جست‌وجوی استاد و پژوهشگر

Melih Kandemirمشاهده پروفایل

دانشیار

• Machine Learning
• Bayesian Inference
• Stochastic Process Modeling
• +۵ مورد دیگر

Melih Kandemir is an Associate Professor of Machine Learning at the University of Southern Denmark, Department of Mathematics and Computer Science. He earned his PhD in 2013 from Aalto University under Prof. Samuel Kaski, followed by postdoctoral work at Heidelberg University (with Prof. Fred Hamprecht) and an assistant professorship at Ozyegin University, Turkey. Prior to joining SDU, he led research at Bosch Center for Artificial Intelligence. Education: PhD (Aalto University, 2013), Postdoc (Heidelberg University). Previous Roles: Assistant Professor (Ozyegin University), Research Group Leader (Bosch CAI). His research focuses on Bayesian inference, stochastic process modeling with deep neural networks, and applications to reinforcement learning and continual learning. He leads the SDU Adaptive Intelligence (ADIN) Lab and is an ELLIS Member, reflecting his standing as a top European AI researcher. His work addresses critical challenges in uncertainty quantification, exploration strategies, and theoretically grounded algorithms for decision-making systems. Recent publications highlight expertise in model-based reinforcement learning (e.g., MOMBO for offline RL), PAC-Bayesian bandits, evidential learning for robust classification, and neural stochastic differential equations. His scientific awards include a Best Paper Award (2017) and ELLIS Membership. He also explores interdisciplinary applications of natural sciences to sustainable technology development.

Giorgio Cristianoمشاهده پروفایل

پژوهشگر

• Circuit Design
• Low-Power Electronics
• Biomedical Electronics
• +۶ مورد دیگر

Giorgio Cristiano is a Post-Doctoral Researcher at ETH Zürich in the Department of Information Technology and Electrical Engineering , affiliated with the Integrated Systems Laboratory . His work focuses on advanced circuit design for Internet of Things (IoT) applications and In-Memory Computing , with a strong emphasis on low-power and CMOS-compatible solutions. His research spans key areas such as: Temperature-stable RC oscillators for IoT nodes High-efficiency DC-DC converters using electromagnetically coupled oscillators Neural recording interface circuits with miniaturized resistors MEMS oscillators for ultra-low power consumption The trends in his publications highlight innovations in energy efficiency (e.g., 1.5pJ/Cycle oscillators), noise reduction (0.4 NEF amplifiers), and biomedical electronics integration. His work often addresses miniaturization and power optimization for wearable and implantable devices. Key institutions involved in his research include: ETH Zürich (primary affiliation) Integrated Systems Laboratory (research group) Collaborations with experts in biomedical engineering and neural networks

Susanne Suterمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Deep Learning
• +۹ مورد دیگر

Prof. Dr. Susanne Suter is a Professor in Data Science at the Institute for Data Science, FHNW School of Computer Science, University of Applied Sciences and Arts Northwestern Switzerland. Her research focuses on Artificial Intelligence, Machine Learning, Deep Learning, and Explainable AI, particularly in medical applications such as Computer Vision, Signal Processing, and Digital Health. She has led projects including Smart Hospital and Hospital @ Home, receiving the Prix D'Excellence SanteNeXt 2023. Her publications emphasize AI-driven solutions in healthcare, spanning medical imaging, real-time clinical decision systems, and high-performance computing. Recent work explores ophthalmic imaging, multimodal ICU data platforms, and tensor-based data compression for visualization. Awards include the Prix D'Excellence SanteNeXt 2023 for innovative healthcare solutions.

• Multiprocessor Systems
• Real-Time Embedded Computing
• Distributed Signal Processing
• +۳ مورد دیگر

Prof. Dr. Matthias Rosenthal is a Professor of Multiprocessor and Real-Time Systems at the ZHAW School of Engineering, Zurich University of Applied Sciences (ZHAW), where he also serves as Head of the Research/Focus Area Realtime Platforms. He holds a PhD and MSc in Electrical Engineering from ETH Zurich (1993–1997). His research focuses on multiprocessor systems, hybrid multicore architectures, distributed signal processing, embedded GPU computing, and real-time embedded systems. Key projects include In-Flight GNSS Interference Detection, dAIrector (automated multi-camera live production), and novel AFM techniques for industrial quality control. He has led over 15 industry-focused projects, including collaborations with Innosuisse and companies like Harman International. His work emphasizes real-time systems, FPGA-GPU co-design, and embedded AI solutions. Education: PhD (ETH Zurich, 1997), MSc (ETH Zurich, 1993) Awards: CTI Startup Label (2005) Teaching: Lectures on digital systems, real-time computing, and information theory Notable contributions include advancements in embedded machine learning for food waste management, secure boot concepts for Zynq MPSoC, and low-latency wireless video systems. His research bridges theoretical computer engineering with practical industrial applications.

Philipp Rehnerمشاهده پروفایل

مدرس

• Classical Density Functional Theory
• Thermodynamic Modeling
• Computer-Aided Molecular Design
• +۵ مورد دیگر

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

Javad Komijaniمشاهده پروفایل

پژوهشگر ارشد

• Particle Physics
• Theoretical Physics
• Lattice Quantum Chromodynamics (QCD)
• +۵ مورد دیگر

Dr. Javad Komijani is a Research Fellow in Computational Physics at the Institute for Theoretical Physics , ETH Zürich. His work focuses on precision calculations in quantum chromodynamics (QCD) and electroweak interactions, particularly in the context of lattice gauge theory and computational methods. Research interests: Lattice QCD simulations for hadronic vacuum polarization (HVP) and baryon mass calculations Electromagnetic effects in QCD+QED frameworks GPU-accelerated algorithms and numerical techniques for large-scale simulations Machine learning applications to quantum field theories Publications emphasize cutting-edge advancements in precision physics, including contributions to the muon g-2 anomaly and isospin symmetry-breaking corrections. Collaborative efforts with experimental initiatives like MUonE highlight his bridge between theoretical and experimental physics. No scientific awards or formal advisees are listed in available records. His research is anchored at ETH Zürich's Institute for Theoretical Physics, where he contributes to both foundational and applied theoretical physics projects.

• Sustainable Energy Systems
• Numerical Optimization
• Deep Learning
• +۳ مورد دیگر

Dr. Manuel Dahmen is the Head of Department at the Institute of Climate and Energy Systems (ICE-1) within the Research Center Jülich. His research focuses on designing sustainable and cost-efficient energy systems through advanced techniques like numerical optimization and deep learning. Key areas include renewable energy integration, process network analysis, and machine learning-driven system design. His work addresses challenges in decarbonizing industries, optimizing energy systems, and developing tailor-made fuels for high-efficiency engines. Dahmen leads interdisciplinary projects combining computational methods with practical engineering solutions, emphasizing robust design under uncertainty. He contributes to open-source tools like COMANDO for energy systems optimization and explores innovations in graph neural networks for molecular property prediction. Research trends reflected in his publications highlight optimization algorithms (e.g., semi-infinite programming), demand response strategies for industrial processes, and physics-informed machine learning for dynamic systems. Dahmen’s contributions bridge theoretical advancements with real-world applications, aiming to accelerate the transition to sustainable energy systems.

Touradj Ebrahimiمشاهده پروفایل

استاد

• Image and Video Compression
• Media Security
• Multimedia Signal Processing
• +۵ مورد دیگر

Touradj Ebrahimi is a Full Professor at École Polytechnique Fédérale de Lausanne (EPFL), where he heads the Multimedia Signal Processing Group (MMSPG) within the School of Engineering's Institute of Electrical Engineering. He holds a Ph.D. in Electrical Engineering from EPFL and serves as the Convenor of the JPEG standardization committee. His research spans AI-powered imaging, multimedia signal processing, and media security. Research Interests: Professor Ebrahimi's work focuses on next-generation image/video compression (including JPEG AI and DNA-based storage), quality of experience in immersive media, trustworthy media authentication (JPEG Trust), and explainable AI for biometric systems. His group pioneers methods for point cloud compression, deepfake detection, and privacy-preserving multimedia. Publication Trends: Recent articles emphasize AI-driven compression standards, quality assessment for emerging media (point clouds, HDR), and explainability in computer vision. Key themes include JPEG AI standardization, DNA storage for visual data, and robustness evaluation of deepfake detectors. Awards & Honors: IEEE Star Innovator Award in Multimedia IEEE and Swiss National ASE Awards ISO Certificates for MPEG-4/JPEG 2000 contributions Best Paper Award (IEEE Trans. Consumer Electronics) Fellow of IEEE and SPIE Leadership: He founded RayShaper SA, Genista SA, and Emitall SA. Represents Switzerland in ISO/IEC JTC1/SC29 and ITU, advising European Commission initiatives and venture capital firms on multimedia technologies. Facilities: Leads the MMSPG lab at EPFL, focusing on intelligent multimedia systems. The group collaborates globally on standardization (JPEG, MPEG) and EU projects.

Jörg Goldhahnمشاهده پروفایل

استاد

• Translational Medicine
• Medical Technology
• Biomechanics
• +۳ مورد دیگر

Prof. Jörg Goldhahn is a Professor at ETH Zurich's Department of Health Sciences and Technology, serving as Translational Science Tutor for the MSc in Human Movement Science and Sport Medical Technology. His research focuses on accelerating medical translation pathways from idea to clinical application, with expertise in biomarker development, wearable devices, and musculoskeletal health technologies. He advises master's students on interdisciplinary coursework including biomechanics, medical implants, and clinical research methods. Goldhahn's research group explores digital health innovations such as sweat-based diagnostics and therapeutic drug monitoring. He contributes to interprofessional education initiatives, emphasizing collaboration between medical and nursing students. His work bridges clinical and engineering disciplines, addressing challenges in orthopedic treatments and aging-related health technologies. Recent projects include developing wearable systems for continuous health monitoring and advancing cartilage regeneration therapies. Goldhahn actively engages in curriculum development at ETH Zurich, promoting inclusive education and ethical considerations in medical technology.

Martin Jaggiمشاهده پروفایل

دانشیار

• Machine Learning
• Optimization
• Artificial Intelligence
• +۳ مورد دیگر

Martin Jaggi is an Associate Professor at EPFL, leading the Machine Learning and Optimization Laboratory (MLO). He holds academic positions within the School of Computer and Communication Sciences (IC), including roles in the SIN, SSC, and EDIC departments. His research focuses on machine learning optimization, federated learning, and large language models. He earned his PhD in Machine Learning and Optimization from ETH Zurich (2011) and a MSc in Mathematics from the same institution. Education: PhD in Machine Learning and Optimization, ETH Zurich (2011) MSc in Mathematics, ETH Zurich Research Interests: Dr. Jaggi's work integrates optimization theory with machine learning applications, emphasizing scalable algorithms and ethical AI. He explores topics like distributed learning systems, privacy-preserving techniques (e.g., federated learning), and model interpretability. His lab develops foundational methods for large language models (LLMs) and their ethical deployment. Teaching & Courses: Optimization for Machine Learning Topics in Machine Learning Systems Advising & Grants: He currently advises 9 PhD students and has supervised 11 past students. His work is supported by grants focusing on federated learning, optimization algorithms, and medical AI applications like Meditron-70b. Collaborations include projects on respiratory disease detection and mental health modeling. Labs & Teams: Academic Director of RCP-GE and oversees MLO lab, which develops open-source tools like FLamby for federated learning benchmarks and Meditron for clinical AI. The lab also advances techniques in distributed optimization and low-precision training.

Pascal Felberمشاهده پروفایل

استاد

• Distributed Systems
• Concurrent Systems
• Trusted Execution Environments
• +۷ مورد دیگر

Pascal Felber is a Full Professor at the Institute of Computer Science within the Faculty of Science at the University of Neuchâtel. His career spans significant industry experience at Oracle Corporation and Bell Labs before transitioning to academia, where he has established himself as a leading researcher in complex computing systems. His research interests focus on the theoretical foundations and practical applications of complex computing systems, particularly reliable, distributed, concurrent, and secure systems. Felber's work addresses critical challenges in large-scale systems including cloud computing, Internet of Things, and big data technologies. His research bridges theoretical computer science with practical system implementations. Analysis of his recent publications reveals a strong emphasis on Trusted Execution Environments (TEEs), secure computing, blockchain security, and privacy-preserving technologies. His work spans multiple domains from secure DNA alignment to phishing detection in smart contracts, demonstrating both theoretical depth and practical impact in addressing security challenges in modern distributed systems. Professor Felber has participated as a (co-)applicant in approximately twenty research projects funded by the EU (including VELOX, SRT-15, LEADS, ParaDIME, SafeCloud, SecureCloud, EBSIS, LEGaTO, VEDLIoT) and the Swiss National Science Foundation (SNSF). His teaching portfolio includes Bachelor's degree courses in French (Programming I & II, Languages and compilation, Web and network technologies, Concurrent and distributed systems) and Master's courses in English (Concurrency: Multi-core programming and data processing, Hot topics in operating systems seminar).

Simon Heneinمشاهده پروفایل

دانشیار

• Micromechanical Design
• Precision Mechanisms
• Flexure Guidance Systems
• +۸ مورد دیگر

Simon Henein is an Associate Professor at the École Polytechnique Fédérale de Lausanne (EPFL) , leading the Micromechanical and Horological Design Laboratory (INSTANT-LAB) under the School of Engineering and Institute of Mechanical Engineering . Since 2024, he holds a Courtesy Appointment at the Digital Humanities Institute (DHI-GE) . He was Tenure Chairholder at Patek Philippe (2012–2020) and a Visiting Professor at the University of Lausanne's Centre d'études théâtrales (2020–2021). His research bridges micromechanical design and creative pedagogy , focusing on flexure mechanisms for aerospace, biomedical devices, and horology, alongside improvisational arts in education. He has authored over 25 peer-reviewed journal articles and 60+ conference papers , with 35 patents. Notable awards include the Best Teacher Award (2023) , OMEGA Scientific Prize (1996) , and Maillefer SA Award (1996) . His teaching involves 12 ECTS credits annually with over 200 students , including mentorship of 26 Master’s and 60 semester projects . He has directed 6 completed PhD theses and currently supervises 4 PhD students , co-supervising 1 more. His lab, INSTANT-LAB, comprises a dozen researchers focusing on centimeter-scale mechanisms with novel kinematics and technologies.

Melika Payvandمشاهده پروفایل

استادیار

• Neuroinformatics
• Artificial Intelligence
• Neuromorphic Computing
• +۵ مورد دیگر

Melika Payvand is an Assistant Professor at the Institute of Neuroinformatics, University of Zurich and ETH Zurich, leading the Emerging Intelligent Substrates lab. She holds MSc and PhD degrees in Electrical and Computer Engineering from the University of California, Santa Barbara (2012 and 2016). Her research focuses on understanding natural intelligence and translating its principles into efficient artificial intelligence systems, particularly exploring hierarchical structures and memristive devices to reduce AI operational costs. She received the 2023 Swiss National Science Foundation Starting Grant and is actively involved in neuromorphic initiatives, including co-coordinating the NEUROTECH project and co-chairing the International Conference on Neuromorphic Systems (ICONS). Her work bridges neuroscience and engineering, emphasizing neuromorphic computing’s potential for scalable and energy-efficient AI. Key contributions include developing the Neurobench benchmarking framework and advancing 3D integration for neuromorphic systems. She also chairs the Neural Systems and Applications committee of the IEEE Circuits and Systems Society, reflecting her leadership in the field. Research interests span neuromorphic architectures, memristive technologies, and spiking neural networks. She has pioneered studies on temporal hierarchy, in-memory computing, and hardware-aware training methods. Her lab’s interdisciplinary approach integrates circuit design, machine learning, and neurobiology to create next-generation intelligent systems.

• Semiconductor Device Modeling
• Field Effect Transistors (FETs)
• Radiation Effects on Integrated Circuits
• +۳ مورد دیگر

Jean-Michel Sallese is a Senior Researcher at École polytechnique fédérale de Lausanne (EPFL), affiliated with multiple units including the Group of Semiconductor Devices (GR-SCI-IEL) , SEL-ENS , and EDMI-GE . His work focuses on semiconductor device modeling, biosensors, radiation effects in electronics, and microfluidic systems. Research Areas: Field Effect Transistor (FET) physics and modeling Radiation-induced soft errors in integrated circuits High-energy particle solid-state sensors Nanowire and junctionless FET biosensors Microfluidic mixer modeling Recent publications highlight advancements in ISFET biosensors , radiation-hard CMOS , and negative capacitance FETs , with applications spanning medical diagnostics, high-energy physics, and industrial monitoring. He supervises PhD students and co-developed the EDLAB initiative for device modeling. Teaching includes core electronics courses and specialized topics like Modeling Micro-/Nano-Field Effect Devices . No scientific awards are explicitly mentioned in the provided texts.

Dmitry Vetrovمشاهده پروفایل

استاد

• Bayesian Deep Learning
• Diffusion Models
• Loss Landscape Analysis
• +۵ مورد دیگر

Dmitry Vetrov serves as a Professor of Computer Science at Constructor University in Bremen, where he founded and leads the Bayesian Methods Research Group. His academic foundation includes graduation from Moscow State University in 2003 and completion of his PhD in 2006, establishing a career centered on advancing probabilistic machine learning methodologies. His educational trajectory features: Undergraduate studies at Moscow State University (2003) Doctoral degree (PhD, 2006) Vetrov's research program critically bridges Bayesian statistics with deep learning architectures, with his group pioneering efficient diffusion model algorithms, loss landscape characterization in neural networks, scalable stochastic optimization tools, tensor decomposition applications for large-scale ML systems, and enhanced conditional text generation frameworks. This work manifests practical implementations across generative AI domains while maintaining theoretical rigor in probabilistic modeling. Analysis of his 2024-2025 publications reveals a concentrated research thrust toward diffusion model innovation, spanning text generation (token embedding smoothing, language model encoding properties), image synthesis (hair transfer, gesture generation), and scientific applications (protein modeling, genetic fine-mapping). Key thematic threads include sampler acceleration, theoretical property analysis of diffusion processes, and robust evaluation frameworks for generative systems. No scientific awards were documented in the source materials. Mentorship outcomes demonstrate significant impact, with three recent PhD students securing research positions at DeepMind. While specific grant details remain undisclosed, the group's prolific output across NeurIPS, ICML, and CVPR indicates sustained research funding. The Bayesian Methods Research Group operates as an integrated innovation hub within Constructor University's academic ecosystem. The research collective he directs maintains active development of Bayesian-deep learning fusion techniques, with current projects emphasizing diffusion model efficiency, theoretical foundations of optimization landscapes, and cross-domain applications in computational biology and multimodal generation.