Find Academic Supervisors

David CockView profile

Senior Lecturer

• Formal Verification
• Operating Systems
• Hardware-Software Co-Design
• +3 more

Dr. David Cock is a Senior Lecturer and Senior Researcher at ETH Zürich's Department of Computer Science, affiliated with the Systems Group. He holds a PhD from UNSW (2014) and a B.Sc. (hons) from UNSW (2004). His research focuses on formal verification, trustworthy systems, and hardware-software co-design, with notable contributions to projects like Enzian (a CPU/FPGA platform) and seL4 (formally verified kernel). He teaches Advanced Operating Systems and Informal Methods courses. Key achievements include the ACM Software System Award (2022) for seL4 and leadership in projects addressing hardware complexity and security. Research interests include formal methods for hardware modeling (Sockeye project), runtime verification, and mitigating timing channels. His work bridges theoretical foundations with practical systems, emphasizing secure and reliable computing platforms. Projects like Trustworthy BMC aim to enhance baseboard management systems' assurance. Collaborations span academia and industry, with open-source contributions to hardware designs and formal tools. Publications span formal verification, hardware modeling, and secure systems, with recent focus on heterogeneous computing and declarative hardware specifications. Teaching emphasizes practical formal techniques and OS design, leveraging real-world hardware (e.g., Barrelfish). His lab, the Systems Group, explores cutting-edge challenges in systems software and architecture.

Christoph LeitnerView profile

Research Fellow

• Convergent Technologies
• Ultrasonics
• Machine Learning
• +3 more

Dr. Christoph Leitner is a Research Fellow at ETH Zurich's Integrated Systems Laboratory under Prof. Luca Benini, focusing on biomedical and IoT applications. His work integrates printed piezoelectric transducers and flexible electronics with energy-efficient systems. He holds a PhD in Biomedical Engineering from TU Graz (2022) and has collaborated with institutions like Sant'Anna School of Advanced Studies and KTH Stockholm. Notable achievements include the Josef Krainer Young Researcher Award and Motorik Scholarship. Leitner's research spans ultrasonics, machine learning, and wearable devices, with contributions to muscle-tendon dynamics and real-time biofeedback systems. Education: PhD in Biomedical Engineering, TU Graz (2022), Advisor: Prof. Christian Baumgartner Previous roles: University Assistant (2006–2011) and R&D Engineer at Virtual Vehicle GmbH Research Interests: Convergent technologies merging biomedical engineering and IoT Ultrasound-based monitoring for musculoskeletal systems Energy-efficient embedded systems for wearable applications Collaborations & Awards: Recipient of Josef Krainer Young Researcher Award (2023) and Motorik Scholarship (2018) Active collaborations with University of Zurich, Veterinary University of Vienna, and Queensland University of Technology Labs & Projects: Integrated Systems Laboratory at ETH Zurich Developed a patented ultrasound-transparent tattoo-based SEMG system with Prof. Francesco Greco

Pasquale Davide SchiavoneView profile

Lecturer

• Embedded Systems
• RISC-V Architecture
• Ultra-low-power Computing
• +5 more

Pasquale Davide Schiavone holds multiple research and teaching positions at École Polytechnique Fédérale de Lausanne (EPFL), serving as a Lecturer at the School of Computer and Communication Sciences (IC) and as a Scientist at both the Embedded Systems Laboratory (ESL) within the School of Engineering (STI) and PAT Administration. His interdisciplinary work bridges computer architecture, embedded systems design, and biomedical applications, with office located at ELG 136 in Lausanne, Switzerland. Dr. Schiavone's research focuses on ultra-low-power computing systems, particularly RISC-V architectures and TinyML applications for edge devices. His work develops open-source hardware platforms like X-HEEP and HEEPOCRATES that enable energy-efficient AI at the edge, with applications spanning biomedical monitoring, neural interfaces, and wearable computing. He explores innovative hardware-software co-design approaches to overcome energy constraints in resource-limited environments. His recent publications reveal a consistent research trajectory centered on open, configurable computing platforms for specialized applications. The work spans from fundamental RISC-V architecture improvements (ARCANE, e-GPU) to application-specific implementations for biomedical contexts (BiomedBench, neural interfaces). A strong emphasis on energy efficiency permeates all his research, whether through novel arithmetic approaches (Posit), system architecture (near-memory computing), or specialized accelerators (Strela, Quadrilatero). Lecturer, School of Computer and Communication Sciences (IC) Scientist, Embedded Systems Laboratory (ESL), School of Engineering (STI) Scientist, PAT Administration, School of Engineering (STI) Dr. Schiavone teaches courses on hardware compilation, presenting algorithms and methods for transforming hardware description languages into optimized circuit implementations. His Embedded Systems Laboratory work places him at the forefront of developing practical, open-source solutions for next-generation computing challenges in energy-constrained environments.

• Natural Language Processing
• Question Answering
• Human-AI Collaboration
• +7 more

Jordan Boyd-Graber is a Professor in the Department of Computer Science at the University of Maryland's College of Computer, Mathematical, and Natural Sciences. He serves as a leading researcher in Natural Language Processing with significant contributions across multiple NLP subfields. His work bridges theoretical advances with practical applications requiring human-AI collaboration. His research interests span Natural Language Processing , Question Answering systems , Human-AI collaboration , Machine Translation , and Topic Modeling . He focuses on developing systems that work effectively with humans rather than replacing them, emphasizing interpretability and user-centered design. His work often involves creating evaluation frameworks that better capture real-world utility rather than just technical metrics. His publication record shows consistent leadership in the field, with numerous papers at top venues including ACL, EMNLP, and NAACL. Recent work (2023-2024) demonstrates strong engagement with LLMs, human evaluation methodologies, and practical applications in health and translation domains. His research often involves student collaborators, indicating active mentorship. ACL Fellow (2021) Program Chair for ACL 2023 Organizer of prompt hacking competition Leader in human-centered NLP evaluation Boyd-Graber has secured substantial funding for his research, particularly in projects involving human-AI collaboration and question answering systems. His work often involves interdisciplinary teams spanning computer science, linguistics, and domain-specific applications. He has mentored numerous graduate students who have gone on to successful careers in academia and industry. He leads research groups focused on developing interpretable NLP systems that work effectively with humans, particularly in high-stakes domains like healthcare and education. His lab frequently develops novel evaluation methodologies that better capture real-world utility rather than just technical metrics.

Paolo PrandoniView profile

Lecturer

• Signal Processing
• Audio Processing
• Image Processing
• +5 more

Paolo Prandoni is a Lecturer at École Polytechnique Fédérale de Lausanne (EPFL) in the School of Computer and Communication Sciences (IC). He serves as a Scientist in the Audiovisual Communications Laboratory (LCAV) and teaches in the SSC-ENS and SIN-ENS units, focusing on signal processing theory and practical applications in audiovisual communications. He earned his PhD from EPFL after completing all prior education there, driven by childhood fascination with long-distance telephony. His doctoral work established foundations in communication systems that continue to inform his research. Prandoni's research spans audio/image processing, machine learning for media analysis, and DSP education. Key areas include computational photography (e.g., spectral imaging, stained glass rendering), speech quality assessment via transfer learning, music information retrieval (e.g., fingering prediction), and audience analytics through his company Quividi. His work consistently bridges theoretical signal processing with real-world implementation. Recent publications reveal a strategic shift toward machine learning integration in signal processing tasks, particularly non-intrusive speech assessment and lensless imaging reconstruction. Simultaneously, he advances DSP pedagogy through MOOC development and hands-on teaching tools using off-the-shelf hardware, emphasizing accessibility and practical skill development. No scientific awards are documented in the provided materials. He has advised PhD student Thanikachalam Niranjan (thesis: Image Based Relighting of Cultural Artifacts , 2016) and teaches Communication Systems and Computer Science courses. His educational impact extends through the open-access textbook Signal Processing for Communications (2008) and tools like MultiPub for maintainable online classes. Industry engagement includes Quividi co-founding (2006) and ongoing CSO role in attention analytics. As a core LCAV laboratory member, he collaborates on interdisciplinary projects including cultural heritage digitization, embedded signal processing systems, and real-time audience measurement, leveraging EPFL's infrastructure for both academic and commercial applications.

Xuming HeView profile

Associate Professor

• Computer Vision
• Machine Learning
• Artificial Intelligence
• +9 more

Xuming He is an Associate Professor at the School of Information Science and Technology (SIST), ShanghaiTech University, where he leads the PLUS Lab. His research spans computer vision and machine learning with a focus on developing algorithms that operate effectively under limited supervision and evolving data conditions. His core research interests include weakly-supervised and few-shot learning for scenarios with sparse annotations, continual learning frameworks for knowledge retention during sequential task acquisition, semantic segmentation techniques for scene understanding, and multimodal vision-language representations. He emphasizes interpretable machine learning to build transparent AI systems capable of human-understandable reasoning, addressing critical challenges in model trustworthiness and deployment reliability. Recent publications reveal strong trends toward novel class discovery in long-tailed recognition scenarios, physics-informed generative modeling for scientific applications, and robust segmentation under distribution shifts. His work increasingly integrates large language models for multimodal reasoning while maintaining focus on efficiency in resource-constrained environments like robotic grasping and medical imaging analysis. He actively mentors students, having supervised Qian He to PhD completion and Chuanyang Hu to Master's degree in 2023. He welcomes prospective graduate students through ShanghaiTech's Computer Science & Technology program and offers undergraduate research projects requiring minimum six-month commitments. The PLUS Lab under his direction drives innovation in learning under supervision constraints, with recent work spanning medical tumor analysis, cross-view geolocation, photonic computing, and semiconductor design verification. The lab's research bridges theoretical advances with practical applications across healthcare, robotics, and scientific discovery domains.

Jakub MacinaView profile

Research Fellow

• Natural Language Processing
• Learning Sciences
• Dialogue Systems
• +6 more

Jakub Macina is a Doctoral Fellow at the ETH AI Center and a PhD Candidate at ETH Zürich . He works in the intersection of Natural Language Processing and Learning Sciences as part of the Language, Reasoning and Education Lab (led by Prof. Mrinmaya Sachan) and the Professorship for Learning Sciences and Higher Education (led by Prof. Manu Kapur). Forbes 30 Under 30 in Science & Education 2023 Recipient of ETH AI Center Fellowship ( Co-founder of a health-tech startup with seed investment Research Interests : Focus on generative large language models (LLMs), dialogue tutoring systems , pedagogical alignment of AI models, and mathematical reasoning . His work explores: Reinforcement learning for pedagogical steering LLM evaluation frameworks Socratic question generation Stepwise error detection and remediation Student-teacher interaction modeling Publications span top conferences like EMNLP , ACL , NeurIPS , and RecSys , with particular emphasis on educational applications of LLMs and dialogue-based learning systems . Scientific Awards : Forbes 30 Under 30 in Science and Education (2023) 2nd Place in ACM IT SPY Computer Science Master's Thesis Competition (2017) ETH AI Center Fellowship (2021) Leadership & Teaching includes: Managing team of 6 data scientists Teaching Assistant for Machine Learning and NLP courses at ETH Zurich Developing large-scale ML pipelines for recommender systems Open-source contributions to Discourse and Google Summer of Code projects

• Machine Learning
• Augmented Intelligence
• Data Science
• +12 more

Simon Ruffieux is a Senior Researcher and Lecturer at the Department of Computer Science, University of Fribourg, and a member of the Human-IST Institute. He currently leads the HIP-Initiative (Human-IST x SwissPost Initiative) and coordinates academic projects related to Swiss Post. His academic roles include Lecturer and Senior Assistant , reflecting his active engagement in teaching and research. His research focuses on leveraging advanced technologies to support individuals, particularly those with special needs. Key areas include: Machine Learning and Data Science for urban systems (e.g., bike-sharing optimization) Human-Computer Interaction (HCI), especially gesture recognition and multimodal interfaces Augmented and Virtual Reality applications in rehabilitation and assistance Development of smart glasses for visually impaired users Physiological signal analysis for workload classification The 15 most recent publications reveal a strong trend in applying AI and data science to real-world challenges, particularly in assistive technologies and urban mobility. His work often involves interdisciplinary collaboration, integrating computer science with psychology, rehabilitation, and industrial applications. There is a consistent emphasis on user-centered design and real-world usability. Simon Ruffieux has not been mentioned as receiving specific scientific awards in the provided text. He has advised or collaborated with several researchers, including Nicolas Spycher, Samuel Torche, and Nicolas Ruffieux, on projects related to forecasting, AR, and gesture recognition. While no formal grant details are listed, his leadership of the HIP-Initiative suggests involvement in externally funded academic projects. His work is closely tied to the Human-IST Institute, where he contributes to interdisciplinary research in human-centered computing. He is actively involved in research teams focused on assistive technologies, gesture interaction, and data-driven urban solutions. The Human-IST Institute serves as the primary hub for his collaborative efforts, particularly through the HIP-Initiative with Swiss Post.

David AtienzaView profile

Professor

• Embedded Systems
• Low-Power Computing
• Edge AI
• +3 more

David Atienza is a Professor in the Department of Electrical Engineering at the School of Engineering, Swiss Federal Institute of Technology in Lausanne (EPFL), renowned for pioneering embedded systems education and research in ultra-low power computing. His innovative teaching methods, including using Nintendo DS consoles and smartphones to teach embedded systems, earned him the 2015 EPFL Teaching Award in Electrical Engineering. His research focuses on Embedded Systems , Edge AI , and Wearable Healthcare , with breakthroughs in energy-efficient hardware-software co-design for biomedical applications. Key contributions include open-source platforms like X-HEEP and HEEPocrates for ultra-low power edge computing, and frameworks like SzCORE for seizure detection benchmarking. His work bridges computer architecture with real-world healthcare challenges, emphasizing privacy-preserving algorithms and sustainable computing. Recent publications (2023-2025) reveal a dominant trend toward biomedical edge AI and sustainable computing , with 70% of articles targeting healthcare wearables (seizure detection, cough monitoring) and 30% addressing energy efficiency in data centers and edge devices. His research consistently integrates open-hardware principles (RISC-V) with novel algorithm-hardware co-design. Awards include: 2015 EPFL Teaching Award in Electrical Engineering section While specific advising details are unreported, his extensive publication record and leadership in multi-partner projects like Sustainable Textile Electronics (STELEC) indicate active graduate supervision and significant research funding. His group develops open-source hardware frameworks used globally in academia and industry. He leads the Embedded Systems Laboratory at EPFL, driving projects in ultra-low power RISC-V architectures, biomedical wearables, and sustainable computing. Current initiatives include carbon-aware data center frameworks and multi-modal health monitoring systems deployable on commercial wearables.

Helen OleynikovaView profile

Lecturer

• Robotics
• Autonomous Systems
• Micro-Aerial Vehicles (MAVs)
• +12 more

Helen Oleynikova is a Lecturer at the Department of Mechanical and Process Engineering at ETH Zürich, where she is part of the Autonomous Systems Lab. She works on the intersection of perception and planning, particularly for micro-aerial vehicles. Her research focuses on real-time onboard mapping, planning, and localization using visual-inertial systems and signed distance fields. Research Interests: Helen's work spans robotics, autonomous systems, and computer vision, with a focus on enabling safe and efficient navigation in complex environments. She specializes in visual-inertial odometry, SLAM, 3D mapping using signed distance fields, and real-time path planning for MAVs. Her projects often involve embedded systems and FPGA-based high-speed vision for obstacle avoidance. Publication Trends: Her recent publications (2023–2019) show a consistent focus on real-time, onboard algorithms for autonomous navigation. Key themes include signed distance function maps, collision-free motion generation, global localization, and efficient exploration. She frequently publishes in top-tier robotics conferences such as ICRA and IROS, and journals like IEEE RA-L and Journal of Field Robotics. Professional Experience: Senior Researcher, Autonomous Systems Lab, ETH Zürich Senior Software Engineer, Isaac 3D Perception, Nvidia Senior Scientist, Microsoft Mixed Reality and AI Lab, Zürich Software Engineer, Google (StreetView) Contributor, Willow Garage (ROS, TurtleBot Arm) Education: PhD in Robotics, ETH Zürich (2019) MSc in Robotics, ETH Zürich BSc in Robotics, Olin College of Engineering (2011) Advising and Grants: While no formal students are listed, she has collaborated extensively with researchers at ETH Zürich and industry labs. Her work has been supported through institutional affiliations and industry research roles. She has contributed to open-source robotics software, particularly in ROS-based systems for manipulation and navigation. Labs and Teams: Helen is a key member of the Mobile Manipulation team at the Autonomous Systems Lab at ETH Zürich. She has also been involved in projects at Nvidia, Microsoft, Google, and Willow Garage, focusing on real-world deployment of autonomous systems.

• Communication Security
• Embedded Systems Security
• Design of Embedded Systems
• +4 more

Alberto Ferrante is a Lecturer and Researcher at the Faculty of Informatics of the Università della Svizzera italiana (USI), affiliated with the IDSIA (Dalle Molle Institute for Artificial Intelligence) USI/SUPSI. His work bridges cybersecurity, embedded systems, and AI applications, particularly in IoT and pest control. He holds a PhD from Università degli Studi di Milano (2006) and an MSc from Politecnico di Milano (2002). Research Interests: Ferrante focuses on Secure communication protocols and embedded systems security Malware detection and resource-optimized hardware solutions Machine learning applications for IoT, including agricultural pest monitoring and healthcare diagnostics Cyber-physical systems design and security-enhanced embedded systems His publications emphasize practical implementations, such as AI-driven UAVs for pest control and low-power drone systems for environmental monitoring. He actively contributes to tech transfer projects with industry partners and participates in major conferences like ICC and Globecom as a TPC member. He teaches the Master’s course Edge Computing in the IoT and collaborates on hardware-software co-design for security-critical systems. His work often integrates real-world constraints like energy efficiency and computational resource limitations. Key contributions include frameworks for dynamic security adaptation in wireless sensor networks and hardware-accelerated security for embedded systems.

• Computer Science
• Electrical Engineering
• Embedded Systems

Solal Pirelli serves as a Lecturer at the Swiss Federal Institute of Technology Lausanne (EPFL), affiliated with the School of Computer and Communication Sciences (IC) and the Institute of Electrical Engineering (SIN). His primary responsibilities center on teaching within the Computer Science curriculum, specifically through the Embedded Systems Laboratory (ENS) under SIN-ENS. His academic focus spans Computer Science and Electrical Engineering, with specialized expertise in embedded systems development, hardware-software co-design, and real-time computing. This specialization aligns with the research priorities of EPFL's Embedded Systems Laboratory, which emphasizes low-power architectures, cyber-physical systems, and IoT security frameworks. No scientific awards or honors were documented in the available institutional records. Professional mentorship activities and research grant involvement are not explicitly referenced in current administrative profiles. His appointment status as an "Invited guest" suggests a non-permanent teaching role without indication of doctoral student supervision. He operates within the Embedded Systems Laboratory (ENS) at SIN, contributing to EPFL's integrated circuit design initiatives and embedded computing education programs through the Computer Science teaching division.

Ivan De Oliveira NunesView profile

Assistant Professor

• Security & Privacy
• Computer Networking
• Embedded Systems
• +6 more

Prof. Ivan De Oliveira Nunes is an Assistant Professor of Cybersecurity at the University of Zurich (UZH), affiliated with the Department of Informatics (IfI) and the Digital Society Initiative (DSI). Previously, he held a similar position at the Rochester Institute of Technology (2021–2025). He earned his Ph.D. from the University of California, Irvine (UCI) in 2021, advised by Prof. Gene Tsudik, and was part of the Sprout Lab. His academic journey includes a B.S. in Computer Engineering from the Federal University of Espirito Santo (Brazil) and an M.Sc. in Computer Science from the Federal University of Minas Gerais (Brazil). His research focuses on Security & Privacy, Computer Networking, Embedded Systems, and Applied Cryptography. Key areas include IoT/CPS Security, System Security, and Provable Execution in resource-constrained systems. Notable contributions include work on remote attestation, secure software updates, and formally verified security protocols. Prof. Nunes advises multiple Ph.D., M.Sc., and undergraduate students, including current Ph.D. candidates Antonio Joia Neto, Liam Tyler, and Alexandra Lengert. Former advisees include Adam Caulfield (now at U. of Waterloo) and Spencer Roth (M.Sc. 2023). He leads the SPINS Research Group, exploring secure systems design, and has secured grants such as the CRII: SaTC grant (2023). His work bridges theoretical foundations and practical implementations, emphasizing real-world applicability in embedded and IoT systems.

• IoT (Internet of Things)
• Platform Business Models
• Data Analytics
• +5 more

Felix Wortmann is a Professor and Senior Lecturer of Information and Operations Management at the University of St. Gallen (HSG), affiliated with the ITEM-HSG Institute. His research focuses on IoT platforms, platform business models, data ecosystems, and the integration of health informatics with automotive systems. He explores topics such as energy efficiency in smart buildings, machine learning applications for driver safety, and blockchain-based decentralized systems. Research Interests: IoT and Platform Ecosystems Data-Driven Business Models Health Technology Integration in Vehicles Smart Energy Systems and Sustainability Machine Learning for Real-Time Decision Making Blockchain and Decentralized Finance Key Contributions: His work bridges technical innovation with business strategy, addressing challenges like peer-to-peer energy trading, non-invasive health monitoring in vehicles, and transforming traditional product companies into service-driven enterprises. Grants & Collaborations: Collaborations include automotive manufacturers, energy companies, and healthcare institutions. Current research focuses on software-defined vehicles, circular economy catalysts, and AI-driven health interventions.

• Security
• Embedded Systems Security
• Cyber-Physical Systems Security
• +13 more

Francesco Regazzoni is a Senior Researcher at the Faculty of Informatics, Università della Svizzera italiana (USI), and affiliated with the Dalle Molle Institute for Artificial Intelligence (IDSIA USI-SUPSI). His work bridges embedded systems, cybersecurity, and artificial intelligence, with a focus on securing hardware and cyber-physical systems. Research Interests: His expertise spans embedded and cyber-physical systems security, side-channel attacks, post-quantum cryptography, hardware trojans, random number generators, and the security of AI and approximate computing. He also contributes to hardware/software co-design and operating systems security. The analysis of his recent publications reveals a consistent focus on hardware and system-level security , particularly in resource-constrained environments like IoT and embedded devices. His work integrates machine learning for attack detection and applies formal methods to ensure trust in hardware. A growing emphasis is placed on securing AI systems from physical and adversarial threats. Scientific Contributions: Over 100 peer-reviewed publications One book and one patent Extensive international collaboration (Belgium, Netherlands, USA, Switzerland, Singapore) Advising and Grants: While specific advisees and grants are not listed, his leadership in funded research projects and involvement with ALaRI and IDSIA suggest active mentorship and project coordination. His work has been supported by industry (e.g., ST Microelectronics, HP), the Swiss National Foundation, and the European Union. Labs and Teams: He is part of the Graph Machine Learning Group (GMLG) at IDSIA, which evolved from the Advanced Learning and Research Institute (ALaRI). This group focuses on graph machine learning, reinforcement learning, and dynamical systems, particularly in non-stationary environments.