جست‌وجوی استاد و پژوهشگر

Federico Wadehnمشاهده پروفایل

مدرس ارشد

• Digital Signal Processing
• Machine Learning
• Deep Learning
• +۶ مورد دیگر

Dr. Federico Wadehn is a Senior Lecturer at the Institute of Signal Processing and Wireless Communications within the School of Engineering at Zurich University of Applied Sciences (ZHAW), specializing in digital signal/image processing and machine learning applications for biomedical and engineering systems. His role includes teaching core courses and supervising graduate theses. Education: PhD in Statistical Signal Processing with Probabilistic Graphical Models, ETH Zürich (2014-2018) Teaching Certificate, ETH Zürich and ZHAW (2017-2018) Visiting Researcher in Neuromonitoring, MIT (2016) MSc/BSc in Electrical Engineering and Information Technology, ETH Zürich (2009-2014) Erasmus Exchange in Control Engineering, Imperial College London (2012-2013) Continuing Education: MATLAB C Coder (2023), Embedded C (2021), IBM Rational Rhapsody (2020) Research Interests: His work bridges Digital Signal Processing (classical, ML, and deep learning), Computer Vision , Sensor Fusion/Kalman Filtering for inertial systems, Probabilistic Graphical Models for navigation, and Physiological System Modeling (respiratory/cerebrovascular). He emphasizes model-based approaches for real-world biomedical applications, particularly in neuromonitoring and respiratory care. Publication Trends: His 2015-2020 publications reveal a consistent focus on advanced signal processing for physiological monitoring, featuring model-based separation of biological signals (eye movements, blood flow), sparse state-space modeling, and Kalman smoothing innovations. The work integrates probabilistic graphical models with biomedical constraints, demonstrating strong cross-disciplinary impact in IEEE journals and conferences. Scientific Awards: Study and doctoral scholarship from German Academic Scholarship Foundation (2014) Abiturpreis Physik from Deutsche Physikalische Gesellschaft (2008) Advising and Professional Activities: He supervises bachelor's/master's theses at ZHAW while maintaining industry connections through prior roles as Systems Engineer at Bosch Sensortec (2021-2023), Research Engineer at Hamilton Medical AG (2019-2021), and Computer Vision Freelancer (2021). His dual academic-industry experience informs practical curriculum development. Laboratories and Teams: He contributes to ZHAW's Digital Health Lab and Medical Systems Platform, focusing on translating signal processing innovations into clinical tools for respiratory monitoring and motion analysis, with recent patent work in ventilation technology.

Charlotte Frenkelمشاهده پروفایل

استادیار

• Neuromorphic Engineering
• AI Processor Design
• Digital and Mixed-Signal IC Design
• +۵ مورد دیگر

Charlotte Frenkel is a Tenure-Track Assistant Professor in the Microelectronics Department at Delft University of Technology (TU Delft), where she leads research in neuromorphic engineering and low-power AI hardware. Her work bridges the gap between biological intelligence and artificial neural networks, focusing on energy-efficient computing at the edge. Dr. Frenkel's research spans digital and mixed-signal IC design, computer architecture, learning algorithms, and neuroscience. She directs the Cognitive Sensor Nodes and Systems (CogSys) lab, which develops neuromorphic processors like ODIN, MorphIC, SPOON, and ReckOn that demonstrate competitive advantages over conventional neural network accelerators. Her publications reveal a strong focus on spiking neural networks, event-based processing, and on-chip learning. Key trends include developing hardware that leverages sparsity for energy efficiency, creating bio-inspired learning algorithms that solve weight transport and update locking problems, and establishing frameworks for benchmarking neuromorphic systems through initiatives like NeuroBench. Scientific Awards: IBM Innovation Award 2021 Nokia Bell Labs Scientific Award 2021 IEEE ISCAS 2020 Best Paper Award NEUROTECH/NICE Best Early Researcher Presentation 2021 AiNed Fellowship Grant Dr. Frenkel is actively expanding her research group through PhD and postdoc positions. She serves as Associate Editor for IEEE Transactions on Biomedical Circuits and Systems and Frontiers in Neuroscience, and has held numerous leadership roles in conference organization including Program Chair for tinyML Research Symposium 2024 and Neuro-Inspired Computational Elements conference 2023-2024. Her service includes extensive reviewing activities for top IEEE journals and conferences in her field.

• Embedded Systems
• Internet of Things
• Smart Systems
• +۳ مورد دیگر

Dominique Kunz is a Lecturer for Embedded Systems at the University of Applied Sciences and Arts Northwestern Switzerland (FHNW) , specifically affiliated with the School of Engineering and Environment . Their work focuses on embedded systems, real-time programming, and IoT applications. Kunz teaches courses on operating systems, memory-safe programming, and C++ applications in embedded systems, while also leading technical computer science laboratories. Research Areas: Embedded Systems, Internet of Things, Smart Systems Teaching: Real-time systems, C++ programming, technical labs Contact: Email: dominique.kunz@fhnw.ch | Phone: +41 56 202 79 75

Bartolomeo Stellatoمشاهده پروفایل

استادیار

• Mathematical Optimization
• Machine Learning
• Optimal Control
• +۳ مورد دیگر

Bartolomeo Stellato is an Assistant Professor at Princeton University's Department of Operations Research and Financial Engineering. He is associated faculty in Electrical and Computer Engineering and Computer Science, and affiliated with Princeton's AI, Robotics, and Center for Statistics and Machine Learning initiatives. His research bridges mathematical optimization, machine learning, and optimal control for real-time decision-making in uncertain environments. PhD advisors: Georgina Hall, Brandon Amos Key collaborators: Bart Van Parys (MIT), Jaime Fernandez Fisac (UPenn), Adrien Taylor (INRIA) His work on Mean Robust Optimization combines robust and distributionally robust optimization through data clustering, achieving 3+ orders of magnitude speedups in portfolio optimization. He also develops learned optimizers (e.g., L2WS, GeNIOS) and OSQP , which won the Beale–Orchard-Hays Prize. Recent grants include NSF CAREER (2023) and ONR Young Investigator (2025) awards. Article Trends : His research spans optimization verification (3/7 of 2025 articles), data-driven methods (5/7 of 2024-2025 articles), control applications (4/15 articles), and equitable resource allocation (2/15 articles). Key subfields include algorithm verification, stochastic optimization, multi-agent systems, and hardware acceleration. Scientific Awards : ONR Young Investigator (2025), Howard B. Wentz Award (2024), NSF CAREER (2023), ISSNAF Strazzabosco (2022) Students : Mentored 12+ graduate students/postdocs including Irina Wang (Wallace Fellowship), Rajiv Sambharya (Best Paper Award at JMLR), and Vinit Ranjan (co-author Mathematical Programming paper) He leads the Princeton Optimization, Learning, and Control Workshop and collaborates with institutions like MIT, INRIA, and Los Alamos National Laboratory. His group develops open-source tools (OSQP, L2WS) with over 300,000 downloads.

• Machine Learning
• AI-Based Audio Signal Processing
• Digital Image Processing
• +۷ مورد دیگر

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.

• Transportation Engineering
• Optimal Control
• Energy Efficiency
• +۳ مورد دیگر

Dr. Dimitris Kouzoupis is affiliated with the Professorship for Transport Systems at ETH Zürich's Institute of Transport Planning and Systems (IVT) , part of the Department of Civil, Environmental and Geomatic Engineering (D-BAUG). His research focuses on energy-efficient train control , real-time optimization , and computational methods for transport systems. He contributes to open-source tools like TTOBench and acados, emphasizing algorithmic efficiency and practical implementation. Key research areas include optimal control strategies for rail systems, embedded optimization frameworks, and decentralized optimization techniques. His work bridges theoretical advancements with real-world applications in transportation automation and energy management. Publications highlight innovations in trajectory optimization, nonlinear model predictive control, and algorithm design for embedded systems. Collaborations involve interdisciplinary teams addressing challenges in railway operations and computational efficiency. No scientific awards are explicitly mentioned in the provided texts. His role in advising students or leading grants is not detailed, though his contributions to software frameworks like TTOBench and acados underscore collaborative and applied research efforts.

Olivier Canévetمشاهده پروفایل

مدرس

• Computer Vision
• Machine Learning
• Deep Learning
• +۳ مورد دیگر

Olivier Canévet is a Lecturer at EPFL, affiliated with both the School of Engineering (STI) and the School of Life Sciences (VPA-AVP-DLE) divisions. He holds a dual role as a Research Engineer at the Idiap Research Institute and serves as a Lecturer in the Department of Electrical Engineering (IEM) and the EDEE-ENS teaching unit. His primary affiliations include the Learning and Information Systems Laboratory (LIDIAP) at EPFL. Education: PhD in Electrical Engineering from EPFL (2012–2016), supervised by Dr. François Fleuret. Engineering diploma from Télécom Bretagne (2007–2012). Additional experiences include a CNES internship on satellite imagery and CERN projects on Invenio ranking systems. He also spent a year managing geophysical observatories in the French Southern and Antarctic Lands. Research Interests: Focuses on Computer Vision, Machine Learning, and Deep Learning applications in human-robot interaction, security systems, and real-time perception. Specializes in depth-based networks for human pose estimation, multi-person tracking, and socially intelligent robotic systems. Article Trends: Recent work emphasizes real-time CNN architectures for depth-based human pose estimation, multi-party human-robot interaction datasets (MuMMER), and security systems leveraging depth maps (UNICITY database). Prior contributions include efficient sample mining techniques and domain adaptation methods. Grants & Funding: His PhD was funded by the Swiss National Science Foundation (DASH project). Current research benefits from EPFL's interdisciplinary infrastructure and Idiap's resources in machine learning and robotics. Labs & Teams: Leads projects at LIDIAP lab (EPFL) focusing on computer vision applications, collaborates with Idiap on security systems, and contributes to teaching initiatives in the EDEE-ENS unit.

• Data-Driven Control
• Predictive Control
• Building Automation
• +۳ مورد دیگر

Yingzhao Lian is a former researcher at École Polytechnique Fédérale de Lausanne (EPFL), contributing to advanced control systems and optimization. Their work focuses on data-driven predictive control methodologies applied to building automation, energy management, and mixed traffic systems. Key collaborations include projects with the National Natural Science Foundation of China and Swiss National Science Foundation. Lian's research emphasizes practical implementation, such as experiments in the Polydome building testbed and real-time control on embedded systems like the TI LaunchPad microcontroller. Their contributions bridge theoretical frameworks (e.g., DeePC algorithms, Willems' Fundamental Lemma) with real-world applications in HVAC systems, demand response programs, and energy harvesting sensor networks. Education: No explicit education details provided. Research Interests: Specializes in data-driven control theory, optimization under constraints, and energy-efficient systems. Recent work addresses challenges in adaptive building control, distributed traffic flow smoothing, and fault detection using kernel methods. Their approach often integrates physics-based principles with data-driven techniques to ensure robustness and scalability. Grants/Funders: Supported by initiatives including BMBF-Project ENSure II, NSF Creative Research Groups, and the NCCR Automation Project. Research units affiliated include EPFL's La3 and La departments, focusing on control systems and embedded technologies. Labs/Teams: Active in teams developing control algorithms for smart buildings, autonomous vehicles, and power grids, with emphasis on distributed computing architectures.

• Control Systems
• Optimization Algorithms
• Machine Learning
• +۳ مورد دیگر

Roland Schwan is a researcher at the Automatic Control Laboratory (LA) within the School of Engineering at the École Polytechnique Fédérale de Lausanne (EPFL) . His work focuses on control systems, optimization algorithms, and the integration of machine learning with physical models. He is actively involved in projects involving model predictive control (MPC), stability verification of neural network controllers, and real-time optimization-based control for robotics applications. His research interests span physics-informed machine learning , quadratic programming solvers , and embedded systems . Key contributions include the development of the PIQP solver for convex quadratic programming and stability analysis frameworks for neural network controllers. He has also explored applications in hovercraft dynamics identification and rocket trajectory control using advanced MPC techniques. Schwan collaborates closely with institutions like the Swiss National Science Foundation and has published extensively in venues such as IEEE Transactions on Automatic Control and IEEE Conference on Decision and Control . His work bridges theoretical control methodologies with practical embedded system implementations.

Urban Lienertمشاهده پروفایل

مدرس

• Jazz Performance
• Music Technology
• Software Development for Music
• +۳ مورد دیگر

Urban Lienert is a Lecturer at the Lucerne School of Music within Lucerne University of Applied Sciences and Arts. His primary affiliations include the Institute of New Music, Composition and Theory. He holds a background in Jazz bass performance and music technology, having studied at Hochschule Luzern – Musik under Heiri Känzig and Herbie Kopf until 1997. Research focuses on the intersection of music performance and technology, including development of software applications for iOS/macOS, musical interface design, and sound installations. Notable projects include MIDI Touch Bar integration (miditouchbar.ch), Pd Blocks external for Roli Blocks, and sound art installations like 'Space Friction' (2022) and 'Sonic Umbrellas' (2022). Teaching responsibilities include Computer Courses, Jazz Theory, and Jazz Ensemble instruction in non-degree programs. Active in open-source communities, maintaining repositories on GitHub including 'blocks' for Roli Blocks integration and 'squeezelite-esp32' for music streaming solutions. Collaborates with artists in Switzerland and internationally through jazz formations like Manufactur and Gilbert Paeffgen Trio.

Daniele Palossiمشاهده پروفایل

پژوهشگر

• Autonomous Systems
• Deep Learning
• Nano Drones
• +۴ مورد دیگر

Dr. Daniele Palossi is a Researcher at ETH Zürich's Department of Digital Integrated Circuits and Systems. His work focuses on autonomous robotics, particularly nano-drones and embedded AI systems. He specializes in developing energy-efficient, vision-based navigation frameworks and deep learning architectures for resource-constrained platforms. Research interests include autonomous systems, computer vision for robotics, and hardware-software co-design for UAV applications. His recent work advances swarm robotics, sim-to-real transfer learning, and ultra-low-power neural networks for nano-scale drones. Key contributions include the FünfiBër-Drone modular platform and secure SOC designs for UAV navigation. His research bridges theoretical AI advancements with practical implementations in miniature robotic systems.

Yawei Liمشاهده پروفایل

مدرس

• Artificial Intelligence
• Computer Vision
• Deep Learning
• +۶ مورد دیگر

Yawei Li is a Lecturer at ETH Zürich, affiliated with the Integrated Systems Institute. His research focuses on efficient deep learning and AI systems for vision, language, and biosignals. Collaborating with Professors Luca Benini and Luc Van Gool, he explores topics like foundation models for biosignals, efficient neural network design, and deployment on RISC-V cores. He has organized workshops such as the NTIRE Challenge on Efficient Super-Resolution and served as a Senior Program Committee member for AAAI and IJCAI. He has supervised numerous PhD and Master students, with open positions available in foundational model development and bio-signal analysis. Key achievements include the Best Poster Presentation at ICVSS 2019 and contributions to projects like TinyYOLO for smart glasses. His work emphasizes practical applications of AI on embedded systems and low-power devices. Teaching includes courses like 'Deep Learning for Image Manipulation' and involvement in Computer Vision Lab initiatives. His research spans image restoration, neural network compression, and cross-modal analysis, with publications in top venues like CVPR, NeurIPS, and ECCV.

• Information Security
• Cybersecurity
• Software Testing
• +۷ مورد دیگر

Dr. Stephan Neuhaus is a Senior Lecturer at the Zurich University of Applied Sciences (ZHAW), School of Engineering, where he contributes to the Research Focus Information Security. His work bridges applied research and practical implementation in cybersecurity, with a focus on software security, embedded systems, and human factors. Research Focus: Information Security Institution: ZHAW School of Engineering Location: Winterthur, Switzerland His research interests span information security, software testing, embedded systems security, network transparency, and usable security. He investigates how security mechanisms can be effectively implemented in real-time systems and how users interact with security-critical systems like password entry and phishing detection. His recent publications, from 2016 to 2025, reflect a strong trend in applied cybersecurity research. Key themes include fuzzing for GNSS modules, real-time security policies using BPF on FPGAs, privilege handling frameworks, and network observability. These works appear in top venues such as IEEE and ACM conferences, demonstrating technical depth and relevance to both industry and academia. Dr. Neuhaus has led and contributed to several applied research projects, including Malicious Node (lateral movement detection), OptiPhish (phishing awareness training), ATLAS (data management for pharma), and Identity Management for Wireless Networks. These projects highlight his leadership and collaboration in interdisciplinary, real-world security challenges. He is actively involved in research teams and collaborates with colleagues across institutions, particularly in projects involving secure embedded systems and network security. His work often involves FPGA implementations, software toolchains, and human-centered security evaluation.

• Embedded Systems
• FPGA
• High Performance Computing
• +۵ مورد دیگر

Alberto Dassatti is a Full Professor at HES-SO University of Applied Sciences Western Switzerland and Director of the Reconfigurable & Embedded Digital Systems (REDS) Institute. He is affiliated with the School of Engineering and Management of the Canton of Vaud. His work focuses on embedded systems, FPGA applications, high-performance computing, and topological data analysis. He has led and collaborated on numerous projects funded by institutions like Innosuisse and HES-SO Rectorat, totaling over CHF 1.2 million in grants. His research spans computational storage systems, medical sensor technologies, and signal processing frameworks for wireless communication. Key projects include developing the CarbonViz energy visualization tool, advancing topological methods for AI reliability, and creating the BB-Sensors system for neonatal vital monitoring. He has contributed to open-source frameworks like giotto-tda and gr-dnn, emphasizing interdisciplinary collaboration between hardware and software domains. His publications span topics such as FPGA-GPU communication, real-time embedded systems optimization, and heterogeneous computing architectures. He actively participates in conferences like NeurIPS and IEEE SOC, showcasing innovations in reconfigurable computing and energy-efficient systems.

• Applied Machine Learning
• Computer Vision
• Signal Processing
• +۷ مورد دیگر

Overview Manfredo Atzori serves as a Scientific Assistant (Adjoint-e scientifique HES A) at HES-SO Valais-Wallis's Haute Ecole de Gestion. His work focuses on advancing machine learning applications in biomedical engineering, digital pathology, and neuroimaging. He leads development of open-source tools like BIDSAlign for EEG standardization and SelfEEG for self-supervised learning in biomedical signals. Research Interests Atzori's research bridges AI and healthcare, emphasizing: Medical Imaging : Histopathology WSI analysis, artifact correction, and cross-modal fusion Prosthetics Control : sEMG-based gesture recognition and adaptive neural interfaces Deep Learning Methodology : Transfer learning optimization, self-supervised pretraining, and reproducibility frameworks Key Contributions 2024 highlights include: Developed RegWSI - winning ACROBAT 2023 challenge for WSI registration Pioneered multimodal WSI-report integration to halve annotation needs Published benchmark studies on Gleason grading deep learning methods Collaborations Active in international projects with institutions like DeeperHistReg group and OpenNeuro. Tools developed are open-source (GitHub: @manfredoatzori) to advance reproducible research practices.