Find Academic Supervisors

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

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

• Human Activity Recognition
• Machine Learning
• Sensor Technology
• +3 more

Anna Ferrari is a Researcher at the Research Institute for Statistics and Information Science, University of Geneva. She holds a Ph.D. from the University of Milano-Bicocca and specializes in human activity recognition through sensor-based systems, particularly using inertial data and deep learning techniques. Her work focuses on model personalization and the development of adaptive classification systems for diverse datasets. Her research interests include machine learning applications in sensor technology, data science methodologies for human activity analysis, and the integration of wearable devices. She has contributed to frameworks for collecting and unifying inertial signals to improve activity recognition accuracy. Anna Ferrari's publications span trends in smartphone-based activity recognition, personalized deep learning models, and sensor data homogenization. She actively maintains professional profiles on ResearchGate, LinkedIn, and Google Scholar, reflecting her commitment to academic collaboration and innovation.

Yi ZhaoView profile

Researcher

• Energy Systems
• Decarbonization Technologies
• Industrial Sustainability
• +3 more

Yi Zhao is a Researcher at École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the School of Engineering and the SCI-STI-FM group (IPESE) at EPFL Valais Wallis. Their work bridges energy systems analysis, industrial decarbonization, and refrigeration engineering. Research Interests Energy storage technologies (e.g., solid oxide electrolysis) Refinery and heavy industry decarbonization strategies Application of genetic algorithms and active learning in energy optimization Low-GWP refrigerant systems for automotive use Development of databases for industrial sustainability (AIDRES project) Modeling of thermodynamic and fluid dynamics in refrigeration systems Scientific Contributions Leading the AIDRES database project for European industrial decarbonization Innovating cost estimation frameworks for hydrogen storage systems Advancing AI-driven optimization in energy system design Investigating CO2 electrolysis safety and efficiency Contact: yi.zhao@epfl.ch

Kyoung-Min KimView profile

Researcher

• Large Language Models (LLMs)
• Database Systems
• Inference Optimization
• +3 more

Kyoung-Min Kim is a Researcher at the Data-Intensive Applications and Systems Laboratory (DIAS) within the College of Computing and Communication Sciences at École Polytechnique Fédérale de Lausanne (EPFL). His work intersects large language models (LLMs) and database systems, focusing on improving inference efficiency and reliability. Email: kyoung-min.kim@epfl.ch Office: BC 224, EPFL, Station 14, Lausanne Research Interests: Recent publications highlight his expertise in: LLM inference optimization GPU resource management Cost modeling for AI systems Scheduling algorithms for scalable inference Preemption strategies reducing computational costs Constraint satisfaction frameworks for system performance Key Contributions: Kim co-developed INFERMAX , an analytical framework comparing LLM inference schedulers. His work demonstrates 30% GPU cost reduction through preemption techniques.

Manuel GilView profile

Researcher

• Bioinformatics
• Evolutionary Genomics
• Computational Biology
• +3 more

Manuel Gil is a Researcher at the Zurich University of Applied Sciences (ZHAW), affiliated with the School of Life Sciences and Facility Management and the Institute of Computational Life Sciences. His work focuses on computational methods in bioinformatics, including ancestral sequence reconstruction, multiple sequence alignment, and semantic integration of biological databases. He leads projects such as computational literature-based drug discovery for natural products and the development of tools like ProPIP and ARPIP. His research interests span evolutionary genomics, bioinformatics software development, and semantic querying of biomedical data. Notable contributions include advancing indel-aware phylogenetic analysis and enabling semantic access to federated bioinformatics databases through projects like Bio-SODA. Publications emphasize methodological advancements in sequence alignment, ancestral reconstruction, and knowledge graph applications in biomedicine. Collaborations include work with institutions like the SIB Swiss Institute of Bioinformatics and involvement in international conferences on text and data mining.

• Data Science
• Machine Learning
• Spatial Analysis
• +5 more

Curdin Derungs is a Lecturer in Data Science at the Lucerne School of Computer Science (HSLU), Switzerland. He specializes in applying statistical and machine learning techniques to energy systems, spatial analysis, and natural language processing. His professional competencies include data-driven automation, deep learning, time series analysis, and energy systems optimization. Derungs holds a PhD in Natural Sciences (University of Zurich, 2013) with a focus on NLP and spatial analysis, and a DAS in Applied Statistics (ETH Zurich, 2019). Earlier degrees include a Master's in Geography (2008) and Atmospheric Physics (ETH Zurich, 2008). His research explores intersections between data science and environmental sustainability, including energy efficiency optimization, spatial language dynamics, and landscape modeling. Recent work focuses on occupant behavior in energy systems, geotagged text analysis, and soil formation modeling. Key projects include SCCER FEEB&D Work Package 2 on renewable energy systems, the Romande Energy Demonstrator, and urban greening studies. He actively contributes to interdisciplinary initiatives like the IGE Innovationswettbewerb 2019 and Innovationspark Zentralschweiz.

Philipp RehnerView profile

Lecturer

• Classical Density Functional Theory
• Thermodynamic Modeling
• Computer-Aided Molecular Design
• +5 more

Dr. Philipp Rehner is a Lecturer at the Department of Mechanical and Process Engineering at ETH Zürich. He holds a master’s degree in process engineering from the University of Stuttgart and completed his Ph.D. there under Professor Joachim Gross, focusing on classical density functional theory and interfacial properties of nanodroplets. Since 2021, he has been a postdoctoral researcher and group leader of the Molecular Design Group at ETH Zürich’s Energy and Process Systems Engineering (EPSE) Group. Research Interests: Development and application of classical density functional theory (DFT) for interfacial systems and adsorption modeling Thermodynamic modeling using equations of state (e.g., PC-SAFT, PCP-SAFT) Computer-aided molecular and process design GPU-accelerated computational methods for large-scale simulations Automated parameter prediction for molecular models using machine learning and SMILES representation Key Contributions: Developed the open-source FeOs framework for equations of state and DFT Advanced group-contribution methods for binary interaction parameters in thermodynamic models Pioneered GPU-accelerated DFT calculations for adsorption in porous materials Recognition: Recipient of the Walter Benjamin Fellowship (German Research Foundation) Labs/Teams: Leader of the Molecular Design Group at ETH Zürich’s EPSE Group Collaborator on interdisciplinary projects involving chemical engineering, computational chemistry, and materials science

Diego Ulisse PizzagalliView profile

Researcher

• Computational Medicine
• Immunology
• Machine Learning
• +5 more

Diego Ulisse Pizzagalli is an independent researcher in Computational Medicine affiliated with the Faculty of Biomedical Sciences at the Università della Svizzera italiana (USI), the Euler Institute, and the Institute for Research in Biomedicine (IRB). He holds roles as a lecturer at USI and teaching assistant, focusing on machine learning in medicine and signal processing. His work bridges immunology and AI, with projects involving wearable device monitoring in chronic diseases and the development of digital biomarkers. His research focuses on applying artificial intelligence to understand immune responses, tissue remodelling, and chronic diseases. Key areas include trajectory analysis of immune cells using intravital microscopy, predictive models for disease complications, and bioimage analysis. He leads the IMMUNEMAP initiative, an open immunology data platform, and collaborates on hardware prototyping for medical applications. Pizzagalli’s recent work emphasizes AI-driven medical imaging tools (e.g., CompositIA for CT scans, deep learning in forefoot morphology analysis) and trajectory-based studies of immune cell behavior. His projects integrate clinical data with computational models, such as predicting hospital admissions in chronic liver disease patients using wearable sensors. He co-leads projects funded by systemsx.ch, Apple Inc., and others. Supervises BSc/MSc students. The Digital Pathophysiology Lab at Euler Institute serves as his primary research base, promoting interdisciplinary collaboration in biomedical imaging, trajectory mining, and wearable sensor development.

Camila ScaffView profile

Researcher

• Language Acquisition
• Child Development
• Cross-Cultural Studies
• +3 more

Camila Scaff is a Postdoctoral Researcher affiliated with the Human Ecology Group. Her work focuses on language acquisition in diverse cultural contexts, particularly among non-industrialized populations such as the Tsimane forager-horticulturalists in Bolivia. She employs long-form audio recordings and cutting-edge methodologies like the ACLEW pipeline and LENA system to analyze language input and developmental patterns. Her research interests span cross-cultural linguistics , infant-directed speech , and the methodological challenges in ecological data collection. She advocates for inclusive research practices to address underrepresentation in language studies. Key contributions include diarization tools for ecological recordings, studies on socioeconomic factors influencing lexical development, and critiques of WEIRD (Western, Educated, Industrialized, Rich, Democratic) biases in academic research. Scaff collaborates internationally with anthropologists and speech technologists to bridge interdisciplinary gaps.

• Bioinformatics
• Text Mining
• Machine Learning Applications in Biomedical Research
• +4 more

Patrick Ruch is a Professor at the Geneva School of Economics and Management (HES-SO), specializing in bioinformatics, text mining, and computational methods for health sciences. He leads research in areas such as machine learning applications in biomedical research, research data management, and natural language processing for scientific literature triage. His work emphasizes improving reproducibility, equity in publishing, and the integration of AI in healthcare. Key affiliations include the SIB Swiss Institute of Bioinformatics and the ArODES initiative. Research interests focus on leveraging AI to enhance data-driven decision-making in medicine, addressing systemic biases in academic publishing, and developing tools for genomic variant curation. He has collaborated extensively on international projects like BiCIKL and DOME, advancing FAIR data principles and open science practices.

Matthias LeeseView profile

Assistant Professor

• Technology and Governance
• International Relations
• Criminology
• +4 more

Matthias Leese is an Assistant Professor for Technology and Governance at ETH Zurich's Department of Humanities, Social and Political Sciences. His research bridges International Relations, Criminology, and Science and Technology Studies, focusing on how technological tools reshape security practices and governance. He leads the CURATE Project (ERC/SERI Grant) examining data quality in European law enforcement and border control, and serves on the DATAMIG EU COST Action Management Committee for migration technology research. His research emphasizes qualitative empirical methods to study socio-technical relations in security contexts, including predictive policing, aviation security, and EU databases. Recent work explores data temporalities, algorithmic accountability, and the socio-political impacts of security technologies. Leese's articles analyze topics like algorithmic accountability in criminal justice, data quality frameworks in migration governance, and the evolution of EU security systems. He co-edits the Journal of International Relations and Development and contributes to policy discussions on digital sovereignty and ethical technology implementation. Grants: CURATE (ERC/SERI Starting Grant), DATAMIG (EU COST Action) Teaching: Focuses on technology governance, security studies, and research methods. Labs/Teams: Leads the Technology and Governance research group at ETH Zurich.

Thomas MyrachView profile

Professor

• E-commerce and Online Marketing
• Digital Data Management
• Requirements Engineering
• +4 more

Prof. Dr. Thomas Myrach serves as Director of the Information Management Department at the Institute for Business Information Systems , affiliated with the Faculty of Business, Economics and Social Sciences at the University of Bern . His academic career spans decades, focusing on E-commerce, Online Marketing, Digital Data Management, and Requirements Engineering . Key research areas include: Value-sensitive Design of Decision Support Systems Platform Control and User Autonomy in Digital Ecosystems Development of Cognitive Cities and Smart Urban Infrastructure Knowledge Commons and Sustainable Digital Artifacts Publications reveal trends in Digital Sustainability (2016-2017), Smart City research (2016), and Cognitive Mapping techniques (2015-2017). His work bridges Information Systems with Environmental Informatics and Human-Computer Interaction .

• Bayesian Probabilistic Models
• Machine Learning
• Optimization
• +3 more

Dr. Thomas Oskar Weinmann is the Head of the Research Focus Area Scientific Computing & Algorithmics at ZHAW School of Engineering, Zurich University of Applied Sciences (ZHAW). His research interests include Bayesian probabilistic models, machine learning, optimization, and visual computing. He holds a PhD in Mathematics from ETH Zürich (2003–2007) and completed a CAS in Visual Computing at ETH Zürich in 2011. Education: PhD in Mathematics, ETH Zürich (2003–2007) CAS Visual Computing, ETH Zürich (2011) His current research projects include: Smart Acquisition for Ultra-High field NMR Spectroscopy (Deputy project leader, ongoing) Raman for Process Analytics (Team member, ongoing) Target Recognition using Artificial Intelligence (TRAI) (Project leader, ongoing) Completed projects include machine learning applications in NMR spectroscopy, varroa mite counting in honeybee colonies, football motion data analysis, and NoSQL database systems. His publications span Bayesian spectral analysis and query optimization in NoSQL systems.

• History of Computing
• Digital Society
• Technology Governance
• +2 more

David Gugerli is Full Professor for History of Technology at ETH Zurich's Institute for History, examining how technologies shape social orders and knowledge regimes. His research pioneers critical studies of computing infrastructures and digital societies. Research analyzes database epistemologies, supercomputing politics, and surveillance cultures. Major works include 'How the World Got Into the Computer' exploring digital reality emergence and studies of Swiss banking computerization. Methodological innovations combine archival research with science studies principles. Recognized through Rudolf Kellerman Prize and Berlin Brandenburg Academy Prize for electrification studies. International fellowships include Wissenschaftskolleg zu Berlin and Stanford University. Teaches courses on technology governance, historical methodology, and infrastructure studies within ETH's humanities curriculum.

• Medical Imaging
• Computational Chemistry
• Data Science
• +5 more

Dr. Qianwei Qu is a Researcher at the Paul Scherrer Institute (PSI), affiliated with the IT Infrastructure and Services group within the PSI Center for Scientific Computing, Theory and Data Science. Their work focuses on advanced imaging techniques, molecular spectroscopy, and computational chemistry, particularly in the context of exoplanet research and medical diagnostics. Research interests span Medical Imaging (including 4D synchrotron CT and time-resolved CT), Molecular Spectroscopy (hyperfine-resolved spectra and ExoMol database development), and Laser Diagnostics (flow tomography and high-speed flow analysis). Recent projects include contributions to molecular line lists for exoplanet atmospheres and thermodynamic modeling of diatomic molecules. Publications highlight innovation in computational methods for rovibronic spectra, laser absorption tomography algorithms, and applications of synchrotron-based imaging. Their interdisciplinary approach bridges physics, chemistry, and data science to address challenges in medical engineering and astrophysics. No awards or grants are explicitly mentioned, but their work demonstrates significant contributions to open science through collaborative projects like the ExoMol database and hackathon initiatives.