIVAL, x-HEEP, and various arithmetic unit implementations. His research group develops energy-efficient platforms and tools for hardware deployment within the RISC-V ecosystem.

• Statistical Data Analysis
• Computer Vision
• Digital Health
• +5 more

Mirko Birbaumer is a Professor at the Lucerne School of Engineering and Architecture (HSLU), specializing in data science and machine learning applications. He holds a PhD in computer-assisted image analysis from ETH Zurich and has completed advanced executive education at MIT in Innovation and Strategy. PhD in Systems Biology (ETH Zurich, 2010) Executive Certificate in Innovation and Strategy (MIT, 2022) Advanced Executive Certificate in Innovation, Strategy, and Technology (MIT, 2024) His research integrates statistical data analysis , computer vision , and digital health , with applications spanning industrial quality control, medical imaging, and environmental monitoring. Recent work focuses on physics-informed neural networks, interpretable anomaly detection, and causal machine learning. His projects include AI-driven solutions for medical diagnostics, predictive maintenance in manufacturing, and environmental anomaly detection. He has supervised over 20 student theses on topics like deep learning for knee osteoarthritis detection, monocular depth estimation, and AI-assisted logo similarity analysis. He leads the Data Science profile in the Master of Science and Engineering program and teaches courses on deep learning, Bayesian machine learning, and computer vision. His methodological expertise includes agent-based modeling, statistical analysis, and explainable AI.

• Embedded Sensors
• Sensor Networks
• Distributed Systems
• +3 more

Bruno Rodrigues is an Assistant Professor of Computer Science (Embedded Sensing Systems) at the University of St. Gallen (HSG). His research focuses on embedded sensors, sensor networks, distributed systems, and network security, with additional expertise in software-defined networks and distributed ledgers. He holds a PhD from the University of Zurich (2020), a Master's from the Polytechnic School of the University of São Paulo (2016), and a Bachelor's from the University of Santa Catarina, Brazil (2013). Prof. Rodrigues' professional career includes roles as a Senior Researcher (2020–2024) and Junior Researcher (2016–2020) at the University of Zurich, where he contributed to projects like the EU-funded CONCORDIA H2020 and collaborations with Swiss innovation agencies (KTI/Innosuisse). His work spans DDoS mitigation, blockchain applications in voting systems, and energy-efficient cloud networks. Notable projects include the 'Passive Wireless Intelligence Tracking System (PasWITS)' and 'Energy-Efficiency to Clouds (E2C)' with Ericsson Research. His research portfolio includes over 50 publications on topics such as secure IoT frameworks (SHIFT, WeTrace), blockchain-based voting systems (ProvotuMN, Æternum), and cybersecurity tools like SecGrid for traffic analysis. He has pioneered solutions for privacy-preserving mobile tracking (LaFlector), real-time medical device inventory systems (PMD-Track), and decentralized financial inclusion tools (AfriBit). Prof. Rodrigues' work bridges theoretical computer science with practical applications, emphasizing sustainability, security, and innovation in distributed systems. He has led interdisciplinary projects at the intersection of blockchain technology, sensor networks, and ethical AI, with a focus on real-world implementation challenges.

Jean-Marc OdobezView profile

Adjunct Professor

• Computer Vision
• Machine Learning
• Deep Learning
• +6 more

Jean-Marc Odobez is a Senior Scientist at the IDIAP Research Institute and Adjunct Professor at the Swiss Federal Institute of Technology Lausanne (EPFL), where he is affiliated with the School of Engineering and serves on the Electrical Engineering Doctoral committee (EDEE). He leads the Perception & Activity Understanding Group at Idiap and has extensive teaching responsibilities across multiple departments. Dr. Odobez received his PhD in Computer Science from Rennes University in 1994. His research focuses on multimodal perception systems combining computer vision, statistical machine learning, and deep learning for activity recognition, behavior understanding, and human-robot interaction. His work spans diverse application domains including human health assessment, social robotics, and media content analysis. His recent research shows strong trends in gaze estimation, human activity recognition, and multimodal processing. His team has developed innovative solutions for gaze tracking, head pose estimation, and activity recognition using depth sensors and neural networks. His work increasingly bridges computer vision with digital humanities, particularly in the analysis of ancient Maya glyphs. IEEE member Associate Editor of Machine Vision and Applications journal Dr. Odobez has supervised numerous PhD students and serves as a committee member for the Electrical Engineering Doctoral program. He has been principal investigator for over 16 European and Swiss research projects and has worked on 10 technology transfer projects with SMEs. He co-founded Klewel SA and Eyeware SA, focusing on eye tracking and attention modeling technologies. His research group actively collaborates with industry partners and maintains strong connections with the computer vision and human-computer interaction research communities.