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Olga Sorkine-HornungView profile

Professor

• Digital Content Creation
• Geometry Processing
• Shape Representation and Editing
• +5 more

Olga Sorkine-Hornung is a Professor of Computer Science at ETH Zurich and head of the Institute of Visual Computing. She leads the Interactive Geometry Lab, focusing on theoretical and practical advancements in digital content creation, geometry processing, and shape modeling. Current position: ETH Zurich, Department of Computer Science Previous roles: Courant Institute (NYU), Technical University of Berlin Education: BSc and PhD from Tel Aviv University, postdoc at TU Berlin Her research spans shape representation, digital fabrication, computer animation, and fundamental geometry processing. Key contributions include Laplacian surface editing, as-rigid-as-possible deformation, and generalized winding numbers. She works on applications in VR, AR, and autonomous systems. Awards include Test of Time Awards (2024), ACM Fellow (2020), ERC Consolidator Grant (2020), and EUROGRAPHICS Young Researcher Award (2008). She has supervised numerous students and co-developed software libraries like libigl and Instant Meshes . Co-chair roles for SIGGRAPH, Eurographics, and Pacific Graphics Editorial board member for ACM Transactions on Graphics and other journals Keynote speaker at VMV, CVPR, and SIAM conferences Her work bridges mathematical rigor with practical implementation, advancing computer graphics and geometry processing through intuitive algorithms that maintain surface detail while enabling efficient computation.

Kyojin ChooView profile

Assistant Professor

• Charge-domain circuits
• Sensor interfaces
• Energy converters
• +4 more

Kyojin Choo is a Tenure Track Assistant Professor at the Swiss Federal Institute of Technology Lausanne (EPFL) in the School of Engineering , affiliated with the Mixed-Signal Integrated Circuits Lab (MSIC-LAB). He also holds teaching roles in Microengineering and Electrical and Electronics Engineering at EPFL. B.S. and M.S. in Electrical Engineering from Seoul National University (2007, 2009) Ph.D. in Electrical Engineering from the University of Michigan (2018) His research focuses on charge-domain analog/mixed-signal circuits , low-power sensor interfaces , and compact ADCs for IoT, wearables, and millimeter-scale systems. He has pioneered charge-injection cell techniques for energy-efficient circuits in energy management, sensor front-ends, and communication. His work emphasizes reducing power consumption to nanowatt levels while enabling ultra-compact designs. His recent publications highlight advancements in compact SAR ADCs , low-power MEMS accelerometers , millimeter-scale imaging systems , and ultra-low-power timing generators . His research integrates charge-domain circuit design with sensor interface optimization , energy harvesting , and high-speed link architectures . He holds over 20 US patents and has taught courses in Microengineering and Electrical Engineering at EPFL. His group (MSIC-LAB) addresses challenges in battery-free sensor design, power-constrained system scaling, and commercialization of wearables with unconventional form factors.

Volker DellwoView profile

Associate Professor

• Phonetics
• Speech Recognition
• Computational Linguistics
• +4 more

Prof. Dr. Volker Dellwo is an Associate Professor of Phonetics and head of the Department of Computational Linguistics. His research focuses on phonetics, speech recognition, computational linguistics, and dialectology, with applications in forensic analysis, voice biometrics, and multimodal emotion recognition. Academic Rank: Associate Professor Department: Computational Linguistics His work explores phonetic convergence , speaker discrimination, and the role of prosodic features in voice recognition. Recent studies analyze whispered speech processing, cross-dialect accommodation, and neural mechanisms of speaker identity encoding. Key article trends include self-supervised learning for speech recognition , multimodal emotion detection , and forensic voice analysis . Subfields span acoustic variability, temporal envelope dynamics, and voice quality metrics. Publications emphasize computational phonetics , cross-linguistic studies , and neural network applications in speaker identification. Research also addresses challenges in forensic audio analysis and synthetic speech dataset generation.

Julian TachellaView profile

Researcher

• Signal Processing
• Machine Learning
• Computational Imaging
• +5 more

Dr. Julian Tachella is a CNRS Research Scientist at the Sisyph Laboratory of École Normale Supérieure de Lyon, with co-founder/CSO roles at Blur Labs. His career spans signal processing, machine learning, and computational imaging, focusing on inverse problems and self-supervised learning. Affiliation: CNRS (French National Centre for Scientific Research), Sisyph Laboratory, École Normale Supérieure de Lyon Co-founder & CSO: Blur Labs (AI/Imaging startup) Research Interests: At the intersection of signal processing and deep learning , his work addresses imaging inverse problems through self-supervised methodologies (e.g., UNSURE, Generalized R2R) that eliminate ground-truth requirements. Key contributions include equivariant imaging frameworks for stability, spline sketches for photon-counting lidar compression, and uncertainty quantification techniques with equivariant bootstrapping. Recent Trends: 2025 publications emphasize lightweight architectures for multi-domain reconstruction (CT, super-resolution) and noise-agnostic SURE methods. 2024 works focus on audio declipping , compressed lidar , and nonlinear algorithm unrolling with applications in autonomous vehicles and medical imaging. Scientific Awards: Best Student Paper Award at ICASSP’22 Collaborations & Leadership: He leads the DeepInverse open-source project and develops algorithms for real-time 3D lidar reconstruction. His team includes researchers from University of Edinburgh and Grenoble INP, with applications in automotive lidar and underwater imaging.

Alexandre de SpindlerView profile

Professor

• Conversational Agents
• Generative AI
• Information Systems
• +8 more

Prof. Dr. Alexandre de Spindler is a Professor of Information Systems at the Zurich University of Applied Sciences (ZHAW) within the School of Management and Law . He co-leads the Center for Information Systems and Technologies and focuses on application development frameworks for conversational interactions with information systems, leveraging multimodal foundation models while enhancing their controllability and reliability. His work extends to improving social skills in digital interactions and teaching agile requirements engineering. University: Zurich University of Applied Sciences School: School of Management and Law Department: Institute of Business Information Technology Academic Rank: Professor His research explores conversational systems , generative AI , and stateful prompt orchestration for complex interactions, particularly in healthcare and financial sectors. Recent work includes frameworks like PROMISE for model-driven prompt management and Science Fiction Prototyping for responsible AI innovation. Teaching contributions span data science programs (BSc, MSc, CAS) covering machine learning, generative AI, and full-stack application validation. He collaborates across disciplines including gerontology and forensic phonetics, with a focus on applying AI to societal challenges.

• Artificial Intelligence
• Medical Imaging
• Data Analysis
• +3 more

Simone Lionetti is a Lecturer and Co-Head (acting) of the Applied AI Research Lab at the Lucerne School of Computer Science and Information Technology, Lucerne University of Applied Sciences and Arts. His academic journey includes a Ph.D. in Physics from ETH Zürich (2018), a Master's in Physics from Università degli Studi di Milano (2013), and a Bachelor's in Physics from the same institution (2010). He has held postdoctoral and research roles at institutions such as Durham University and ETH Zürich. His research focuses on AI applications in healthcare, including medical anomaly detection, dermatology, and bioaerosol monitoring. He also explores theoretical particle physics and software engineering for scientific computing. Key competencies include data analysis, algorithm design, and self-supervised learning techniques. Recent work emphasizes robust AI systems for medical imaging, data quality audits, and scalable foundation models. His projects bridge technical innovation with societal needs, such as addressing diversity gaps in dermatological AI and improving real-time environmental monitoring. He has contributed to over 20 peer-reviewed articles and holds two notable awards/honors (specific details not disclosed in text). His work integrates cross-disciplinary approaches, combining theoretical physics insights with practical AI solutions for healthcare and environmental challenges.

• Biomedical Engineering
• Signal Processing
• Machine Learning
• +3 more

Andrea Ronco is a Researcher affiliated with the Department of Rehabilitation Engineering at ETH Zürich. His work focuses on advancing sensor technologies and their applications in biomedical engineering, autonomous systems, and IoT. He leads research in mmWave radar systems for non-invasive vital sign monitoring, in-ear gesture recognition, and low-power embedded sensor solutions. Professional Affiliation: Staff of Professorship for Rehabilitation Engineering Research interests span radar technology optimization, real-time signal processing for healthcare, and robust sensor fusion for autonomous vehicles. His recent work emphasizes miniaturization and energy efficiency in wearable and embedded systems. Key contributions include TinyssimoRadar and In-ear-voice platforms that integrate advanced sensing with minimal power consumption. Ronco's publications address challenges in sensor robustness against environmental conditions and real-world deployment scenarios. His research outputs demonstrate expertise in interdisciplinary domains combining electrical engineering, machine learning, and biomedical applications. Current projects likely explore next-generation sensing modalities for wearable health monitoring and smart infrastructure systems.

• Embedded Systems
• AI Algorithms
• FPGA/VHDL Design
• +5 more

Jürgen Wassner is a Professor of Edge Computing and Co-Head of the Competence Center for Intelligent Sensors and Networks at the Lucerne School of Engineering and Architecture (HSLU T&A). He holds a master's degree from Technical University of Dresden and a PhD from ETH Zurich. His career spans industry roles in Silicon Valley Group Inc. and telecommunication R&D before joining academia in 2007. His research focuses on embedded systems, AI at the edge, FPGA/VHDL design, real-time systems, and powerline communication . He leads projects such as 'Visual-Servoing Testbed with AI-Hardware in the Loop' and 'Low-Cost High-Performance Intelligent Camera for Space Debris Mitigation,' collaborating with companies like Diehl Aerospace. Recent publications emphasize wire fault detection using powerline communication, AI-based odor classification systems, and hardware acceleration for CNNs. His work bridges theoretical research with practical applications in avionics, rail freight, and space debris mitigation. Wassner advises Master of Science in Engineering students and has been recognized for his media engagement including a feature in the Luzerner Zeitung. He teaches Digital Design (FPGA, VHDL) and Digital Signal Processing , emphasizing hands-on implementation of cutting-edge systems.

Sagie BenaimView profile

Assistant Professor

• computer vision
• machine learning
• computer graphics
• +4 more

Sagie Benaim is an Assistant Professor at the School of Computer Science and Engineering at the Hebrew University of Jerusalem. Previously, he was a postdoctoral researcher at DIKU (Department of Computer Science, University of Copenhagen) working with Professor Serge Belongie and was a member of the Pioneer Center for AI. He completed his PhD at Tel Aviv University in the Deep Learning Lab under the supervision of Professor Lior Wolf. Dr. Benaim's research spans computer vision, machine learning, and computer graphics, with a particular emphasis on generative models, neural signal representations, and inverse graphics. His work explores how disentangled representations can be leveraged to better understand and manipulate visual content. He has made significant contributions to 3D scene manipulation, image-to-video generation, and neural rendering techniques. His recent publications reveal a strong trajectory toward advancing generative AI capabilities, particularly in 3D understanding, multimodal generation, and video synthesis. His research consistently bridges theoretical foundations with practical applications, demonstrating innovation in neural representation manipulation for both creative and analytical purposes. Dr. Benaim is actively seeking excellent students and postdocs to join his research group, indicating his commitment to mentoring the next generation of researchers in computer vision and machine learning. His position at the Hebrew University of Jerusalem places him within a vibrant academic community focused on advancing the frontiers of computer science.

• Language Acquisition
• Cognitive Science
• Computational Modeling
• +7 more

Emmanuel Dupoux is a Professor at École des Hautes Études en Sciences Sociales (EHESS), affiliated with the School of Advanced Studies in Social Sciences and the Laboratory of Cognitive Science and Psycholinguistics (LSCP). His work bridges cognitive science, computational linguistics, and machine learning to study infant language acquisition and social cognition. Co-creator and director of EHESS Cognitive Science Master program Former LSCP laboratory director (1998-2009) Current research focuses on textless speech modeling and ecological audio analysis Research areas include: Modeling early language acquisition mechanisms using Bayesian models and HMM Investigating phonological 'deafness' and critical age plasticity Studying social cognition development through infant-toddler experiments His technical contributions span speech processing toolkits like Shennong, emergent communication frameworks (STOP dataset), and innovative approaches to self-supervised speech modeling. Recent publications demonstrate breakthroughs in: Topography-inspired CNN designs for better memory efficiency Prosody-aware generative spoken language models (pGSLM) Textless emotion conversion systems He actively explores how machine learning can reverse-engineer infant language learning processes from ecological audio data, with applications in neurodegenerative disease diagnostics. Key collaborations include: INRIA's Cognitive Machine Learning (CoML) team Facebook AI Research (FAIR) partnerships VoxPopuli multilingual speech corpus development

• Artificial Intelligence in Healthcare
• Medical Imaging
• Dermatology
• +3 more

Ludovic Amruthalingam is a Lecturer and Senior Researcher at the Lucerne School of Computer Science and Information Technology, Lucerne University of Applied Sciences, Switzerland. He holds a PhD in Artificial Intelligence from the University of Basel, a Master of Science in Computer Science from ETH Zürich, and a Bachelor of Science in Computer Science from EPFL. His professional activities include co-founding the startup Intelligent Orthopaedics in Chennai, India (2017–present), and leading research projects such as the Health Innovation Transfer Study (HIT) , Derma consultation documentation with AI , and the HSLU Medtech Joint Venture . His work focuses on leveraging AI for medical applications, particularly in dermatology, teledermatology, and medtech innovation. Research interests include medical anomaly detection, self-supervised learning, hyperbolic neural networks, and overcoming data scarcity in healthcare. He has pioneered projects addressing skin condition analysis, automated anatomical mapping, and diversity-inclusive medical imaging datasets. His recent publications highlight advancements in AI for dermatological diagnosis (e.g., vulvar lichen sclerosus pilot studies), hyperbolic space applications for medical anomalies, and benchmarking frameworks for image data cleaning (e.g., CleanPatrick ). He also contributes to foundational AI tools for scalable digital dermatology and equitable healthcare access through global skin imaging initiatives. Ludovic’s work bridges academic research and industry, driving innovations in medtech through collaborations like the ABIZ MedTech Expansion and Counteracting Data Scarcity in MedTech projects. His research emphasizes clinical translation of AI systems for real-world healthcare challenges.

• Machine Learning
• Data Science
• Computer Vision
• +3 more

Dr. Andreas Streich is a Lecturer at the Department of Computer Science at ETH Zurich. His research focuses on Machine Learning, Data Science, and their applications in fields like medical imaging and signal processing. He teaches courses including Computer Science I, Building ML/AI Applications, and Data Science & Machine Learning. His work spans neural networks, clustering algorithms, and multi-label classification. He has contributed to advancements in medical diagnostics via deep learning and acoustics-based signal processing. No scientific awards or student advisees are explicitly mentioned in the provided texts.

• Machine Learning
• AI-Based Audio Signal Processing
• Digital Image Processing
• +7 more

Dr. Martin Loeser is a Professor and Program Director of Electrical and Computer Engineering at the ZHAW Zurich University of Applied Sciences, School of Engineering, Department of Information Technology, Electrical Engineering and Mechatronics. He holds a Ph.D. in Computational Physics from ETH Zurich (2008) and a Master's degree in Electrical and Computer Engineering from the Technical University of Munich (2003). His educational background includes: Ph.D. in Computational Physics, ETH Zurich (2004-2008) Master of Science in Economics and Management, University of Hagen (2010-2012) Master of Science in Electrical and Computer Engineering, Technical University of Munich (1997-2003) Study Abroad Degree in Applied Physics, University of Sydney (2001-2002) Dr. Loeser's research spans multiple interdisciplinary fields with a strong focus on Machine Learning, AI-Based Signal Processing, and Digital Image Processing . His work bridges theoretical computational methods with practical applications in healthcare, education, and optoelectronics. Recent research has focused on using machine learning and virtual reality for early detection of cognitive impairments, developing AI-based educational tools, and creating medical diagnostic devices. His earlier work centered on computational physics and optoelectronic device modeling, particularly for LEDs and solar cells. His publication record shows a clear evolution from fundamental computational physics and optoelectronics (2006-2010) toward AI applications in healthcare and education (2015-present). This transition reflects his ability to adapt core computational expertise to emerging technological challenges. His recent work demonstrates strong interdisciplinary collaboration, particularly with medical researchers for cognitive impairment detection and with educational specialists for AI-enhanced teaching tools. Among his recognitions is being named "Best graduate of the year in the Master's degree program in Management" from the University of Hagen in June 2012. Dr. Loeser maintains active research collaborations across disciplines and institutions, as evidenced by his diverse publication record spanning computer science, healthcare technology, education, and traditional engineering fields. His current research program focuses on edge AI applications, particularly in healthcare diagnostics and educational technology.

• Deep Learning
• Machine Learning
• Computer Vision
• +4 more

Javier Montoya is a Senior Lecturer at the Lucerne School of Computer Science and Information Technology, part of the Lucerne School of Applied Sciences and Arts (HSLU). He holds a PhD in Computer Vision and Machine Learning from ETH Zurich (2016), with earlier Master’s degrees from INRIA/Grenoble Institute of Technology (France) and UNICAMP (Brazil). His research focuses on Deep Learning, Machine Learning, and Computer Vision, particularly in healthcare applications such as medical image processing and digital health solutions. Education: PhD: Swiss Federal Institute of Technology (ETH Zurich), 2016 Master’s: INRIA/Grenoble Institute of Technology (France), 2010 Master’s: University of Campinas (UNICAMP, Brazil), 2007 Montoya’s professional expertise spans Machine Learning & Deep Learning, Image Processing & Computer Vision, and Digital Health. His work bridges academic research with applied projects, including roles as Senior Research Scientist at Zurich University of Applied Sciences (ZHAW, 2020–2021) and Research Scientist at ETH Zurich (2017–2019). His recent publications emphasize medical imaging advancements, motion artifact mitigation, and deep learning applications in healthcare. His research trends highlight innovations in medical image segmentation (e.g., polyp detection, cardiac imaging) and cross-disciplinary projects like solar energy systems and road network analysis. While no specific grants or awards are listed, his extensive publication record underscores contributions to both healthcare and engineering domains.

Teodora VukovicView profile

Research Fellow

• Computational Linguistics
• Multimodal Analysis
• Human-Computer Interaction
• +5 more

Dr. Teodora Vuković is a computational linguist at the University of Zurich's Faculty of Arts, specializing in multimodal analysis of human interaction and language technology. As Principal Investigator of the CAPIRE and LCP projects, she leads development of automatic tools for analyzing speech, gestures, and facial expressions to identify person-specific patterns while implementing data anonymization techniques.