جست‌وجوی استاد و پژوهشگر

Maria Giulia Pretiمشاهده پروفایل

مدرس

• Neuroimaging
• Brain Connectivity
• Alzheimer's Disease
• +۳ مورد دیگر

Maria Giulia Preti is a Senior Scientist at MIPLAB, Research Staff Scientist at the CIBM Center for Biomedical Imaging (Switzerland), and a Lecturer (Maître-Assistante) at the University of Geneva. She holds a PhD in Bioengineering from Politecnico di Milano (2013) and completed postdoctoral research in Dimitri Van De Ville’s group at EPFL. Her work focuses on integrating neuroimaging techniques (fMRI, DTI, EEG, fNIRS) with graph signal processing to study brain function-structure relationships in health and disease. Key clinical applications include Alzheimer’s disease, epilepsy, multiple sclerosis, and stroke. She has pioneered methods like groupwise fMRI-guided tractography to study neurodegenerative pathologies. Education: PhD in Bioengineering (2013), Politecnico di Milano MSc in Biomedical Engineering (2009), Politecnico di Milano BSc in Biomedical Engineering (2007), Politecnico di Milano Research emphasizes multimodal neuroimaging integration and computational neuroscience. Notable achievements include developing connectome embedding techniques and demonstrating brain fingerprinting via spectral signatures. She received the Progetto Rocca fellowship (2011) supporting her work at MIT/Harvard Medical School. Ongoing projects investigate structure-function coupling dynamics, drowsiness markers, and DBS mechanisms in depression. Labs/Teams: MIP:Lab (part of the Van De Ville group at EPFL and University of Geneva collaborations).

• Multi-agent systems
• Real-Time Systems
• Explainable AI (XAI)
• +۵ مورد دیگر

Davide Calvaresi is an Associate Professor at HES-SO Valais-Wallis (University of Applied Sciences and Arts Western Switzerland) in the Engineering and IT school within the Computer Science and Communication Systems department. His research focuses on multi-agent systems , real-time systems , explainable AI (XAI) , and blockchain for trustworthy applications in healthcare and safety-critical domains. Current projects include EXPECTATION (Personalized Explainable AI for decentralized agents) and past projects like Axe Santé 2020 (interdisciplinary health research) and Simulator of multi-agent systems (timing reliability analysis). Collaborations include researchers from University of Bologna, University of Luxembourg, and Ozyegin University. Recent publications focus on swarm intelligence in IoT , ethical AI for aging populations , and explainable health coaching systems Technical contributions include DEXiRE (rule extraction from neural networks), SCoDES (blockchain-based consent management), and HAExA (parsimonious XAI architecture)

• Computer Vision
• Machine Learning
• Deep Learning
• +۲ مورد دیگر

Thomas Koller is a Professor and Head of the Master's Program in Engineering at Lucerne University of Applied Sciences and Arts, affiliated with the School of Computer Science and Information Technology. His research focuses on computer vision, machine learning, and medical image analysis applications. His primary research domains include: Computer Vision and Image Processing Deep Learning and Reinforcement Learning Medical Imaging and Diagnostic Applications His publications demonstrate strong focus on applying computer vision and deep learning techniques to medical challenges, particularly in dermatology and vascular imaging. Recent work shows increasing emphasis on real-world healthcare applications and interactive systems. Scientific Awards: Best Poster Video Award (2022) aha! Award des Allergiezentrums Schweiz (2017)

• Aerothermodynamics
• Mechanical Engineering
• Process Engineering
• +۳ مورد دیگر

Dr. Ndaona Chokani is a Lecturer at the Department of Mechanical and Process Engineering at ETH Zürich. Their research focuses on aerothermodynamics, mechanical engineering, and process engineering, with a recent emphasis on interdisciplinary applications in computer vision and image processing. Affiliated with the Professur f. Aerothermodynamik, their work spans theoretical and applied domains. While explicit educational background details are not provided, their academic career includes contributions to fields such as fluid dynamics and process systems engineering. Research interests are inferred from departmental affiliation and recent publications, emphasizing computational methods in mechanical systems and image quality assessment. Recent publications highlight work in perceptual quality metrics for images and videos, immersive systems (VR/360 content), and machine learning applications in 3D pose estimation and generative models. Key areas include diffusion models for low-light imaging, spatial-temporal geometric networks, and benchmarking frameworks for image harmonization. No awards or grants are explicitly listed in the provided text. Their research often involves collaborations on datasets like Salient360! and tools for gaze data analysis in 3D environments. Future work appears to focus on improving perceptual metrics and expanding applications in healthcare and immersive technologies.

• Augmented Reality
• Game Technology
• Medical Imaging
• +۴ مورد دیگر

Dr. Fabio Zünd is a Lecturer at the Department of Computer Science, ETH Zurich, affiliated with the Game Technology Center (GTC). He is based in Zurich, Switzerland, and can be reached at fabio.zund@inf.ethz.ch . His work focuses on innovative applications of Augmented Reality (AR), Game Technology, and Human-Computer Interaction (HCI), with notable contributions to medical AR integration, facial image anonymization, and interactive storytelling systems. Research interests include AR-driven medical education, personalized clinical imaging tools, and the development of immersive technologies that bridge real and virtual worlds. His projects often address challenges in user-centered design, such as scent-based gaming interfaces and emotion-aware interaction techniques. Recent work highlights include the VerA system for clinical facial image anonymization, scent prediction in video games, and latent diffusion models for art generation. He explores interdisciplinary applications in healthcare, education, and entertainment through collaborations with institutions like the ETH Library and industry partners. No scientific awards are explicitly mentioned in the provided information. His advising record remains unspecified, though he contributes to ETH’s educational programs through courses like the Seminar on Game Technology . Ongoing projects involve the Game Technology Center’s exploration of tangible interaction systems and wearable input devices such as the DigiGlo glove technology.

Lidia Alecciمشاهده پروفایل

پژوهشگر

• Wearable Computing
• Affective Computing
• Machine Learning
• +۵ مورد دیگر

Lidia Alecci is a Ph.D. student at the Faculty of Informatics, Università della Svizzera italiana (USI) since October 2021. She holds a double Master’s degree in Informatics from USI and the University of Milano-Bicocca (2021), with a thesis focused on robust sleep behavior recognition using wearable sensors. Her research combines wearable computing, affective computing, and machine learning to address interpersonal variability in physiological and behavioral patterns. Education: Master in Informatics - Double Degree (2019-2021), USI and UniMiB Bachelor in Computer Science (2015-2018), University of Padova Research Interests: Wearable Computing Affective Computing Machine Learning for Healthcare Data Science Awards: Italian finals of international championships in mathematical games (2014, 2015) Experience: Roche Internship (2022-2022): Developed ML pipelines for Huntington's disease symptom prediction Wintech Internship (2018): Built web applications for contact management and campaign analysis Labs/Teams: Silvia Santini’s research group at USI

• Seismology
• Earthquake Dynamics
• Geophysics
• +۳ مورد دیگر

Dr. Kadek Hendrawan Palgunadi is a researcher at the Swiss Seismological Service (SED) under ETH Zurich. His work focuses on seismology, earthquake dynamics, and fault system analysis with a particular emphasis on Indonesia and Türkiye. He has contributed significantly to understanding cascading earthquake mechanisms, fault geometry characterization, and ground motion modeling. His recent studies include analysis of the 2023 Türkiye earthquake doublet, the 2022 Cianjur earthquake, and the newly identified Kalaotoa Fault system. Key research areas include multiscale seismicity patterns, fracture energy analysis in fault networks, and innovative methods for seismic waveform generation using machine learning. He has developed methodologies for hypocenter relocation and rupture dynamics modeling, contributing to improved seismic hazard assessments in active tectonic regions. His work also extends to interdisciplinary projects such as the Sea-VL dataset for Southeast Asia vision-language studies and sustainable agriculture systems. Notable contributions include insights into fault slip localization, stress regime analysis in complex fault systems, and applications of denoising diffusion models for high-resolution seismic data processing. Collaborations involve international institutions and fieldwork in regions like Sulawesi, Sumatra, and the East Anatolian Fault zone.

Alexandre Refregierمشاهده پروفایل

استاد

• Cosmology
• Astrophysics
• Dark Energy
• +۵ مورد دیگر

Prof. Alexandre Refregier is a Full Professor at the Department of Physics, ETH Zürich. He leads research in cosmology and astrophysics, focusing on dark energy, dark matter, and large-scale structure analysis using observational, theoretical, and instrumental approaches. His work spans gravitational lensing, cosmic microwave background, and galaxy cluster studies. He has contributed to major projects like the Dark Energy Survey and Euclid mission, leading instrumental development and data analysis efforts. Refregier holds a PhD from Columbia University (1997) and has held positions at institutions including Princeton University and the University of Cambridge. Education: PhD in Physics, Columbia University, 1997 M.Phil. and M.A. in Physics, Columbia University, 1992–1993 B.S. in Physics (Summa cum Laude), University of Texas at Austin, 1991 Research Interests: Dark energy/dark matter dynamics, weak lensing, baryon acoustic oscillations, cosmic microwave background analysis, and cosmological probes. His interdisciplinary methods combine theoretical modeling with cutting-edge observational techniques and instrument design. Key Contributions: Over 200 published papers (e.g., on Euclid mission instrumentation, joint lensing-CMB analyses, and baryonic feedback modeling). Active in developing simulation tools like PyCosmo and GalSBI for cosmological inference. Awards/Grants: Not explicitly listed, but his leadership in major collaborations implies significant funding and recognition. Active in Simons Foundation-supported research. Labs/Teams: Leads the Cosmology Group at ETH Zurich, involved in Euclid's science and instrument teams, and collaborates on projects like HIRAX and SKA simulations.

• Machine Learning
• Biomedical Informatics
• Medical Genomics
• +۳ مورد دیگر

Prof. Gunnar Rätsch is a Full Professor in the Department of Computer Science at ETH Zürich and Deputy Head of the Institute for Machine Learning. His research focuses on developing machine learning methods for biomedical applications, including genomics, medical imaging, and clinical decision support systems. He specializes in integrating multi-omics data, spatial transcriptomics, and time-series analysis to address challenges in precision medicine and critical care. His work emphasizes ethical AI frameworks, algorithmic fairness, and robust clinical prediction models. Key research areas include: Deep learning for medical imaging and histopathology Single-cell analysis and tumor profiling Reinforcement learning for treatment optimization in ICUs Multimodal data integration for clinical applications Recent work highlights advancements in: Standardizing single-cell cytometry readouts for clinical use Developing foundation models for critical care time-series analysis Creating interpretable survival models for ICU patients His contributions span foundational machine learning theory and applied healthcare technologies, with a focus on translational research to improve clinical outcomes. Current projects include the Tumor Profiler Study for multi-omic tumor analysis and ethical frameworks for clinical AI systems.

Gerhard Schrattمشاهده پروفایل

استاد

• Molecular Neuroscience
• Synaptic Plasticity
• Non-coding RNAs
• +۱ مورد دیگر

Gerhard Schratt is Full Professor and Head of the Institute for Neuroscience at ETH Zürich. His laboratory investigates molecular mechanisms of synapse development and plasticity, with emphasis on non-coding RNA regulation in neurological disorders. Research integrates molecular neurobiology with computational approaches to study microRNA functions in neuronal development, synaptic homeostasis, and stress responses. Current projects examine RNA-based therapies for neurodegenerative conditions and computational pathology platforms for digital histology. Professor Schratt's group develops AI frameworks for neurological assessment including multimodal dementia diagnosis, voice-based cognitive testing with privacy protection, and digital pathology for kidney disease. Collaborative projects with the Chan Zuckerberg Initiative explore mitochondrial regulation by microRNAs. He teaches molecular neurophysiology and neuroscience courses, supervising doctoral candidates in systems neuroscience approaches.

Charalampos Anastasiouمشاهده پروفایل

استاد

• Theoretical Particle Physics
• Quantum Field Theory
• Particle Phenomenology
• +۳ مورد دیگر

Charalampos Anastasiou is a Full Professor of Theoretical Particle Physics at ETH Zurich's Department of Physics. He leads research in quantum field theory and its applications to particle physics phenomenology, particularly in predicting Higgs boson signals at the Large Hadron Collider (LHC). His work includes developing mathematical methods for Feynman diagrams and precision calculations for collider experiments. He has held academic positions at ETH Zurich since 2007, including Associate Professor (2010–2013) and Full Professor (2013–present). His career includes research roles at CERN and Stanford Linear Accelerator Center. Education: Ph.D. in Particle Theory, Durham University, UK (2001) Research Interests: Anastasiou's research focuses on advancing theoretical frameworks for particle physics, with emphasis on perturbative quantum field theory, collider phenomenology, and precision calculations for Higgs boson interactions. His work bridges mathematical rigor and experimental validation, contributing to the understanding of fundamental forces and particle interactions. Publications: His recent publications span superconducting magnet design for future colliders, quantum-resistant cryptographic methods, and material science studies. These reflect his interdisciplinary approach to addressing challenges in high-energy physics and technology. Awards & Grants: ERC Advanced Grant: PertQCD (2016) ERC Advanced Grant: IterQCD (2010) University Latsis Prize (2009) Advising & Labs: Anastasiou collaborates on projects like the Muon Collider and LHC upgrades. His lab develops novel superconducting magnet technologies and computational tools for high-energy physics experiments. He actively contributes to international initiatives like the Future Circular Collider (FCC) and European Strategy for Particle Physics.

• Medical Imaging
• Artificial Intelligence
• Image Processing
• +۳ مورد دیگر

Prof. Jérôme Schmid is a Full Professor (Professeur HES ordinaire) at the Haute école de santé - Genève, part of the Faculty of Health. His expertise spans medical image processing, artificial intelligence (AI), and their clinical applications. He leads innovative projects addressing challenges in diagnostics, surgery planning, and medical education. Key roles include Principal Investigator in grants funded by Swiss National Science Foundation, Swiss Innovation Agency, and others. Research Focus: Combines AI with medical imaging for applications such as Parkinson’s disease detection via SPECT, breast lesion analysis using ultrafast MRI, and AI-driven radiography training tools. Projects emphasize interdisciplinary collaboration with hospitals and industry partners. Projects: DeepDAT (2022–2025): AI for Parkinson’s diagnosis via SPECT imaging. SUBREAM (2022–2025): Rapid breast MRI protocols with AI integration. AIRx (2019–2020): AI-based radiography simulation for student training. MyHip (2012–2014): Patient-specific hip arthroplasty planning. Publications: Focus on AI-driven diagnostics, imaging techniques, and medical education. Recent works address drowning detection via post-mortem CT, multimodal AI fusion for breast cancer, and serious games in radiology training. Grants & Partnerships: Swiss National Science Foundation: FAI analysis via multi-modal imaging. Innosuisse: Low-cost X-ray detectors for developing countries (GlobalDiagnostiX). Swiss Cancer Research Foundation: Breast MRI advancements (SUBREAM). Labs/Teams: Collaborates with the Geneva University Hospitals, EPFL, and industry (e.g., Medacta International SA) on hardware and clinical AI solutions.

• Human-Machine Interaction
• Human-Robot Interaction
• Multimodal Interaction
• +۷ مورد دیگر

Nabil Ouerhani is a Professor in Computer Science at Haute Ecole Arc // HES-SO, leading the 'Interaction Technologies' research group focused on Human-Machine Interaction (HMI), Human-Robot Interaction (HRI), and Industry 4.0 applications. His work integrates IoT, cyber-physical systems, and multi-agent systems to develop innovative industrial solutions. Education: BSc HES-SO in Computer Science - Haute Ecole Arc - Ingénierie Key Research Areas: Collaborative robotics and AI-driven automation Thermal error prediction in machine tools IoT-enabled energy efficiency solutions Virtual/Augmented Reality for industrial safety Recent Projects: BonsAPPs (EU H2020): AI-as-a-Service platform for Deep Edge applications Decolleteur 4.0 (Innosuisse): AI-based Swiss-Type lathe programming system TherMoMac : Hybrid thermal error prediction for machine tools Grants & Funding: BonsAPPs: CHF 5,750,000 (EU H2020) Decolleteur 4.0: CHF 1,217,809 (Innosuisse) ECOMAC25: CHF 439,062 (Innosuisse) Research Collaborations: Industry partners: Tornos SA, NVISO SA, ST Microelectronics Academic partners: University of Bologna, ETH Zurich, SUPSI

• Medical Imaging
• Artificial Intelligence
• Image Processing
• +۵ مورد دیگر

Prof. Jérôme Schmid is a Professor at the Geneva School of Health Sciences (Haute école de santé - Genève) in the Department of Medical Radiology Techniques. He specializes in medical image processing, artificial intelligence applications in healthcare, and computer-aided diagnosis systems. His work focuses on improving diagnostic accuracy through machine learning, deformable models, and multimodal imaging fusion. Education includes a Master of Science (MSc) in Health Sciences from HES-SO/UNIL and a Bachelor of Science (BSc) in Medical Radiology Techniques from HES-SO. He also contributed to advanced education programs in 3D technologies in orthopedics and sports medicine through collaborations with the Geneva University Medical Faculty. Research interests revolve around AI-driven medical imaging analysis, specifically in breast lesion detection via MRI, Parkinson's disease diagnosis through SPECT imaging, and fracture recognition in radiographs. He leads projects addressing global healthcare challenges such as affordable imaging systems for low-resource countries and AI integration into radiology education through platforms like AIRx and ImmeRgaMe. Current projects (2022-2025) include DeepDAT (AI for Parkinson's diagnosis using SPECT) and SUBREAM (ultrafast breast MRI protocols). Completed projects span hip arthroplasty planning (MyHip), thermal-tolerant X-ray detectors, and FAI impingement modeling. His work is funded by Swiss National Science Foundation, Innosuisse, and private sector partnerships. Publications span 2015-2025, focusing on image segmentation, AI integration in diagnosis, and medical education innovations. He collaborates with academic partners like Hôpitaux Universitaires de Genève and industry leaders such as Medacta International.

• Learning Analytics
• Artificial Intelligence in Education
• Educational Technology
• +۳ مورد دیگر

Paola Mejia is a current researcher at the Swiss Federal Institute of Technology in Lausanne (EPFL), serving as primary contact for the Machine Learning for Education (ML4ED) research unit. Her work is centered in the School of Computer and Communication Sciences, where she advances AI applications in educational contexts through collaborative projects with international institutions. Her research focuses on leveraging machine learning to enhance learning analytics, with expertise spanning adaptive educational systems, student modeling, and data-driven pedagogy. Key interests include developing intelligent tutoring systems and analyzing learner behavior patterns through computational methods, often in partnership with researchers like Tanja Käser from Monash University. Mejia's scholarly contributions are supported by major funding bodies including the Australian Research Council and Swiss State Secretariat for Education, Research and Innovation. She actively publishes in top venues like the International Conference on Learning Analytics and Knowledge (LAK) and Artificial Intelligence in Education (AIED) series, with recent work emphasizing ethical AI implementation in diverse educational settings. Her research trajectory shows increasing focus on cross-cultural learning analytics and multimodal data integration. As a core member of EPFL's digital education ecosystem, she contributes to the AVP-E-LEARN unit's mission of transforming pedagogical practices through technology. She maintains professional collaborations across institutions including Monash University and Berliner Hochschule für Technik, with all correspondence directed to paola.mejia@epfl.ch .