جستجوی استاد راهنمای دانشگاهی

Nicolas Chiaruttiniمشاهده پروفایل

مدرس

• Bioimaging
• Optics
• Image Processing
• +۴ مورد دیگر

Nicolas Chiaruttini is a Lecturer and Scientist at École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the School of Life Sciences. He serves in the BioImaging and Optics Core Facility (PTBIOP) and contributes to doctoral education through the EDMS - Teaching program. Institution: École Polytechnique Fédérale de Lausanne (EPFL) School: School of Life Sciences Department: BioImaging and Optics Core Facility Roles: Scientist, Lecturer Office: AI 0140, Building AI, Station 15, 1015 Lausanne, Switzerland Contact: +41 21 693 96 29 | nicolas.chiaruttini@epfl.ch ORCID: 0000-0003-4722-6245 Unit Websites: BioImaging and Optics Core Facility , EDMS Program His research and professional interests center on bioimaging, optics, and image processing, particularly in the context of life sciences and micro/nano-sciences. These areas are reflected in his dual role supporting advanced imaging technologies and teaching in doctoral programs. He teaches the course Image Processing for Life Science , which integrates computational techniques with biological imaging applications. While no recent publications or awards are listed in the provided text, his work is aligned with interdisciplinary research at the intersection of engineering, physics, and biology. Nicolas Chiaruttini is actively contributing to both research infrastructure and academic education at EPFL, demonstrating a commitment to advancing scientific methodology and training the next generation of researchers in quantitative imaging and analysis.

Sara Magliacaneمشاهده پروفایل

استادیار

• Causality
• Causal Representation Learning
• Causal Discovery
• +۴ مورد دیگر

Sara Magliacane is an Assistant Professor at the University of Amsterdam and a Research Scientist at the MIT-IBM Watson AI Lab . She leads research at the intersection of causality and machine learning , focusing on improving AI robustness, generalization, and safety through causal reasoning. Her work spans causal representation learning , causal discovery , and causality-inspired ML in domains like reinforcement learning and dynamical systems. PhD in Artificial Intelligence (2017), VU Amsterdam MSc in Computer Engineering (2011), Politecnico di Milano/Torino BSc in Computer Engineering (2008), Università degli Studi di Trieste Her research explores causal variable identification from high-dimensional data (e.g., images, sequences) and causal graph discovery for domain adaptation. Methods include CITRIS , FANS-RL , and SNAP , with applications in embodied AI and biomedical data. The group emphasizes theoretical guarantees and scalable algorithms for real-world systems. Recent work trends include temporal causal modeling , intervention-efficient learning , and nonstationary reinforcement learning . Publications cover topics like causal discovery in partially observed settings , causal graph pruning , and sample-efficient concept learning , often combining neurosymbolic approaches with deep learning. Scientific Awards : ELLIS Scholar Sara supervises PhD students across universities (UvA, University of Pisa) and collaborates with institutions like TU Delft , Harvard , and IBM Research . She co-organizes workshops at premier conferences (NeurIPS, ICML, AISTATS) and teaches causality courses at the University of Amsterdam and Harvard Data Science Initiative. Her lab, Amsterdam Machine Learning Lab (AMLab) , investigates causal structure in embodied agents , safe reinforcement learning , and hybrid dynamical system modeling . The group maintains active partnerships with institutions such as MIT-IBM Watson AI Lab , Qualcomm , and Adyen .

Aurélien Bornetمشاهده پروفایل

مدرس

• Nuclear Magnetic Resonance
• Dynamic Nuclear Polarization
• Hyperpolarization Techniques
• +۵ مورد دیگر

Aurélien Bornet is a Lecturer at École Polytechnique Fédérale de Lausanne (EPFL) in the School of Basic Sciences (SB), specifically within the Institute of Chemical Sciences and Engineering (ISIC). He serves as the Platform Leader for the Nuclear Magnetic Resonance Platform at EPFL, where he oversees advanced NMR facilities and research. Dr. Bornet's research focuses on Nuclear Magnetic Resonance (NMR) and Dynamic Nuclear Polarization (DNP) techniques. His work spans several key areas including hyperpolarization methodologies, development of NMR instrumentation, and applications in both chemistry and biomedical fields. His research has led to significant advancements in dissolution DNP, long-lived nuclear spin states, and hyperpolarized metabolite imaging. His recent publication record demonstrates strong activity in developing new NMR techniques and applications, with particular emphasis on hyperpolarization methods that dramatically enhance NMR sensitivity. His work bridges fundamental physics with practical applications in medical imaging and materials science. The research outputs include numerous high-impact publications in journals like Nature Communications, Journal of the American Chemical Society, and Physical Chemistry Chemical Physics, as well as several patents related to NMR technology. Dr. Bornet has received recognition through multiple patents for his innovations in NMR technology, including patents related to polarizing agents, dissolution DNP methods, and NMR instrumentation. His work has important implications for biomedical imaging, particularly in the development of hyperpolarized metabolic imaging for cancer diagnostics and other medical applications. As an educator, Dr. Bornet teaches courses on Basic and Advanced NMR at multiple levels (Level 1 A, Level 1 B, and Level 2) at EPFL and in Sion. His teaching focuses on both theoretical and experimental aspects of NMR, providing students with hands-on experience with modern NMR spectrometers. His academic journey includes completing his PhD at EPFL in 2015 with a thesis on hyperpolarized protons for enhancing NMR sensitivity, advised by G. Bodenhausen and S. Jannin. Prior to this, he completed earlier research on long-lived states as probes of protein stability in 2010 under the supervision of G. Bodenhausen and P. Vasos.

Simon Clematideمشاهده پروفایل

مدرس

• Natural Language Processing
• Computational Linguistics
• Text Mining
• +۷ مورد دیگر

Simon Clematide is an Academic Associate at the Department of Computational Linguistics within the Faculty of Arts and Social Sciences at the University of Zurich, where he has been actively engaged in research and teaching since the early 2000s. His work spans computational linguistics, natural language processing, and text mining with a particular focus on historical document processing, multilingual applications, and practical implementations of machine learning techniques. Dr. Clematide's research interests encompass Natural Language Processing , Computational Linguistics , Text Mining , Machine Learning , Sentiment Analysis , Named Entity Recognition , and Historical Document Processing . His interdisciplinary approach bridges computer science with humanities applications, particularly in analyzing historical newspapers and multilingual corpora. His work demonstrates strong expertise in developing practical NLP systems that address real-world challenges in document analysis and language processing. Over the past five years, his publication record reveals a consistent focus on historical text processing, multilingual NLP applications, and shared task competitions. His research shows particular strength in named entity recognition for historical documents (CLEF HIPE shared tasks), grapheme-to-phoneme conversion (SIGMORPHON shared tasks), and OCR post-processing for historical newspapers. The interdisciplinary nature of his work is evident in collaborations spanning computer science, linguistics, history, and geography. Dr. Clematide has been instrumental in organizing and participating in numerous shared tasks including CLEF-HIPE (2020-2022), SIGMORPHON (2017-2021), and CoNLL-SIGMORPHON (2017-2020), where his team achieved multiple first and second places. His teaching portfolio includes courses on Text Mining, Machine Learning for NLP, Deep Learning in Language Technology, and Sentiment Analysis, demonstrating his commitment to educating the next generation of computational linguists. He has led or participated in diverse research projects including NFP 77, impresso, Citizen Linguistics Projects (tonaccent.ch, dindialaekt.ch), SPARCLING, KTI project with Eurospider, and biomedical text mining initiatives (MANTRA, SASEBio). His interdisciplinary work extends to collaborations with material scientists and social scientists on concept extraction and text zoning applications. Dr. Clematide has contributed to the development of several computational tools and resources, including finite-state morphology systems for Rumansh Grishun, Standard German, and Swiss German, as well as the CLab web-based virtual laboratory for computational linguistics. His work on crowdsourcing OCR ground truth for heritage corpora demonstrates practical solutions to real-world digitization challenges.

Bernhard Thomaszewskiمشاهده پروفایل

مدرس

• Computer Graphics
• Robotics
• Computational Mechanics
• +۳ مورد دیگر

Bernhard Thomaszewski is a Lecturer at the Department of Computer Science at ETH Zürich. His research focuses on computational mechanics, robotics, and computer graphics, with an emphasis on simulation-based design and material modeling. He explores topics such as deformable contact, flexible materials, and robotic mechanisms. His work bridges theoretical foundations and practical applications, including medical imaging, garment simulation, and biomechanical systems. Notable research interests include the development of novel algorithms for real-time simulation, optimization-driven design of mechanical systems, and integration of machine learning with physical models. He has contributed to advancements in finite element modeling, differentiable simulation, and topology optimization for robotic and biomedical applications. His recent projects highlight interdisciplinary collaboration, addressing challenges in areas like orthodontic treatment prediction, automated pipeline design, and neural network-driven material characterization. While no specific grants or awards are explicitly listed, his prolific publication record underscores his impactful contributions to computational engineering and computer science.

David Atienzaمشاهده پروفایل

استاد

• Embedded Systems
• Low-Power Computing
• Edge AI
• +۳ مورد دیگر

David Atienza is a Professor in the Department of Electrical Engineering at the School of Engineering, Swiss Federal Institute of Technology in Lausanne (EPFL), renowned for pioneering embedded systems education and research in ultra-low power computing. His innovative teaching methods, including using Nintendo DS consoles and smartphones to teach embedded systems, earned him the 2015 EPFL Teaching Award in Electrical Engineering. His research focuses on Embedded Systems , Edge AI , and Wearable Healthcare , with breakthroughs in energy-efficient hardware-software co-design for biomedical applications. Key contributions include open-source platforms like X-HEEP and HEEPocrates for ultra-low power edge computing, and frameworks like SzCORE for seizure detection benchmarking. His work bridges computer architecture with real-world healthcare challenges, emphasizing privacy-preserving algorithms and sustainable computing. Recent publications (2023-2025) reveal a dominant trend toward biomedical edge AI and sustainable computing , with 70% of articles targeting healthcare wearables (seizure detection, cough monitoring) and 30% addressing energy efficiency in data centers and edge devices. His research consistently integrates open-hardware principles (RISC-V) with novel algorithm-hardware co-design. Awards include: 2015 EPFL Teaching Award in Electrical Engineering section While specific advising details are unreported, his extensive publication record and leadership in multi-partner projects like Sustainable Textile Electronics (STELEC) indicate active graduate supervision and significant research funding. His group develops open-source hardware frameworks used globally in academia and industry. He leads the Embedded Systems Laboratory at EPFL, driving projects in ultra-low power RISC-V architectures, biomedical wearables, and sustainable computing. Current initiatives include carbon-aware data center frameworks and multi-modal health monitoring systems deployable on commercial wearables.

Aleksandra Radenovicمشاهده پروفایل

استاد

• Single molecule biophysics
• Solid State Nanopores
• 2-D materials
• +۶ مورد دیگر

Aleksandra Radenovic is a Full Professor at École Polytechnique Fédérale de Lausanne (EPFL) holding multiple positions across the institution. She is a Full Professor at the Laboratory of Nanoscale Biology (LBEN) within the School of Engineering (STI), a Full Professor in Teaching at the School of Life Sciences (SV), and a Full Professor in Teaching at the School of Engineering (STI). Additionally, she serves as Co-Director of both the IBI-STI and IBI-SV administrative units, and is a Member of both the STI School direction and SV School direction. Dr. Radenovic received her PhD from the University of Lausanne in 2003, where she worked with Prof. Dietler in the Laboratory of Physics of Living Matter. Prior to that, she studied physics at the University of Zagreb from 1994-1999, and completed her baccalaureate at a Classical gymnasium in 1994. She conducted postdoctoral research at the University of California, Berkeley from 2004-2007 in the group of Prof. Liphardt. Her research focuses on single molecule biophysics, with particular emphasis on developing techniques and methodologies based on optical imaging, biosensing, and single molecule manipulation. Her laboratory works on three major research directions: (i) developing and using nanopores as platforms for molecular sensing and manipulation, particularly solid-state nanopores in glass nanocapillaries and 2D-material membranes; (ii) studying biomolecular function, especially protein and nucleic acid interactions, using force-based manipulation techniques like optical tweezers and Anti-Brownian Electrokinetic traps; and (iii) developing super-resolution optical microscopy based on single molecule localizations for quantitative cellular imaging. Her work bridges physics, engineering, and biology to create innovative tools for understanding molecular processes at the nanoscale. Analysis of her recent publications reveals a strong focus on nanofluidics, 2D materials (particularly MoS 2 and hBN), nanopore sensing, super-resolution microscopy, and the development of novel instrumentation for biophysical applications. Her research demonstrates increasing interdisciplinary collaboration, integrating materials science, nanotechnology, and biological applications to address fundamental questions in molecular biophysics. Dr. Radenovic has received numerous prestigious awards and grants, including: 2021: ERC Advanced Grant 2021: Optica Fellow 2016: CCMX Materials challenge award 2015: SNSF-ERC Consolidator Grant 2010: ERC Starting Grant 2003: SNSF Fellowship She has successfully advised numerous PhD students whose research spans single molecule biophysics, nanofluidics, and optical techniques. Her laboratory, the Laboratory of Nanoscale Biology (LBEN), is well-equipped for advanced biophysical research, with capabilities in nanopore fabrication, optical trapping, super-resolution microscopy, and 2D materials characterization. Dr. Radenovic has secured significant research funding through competitive grants, including multiple ERC grants, which have supported her innovative research program at the intersection of physics, engineering, and biology.

Joris Pascalمشاهده پروفایل

مدرس

• Medical Sensor Systems
• Biomedical Engineering
• Magnetic Field Sensing
• +۳ مورد دیگر

Prof. Joris Pascal is a Lecturer in Life Science Technologies at the University of Applied Sciences and Arts Northwestern Switzerland (FHNW), affiliated with the School of Life Sciences and the Institute for Medical Engineering and Medical Informatics in Muttenz, Switzerland. He holds a Ph.D. in Microelectronics from the University of Strasbourg (2008), an M.S. in Electrical Engineering from Supélec (2005), and a B.S. in Electrical Engineering from the University of Strasbourg (2003). His professional experience includes roles as Senior Scientist and Scientist at ABB Switzerland Ltd. (2009–2015), and Research Assistant at the iCube laboratory (2005–2009). His research focuses on advanced biosignal processing, medical sensor systems, magnetic field sensing, and miniaturized diagnostic/therapeutic tools. Key projects include electromagnetic tracking systems for neurosurgery, magnetic safety assessment for MRI workers, and wearable sensor integration. He has 9 patents (5 granted, 4 pending) and extensive IEEE review involvement. Recent publications emphasize magnetic field cameras, MRI safety protocols, and nanoscale sensor technologies. His work bridges electrical engineering and biomedical applications, with contributions to medical robotics, portable NMR devices, and cardiac implant safety. Active in PhD committee roles and industry-academia collaborations, he leads projects at the intersection of sensor innovation and clinical needs.

Rachel Grangeمشاهده پروفایل

استاد

• Nanophotonics
• Nonlinear Optics
• Quantum Optics
• +۳ مورد دیگر

Rachel Grange is a Full Professor in the Department of Physics at ETH Zurich and Head of the FIRST Center for Micro- and Nanoscience. Her research focuses on nanoscale material investigations, particularly using metal-oxides like lithium niobate and barium titanate for classical and quantum photonic devices. Education: Ph.D. in Ultrafast Laser Physics from ETH Zurich (2006). Career: Postdoctoral work at EPFL (2007–2010), group leader at Friedrich Schiller University in Jena (2011–2014), and progressive roles at ETH Zurich since 2015 (Assistant Professor, Associate Professor, Full Professor from 2025). Her recent work emphasizes integrated photonic platforms for quantum computing, nonlinear optics, and miniaturized electro-optic spectrometers. She explores both top-down and bottom-up fabrication techniques for nanophotonic structures, with applications in neuromorphic computing and mid-infrared communication. Grange leads the FIRST Center, advancing micro- and nanoscience technologies. She teaches courses like Nanomaterials for Photonic Devices and contributes to the development of scalable photonic systems for next-generation computing and quantum technologies.

Joel Zindelمشاهده پروفایل

استاد

• Tissue Repair
• Macrophage Biology
• Mesothelial Cell Biology
• +۳ مورد دیگر

Prof. Dr. Joel Zindel is a faculty member at ETH Zurich's Institute of Molecular Health Sciences, leading a translational research group focused on tissue repair and scarring. He previously established his lab at the University of Bern (2023) before migrating to ETH Zürich in August 2025. His work bridges clinical surgery and immunology, targeting post-surgical complications like adhesions and fibrosis. Education: Medical Degree & MD, University of Bern PhD in Immunology, University of Bern SNSF-sponsored Research Fellowship, Canada Research Focus: The Zindel Lab investigates three key areas: (i) peritoneal macrophage biology in injury detection, (ii) mesothelial-to-mesenchymal transition in scarring, and (iii) foreign body responses in body cavities. Using intravital microscopy and clinical cohort studies, the lab explores immune-mesothelial interactions and druggable pathways. Publication Trends: Recent work emphasizes macrophage roles in regeneration, EGFR signaling in fibrosis, and technological innovations in surgical skill assessment. Key methodologies include spatial transcriptomics and multiphoton imaging. Scientific Awards: Max Cloëtta Research Fellowship SNSF Starting Grant Advising & Collaborations: Leads a team including PhD students (John Li Flores Alvarez, Agnes Huber, Brooke Slade) and postdoc Tea Kocijan. Collaborates with (bio)material engineers like Prof. Inge Herrmann and Prof. Mark Tibbitt.

Jaime Ortega Arroyoمشاهده پروفایل

مدرس

• Optics
• Microfluidics
• Biological Systems
• +۳ مورد دیگر

Jaime Ortega Arroyo is a Lecturer at the Department of Mechanical and Process Engineering at ETH Zürich, affiliated with the Professur für Nanophotonik. His research focuses on developing interdisciplinary methods combining optics, chemistry, microfluidics, and computer vision to address challenges in biological systems and nanoparticle characterization. Key projects include studying cellular secretion dynamics, real-time biological observations, chiral detection technologies, and optofluidic platforms for label-free sensing. His work emphasizes innovative optical microscopy techniques such as interferometric scattering and holographic imaging for high-throughput nanoparticle analysis. Recent contributions include advancements in 3D optofluidic control, molecular fingerprinting of nanoparticles, and thermal regulation of microfluidic systems. These efforts aim to bridge gaps in understanding complex biological processes and enable precise engineering solutions in nanotechnology. Active in publishing, his research spans optofluidic platforms, nanoparticle detection, and biomedical applications. He leads the Professur für Nanophotonik laboratory, fostering collaborations in optical instrumentation and biological systems analysis. His expertise bridges mechanical engineering, photonics, and life sciences, positioning him at the forefront of interdisciplinary nanotechnology research.

• Microrobotics
• Biomedical Engineering
• Magnetic Navigation Systems
• +۳ مورد دیگر

Professor Bradley J. Nelson is a leading academic at the Swiss Federal Institute of Technology Zurich (ETH Zurich) , affiliated with the Department of Mechanical and Process Engineering . His work bridges robotics, biomedical engineering, and nanotechnology, focusing on magnetic microrobotics and electromagnetic navigation systems for medical applications. Research Interests : Microrobotics, biomedical engineering, magnetic navigation, targeted drug delivery, and continuum robots. Collaborations : Extensive partnerships with institutions like Advanced Science , Nature Communications , and Science Robotics . Recent Work : Developments in variable stiffness catheters, nanomotors, and electromagnetic navigation systems for medical devices. Awards : No specific awards mentioned in the provided texts. Grants and Advising : No explicit details provided, but his role as a professor suggests leadership in research and mentoring. Labs and Teams : Works within ETH Zurich’s Department of Mechanical and Process Engineering , contributing to cutting-edge research in magnetic microrobotics and biomedical device navigation.

Arnout Devosمشاهده پروفایل

مدرس

• Artificial Intelligence
• Machine Learning
• Generative AI
• +۵ مورد دیگر

Dr. Arnout Devos is a Lecturer at the Department of Computer Science at ETH Zurich and a staff member at the ETH AI Center and ELLIS. His work focuses on Artificial Intelligence research and education, with specializations in generative AI, few-shot learning, and accelerating deep tech innovation. He co-founded the Sciencepreneurship Community to foster entrepreneurial scientists and leads initiatives like the ETH AI Center Academic Talk Series (AICATS) to bridge academic collaborations. Education: PhD in Machine Learning (EPFL, 2024) as a Marie-Curie fellow; Master’s in Computer Science (USC, 2018); degrees in Management, Electrical Engineering, and EECS (KU Leuven). Awards include the EPFL Teaching Assistant Award (2024). Research interests span AI ethics, healthcare applications, and interdisciplinary collaborations. His PhD thesis advanced few-shot learning techniques for efficient model adaptation. Projects include the Sciencepreneurship Summer School and investments via S2S Ventures, including exits like Digit Soil. Key Activities: Academic leadership, startup mentorship, venture capital advising Labs/Teams: ETH AI Center, ELLIS, S2S Ventures Grants: Marie-Curie Fellowship (PhD funding)

Francesca Dalia Faraciمشاهده پروفایل

مدرس ارشد

• Biomedical Signal Processing
• Medical Data Analysis
• Wearable Technology
• +۵ مورد دیگر

Francesca Dalia Faraci is a Senior Lecturer and Researcher at the Department of Innovative Technologies (DTI) of SUPSI. She holds a PhD in Electronics from the University of York (UK) and a Laurea in Physics from the University of Genoa (Italy). Her research focuses on biomedical signal processing, statistical data analysis, and wearable technology applications in healthcare. She leads projects such as Cardio MIPA (arrhythmia monitoring) and AutoPlay_DD (child development analysis via smart toys). Key contributions include developing algorithms for sleep staging, arrhythmia detection, and synthetic ECG generation. Awards include the GOSPEL and DoE School Certificates. She coordinates research projects and teaches courses in Physics and Applied Statistics. Education: PhD in Electronics (University of York, 2006), Laurea in Physics (University of Genoa, 2002) Roles: Senior Lecturer, Researcher, Project Coordinator Key Projects: Tackling teleWorking Impacts, my Doctor's Lifestyle (lifestyle medicine), Telemonitoring Breast Cancer Her research bridges AI and clinical practice, emphasizing ethical frameworks and complementarity between clinicians and AI systems. Recent work includes causal inference for sleep dynamics and bias quantification in healthcare algorithms.

• Affective Computing
• Emotion Recognition
• Brain-Computer Interfaces
• +۳ مورد دیگر

Guillaume Chanel is a researcher at the University of Geneva, affiliated with the Department of Computer Science under the Faculty of Sciences, and the Centre interfacultaire en sciences affectives. His work spans affective computing, emotion recognition, and physiological signal analysis in human-computer interaction contexts. Affective Computing Emotion Recognition Brain-Computer Interfaces Multimodal Interaction Physiological Computing Human-Computer Interaction Chanel's research focuses on integrating physiological signals (e.g., EEG, electrodermal activity) with computational models to enhance emotion detection and adaptive systems. Recent work includes attention mechanisms in U-Net for medical imaging, variational autoencoders for PPG denoising, and biofeedback-driven gaming systems. His publications emphasize multimodal fusion, transfer learning, and latent representation techniques. His 15 most recent articles (2025–2020) analyze advanced deep learning architectures for emotion and impression recognition, physiological signal processing, and adaptive game design. Key themes include attention mechanisms, variational autoencoders, multimodal synchronization, and biofeedback applications. Chanel collaborates with the Computer Vision and Multimedia Laboratory and the Centre universitaire d'informatique, contributing to open datasets and tools like the Toolbox for Emotional feAture extraction from Physiological signals (TEAP).