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Francesca StradoliniView profile

Lecturer

• Bioelectronics
• IoT for Medical Monitoring
• Anesthesia Delivery Systems
• +3 more

Francesca Stradolini is a Lecturer at École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the Electrical Engineering Doctoral School (EDEE). She holds a PhD in Electrical Engineering from EPFL (2018), with prior education in Bio Engineering and Neuroscience from the University of Genoa (2012, 2015). Her current work focuses on R&D engineering for medical devices, emphasizing system lifecycle management, human factors engineering, and compliance with CE/FDA regulations. As an invited guest, she contributes to usability engineering education and interdisciplinary research at EPFL. PhD: IoT Bio-Electronic Multi-Panel Device for On-line Monitoring of Anaesthesia Delivery (EPFL, 2018) MSc: Bio Engineering (University of Genoa, 2015) BSc: Bio Engineering (University of Genoa, 2012) Her research spans bioelectronics, IoT-enabled medical monitoring, and electrochemical sensor development for clinical applications. Key areas include anesthesia delivery systems, wearable diagnostics, implantable devices, and human factors engineering. Her work integrates fluidic systems, noble metal nanostructures, and open-source hardware (e.g., Raspberry Pi) to address challenges in traceability, fouling mitigation, and pharmacokinetic modeling. Scientific recognition includes the Ville de Lausanne Award (2019) , EPFL Outstanding PhD Thesis Distinction (2018) , and a Best Paper Award at MOBIHEALTH (2016) . She has published extensively in journals like IEEE TBIOCAS, Sensors and Actuators B, and IEEE Sensors Journal, with collaborative studies on propofol monitoring, carbon nanoallotrope drug detection, and implantable sensor validation. Her technical expertise includes managing medical device development lifecycles, designing IoT architectures for intensive care, and optimizing electrochemical sensors for stability in undiluted biological fluids. She has contributed to conferences such as IEEE BioCAS, ISCAS, and MOBIHEALTH, focusing on translational studies in personalized medicine and 3D-printed medical systems.

Peter BühlmannView profile

Professor

• High-dimensional Statistics
• Causal Inference
• Machine Learning
• +3 more

Peter Bühlmann is a Professor at ETH Zürich within the Seminar für Statistik , focusing on high-dimensional statistics, causal inference, and machine learning. His work bridges theoretical advancements with practical software implementations in R packages like pcalg , mboost , and glmmlasso , impacting fields such as genomics, proteomics, and intensive care analytics. Key Contributions : Causal structure learning, stability selection, anchor regression, and deconfounding. Software : Developed widely used R packages for statistical modeling and causal inference. Teaching : Courses on high-dimensional statistics at ETH Zürich and international institutions. Research Trends : Recent articles emphasize causal robustness, domain adaptation, and applications in medicine. His work addresses challenges in heterogeneous data, missing values, and covariate shifts using methods like spectral deconfounding and residual prediction tests. Scientific Recognition : Co-author of a paper designated as a New Hot Paper (Meinshausen and Bühlmann, 2006) by Essential Science Indicators, indicating significant impact in high-dimensional multiple testing.

Michael MoorView profile

Assistant Professor

• Medical AI
• Machine Learning in Healthcare
• Causal Learning
• +3 more

Prof. Michael Moor is a tenure-track Assistant Professor for Medical AI at ETH Zurich's Department of Biosystems Science and Engineering in Basel. Previously, he conducted postdoctoral research at Stanford University under Prof. Jure Leskovec, focusing on medical foundation models. His work spans causal learning, multimodal AI, and sepsis prediction. Moor holds an MD from the University of Basel and a PhD from ETH Zurich's Machine Learning and Computational Biology Lab under Prof. Karsten Borgwardt. Research Interests: Generalist medical AI models Multimodal medical reasoning Zero-shot and few-shot learning Clinical causal inference Retrieval-augmented language models Sepsis prediction systems Key Achievements: Published foundational work in Nature (2023) on generalist medical AI Developed Med-Flamingo multimodal model (2023) Zero-shot causal learning framework accepted to NeurIPS 2023 (Spotlight) International sepsis prediction study with 156k ICU patients (2023) Lab Activities: Leads ETH's Medical Foundation Models group, collaborating with Stanford HAI and NASA Ames. Active in creating medical AI benchmarks like AgentClinic and developing retrieval-augmented systems like Almanac.

Claudia R. BinderView profile

Professor

• Urban Sustainability
• Transition Research
• Urban Metabolism
• +5 more

Claudia R. Binder is Full Professor at EPFL's School of Architecture, Civil and Environmental Engineering, leading the Laboratory for Human-Environment Relations in Urban Systems (HERUS) since 2016. Previously, she held professorships at the University of Munich (2011-2016), University of Graz (2009-2011), and University of Zurich (2006-2009). She served as Dean of EPFL's ENAC School from 2020-2023 and holds advisory roles with Swiss federal institutions including the Mercator Foundation since 2024. Her academic foundation includes a Biochemistry degree and PhD in Environmental Sciences from ETH Zurich, followed by postdoctoral research at the University of Maryland. This interdisciplinary background underpins her research approach spanning natural and social sciences. Professor Binder's work centers on urban sustainability transitions, examining urban metabolism dynamics through systems science frameworks. She investigates energy-food-transport interdependencies in cities using transdisciplinary methods that integrate material flow analysis, spatial modeling, and socio-technical assessments. Her research particularly emphasizes regulatory mechanisms and transformation drivers in human-environment systems, with case studies across Swiss and global urban contexts. Recent publications reveal evolving focus from foundational urban metabolism studies toward actionable transition strategies. Her 2024-2025 work increasingly addresses social tipping dynamics, circular decarbonization, and spatially explicit waste management, demonstrating methodological innovation through geo-referenced material flow analysis and participatory backcasting frameworks. Key thematic clusters include energy innovation diffusion, plastic waste governance, and demand-side flexibility in residential systems. She actively mentors 7 current PhD candidates while supervising 11 graduates since 2018, with research spanning urban metabolism modeling, sustainability assessment, and transition governance. Her leadership extends to Swiss National Science Foundation committees and National Research Program 71 on energy consumption management. At EPFL, she directs the HERUS laboratory which develops the Sustainability Solution Space methodology for urban assessment. The lab operates at the intersection of data science, environmental engineering, and social theory, maintaining strong field connections in Switzerland, Indonesia, and Germany for empirical validation of transition models.

Siyu TangView profile

Assistant Professor

• Computer Vision
• Machine Learning
• Digital Humans
• +5 more

Siyu Tang is an Assistant Professor in the Department of Computer Science at ETH Zürich, where she leads the Computer Vision and Learning Group (VLG) at the Institute of Visual Computing. Her research focuses on computational models for human perception and digitalization through computer vision and machine learning. Her educational background includes: PhD in Computer Science, Max Planck Institute for Informatics (2017), supervised by Prof. Bernt Schiele Master of Science in Media Informatics, RWTH Aachen University Bachelor of Science in Computer Science, Zhejiang University, China Dr. Tang specializes in human-centric computer vision, developing statistical models for motion analysis, pose estimation, and digital human creation. Her work integrates machine learning with optimization techniques to enable machines to interpret human activities from visual data, with applications spanning virtual reality, healthcare, and human-computer interaction. Key research thrusts include generative models for content creation, egocentric vision, and human motion synthesis. Her recent publications (2024-2025) demonstrate intense focus on 3D human modeling and neural rendering, with Gaussian splatting emerging as a dominant technique for efficient avatar creation and scene reconstruction. Significant themes include text-driven motion synthesis using diffusion models, relightable avatars, surgical training applications, and egocentric multimodal pretraining. This work bridges computer vision, graphics, and machine learning to advance human digitalization. No scientific awards were mentioned in the provided text. Dr. Tang leads the VLG research group at ETH Zürich, mentoring PhD and Master's students in human-centric AI. She previously secured an early career research grant from the Max Planck Institute for Intelligent Systems to establish her independent research program. Her group actively pursues funding for projects in human motion analysis, 3D reconstruction, and generative modeling, with strong industry and clinical collaborations. The Computer Vision and Learning Group (VLG) operates within ETH's Institute of Visual Computing, maintaining dedicated facilities for motion capture, 3D scanning, and high-performance computing. The team collaborates internationally with institutions like the Max Planck Society and focuses on scalable solutions for real-world human digitalization challenges, including surgical training systems and immersive virtual environments.

Reinhard HeckelView profile

Professor

• Machine Learning
• Deep Learning
• Computational Imaging
• +5 more

Reinhard Heckel is a Tenured Associate Professor (equivalent to Professor) of Machine Learning at the Department of Computer Engineering, Technical University of Munich (TUM), and Adjunct Faculty in Electrical and Computer Engineering at Rice University. He was previously an Assistant Professor at Rice (2017–2019), a postdoc in the Berkeley Artificial Intelligence Research (BAIR) Lab at UC Berkeley, and a researcher at IBM Research Zurich. Education: PhD, 2014 – ETH Zurich Visiting PhD student – Department of Statistics, Stanford University Research Interests: His work centers on machine learning and information processing with three major thrusts: (1) developing algorithms and theoretical foundations for deep learning, especially for accelerated magnetic resonance imaging ; (2) establishing rigorous mathematical and empirical underpinnings for modern machine-learning systems; and (3) leveraging DNA as a digital information-storage medium , including error-correction coding and system design for DNA-based storage. Across more than 100 peer-reviewed papers since 2017, Heckel’s research exhibits a strong interdisciplinary blend of computational imaging , machine-learning theory , and molecular data storage . Recent 2024–2025 publications show intensive focus on robust MRI reconstruction using diffusion priors, evaluation of bias in large web-text corpora, and state-of-the-art error-correcting codes for DNA storage channels. A forthcoming book, Deep Learning for Computational Imaging (Oxford University Press), consolidates his contributions to the field. Outreach & Media: Keynote and panel talks at DLD, TUM, and major ML conferences Op-eds in Frankfurter Allgemeine on ChatGPT and DNA storage Science features on Netflix, BBC, and German television (Galileo, “Gut zu Wissen”) Research Environment: At TUM he leads a group investigating theoretical and applied aspects of deep learning, compressed sensing, and coding for DNA storage. Open-source repositories on GitHub (e.g., dna_data_storage , supplement_deep_decoder ) provide code and data supplements accompanying his publications.

Anastasia AilamakiView profile

Professor

• Database Management Systems
• Scientific Applications
• Computer Architecture
• +3 more

Anastasia Ailamaki is a Full Professor at the École Polytechnique Fédérale de Lausanne (EPFL) and a visiting researcher at Google. She co-founded RAW Labs SA as Chair of the Board of Directors, focusing on big data systems. Her academic affiliations span EPFL's School of Computer and Communication Sciences and its Data-Intensive Applications and Systems Laboratory (DIAS). PhD in Computer Science from the University of Wisconsin-Madison (2000) Her research focuses on data-intensive systems and applications, particularly: (1) strengthening database software interactions with emerging hardware/I/O devices, and (2) automating data management for computationally demanding scientific applications. She has supervised numerous PhD students and mentored teams at EPFL's DIAS, SIN, and SSC laboratories. Notable scientific honors include the ACM SIGMOD Edgar F. Codd Award, VLDB Women in Database Research Award, ERC Consolidator Award, and recognition by the Presidents of Cyprus and Greece. She holds fellowships from ACM and IEEE, and serves on multiple national research councils.

• Statistical Seismology
• Seismic Hazard Assessment
• Induced Seismicity
• +3 more

Dr. Laura Gulia is a Researcher at the Swiss Seismological Service (SED) affiliated with ETH Zurich. As the Principal Investigator of the ERC Advanced Grant FARIA, her work focuses on statistical seismology, earthquake forecasting, and induced seismicity. She pioneered the Foreshock Traffic Light System (FTLS), a real-time tool to distinguish between aftershock decay and foreshock sequences, published in Nature (2019) and further developed in Seismological Research Letters (2024). Her research emphasizes b-value analysis, data quality validation, and correlations between seismic activity and reservoir compaction rates in induced seismicity. Key Affiliation: Swiss Seismological Service (SED), ETH Zurich Grants: ERC Advanced Grant FARIA Her research interests include: - Real-time seismic hazard assessment - Artifact detection in seismic catalogs - Macroseismic data analysis using citizen-reported intensity data - Link between gas production and induced seismicity in Groningen Her publications demonstrate advancements in: - Foreshock/aftershock differentiation algorithms - Quantifying induced seismicity risks through b-value metrics - Integrating citizen science data for rapid earthquake response Awards: ERC Advanced Grant FARIA (2020) Advising and Collaborations: - Leading the FTLS development team at SED - Collaborating with European Mediterranean Seismological Centre (EMSC) on real-time macroseismic systems - Contributing to the Groningen gas field seismicity monitoring Labs/Teams: - Swiss Seismological Service (SED) research group - ERC-funded FARIA project consortium

• Data-Intensive Systems
• Resource-Aware Computing
• Machine Learning Systems
• +7 more

Pınar Tözün is an Associate Professor at the IT University of Copenhagen (ITU), Denmark, where she serves as the Section Head of the Data, Systems, and Robotics section and leads the Resource-Aware Data Systems (RAD) research team. She has been a faculty member at ITU since 2018, contributing to advanced research in data-intensive computing systems. Her research interests span the efficient utilization of modern hardware in data systems, with a focus on resource-aware machine learning, data processing on resource-constrained devices, and the integration of emerging technologies such as SSDs and Compute Express Link (CXL) into data management architectures. Her work bridges systems, databases, and machine learning, aiming to optimize performance and efficiency in next-generation computing environments. Pınar Tözün's research has been supported by major funding agencies including the Independent Research Fund Denmark, Novo Nordisk Foundation, Innovation Fund Denmark, Swiss National Science Foundation, and the European Union’s Horizon 2020 programme. These grants reflect the impact and relevance of her work in both academic and industrial contexts. She previously worked as a research staff member at IBM Almaden Research Center in San Jose, CA, USA, where she contributed to the development of IBM Db2 Event Store. She received her PhD from École polytechnique fédérale de Lausanne (EPFL) in 2014 under the supervision of Prof. Anastasia Ailamaki, where she was a key developer of the Shore-MT storage manager. Her bachelor’s degree is in Computer Engineering from Koç University, Istanbul, Turkey, where she was advised by Prof. Serdar Taşıran. She is actively involved in research leadership and innovation, guiding the RAD team in advancing data systems research. Her work continues to influence the design of efficient, scalable, and hardware-aware data platforms.

• Computer Architecture
• Memory Systems
• Solid-State Drives
• +2 more

Dr. Seyyedmohammad Sadrosadati is a Lecturer in the Department of Information Technology and Electrical Engineering at ETH Zurich. His expertise centers on storage systems and computer architecture, with particular focus on solid-state drive (SSD) design and optimization. Research focuses on: Modern SSD architecture and performance enhancement Memory hierarchy optimization Hardware accelerators for data-intensive applications Energy-efficient storage solutions His teaching includes courses on SSD design and advanced computer architecture.

• Seismology
• Earthquake Early Warning
• Real-Time Seismic Monitoring
• +4 more

Dr. Maren Boese is a Senior Research Scientist at the Swiss Seismological Service (SED) and Seismology and Geodynamics (SEG) groups at ETH Zurich. Her expertise spans seismology, earthquake early warning systems, and planetary seismology. She leads projects like the CISN ShakeAlert system for California and contributed to NASA's InSight mission to Mars. She earned her Ph.D. from Karlsruhe Institute of Technology (2006) and a Master's from Kiel University (2002). Research Focus: Real-time seismic event detection, finite-fault rupture analysis, ground-motion simulation, and Marsquake seismology. Key contributions include developing the FinDer algorithm for the ShakeAlert system and advancing early warning methodologies in Central America and Switzerland. Awards: 2011: Outstanding Publication Award (Luxembourg National Research Fund) 2010: Ausgewählter Ort im Land der Ideen (BMBF Project EWS Transport) 2006: European IST Prize (Early Warning for Strong Earthquakes) Grants & Leadership: Led the CISN ShakeAlert team (2009–2014), managed the German CRC 461 project, and coordinated international collaborations like the InSight Marsquake Service. Labs/Teams: Swiss Seismological Service (ETH Zurich), NASA InSight Mission, and the CHEAP smartphone network project in Chile.

Tom BeuclerView profile

Assistant Professor

• Geo-Environmental Data Science
• Climate Informatics
• Atmospheric Physics
• +5 more

Tom Beucler is a Conditional Pre-Tenure Assistant Professor in Geo-Environmental Data Science at the University of Lausanne’s Institute for Earth Surface Dynamics (IDYST). He holds a Master’s degree in Science and Mechanics from École Polytechnique (2014) and a PhD in Atmospheric Science from MIT (2019). Postdoctoral research at Columbia University and UC Irvine focused on machine learning applications in climate science under Professors Pierre Gentine and Michael Pritchard. Research Interests: Climate informatics, atmospheric physics, fluid dynamics, tropical meteorology, and integrating machine learning into climate models for extreme weather prediction and hydrological cycle modeling. Collaborations: Works with environmental scientists and computer engineers to improve climate models using neural networks and causal discovery methods. Initiatives: Organizes weekly brainstorming sessions to promote machine learning adoption in environmental sciences. Publications span climate-invariant machine learning, data-driven parameterizations, and hybrid AI-climate modeling frameworks like ClimSim. His work emphasizes causal consistency and generalizability across climate conditions.

• Data Ethics
• Machine Ethics
• Artificial Intelligence
• +7 more

Sylvie Delacroix is the Inaugural Jeff Price Chair in Digital Law and Director of the Centre for Data Futures at King's College London. She is also a Visiting Professor at Tohoku University, focusing on bridging theory and practice in ethics and public policy initiatives. Her research spans Data & machine ethics Ethical agency and habit Social sustainability of data ecosystems Participatory infrastructure for AI systems Uncertainty communication in LLMs Legal theory and digital law Recent publications address LLM interfaces as transitional spaces for democratic revival and sustainable data rivers in generative AI. Her work has attracted significant grant funding from the Patrick J. McGovern Foundation, Wellcome Trust, and Omidyar Network. Scientific awards include the Leverhulme Prize Peter Birks 2d Prize for Outstanding Legal Scholarship Montgomery Fellowship at Dartmouth College She supervises PhD research on large language models and uncertainty communication, as well as habit in ethical lives. Her policy contributions include advising the Law Society of England and Wales on algorithmic justice systems.

Manos AthanassoulisView profile

Associate Professor

• Data Systems
• Cloud Data Management
• Database Systems
• +7 more

Manos Athanassoulis is an Associate Professor in the Department of Computer Science at the College of Arts and Sciences, Boston University. He is the Founder and Director of the BU Data-intensive Systems and Computing (DiSC) lab and a member of the BU MiDAS group. His research focuses on data systems, particularly cloud data management, hybrid transactional/analytical workloads, and integration with emerging hardware such as non-volatile memory and heterogeneous computing. His educational background includes a PhD from EPFL (2014), an MSc in Computer Systems Technology, and a BSc in Informatics and Telecommunications from the University of Athens, Greece. Prior to BU, he was a Postdoctoral Researcher and Research Associate at Harvard University, supported by a SNSF Postdoc Mobility Fellowship. His research interests span data systems, database architectures, LSM trees, indexing, storage systems, and performance optimization. He explores how novel hardware can be leveraged to improve data management efficiency and scalability, especially in cloud environments. His recent publications (2021–2025) predominantly focus on LSM trees, covering topics such as compaction policies, Bloom filter tuning, DPU offloading, adversarial resilience, and sustainable caching. Earlier works include foundational contributions on access methods (RUM Conjecture) and optimal key-value stores (Monkey). The trend shows a consistent focus on data system efficiency, adaptability, and robustness under varying workloads and hardware constraints. Scientific Awards: NSF CAREER Award (2022) Facebook Faculty Research Award (2020) NSF CRII Award (2019) Best of VLDB 2017 and Best of SIGMOD 2017 SIGMOD Most Reproducible Paper Award (2017) Multiple ACM SIGMOD Distinguished PC Member recognitions (2018–2025) VLDB 2023 Best Demo Award RedHat Collaboratory Research Incubation Awards (multiple, 2021–2023) SNSF Postdoc Mobility Fellowship (2015–16) IBM PhD Fellowship (2011–12) Dr. Athanassoulis has advised numerous students and collaborators, evident from his co-authorship on works with researchers such as Niv Dayan, Stratos Idreos, and A. Ailamaki. His grants include major awards from NSF, Facebook, and RedHat, supporting research in robust data systems, hardware-software co-design, and learned cost models. He has also been recognized for teaching excellence at Harvard University. He leads the DiSC lab at Boston University, which focuses on data-intensive computing and systems research. The lab explores next-generation data architectures, particularly in cloud and hardware-aware environments. Collaborations with the BU MiDAS group enhance interdisciplinary research in data science and AI.

• Particle Physics
• Muon Physics
• Detector Development
• +4 more

Dr. Stefan Ritt is a prominent researcher and Group Leader of the Muon Physics group at the Paul Scherrer Institute (PSI) in Switzerland. With over 30 years of experience in particle physics, he has made significant contributions to muon decay experiments and detector development. His research focuses on precision measurements of muon properties and searches for physics beyond the Standard Model. Ritt's primary research interests encompass particle physics, muon physics, detector development, and data acquisition systems. His work has been instrumental in advancing high-precision measurements of muon decay processes, particularly in the search for lepton flavor violation. He has pioneered developments in waveform digitizing technology, most notably through the Domino Ring Sampler (DRS) series of chips, which have revolutionized data acquisition in particle physics experiments. Analysis of his recent publications reveals a strong focus on the MEG and MEG II experiments, which search for the rare decay μ+→e+γ. His work spans detector design, data acquisition systems, trigger implementation, and precision analysis techniques. The publications demonstrate expertise in liquid xenon detectors, silicon photomultipliers, timing resolution, and high-speed waveform digitization. 1984 Jugend Forscht Landessieger 2011 IEEE Senior Member 2016 IEEE Fellow for the development of the Domino Ring Sampler series of chips 2020 IEEE Emilio Gatti Radiation Instrumentation Technical Achievement Award for contributions to the development and democratization of ultra high-speed digitizers Ritt has served as a thesis examiner for institutions including INFN Pisa and ETH Zurich, demonstrating his role in academic mentoring. His leadership extends to coordinating beam time for PSI's secondary particle beam lines and organizing major international workshops. He has been instrumental in developing the Mu3e experiment and advancing muon beam technology at PSI. As head of the Muon Physics group (comprising 12 members), Ritt oversees fundamental particle physics experiments at PSI's secondary beam lines. His group is responsible for the design and implementation of data acquisition hardware and software for the MEG II experiment and serves as co-spokesperson for the Mu3e experiment.