Prof. Hedan Bai is an Assistant Professor at the Department of Materials, ETH Zürich, specializing in robotics materials, bio-inspired systems, and soft robotics. Their research focuses on developing advanced materials for sensing, energy-efficient systems, and biomedical applications. Notable projects include the SmartSuit architecture for space exploration and self-healing optical sensors for soft robots. Research interests span bioelectronics, stretchable sensors, haptic interfaces, and biomimetic materials. Bai's work integrates material science with robotics to create adaptive, sustainable, and intelligent systems. Key areas include wireless implants for neuromodulation, environmental-responsive textiles, and energy-harvesting devices. Publications highlight innovations in optical waveguides, self-healing materials, and wearable technologies. Their interdisciplinary approach bridges robotics, biomedical engineering, and aerospace applications. No formal awards are listed, but contributions to next-gen materials for robotics are prominent. Advising and grants are not detailed in the text, but Bai's lab focuses on projects like SmartSuit for extravehicular activities and synthetic afferent neural networks. Collaborations likely involve aerospace and biomedical sectors.
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.
Pierre Vandergheynst is a Full Professor at the Swiss Federal Institute of Technology Lausanne (EPFL) in the Department of Electrical Engineering, with a courtesy appointment in Computer and Communication Sciences. He serves as EPFL’s Vice-Provost for Education since 2015 and leads the Signal Processing Laboratory 2 (LTS2). His research spans harmonic analysis, sparse approximations, mathematical data processing, and applications in signal/image processing, computer vision, machine learning, and graph-based data analysis. PhD in Mathematical Physics (1998), Université catholique de Louvain Postdoctoral Researcher at EPFL (1998-2001) Assistant Professor at EPFL (2002-2007) His research explores geometry/symmetry in high-dimensional data, redundant dictionaries for dimensionality reduction, and computational harmonic analysis on manifolds. Recent work focuses on protein structure modeling, geometric deep learning, and graph-based signal processing. Key article trends include graph neural networks for protein analysis, geometric deep learning in neuroscience, and structured knowledge priors in neural models. His 2023-2025 publications emphasize interpretable AI, long-range dependencies in graphs, and molecular representation learning. Scientific Awards: IEEE Signal Processing Magazine Best Paper Award (2023) Signal Processing Society Best Paper Award (2022) Apple ARTS Award (2007) De Boelpaepe Prize, Royal Academy of Sciences of Belgium (2009-2010) He has supervised over 30 PhD theses and contributed to foundational work in graph signal processing, compressive sensing, and geometric deep learning. His lab develops tools for data science on non-Euclidean structures, with applications in medicine, astronomy, and wireless systems.
Prof. Roger Wattenhofer is a Full Professor at the Department of Information Technology and Electrical Engineering, ETH Zurich, Switzerland, and Deputy head of the Computer Engineering and Networks Lab. He holds a doctorate in Computer Science from ETH Zurich (1998) and has held positions at Brown University and Microsoft Research before returning to ETH. His research focuses on distributed computing, wireless networks, and algorithmic systems design, with contributions to Byzantine agreement protocols, blockchain technologies, and neural network architectures. He teaches courses such as Distributed Systems and Computational Thinking. Education: Ph.D. in Computer Science (ETH Zurich, 1998). Research interests include distributed systems, network algorithms, and the intersection of machine learning with distributed computing. His work spans both theoretical foundations and practical implementations, addressing challenges in fault tolerance, consensus mechanisms, and algorithmic efficiency. Recent publications explore topics like adversarial robustness in voting systems, privacy in reinforcement learning, and generative music models. He actively contributes to open-source frameworks and benchmarks for neural algorithmic reasoning.
Prof. Raffaello D'Andrea is a Full Professor at ETH Zürich's Department of Mechanical and Process Engineering, affiliated with the Institute for Dynamic Systems and Control. His research focuses on bridging digital and physical worlds through robotics, control systems, and autonomous systems. He has pioneered work in aerial robotics, swarm systems, tactile sensing, and soft robotics. His philosophy emphasizes solving 'easy' problems with scalable, robust solutions, prioritizing simplicity and replicability. Key research areas include UAV navigation, distributed control, tactile sensor design, and fault-tolerant systems. He has founded multiple organizations and led roles as CTO/CEO, emphasizing cross-disciplinary innovation. His work has commercial applications in logistics, healthcare, and automation, driven by a belief in technology's role in improving human life. Notable projects include the Cubli robotic cube, aerial vehicle swarms, and optical tactile sensors for robotics. His lab emphasizes collaboration and team leadership, aiming to translate theoretical insights into practical, scalable technologies. Scientific awards: None explicitly listed in the provided texts. Advising and grants: No students listed in the provided texts; grants information not detailed. Labs/Teams: Leads research at ETH Zurich's Institute for Dynamic Systems and Control, collaborating with industry and academic partners globally.
Michael Gastpar is a full Professor at École Polytechnique Fédérale de Lausanne (EPFL) in the School of Computer and Communication Sciences, where he leads the Laboratory for Information in Networked Systems (LINX). He previously held faculty positions at the University of California, Berkeley (2003-2011, earning tenure in 2008) and Delft University of Technology. His research spans information theory, signal processing, communications, and systems neuroscience. His research interests focus on network information theory and related coding and signal processing techniques, with applications to sensor networks and neuroscience. Recent work demonstrates a strong shift toward exploring the theoretical foundations of modern machine learning, particularly investigating transformer architectures from an information-theoretic perspective. His research group at EPFL explores how information theory principles can provide fundamental limits and novel approaches for contemporary machine learning problems. His recent publications reveal a clear trend toward bridging classical information theory with modern machine learning. The 15 most recent papers show increasing focus on theoretical analysis of transformers, rate-distortion frameworks for language models, universal prediction methods, and applications of information measures to machine learning theory. This represents a strategic evolution from his earlier work on sensor networks and physical-layer network coding toward foundational questions in artificial intelligence. Scientific Awards: IEEE Fellow 2013 Communications Society & Information Theory Society Joint Paper Award Information Theory Society Distinguished Lecturer (2009-2011) ERC Starting Grant (2010) Okawa Foundation Research Grant (2008) NSF CAREER award (2004) 2002 EPFL Best Thesis Award Professor Gastpar has advised over 20 PhD students who have gone on to successful careers in both academia and industry. His research has been generously supported by major grants including an ERC Starting Grant "ComCom" (2011-2016) and ongoing support from the Swiss National Science Foundation. He has served in significant editorial roles, including as Associate Editor for Shannon Theory for the IEEE Transactions on Information Theory (2008-11) and as Technical Program Committee Co-Chair for the IEEE International Symposium on Information Theory in 2010 and 2021. He leads the Laboratory for Information in Networked Systems (LINX) at EPFL, which brings together researchers working at the intersection of information theory, machine learning, and networked systems. The lab maintains strong connections with both theoretical research communities and practical applications in communications and neuroscience.
Andreas Peter Burg is a Tenured Associate Professor at the École Polytechnique Fédérale de Lausanne (EPFL), where he leads the Telecommunications Circuits Laboratory (TCL) within the School of Engineering. He holds multiple academic and administrative roles at EPFL including Associate Professor in Teaching (SEL, EDMI, EDEE), Director of SEL Management, and Member of the Doctoral Program Committee for Electrical Engineering. Dr. Burg received his Dipl.-Ing. degree in 2000 and Dr. sc. techn. degree in 2006 from ETH Zurich. His academic career includes positions as SNF Assistant Professor at ETH Zurich (2009-2011) before joining EPFL in January 2011 as a Tenure Track Assistant Professor, where he was promoted to Tenured Associate Professor in June 2018. His research focuses on circuits and systems for telecommunications , with particular expertise in silicon implementation of communication technologies, communication algorithms optimization for hardware, low-power VLSI signal processing, and digital integrated circuits. His work bridges theoretical communication concepts with practical circuit implementations, addressing challenges in wireless and wired communication systems. His recent publications (2024-2025) demonstrate a strong focus on next-generation communication technologies including 6G systems, advanced error correction coding, wireless sensing applications, and ultra-low power circuit design. These works span multiple subfields from LDPC and polar code decoding to RF signal processing and machine learning applications in wireless systems. Willi Studer Award (2000) ETH Medal for diploma thesis (2000) ETH Medal for Ph.D. dissertation (2006) Swiss National Science Foundation Assistant Professorship grant (2008) Dr. Burg has been involved in the development of more than 25 ASICs throughout his career and co-founded Celestrius, an ETH spinoff in MIMO wireless communication. His laboratory work focuses on practical implementations of communication algorithms with emphasis on power efficiency and hardware optimization. Current research directions include 6G technologies, wireless sensing applications, and novel error correction techniques for next-generation communication systems.
Dr. Chenhao Chu is a Professor at ETH Zürich, holding the Professur für Elektronik (Professorship for Electronics). He specializes in RF/mm-Wave circuits, AI-driven design methods, and advanced power amplification technologies. His research focuses on energy-efficient, wideband systems, antenna-in-package solutions, and GaN-based applications for 6G and beyond. Education: Ph.D. in Electronic Engineering, University College Dublin (2022) M.Sc. in Electronic Information Engineering, City University of Hong Kong (2017) Research Interests: His work bridges AI and hardware design, emphasizing reconfigurable circuits , high-linearity power amplifiers , and mm-Wave phased arrays . Key areas include: AI-assisted rapid design synthesis III-V/Si co-design for mm-Wave Efficient antenna integration Dynamic load modulation techniques Awards: Award-winning researcher with distinctions including the First Place Best Student Paper Award (2022 Royal Irish Academy Colloquium) and multiple HEPA-SDC Competition Awards (2021-2022). Recognized for innovations in PA efficiency and design automation. Advising & Grants: Leading projects on 6G PA architectures and AI-driven RF design. Active in IEEE with contributions to conferences like IMS and ARFTG. No explicitly stated grants mentioned but widely cited in industry-academia collaborations. Labs & Teams: Associated with ETH Zürich's Electronics Laboratory, focusing on next-generation wireless systems. Collaborates internationally on 5G/6G infrastructure and mm-Wave innovations.
Anja Skrivervik is a Full Professor at École Polytechnique Fédérale de Lausanne (EPFL), holding multiple key academic positions across the institution. She serves as a Full Professor in the School of Engineering (STI) within the Electromagnetics and Antennas group, as Director of the Electrical Engineering Doctoral Program, and as a Full Professor in multiple teaching units including EDEE, SEL, and EDMI. Her extensive institutional affiliations demonstrate her significant leadership role within EPFL's engineering and educational frameworks. Professor Skrivervik's research spans multiple cutting-edge domains in electromagnetic engineering with a particular focus on antenna design for specialized applications. Her work prominently features implantable medical devices, where she develops antennas and wireless power transfer systems for deep-body bioelectronics. She has made significant contributions to mm-Wave technology, particularly in multibeam systems for 5G applications and wireless power transfer. Her research also encompasses tissue phantom development for electromagnetic characterization, with recent work on biodegradable alternatives to traditional materials. The interdisciplinary nature of her work bridges electrical engineering, biomedical applications, and materials science. An analysis of her recent publications (2023-2025) reveals a consistent research trajectory focused on solving practical challenges in antenna design for constrained environments. Her work shows particular strength in optimizing antenna performance for implantable medical devices, where size, efficiency, and biocompatibility present unique challenges. She has developed novel approaches to beamsteering, mutual coupling reduction, and RF radiation modeling specifically tailored for medical applications. Her contributions to tissue phantom development represent an important methodological advancement for testing and validating implantable devices. As Director of the Electrical Engineering Doctoral Program and through her multiple professorial appointments, Professor Skrivervik plays a central role in shaping graduate education at EPFL. Her leadership extends to serving on the Doctoral Commission, where she helps set standards and policies for doctoral education across the institution. While specific grant information isn't detailed in the available materials, her extensive publication record across top journals and conferences suggests successful funding of her research activities. Her work appears to be conducted within EPFL's Electromagnetics and Antennas group (SCI STI AS), which likely houses specialized laboratories for antenna measurement, electromagnetic simulation, and biomedical device testing. The focus on tissue phantoms and implantable devices suggests dedicated facilities for biomedical electromagnetic testing, while her mm-Wave and 5G research indicates capabilities in high-frequency measurement and characterization.
Yujia Zhang is a Tenure Track Assistant Professor at the School of Engineering , École Polytechnique Fédérale de Lausanne (EPFL), leading the Laboratory for Bio-Iontronics (BION) since January 2025. His work focuses on developing iontronic biointerfaces and hybrid intelligent systems for biomedical applications. Academic Affiliations: EPFL School of Engineering, STI-SMT SMT-ENS PhD program committee Research Themes: Droplet-based iontronics, synthetic tissues, advanced manufacturing Research Trends from his publications emphasize microscale droplet iontronics , soft energy systems , and biohybrid interfaces , with applications in neurostimulation , tumor modeling , and biomedical devices . Scientific Awards : 2023: Early-career Research Scientist Representative, UK Parliamentary & Scientific Committee 2022: Excellent Doctoral Dissertation, Chinese Academy of Sciences 2021: Outstanding Doctoral Thesis, Chinese Institute of Electronics 2020: Special Prize for President Scholarship, Chinese Academy of Sciences Academic Contributions include mentoring PhD students and teaching microfabrication technologies. His lab develops 3D-printed synthetic tissues and droplet networks for interactive biological communication.
Prof. Taekwang Jang is an Associate Professor at the Department of Information Technology and Electrical Engineering, ETH Zürich. He leads the Energy Efficient Circuits and IoT Systems Group, focusing on analog and mixed-signal circuits for energy-constrained applications such as wireless sensor nodes and biomedical electronics. His research includes sensor interfaces, energy harvesters, power converters, and communication systems. He holds 15 patents and has authored over 80 peer-reviewed publications. Key awards include the 2024 IEEE Solid-State Circuits Society New Frontier Award and the SNSF Starting Grant. Educations: B.S. and M.S. in Electrical Engineering, KAIST (2006, 2008) Ph.D. in Electrical Engineering, University of Michigan (2017) Affiliations: Chair of IEEE Solid-State Circuits Society, Switzerland Chapter Associate Editor for Journal of Solid-State Circuits (JSSC) Research Interests: His work spans energy-efficient integrated circuits, biomedical interfaces, and IoT systems. Notable contributions include low-power keyword spotting ICs, ultra-low-noise amplifiers, and neural stimulation systems. He emphasizes practical applications in healthcare and wearable devices. Awards: 2024 IEEE Solid-State Circuits Society Distinguished Lecturer 2022 IEEE ISSCC Jan Van Vessem Award 2009 IEEE CAS Guillemin-Cauer Best Paper Award Advising & Grants: Supervises a team of researchers and has secured grants including the SNSF Starting Grant. His lab collaborates with institutions like the Competence Center for Rehabilitation Engineering and Science. Labs & Teams: Leads the Energy-Efficient Circuits and Intelligent Systems group at ETH Zurich, focusing on interdisciplinary projects at the intersection of circuits, systems, and biomedical engineering.
Silvia Santini is an Associate Professor at the Faculty of Informatics of USI since 2016, leading the People-Centered Computing Lab. Previously, she held roles at TU Dresden (2014–2016) and TU Darmstadt (2011–2014), focusing on embedded systems and wireless sensor networks. She completed her PhD at ETH Zurich in 2009 and earned a Telecommunication Engineering degree from Sapienza University of Rome (2004). Her research centers on wearable computing, human-computer interaction, and sensor networks applied to health monitoring, activity recognition, and physiological signal analysis. Notable projects include BiHeartS (bilateral heart rate monitoring) and studies on sleep quality using wearable devices. Key achievements include being ranked among the World’s top 2% of Scientists (2022) and securing grants for projects like XAI-FinCrime. She advises on PhD and postdoc positions, such as the 2025 Innosuisse-funded project in explainable AI for financial crime detection. Labs/Teams: People-Centered Computing Group (USI), collaborating on cross-disciplinary projects in pervasive computing and healthcare technology.
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.
Daniele Allegri is a Professor of 'Programmable and Integrated Microelectronic Systems' at the University of Applied Sciences and Arts of Southern Switzerland (SUPSI), leading the Department of Innovative Technologies. He serves as Director of the Institute for Systems and Applied Electronics (ISEA) since 2023 and previously held roles including Head of the 'Digital Electronics, Microelectronics, and Bioelectronics' research area (since 2019). His professional career spans engineering roles at Mandozzi Elettronica SA (1998–2014) and academic research at SUPSI (2014–present). Education: Ing.-Dipl. in Electronic Engineering from ETH Zurich (1998), PhD in Microelectronics from the University of Pavia (2017). Research Focus: Development of integrated circuits and embedded systems for biomedical applications, mixed analog-digital circuits, FPGA systems, signal processing, imaging technologies, and Edge AI. His work addresses clinical needs like real-time hydration monitoring for dialysis patients and advanced solar telescope instrumentation. Key Projects: Probing semiconductor rad-hardness via ions/lasers Next-gen tunnel safety sensors High-precision 3D imaging systems Cardio Pulmonary Rescue Support System Wireless pulmonary edema sensing networks Publications emphasize biomedical instrumentation and astrophysical engineering. No scientific awards explicitly listed in text, but contributions suggest strong peer recognition. Advising activity details not provided in source text. Current affiliations include leadership roles at SUPSI's ISEA and teaching responsibilities in multiple electronics-related courses.
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.
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