Dr. Anna Scampicchio is a Researcher at ETH Zürich, affiliated with the Professorship for Intelligent Control Systems. Her work focuses on advancing control theory and machine learning methodologies, particularly in data-driven control systems, model predictive control, and Bayesian learning techniques. She has contributed to the development of algorithms for system identification, optimal control, and robust control strategies. Her research integrates theoretical analysis with practical applications in robotics and dynamical systems. Key publications include studies on kernel methods, randomized signatures for learning dynamics, and Bayesian multi-task learning approaches. Dr. Scampicchio holds a Ph.D. (implied by her title) and has published extensively in top-tier journals and conferences, addressing challenges in intelligent control systems and machine learning integration.
Dr. Evren Mert Turan is a Lecturer at ETH Zürich's Department of Energy and Process Systems Technology. His academic background includes a Bachelor's and Master's in Chemical Engineering from the University of Cape Town, followed by a PhD in Process Systems Engineering at the Norwegian University of Science and Technology. His research focuses on integrating machine learning and optimization techniques to address decision-making challenges under uncertainty in energy systems and process engineering. Evren's expertise spans model predictive control, real-time optimization, and data-driven approaches for complex systems. He has contributed to advancements in semi-infinite programming, feedback control policies, and steady-state detection algorithms. His work emphasizes practical applications in sustainable energy systems and industrial process optimization. Key research trends include the development of neural network-based control strategies, convex optimization methods for reduced computational complexity, and experimental validation of novel algorithms. His publications highlight interdisciplinary approaches blending machine learning with traditional engineering methodologies. Evren currently teaches the course 'Introduction to Modeling and Optimization of Sustainable Energy Systems' and actively engages in collaborative research at ETH Zürich. His contributions to scientific machine learning aim to enhance robustness and reliability in dynamic systems analysis.
Stefan Gysin, MD, PhD, MME, is a faculty member at the University of Lucerne’s Faculty of Health Sciences and Medicine . As Head of the Joint Medical Master’s Program (University of Lucerne and Zurich), he focuses on curriculum design, academic quality assurance, and interprofessional education. His work includes coordinating master’s theses, advising students, and organizing academic events.
Andreas Fischer is an Ordentlicher Professor at the Fribourg School of Engineering and Architecture (HES-SO), specializing in Pattern Recognition, Machine Learning, and Document Analysis. His research focuses on handwriting recognition, graph-based methods, and applications in cultural heritage preservation and medical imaging. Education: BSc in Computer Science from Fribourg School of Engineering and Architecture Research Interests: Graph Neural Networks for automata universality analysis Hybrid systems for Vietnamese stele keyword spotting Medical image analysis (colorectal cancer, tumor budding) Large language models for post-OCR correction Key Projects: TAINA Technology (handwriting validation for tax forms) Swisscom (Swiss German to High German translation) Hasler Foundation (Vietnamese stele graph-based analysis) Publications: Over 30 peer-reviewed articles in top journals/conferences (IEEE Access, Medical Image Analysis, Pattern Recognition, etc.), with focus on graph-based methods, handwriting recognition, and medical applications. Grants & Roles: Principal Applicant for multiple industry-funded projects (TAINA, Swisscom) Co-developer of DIVA-DAF deep learning framework
Zahno Silvan is a Professor at HES-SO Valais-Wallis - Haute Ecole d'Ingénierie, affiliated with the School of Engineering and IT and the Department of Industrial Systems. His work focuses on advancing Industry 4.0, Data Science, and embedded technologies. He leads and collaborates on innovative projects addressing challenges in manufacturing automation, predictive maintenance, and teleoperation. Research interests include FPGA-based systems, machine learning integration for quality control, and IoT-driven industrial solutions. Key projects include the PmPm framework for interactive ML in manufacturing, AT-Com for teleoperated construction machinery, and collaborations with companies like Constellium and Eversys to reduce production costs and improve efficiency. He has secured significant funding, including Innosuisse grants and industry partnerships, totaling over CHF 3 million across ongoing and completed projects. His contributions span academic-industry collaborations, emphasizing digital transformation and sustainable manufacturing practices.
Dr. Xiang-Zhao Kong is a Lecturer at the Institute of Geophysics, ETH Zurich, within the Department of Earth and Planetary Sciences (D-EAPS). He holds a PhD in Environmental Engineering from ETH Zurich (2010), where he was awarded the ETH Medal for his dissertation. His career includes postdoctoral research at the University of Minnesota and a Research Fellowship at the University of Queensland before returning to ETH Zurich in 2015. His research focuses on geothermal energy, flow and transport processes in porous media, reactive transport modeling, and subsurface engineering. Key areas include fractured formations, geothermal reservoir optimization, and CO₂ sequestration. He employs advanced computational methods like lattice-Boltzmann solvers and machine learning for subsurface flow modeling and reservoir characterization. Dr. Kong’s work bridges experimental and theoretical approaches, with notable contributions to mineral precipitation dynamics, fluid-rock interactions, and phase transition fracturing. His publications span geothermal systems, carbon capture, and subsurface energy storage. Recent efforts emphasize de-risking CO₂-Plume Geothermal (CPG) technologies and advancing fracture modeling via neural networks. Awards: ETH Medal for PhD Dissertation (2011) Teaching: Leads the 'Groundwater' course (Autumn Semester 2025).
Dr. Andreas Lichtenberger is a Lecturer at the Department of Physics at ETH Zürich. His research focuses on advancing physics education through innovative technologies like augmented reality and exploring effective teaching methodologies. His work emphasizes conceptual understanding in electromagnetism, kinematics, and vector fields, with a particular interest in formative assessment strategies and the role of multiple external representations (MER) in learning. Key research areas include: Technology-enhanced learning (AR/VR in physics education) Cognitive aspects of physics concept acquisition Gender differences in representational competence Experimental validation of educational interventions His publications highlight contributions to: Designing AR tools for Lorentz force visualization Concreteness fading pedagogy in secondary physics Eye-tracking analysis of student problem-solving Development of competency assessment inventories No scientific awards are explicitly listed. He collaborates widely with educational researchers and physicists, contributing to both theoretical and applied aspects of STEM education.
Prof. Christoph Studer is a Full Professor of Integrated Information Processing at ETH Zurich's Department of Information Technology and Electrical Engineering. He leads the Integrated Systems Laboratory and directs SwissChips. His research focuses on wireless communication, machine learning, signal processing, and hardware-efficient algorithms, with applications in B5G systems, sensing-communication integration, and low-power signal processing. He holds a Ph.D. and M.S. from ETH Zurich (2009 and 2006) and has held academic positions at Cornell University before returning to ETH in 2020. Notable honors include the NSF CAREER Award (2017), ETH Medal for Doctoral Dissertation (2011), and multiple teaching awards. Education: M.S. and Ph.D. in Information Technology and Electrical Engineering, ETH Zurich (2006, 2009) Visiting Researcher, Stanford University (2005) Research Interests: Develops algorithms and hardware for high-throughput, low-power wireless systems. Key areas include: B5G multi-antenna systems and simultaneous sensing-communication (SISCO) Analog-to-feature (A2F) conversion for low-power signal classification Hardware-software co-design for efficient microchip integration Publications: Focus on channel charting, jammer mitigation, and deep learning for communication. Recent work includes CSI2Vec, jammer-resilient synchronization, and distributed MIMO systems. Awards: US NSF CAREER Award (2017) Michael Tien Teaching Award (2016) ETH Medal for Doctoral Thesis (2011) Labs/Teams: Leads the Integrated Systems Laboratory at ETH and directs SwissChips, a national initiative for integrated circuit development.
Dr. Chakaveh Ahmadizadeh is a Lecturer at the Department of Health Sciences and Technology, ETH Zürich. Her research focuses on biomedical and mobile health technology, particularly in wearable sensing systems for physiological and movement monitoring. Position: Lecturer Institution: ETH Zürich Research Interests: Wearable sensors, machine learning for health, smart textiles, physiological signal processing Recent work highlights include: Textile-based capacitive strain sensors for robust hand gesture recognition Multi-plane knee angle monitoring systems Machine learning integration for FMG signal analysis Passive wireless textile sensors for movement tracking Sweat resistance testing for wearable health applications
Cesare Alippi is a Full Professor at the Faculty of Informatics, Università della Svizzera italiana (USI), and also holds a professorship at Politecnico di Milano, Italy. He serves as a visiting Professor at Guangdong University of Technology (China) and Consultant Professor at Northwestern Polytechnic of Xi'an (China). His academic leadership extends to multiple international institutions where he has served as a visiting researcher including UCL (UK), MIT (USA), ESPCI (France), CASIA (China), A*STAR (Singapore), and University of Kobe (Japan). Professor Alippi's research interests center around graph-based learning, adaptation and learning in non-stationary environments, and intelligence for embedded, cyber-physical systems and IoT. His work bridges theoretical foundations with practical applications in sensor networks, environmental monitoring, and industrial processes. He has established significant research infrastructure including the Wireless Embedded Systems (WEmSy) Lab and the Internet of Things Lab, with notable deployments for marine environment monitoring in Queensland, Australia and the Fiji Islands, as well as rockfall and landslide monitoring systems across Italy and Switzerland. His research output shows a clear evolution toward graph-based deep learning approaches for time series analysis, anomaly detection, and spatiotemporal forecasting, reflecting the growing importance of graph neural networks in handling complex relational data in non-stationary environments. Major Awards: IEEE CIS Enrique Ruspini Meritorious Service Award (2024) IEEE CIS Outstanding Computational Intelligence Magazine Paper Award (2018) Gabor Award from International Neural Network Society (2016) IBM Faculty Award (2013) IEEE Instrumentation and Measurement Society Young Engineer Award (2004) Professor Alippi has held significant leadership roles including Past Board of Governors member of the International Neural Network Society, Past member of the Administrative Committee of the IEEE Computational Intelligence Society, and Past Vice-President for Education of the IEEE Computational Intelligence Society. He has served as Associate Editor for Proceedings of IEEE and several other prestigious journals. His research has been supported through numerous grants including an IBM Faculty Award in 2013 specifically for research on Intelligent Embedded Systems working in non-stationary environments. His research infrastructure includes the Wireless Embedded Systems (WEmSy) Lab and the Internet of Things Lab, with notable deployments including a sophisticated automatic, adaptive, sustainable and reliable wireless monitoring system for marine environments deployed in Queensland, Australia (2007) and under deployment at the Fiji Islands (2014-2015). He has also led several top-world deployments for rockfall and landslide monitoring across Italy and Switzerland since 2010, demonstrating the practical impact of his research in real-world harsh environments.
Prof. Ingo Scholtes is a Full Professor of Machine Learning for Complex Networks at Julius-Maximilians-Universität Würzburg's Center for Artificial Intelligence and Data Science (CAIDAS). He holds a doctorate in computer science and mathematics from the University of Trier and has held roles including SNSF Professor at the University of Zurich, Full Professor at Bergische Universität Wuppertal, and Senior Assistant at ETH Zürich. His research focuses on higher-order graph analytics for temporal networks, machine learning, and computational social science. Education: PhD in Computer Science (University of Trier, Germany), Postdoctoral Research at ETH Zürich (2011–2016), and prior roles at Karlsruhe Institute of Technology and CERN. Research Interests: Machine learning on graphs, temporal network analysis, higher-order network models, and their applications in software engineering and social systems. He develops open-source tools like pathpy and git2net for network analysis. Recent Work: Focuses on causality-aware graph neural networks, temporal graph isomorphism, and network science applications in AI. Recent articles include studies on temporal network dynamics, path prediction, and Bayesian inference of network transitions. Awards: SNSF Professorship (2018), Junior-Fellowship (2014), and German Academic Scholarship Foundation (2004-2005). Active in editorial roles for EPJ Data Science and leadership in GI's Computational Social Science working group.
Dr. Nicolò Pagan is a Postdoctoral Researcher at the Social Computing Group within the Department of Informatics at the University of Zurich. He is also a member of the National Centre of Competence in Research (NCCR) Automation. Pagan holds a Ph.D. from ETH Zurich (2021), an M.Sc. from EPF Lausanne, and a B.Sc. from Politecnico di Torino. His research focuses on AI ethics, fairness in automated decision-making systems, and generative AI applications for health interventions. He has published in top venues like Nature Communications and has supervised multiple student projects on topics like feedback loops in recommendation systems and generative AI modeling. Education: Ph.D. in Automatic Control, ETH Zurich (2021) M.Sc. in Computational Science and Engineering, EPF Lausanne B.Sc. in Applied Mathematics, Politecnico di Torino Research Interests: AI ethics and societal impact Feedback loops in automated systems Generative AI for health interventions Algorithmic fairness in social media Network formation dynamics His recent work explores fairness effects of algorithmic systems and uses LLM-based generative AI to model large-scale human behavior for health campaigns. Over 10 peer-reviewed publications span topics like recommendation systems' societal impact and game-theoretic network analysis. Advising and Grants: Supervised 5+ student projects on topics like opinion dynamics and fairness in ride-hailing systems. Active in NCCR Automation's interdisciplinary research initiatives. Labs/Teams: Member of the Social Computing Group and NCCR Automation, collaborating on projects linking AI ethics to real-world systems.
Prof. Dr. Yulia Sandamirskaya is a faculty member at Zurich University of Applied Sciences (ZHAW) in the School of Life Sciences and Facility Management. She leads the Institute of Computational Life Sciences and is an ORCID-registered researcher with a focus on neuromorphic computing and cognitive systems. Her work bridges computational neuroscience with practical robotics applications. Robotics for dementia care Neuromorphic computing Embodied AI systems Spiking neural networks Industrial force-control systems Human-robot interaction Her recent publications demonstrate expertise in neuromorphic architectures for sensory processing, including visual scene understanding, spectral classification, and real-time control systems. She actively contributes to setting benchmarks in neuromorphic computing and develops practical applications in healthcare robotics. Current projects include RobotCare (service robots for elderly care), Neuromorphic Technology for Embodied AI , and Emerging AI (completed). She collaborates with institutions in Switzerland, UAE, and Italy while working on industrial applications of neuromorphic systems.
Matthias Kurt Muntwiler is a Researcher at the Paul Scherrer Institute (PSI) in the Photon Science Division's Laboratory for X-ray Nanoscience and Technologies (LXN). He manages the PEARL beamline at the Swiss Light Source and is a member of the Swiss Nanoscience Institute, supporting user experiments across Europe. He earned his diploma (2000) and PhD (2004) in experimental physics from the University of Zurich, Switzerland, followed by postdoctoral work at the University of Minnesota under Prof. Xiaoyang Zhu focusing on ultrafast polaron dynamics. Muntwiler specializes in surface and interface science of ultrathin films, organic semiconductors, and two-dimensional materials. His expertise spans photoelectron spectroscopy for chemical analysis, band mapping, atomic structure determination, and dynamic processes, emphasizing structure-property relationships. He also develops software for experiment control and machine learning-based structural modeling. His publication record (2008-2020) reveals consistent focus on advanced synchrotron-based characterization of nanomaterials, particularly using photoelectron diffraction to resolve atomic-scale interfaces in systems like h-BN nanomeshes, metal-organic networks, and ferroelectric materials. As designer and manager of the PEARL beamline, Muntwiler integrates soft X-ray spectroscopy with scanning tunneling microscopy to enable atomic-resolution studies of novel materials and molecular adsorbates.
Fariba Moghaddam is an Ordinary Professor at HES-SO Valais-Wallis - Haute Ecole d'Ingénierie, leading the Power & Control orientation under the Institut Systèmes industriels. She specializes in renewable energy systems, control engineering, and educational technology, with a focus on solar energy optimization, remote laboratories, and sustainable development initiatives in the Global South. Her work spans academic leadership, research, and pedagogical innovation. Education: PhD in Control Engineering from École Polytechnique Fédérale de Lausanne (EPFL), 1996. Additional academic background in electrical and mechanical engineering. Research Interests: Solar tracking systems, photovoltaic-thermal (PVT) integration, machine learning applications in energy systems, remote experimentation platforms, and collaborative educational infrastructure. Recent projects include the Solar Energy Optimization via Remote Platforms and DEAR MENA (Digital Education & Research for MENA). Grants & Projects: Solar Energy Optimization (2022-2026): Swiss National Science Foundation-funded project developing cost-effective sun tracking solutions. DEAR MENA (2019-2024): Cluster fostering digital education partnerships between Swiss and MENA institutions. Sustainable Laboratories (2020-2021): Platform for redistributing lab equipment to emerging economies. MOOLs (2018-2021): Massive Open Online Laboratories for global engineering education. Labs & Infrastructure: Spearheaded the Remote Real-Time Research Platform for control engineering, enabling global access to lab equipment via secure web interfaces. Collaborates on IoT-integrated systems for remote experimentation and industrial automation. Awards: None explicitly listed, but recognized for contributions to sustainable energy and educational equity through numerous funded projects.
