جست‌وجوی استاد و پژوهشگر

Philippe Cudré-Maurouxمشاهده پروفایل

استاد

• Exascale Information Management
• Big Data
• Artificial Intelligence
• +۱۳ مورد دیگر

Philippe Cudré-Mauroux is a Full Professor at the University of Fribourg, Switzerland , where he leads the eXascale Infolab . He has held visiting researcher positions at MIT and Microsoft CISL , and serves on the Research Council of the Swiss National Science Foundation and the Scientific Advisory Board of the CHIST-ERA EU Research Programme . Research Interests: His work spans exascale information management , big data , AI , knowledge graphs , linked data , time series data repair , and emergent semantics . He focuses on building scalable, intelligent data systems that integrate storage, computation, and semantics. Publication Trends: His recent work emphasizes schema-aware knowledge graph completion , time series imputation and benchmarking , hardware-accelerated data systems , and large language models for data cleaning . His research bridges database systems, AI, and systems architecture, often targeting high-performance, real-world applications. Scientific Awards: ERC Consolidator Grant (2016) Google Faculty Research Award (2013) Verisign Internet Infrastructures Award (2012) Best Paper Awards at VLDB (2020), AAMAS (2019), and Swiss Data Science Conference (2020) EPFL Doctorate Award and Press Mention (2007) Best Mentor Award at ISWC (2010) Advising and Grants: He mentors a large group of researchers and students, many of whom are co-authors on his publications. He has secured significant funding, including a €2M ERC Grant and multiple Google and Amazon grants, supporting a vibrant research lab focused on next-generation data infrastructure. Labs and Teams: He leads the eXascale Infolab at the University of Fribourg, a dynamic research group actively publishing in top-tier venues and developing innovative tools for data management and AI integration.

Gavin Brownمشاهده پروفایل

استاد

• Machine Learning
• Statistics
• Information Theory
• +۳ مورد دیگر

Professor Gavin Brown is a faculty member in the Department of Computer Science, focusing on theoretical and methodological foundations of Machine Learning. His work bridges statistics, information theory, and information geometry to develop robust ML methods for reproducibility and hypothesis testing in non-standard scenarios. His research has been applied to predictive policing, clinical trials, and energy-efficient ML algorithms for plastic electronics. Key publications include A Unified Theory of Diversity in Ensemble Learning (JMLR, 2023) and On the Stability of Feature Selection in the Presence of Feature Correlations (ECML, 2019), supported by EPSRC funding. Recent work (2024) investigates the distinction between bias-variance and approximation-estimation error, alongside hardware-efficient ML solutions for FPGAs and flexible electronics. His book How to Get Your PhD (OUP, 2021) offers practical and emotional guidance for doctoral students, co-authored with 12 experts on career planning, diversity in science, and risk management in research. Developed theories for ensemble diversity (2020-2023) Created stability metrics for feature selection (2016-2022) Explored ML applications in healthcare and public safety Current activities include a research sabbatical (2021-2022) working on novel ML frameworks. He leads a research group at the Kilburn Building (Office G11) and contributes to pedagogy discussions, particularly around PhD training and conceptual understanding in deep learning.

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.

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.

• 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.

Alexandre Sébastien Julien Levisseمشاهده پروفایل

مدرس

• Computer Architecture
• VLSI Design
• Low-Power Electronics
• +۴ مورد دیگر

Alexandre Sébastien Julien Levisse is a Lecturer at the Institute of Electrical Engineering within EPFL's School of Engineering, with additional roles as System Specialist in PAT/SEL departments and Scientist in IEM. His work centers on energy-efficient hardware architectures for edge computing and AI applications. Research focuses on: Computer Architecture & VLSI Design Low-Power Electronics for edge devices Emerging Memory Technologies (RRAM, OxRAM) Neuromorphic & Approximate Computing In-Memory/ Near-Memory Computing Edge AI Hardware Acceleration His 2022-2025 publications reveal strong emphasis on specialized edge AI hardware, including near-DRAM architectures (SideDRAM), RISC-V microcontrollers for healthcare (HEEPOcrates), and overflow-free memory systems. Key innovations involve hardware-software co-design for energy efficiency, variable precision computation, and novel memory hierarchies targeting wearable sensors and edge inference. Levisse teaches Fundamentals of VLSI Design and Lab in Advanced VLSI Design at EPFL. He actively contributes to digital education as a member of EPFL's Centre for Digital Education (CCE), developing curricula for next-generation hardware engineers.

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.

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).

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.

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.

Thomas Sanchezمشاهده پروفایل

پژوهشگر ارشد

• Medical Imaging
• Magnetic Resonance Imaging
• Fetal Imaging
• +۳ مورد دیگر

Thomas Sanchez serves as a Research Fellow at the Center for Biomedical Imaging (CIBM) under the University of Lausanne and CHUV hospital, working within the Computational Neuroanatomy & Fetal Imaging Section at the Medical Image Analysis Laboratory (MIAL) led by Dr. Meritxell Bach Cuadra. His academic credentials include: Master's degree in Computational Science and Engineering from EPFL (2018) PhD in Computer Science from EPFL (2022) Sanchez specializes in medical imaging with emphasis on magnetic resonance imaging optimization. His doctoral work developed reinforcement learning and deep learning approaches for Cartesian sampling mask optimization in accelerated MRI, balancing acquisition speed against image quality. Current projects focus on deep learning-based quality control systems for fetal brain MRI and 3D super-resolution reconstruction techniques, advancing diagnostic capabilities in prenatal neuroimaging. MIAL operates as a collaborative research hub within CIBM, integrating computational methods with clinical radiology applications at CHUV Radiologie.

Bingcong Liمشاهده پروفایل

پژوهشگر

• Machine Learning
• Optimization
• Signal Processing
• +۳ مورد دیگر

Bingcong Li is a postdoctoral researcher at ETH Zurich collaborating with Prof. Niao He and the ODI group. Previously, they completed doctoral studies at the University of Minnesota under Prof. Georgios B. Giannakis, followed by industry experience focused on large language models (LLMs). Education includes a PhD from the University of Minnesota under Prof. Georgios B. Giannakis. Research centers on making computation efficient, accessible, and affordable across heterogeneous resources—from GPU clusters to consumer hardware—through interdisciplinary approaches combining deep learning, optimization, and signal processing. Key research areas address foundational computing architectures, large-scale system sustainability, and personalized AI access. Their work develops theoretically grounded methods for explainable systems, with recent focus on LLM fine-tuning efficiency. Publication trends show consistent contributions to top conferences (NeurIPS, ICML, ICLR) with emphasis on optimization techniques for resource-constrained LLM deployment. Their advising and grant activities aren't explicitly detailed, though they actively participate in academic service through conference talks (EUROPT 2025, ICASSP 2025) and co-organizing events like the Efficient LLMs Fine-tuning Track at AI+X Summit. Lab affiliation centers on ETH Zurich's ODI group under Prof. Niao He, focusing on optimization-driven AI solutions.

Paolo Favaroمشاهده پروفایل

استاد

• Computer Vision
• Computational Photography
• Machine Learning
• +۴ مورد دیگر

Prof. Dr. Paolo Favaro is a full Professor and Head of the Computer Vision Group (CVG) at the University of Bern's Institute of Computer Science. He holds a Laurea (BSc+MSc) from the University of Padova, Italy, and M.Sc. and Ph.D. in Electrical Engineering from Washington University in St. Louis. His career includes postdoctoral roles at UCLA and Cambridge University, followed by work in medical imaging at Siemens Corporate Research. From 2006–2011, he was Lecturer and Reader at Heriot-Watt University, and Honorary Fellow at the University of Edinburgh before joining Bern in 2012. His research focuses on computer vision , computational photography , machine learning , and signal/image processing , with contributions to inverse problems and variational techniques. His work spans theoretical advancements and practical applications, such as video diffusion models, unsupervised segmentation, and image restoration. He is an active member of the IEEE Society. Recent research trends emphasize generative models (e.g., diffusion models, video generation) and self-supervised learning , addressing challenges in real-world image/video denoising and sparsity-driven methods. His articles explore topics like Kalman-based optimization, multimodal world models, and spatio-temporal representation learning. Prof. Favaro leads the CVG, fostering interdisciplinary research at the intersection of computer vision and AI. His lab develops cutting-edge solutions for 3D reconstruction, medical imaging, and autonomous systems.