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Chenhao ChuView profile

Professor

• RF/mm-Wave Circuits
• Power Amplification
• AI-Assisted Design Synthesis
• +4 more

Dr. Chenhao Chu is a Professor at ETH Zürich, holding the Professur für Elektronik (Professorship for Electronics). He specializes in RF/mm-Wave circuits, AI-driven design methods, and advanced power amplification technologies. His research focuses on energy-efficient, wideband systems, antenna-in-package solutions, and GaN-based applications for 6G and beyond. Education: Ph.D. in Electronic Engineering, University College Dublin (2022) M.Sc. in Electronic Information Engineering, City University of Hong Kong (2017) Research Interests: His work bridges AI and hardware design, emphasizing reconfigurable circuits , high-linearity power amplifiers , and mm-Wave phased arrays . Key areas include: AI-assisted rapid design synthesis III-V/Si co-design for mm-Wave Efficient antenna integration Dynamic load modulation techniques Awards: Award-winning researcher with distinctions including the First Place Best Student Paper Award (2022 Royal Irish Academy Colloquium) and multiple HEPA-SDC Competition Awards (2021-2022). Recognized for innovations in PA efficiency and design automation. Advising & Grants: Leading projects on 6G PA architectures and AI-driven RF design. Active in IEEE with contributions to conferences like IMS and ARFTG. No explicitly stated grants mentioned but widely cited in industry-academia collaborations. Labs & Teams: Associated with ETH Zürich's Electronics Laboratory, focusing on next-generation wireless systems. Collaborates internationally on 5G/6G infrastructure and mm-Wave innovations.

Yi GuoView profile

Researcher

• Power Systems
• Control Systems
• Smart Grids
• +3 more

Dr. Yi Guo is an External Scientific Staff member at the Power Systems and High Voltage Lab, part of ETH Zurich's Department of Information Technology and Electrical Engineering. His research focuses on advancing smart grid technologies, particularly in power system coordination, stochastic control, and distributed energy resource integration. His work emphasizes real-time operational frameworks for integrated transmission-distribution systems, flexibility modeling, and robust optimization under uncertainty. Collaborations include projects funded by NCCR Automation (SNF). Key research areas include: - Real-time grid control and NMPC applications - Stochastic modeling of distributed energy resources (DERs) - Sparsity-promoting control design for power grids - Joint optimization-estimation architectures for distribution networks - Two-stage electricity market frameworks for DER participation Recent publications (2020-2024) highlight contributions to grid resilience, DER aggregation, and sensor placement optimization. His work addresses challenges in energy transition through advanced control systems and market mechanisms. Lab affiliations include the Power Systems and High Voltage Lab, collaborating on projects like NCCR Automation Phase I. His research bridges theoretical control advancements with practical grid implementation.

• Natural and Artificial Lighting
• Solar Energy (Passive and Active)
• Building Technology
• +3 more

Jean-Louis Scartezzini is an Honorary Professor at École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the School of Architecture, Civil and Environmental Engineering (ENAC) and the Solar Energy and Building Physics Laboratory (LESO-PB). His research focuses on natural/artificial lighting, solar energy systems, and building technology, with a strong emphasis on energy efficiency and sustainability. Director of LESO-PB since 1994 Founded and led several institutes, including the Institute for Infrastructure, Resources, and Environment (2002–2009) Doctorat in Physics from EPFL (1986) Extensive international collaborations, including visiting roles at NUS (2009) and LBNL/UCLA (1988) Research interests include: - Daylighting and lighting control systems - Passive/active solar technologies - Urban microclimate and energy systems - Stochastic simulation and predictive control Recent work addresses climate change impacts on energy systems, urban sustainability, and machine learning applications in energy optimization. Key publications span lighting health impacts, renewable integration, and microclimate modeling Awards include the European Solar Prize (2001/2002) and Walsh-Weston Bronze Medal (1998) Mentored over 20 PhD students, many leading in academia and industry (e.g., Marilyne Andersen at EPFL, Flavio Foradini at E4Tech).

Onur MutluView profile

Professor

• Computer Architecture
• Computer Systems
• Hardware Security
• +5 more

Onur Mutlu is a Professor of Computer Science at ETH Zurich, affiliated with the Department of Information Technology and Electrical Engineering. He also holds adjunct professorships at Carnegie Mellon University and Bilkent University. His research focuses on computer architecture, systems security, bioinformatics, and energy-efficient computing. He has pioneered work on memory-centric computing paradigms, RowHammer security vulnerabilities, and bio-inspired computing systems. He teaches courses such as Digital Design & Computer Architecture and supervises the SAFARI research group, which explores cutting-edge topics in memory systems, AI accelerators, and genomics. Recent activities include keynote talks at ISCA, HiPEAC, and IEEE conferences, emphasizing emerging hardware-software co-design principles. Key contributions include foundational work on memory reliability, cross-layer system design, and accelerating genomic data analysis. His research has been showcased in over 200 publications and industry collaborations with tech leaders like Intel, Huawei, and Micron.

Xuming HeView profile

Associate Professor

• Computer Vision
• Machine Learning
• Artificial Intelligence
• +9 more

Xuming He is an Associate Professor at the School of Information Science and Technology (SIST), ShanghaiTech University, where he leads the PLUS Lab. His research spans computer vision and machine learning with a focus on developing algorithms that operate effectively under limited supervision and evolving data conditions. His core research interests include weakly-supervised and few-shot learning for scenarios with sparse annotations, continual learning frameworks for knowledge retention during sequential task acquisition, semantic segmentation techniques for scene understanding, and multimodal vision-language representations. He emphasizes interpretable machine learning to build transparent AI systems capable of human-understandable reasoning, addressing critical challenges in model trustworthiness and deployment reliability. Recent publications reveal strong trends toward novel class discovery in long-tailed recognition scenarios, physics-informed generative modeling for scientific applications, and robust segmentation under distribution shifts. His work increasingly integrates large language models for multimodal reasoning while maintaining focus on efficiency in resource-constrained environments like robotic grasping and medical imaging analysis. He actively mentors students, having supervised Qian He to PhD completion and Chuanyang Hu to Master's degree in 2023. He welcomes prospective graduate students through ShanghaiTech's Computer Science & Technology program and offers undergraduate research projects requiring minimum six-month commitments. The PLUS Lab under his direction drives innovation in learning under supervision constraints, with recent work spanning medical tumor analysis, cross-view geolocation, photonic computing, and semiconductor design verification. The lab's research bridges theoretical advances with practical applications across healthcare, robotics, and scientific discovery domains.

Dusan LicinaView profile

Associate Professor

• Sustainable and healthy built environment
• Indoor air pollutant dynamics
• Human exposure assessment
• +3 more

Dusan Licina is an Associate Professor at the École polytechnique fédérale de Lausanne (EPFL) and director of the Human-Oriented Built Environment Laboratory (HOBEL) within the Smart Living Lab initiative. His research bridges building science, environmental engineering, and human health. Education: BSc/MSc in Mechanical Engineering (University of Belgrade), joint PhD (National University of Singapore & Technical University of Denmark), postdoc (University of California Berkeley) Key research areas: Indoor air quality, human exposure science, building ventilation, IoT sensing, energy-efficient housing His 15 most cited works focus on particle dynamics, ozone chemistry, and occupant-driven building systems. Notable trends include human-clothing interactions in pollution transport, nanocluster aerosol formation, and real-time CO2 monitoring behavior. Awarded Yaglou award (ISIAQ) for sustainable healthy buildings research Ralph G. Nevin’s award (ASHRAE) for human exposure studies and serving on the Indoor Air editorial board. Current PhD students Dong Hui, Liu Tianqi, Loizou Maria, Medina Taylor Oran explore: Occupant behavior in energy systems Smart sensor networks Radon measurement technologies Work-from-home environmental quality His laboratory develops Human-Centric Sensing Platforms and Occupant-Centric Building Design methodologies through experimental chambers and computational models.

Giorgia RamponiView profile

Assistant Professor

• Machine Learning
• Mathematical Modelling
• Reinforcement Learning
• +3 more

Giorgia Ramponi is an Assistant Professor with Tenure Track at the Faculty of Business, Economics and Informatics at the University of Zurich. She is also an affiliated professor at the ETH AI Center and the Data Science and AI, Computer Science and Engineering department at Chalmers University of Technology. Her educational background includes a Ph.D. in Information Technology from Politecnico di Milano (completed June 2021 with honors), advised by Marcello Restelli, and a Master of Science in Computer Science with Honours Programme (110/110 cum laude) from la Sapienza (July 2017), advised by Flavio Chierichetti and Alessandro Panconesi. Dr. Ramponi's research focuses on machine learning and mathematical modeling, with particular emphasis on reinforcement learning and multiagent learning. Her work bridges theoretical foundations with practical applications, exploring how learning algorithms can make optimal decisions in complex environments. She has made significant contributions to areas including inverse reinforcement learning, multi-agent systems, constrained Markov decision processes, and human-AI interaction through preference learning. Her recent publications demonstrate a strong trend toward addressing fundamental challenges in reinforcement learning, particularly in multi-agent settings, constrained optimization, and learning from human feedback. Her work combines theoretical rigor with practical applications across robotics, economics, and decision-making systems. Hassler Research Grant for "Unified Feedback Integration Framework for Reinforcement Learning" Dr. Ramponi actively contributes to the academic community through conference participation, invited lectures (including at the Mediterranean Machine Learning Summer School), and teaching. She designed and taught the "Data Science and Machine Learning" course for the ETH-Ashesi Master program. She is also a member of the ELLIS community, which connects excellence in AI research across Europe. Her research group focuses on developing frameworks for reinforcement learning with various feedback types, including preferences, rewards, and demonstrations. The group aims to advance the theoretical understanding of learning algorithms while addressing practical challenges in real-world applications.

Mattia BianchiView profile

Lecturer

• Control Theory
• Game Theory
• Machine Learning
• +3 more

Mattia Bianchi is a Lecturer at the Department of Information Technology and Electrical Engineering, ETH Zurich, Switzerland. He is affiliated with the Automatic Control Laboratory under Prof. Florian Dörfler, with office location at ETL I 34, Physikstrasse 3, Zurich. His research focuses on developing distributed, efficient, and robust methods for decision and control problems in complex network systems, including power grids and cognitive radio networks. Bachelor’s degree in Information and Communication Engineering (2016), University of L’Aquila, Italy Master’s degree in Systems Engineering (2018), University of L’Aquila, Italy PhD in Systems and Control (2018–2023), TU Delft, The Netherlands Postdoctoral researcher (2023–present), ETH Zurich, Switzerland His methodological approach integrates operator theory, learning algorithms, game theory, and data-driven control. Key research themes include uncovering common structures in optimization and control algorithms, with applications in distributed feedback optimization, Nash equilibrium seeking, and stabilization of constrained systems. Current work explores partial-decision information frameworks and linear convergence guarantees. For detailed information on his publications, visit his Google Scholar profile . Mattia actively supervises Master’s theses and semester projects, inviting candidates to contact him with their academic credentials.

Tova MiloView profile

Professor

• Databases
• Big Data Management
• Machine Learning for Data Management
• +7 more

Professor Tova Milo is the Chair for Information Management at the School of Computer Science, Tel Aviv University, leading the prominent Databases Lab (DB Group). Her research spans databases, big data management, crowd-based data sourcing, and business process querying, with significant contributions to data integration and semi-structured data systems. Her research interests focus on innovative approaches to data management challenges through machine learning integration, crowd computing, and business process analysis. Key projects include Business Process Querying (BPQ) for analyzing BPEL specifications, MoDaS for crowd-based data management, and PROX for data provenance summarization. Her work bridges theoretical foundations with practical applications in fraud detection, recommendation systems, and data cleaning. Analysis of her recent publications reveals strong trends in human-in-the-loop data management systems, with growing emphasis on crowd integration for data cleaning and knowledge acquisition. Her research increasingly combines traditional database theory with machine learning techniques for scalable big data processing, while maintaining focus on business process modeling and provenance tracking. ACM PODS Alberto O. Mendelzon Test-of-Time Award (2010) ERC Advanced Investigators grant MoDaS (2011) The Weizmann Prize for Exact Sciences (2017) VLDB Women in Database Research award (2017) IEEE TCDE Impact award (2022) ISF Breakthrough Research Grant (2022) Doctorate Honoris Causa, University of Zurich (2023) ACM Fellow Member of Academia Europaea Professor Milo has advised over 30 graduate students including PhD candidates like Yael Amsterdamer and Ohad Greenshpan, and numerous MSc students working on projects including MoDaS, BPQ, and EDOS. Her research has been supported by major grants including the ERC Advanced Investigators grant and ISF Breakthrough Research Grant. She actively collaborates with industry partners including IBM and Microsoft, particularly in business process management standards. She directs Tel Aviv University's Databases Lab, which maintains the DB Group with multiple research streams including business process querying (BPQ), crowd-based data management (MoDaS), and self-adaptive data dissemination (EDOS/COLT). The lab operates from the Schreiber Building (M-20) and maintains strong international collaborations, particularly with European institutions through the ERC-funded MoDaS project.

Fabio NobileView profile

Professor

• Scientific Computing
• Uncertainty Quantification
• Numerical Analysis
• +5 more

Fabio Nobile is a Full Professor at the École Polytechnique Fédérale de Lausanne (EPFL) in the School of Basic Sciences (SB), Department of Mathematics (MATH), holding the CADMOS Chair in Scientific Computing and Uncertainty Quantification. He leads the CSQI (Chair of Scientific Computing and Uncertainty Quantification) group. His work focuses on numerical methods for partial differential equations (PDEs), uncertainty quantification, stochastic modeling, and computational fluid dynamics. He is involved in collaborative projects involving fluid-structure interaction, cardiac electro-mechanics, and energy systems. Professor Nobile has extensive teaching experience, including courses on advanced analysis, stochastic simulation, and numerical integration of stochastic differential equations. He supervises numerous PhD students and has contributed to over 200 peer-reviewed publications, covering topics such as low-rank approximation methods, multilevel Monte Carlo techniques, and optimal control under uncertainty. His research emphasizes interdisciplinary applications, including biomedical engineering (e.g., personalized cardiac simulations) and renewable energy (e.g., probabilistic load forecasting). He collaborates with industries and academic institutions globally, advancing computational methodologies for engineering and scientific challenges.

• Speech Technology
• Machine Learning
• Signal Processing
• +3 more

Shashi Kumar is a doctoral student in the Doctoral Program in Electrical Engineering (EDDEE) at École Polytechnique Fédérale de Lausanne (EPFL) , affiliated with the School of Engineering (STI) and the IDIAP Research Institute (LIDIAP) . He holds the role of Doctoral Assistant at LIDIAP, contributing to research in speech technology and machine learning. His work focuses on advancing automatic speech recognition (ASR), optimal transport frameworks, and variational autoencoders for speech enhancement and signal processing. Research interests include speech recognition systems , multimodal task unification , far-field speech processing , and machine learning applications in signal processing and computer vision. His publications highlight contributions to SLAM-ASR performance analysis, joint speaker change detection, and PCB defect classification using image segmentation techniques. Shashi's research is anchored at the IDIAP Research Institute , where he collaborates on projects involving deep learning, audio signal processing, and speech technology. While no awards or grants are explicitly listed, his work reflects active engagement in international challenges like the Interspeech DiCOVA competition.

Adrian PerrigView profile

Professor

• Network Security
• Cybersecurity
• Distributed Systems
• +3 more

Adrian Perrig is a Full Professor at the Department of Computer Science at ETH Zürich. He leads research in network security, distributed systems, and internet architecture, focusing on projects like the SCION secure internet architecture and its commercialization through Anapaya Systems. His work emphasizes secure communication, denial-of-service defense, and public key infrastructure (PKI) innovations. Affiliations: ETH Zürich, Institute for Information Security Key Contributions: SCION, SAGE, RHINE, F-PKI Research interests include path-aware networks, cryptographic protocols, and resilient systems. His publications span over 295 results since 2005, with notable awards including the Best Paper Award (CoNEXT 2021) and ANRP 2023. He has contributed to foundational work in secure routing, DNS security, and GPU attestation. Scientific awards include Best Paper Awards at CoNEXT and ACM SIGCOMM, as well as recognition for applied networking research. His work bridges academia and industry, addressing challenges in global network security and scalability.

Daniel KressnerView profile

Professor

• Numerical Linear Algebra
• High-Performance Computing
• Matrix Equations
• +4 more

Prof. Daniel Kressner is a Professor at the École Polytechnique Fédérale de Lausanne (EPFL), holding positions in the School of Basic Sciences (SB), Mathematics Institute (MATH), and the Numerical Algorithms and High-Performance Computing (ANCHP) group. He also leads the SMA-ENS unit within the SB-SMA division. His research focuses on numerical linear algebra, high-performance computing, and tensor approximation methods, with applications in scientific computing and data science. Education details are not explicitly listed, but his career at EPFL includes leadership in key research groups and doctoral programs. He supervises multiple doctoral students, including Alice Cortinovis, Peter Effenberger, and others. Research interests emphasize low-rank methods, matrix equations, and efficient algorithms for large-scale problems. Recent work includes advancements in randomized algorithms, tensor networks, and preconditioning techniques for eigenvalue problems. His publications span high-impact journals like Siam Journal on Matrix Analysis and Applications and Numerical Linear Algebra with Applications, addressing topics such as compressed sensing, multigrid methods, and distributed signal processing. Prof. Kressner advises doctoral candidates and contributes to the Program doctoral Mathématiques (EDMA-GE) committee. His lab, ANCHP, develops software tools for hierarchical matrices and tensor computations, such as the hm-toolbox for HODLR and HSS matrices.

• Software Composition
• Web 2.0 Mashups
• Cloud Computing
• +6 more

Cesare Pautasso is a Full Professor at the Software Institute within the Faculty of Informatics at the Università della Svizzera italiana (USI) in Lugano, Switzerland. His research focuses on software composition techniques, Web 2.0 mashups, cloud computing, business process management, and RESTful API design. He has led projects like JOpera, a service composition tool, and contributed to frameworks such as Liquid Software for adaptive distributed systems. Education: PhD in Computer Science from ETH Zurich (2004) and a Computer Science Engineering Degree (cum laude) from Politecnico di Milano (2000). Research Interests include model-driven engineering, service-oriented architectures, and API analytics. He has authored over 200 publications and serves on numerous program committees, including IEEE and ACM. His work has been funded by grants from Swiss National Science Foundation (SNSF), EU projects like S-CUBE, and industry collaborations. Teaching and Training: Focuses on Web Development, Enterprise Architecture, and emerging technologies. Active in conferences as general chair of ECOWS 2011 and ICWE 2016. Labs/Teams: Leads research groups exploring API design, blockchain applications, and adaptive systems. Collaborates with institutions like HPI Potsdam and ETH Zurich on projects like FC4MC and API-ACE.

Kirsten Andrea SchnorrView profile

Researcher

• Ultrafast Science
• Atomic Physics
• Molecular Physics
• +6 more

Kirsten Andrea Schnorr is a Researcher at the Paul Scherrer Institute (PSI) in Switzerland, working within the Center for Photon Science and Laboratory for Femtochemistry. She joined the SwissFEL team in 2018 to develop the Maloja endstation for atomic, molecular, and non-linear physics, leading its design, construction, and operational commissioning for cutting-edge XUV/X-ray experiments. Her educational background includes: PhD in Physics (2014), Ruprecht Karl University Heidelberg, completed at the Max Planck Institute for Nuclear Physics under PD Dr. Robert Moshammer; thesis focused on XUV pump-probe experiments of electron rearrangement and interatomic Coulombic decay in diatomic molecules. Schnorr's research centers on photo-induced ultrafast relaxation mechanisms in atoms, molecules, and nanoparticles using time-resolved techniques at Free-Electron Lasers and High Harmonic Generation sources. She pioneers multi-color pump-probe schemes with ultrashort X-ray pulses to steer non-local decay processes like Interatomic Coulombic Decay and Electron Mediated Decay, enabling real-time observation of electron dynamics and proton transfer in molecular systems. Her publication trends (2025-2020) reveal dual expertise in fundamental molecular dynamics and instrumental innovation. Key themes include proton transfer in water dimers (Science Advances 2023), Coulomb explosion in iodinated compounds (2025), and engineering breakthroughs like compact gas attenuators (2023) and polarization control systems (2024), frequently published in Physical Review Letters, Nature Communications, and Journal of Synchrotron Radiation. Scientific awards: Peter Paul Ewald Fellowship from the Volkswagen Foundation (2015), supporting her research on non-linear relaxation processes at UC Berkeley's Physical Chemistry Department under Prof. Stephen Leone. No formal advisees or student supervision are documented. The Volkswagen Foundation fellowship served as her primary grant, funding postdoctoral work on real-time relaxation studies; no additional grants are specified. Her instrumental leadership at SwissFEL suggests mentorship of junior scientists, though no individual students are named. Schnorr directs the Maloja instrument at SwissFEL while contributing to the ATHOS beamline development. She collaborates extensively with PSI's detector teams (e.g., JUNGFRAU advancements) and international groups like UC Berkeley's Physical Chemistry Department, driving initiatives in ultrafast beamline technology and molecular dynamics experiments.