Saleh Ashkboos is a Ph.D. student in the Computer Science Department at ETH Zurich, advised by Professors Torsten Hoefler and Dan Alistarh. He is also a Research Assistant at the Scalable Parallel Computing Lab and an affiliated doctoral student of the ETH AI Center. His research focuses on accelerating deep neural network training and developing systems for large-scale graph processing. Prior to ETH Zurich, he earned his Master's degree in Computer Science from Sharif University of Technology, advised by Professor Amir Daneshgar. His work has led to notable contributions, including the best paper award at SC22 for 'ProbGraph.' Recent research emphasizes efficient LLM training and quantization techniques, with publications on topics like 4-bit inference, quantization-aware training frameworks, and scalable meteorological modeling. He has interned at Apple and Microsoft, and his work is accessible via Google Scholar and GitHub. Key projects include GPTQ (post-training quantization for transformers), SliceGPT (LLM compression), and ProbGraph (high-performance graph mining). His technical contributions span distributed systems, neural network optimization, and climate-related machine learning.
Dr. Andrea Guerrieri is an Associate Professor at the University of Applied Sciences and Arts Valais-Wallis - School of Engineering , specializing in Reconfigurable Computing, Electronics Design Automation (EDA), and Post-Quantum Cryptography . His work focuses on accelerating FPGA compilation through tools like DynaRapid and optimizing security protocols via Dynamatic , with technologies adopted by industry leaders including Intel, AMD-Xilinx, and CERN . He holds a BSc in Industrial Systems and a MSc in Engineering from HES-SO, and teaches courses in Digital Design and Embedded Hardware. BSc HES-SO in Industrial Systems (2019) BSc HES-SO in Computer and Communication Systems (2017) MSc HES-SO in Engineering (2021) Guerrieri’s research bridges High-Level Synthesis (HLS) and Reconfigurable Architectures to enhance FPGA performance for both terrestrial and space applications . His 2025 publications highlight advancements in heterogeneous computing , energy-efficient PQC , and rapid compilation frameworks . Notably, his 2024 work on DynaRapid achieved 20× speedup in C-to-FPGA implementation. Key scientific contributions span dataflow circuit optimization , dynamic scheduling , and automated code transformations . His 2023 book Applications Enabled by FPGA-Based Technology and 2021 textbook System-on-Chip Design with Arm established foundational references in embedded systems. Awards include Best Paper at FPL 2024 , Outstanding TPC Member at DAC 2024 , and IEEE Senior Membership (2021) . 2024: Best Paper Award (FPL), Outstanding Short Paper Award (HPEC) 2023: H-Saber publication on PQC optimization 2021: IEEE Senior Member recognition As Chair of Onboard Computing for the CHEESE-NASA SSERVI consortium, he leads international collaborations with ETH Zurich, University of Geneva, California State University , and companies like NVIDIA, Arm, and NASA . His projects include the Innosuisse-funded DyReCte initiative (2019–2021) for reconfigurable cryptoengines in nanosatellites.
Andrea Guerrieri serves as an Associate Professor at the School of Engineering, University of Applied Sciences and Arts Western Switzerland Valais (HES-SO Valais-Wallis), specializing in reconfigurable computing and electronics design automation. His research has established significant industry impact through tools like DynaRapid and Dynamatic, with technology adopted by major semiconductor companies including MIPS, Intel, and AMD-Xilinx. BSc HES-SO in Industrial Systems - System-on-Chip specialization BSC HES-SO in Computer and Communication Systems - Digital Design specialization MSc HES-SO in Engineering - Embedded Hardware and Firmware specialization Professor Guerrieri's research focuses on reconfigurable computing, electronics design automation (EDA), and security, with particular emphasis on FPGA design, high-level synthesis, and post-quantum cryptography implementations. His work bridges the gap between theoretical computer architecture and practical hardware implementations, with strong applications in space technology and embedded security systems. His recent publications demonstrate increasing focus on energy-efficient implementations for space applications and quantum-resistant cryptographic systems. Analysis of his 15 most recent publications reveals a clear research trajectory toward optimizing FPGA implementations for post-quantum cryptography and space applications. His work consistently addresses the performance bottlenecks in high-level synthesis while maintaining practical applicability for industry partners like NASA, CERN, and major semiconductor companies. The recent surge in best paper awards (three in 2024 alone) reflects growing recognition of his contributions to efficient FPGA compilation techniques and cryptographic implementations. Scientific Awards: Best Paper Award at FPL 2024 Best Paper Award at HPEC 2024 Best Paper Award at ISFPGA 2020 Outstanding Short Paper Award at IEEE HPEC 2024 Outstanding TPC Member Award at DAC 2024 IEEE Senior Member (2021) Multiple Best Paper nominations (FCCM 2022, FPL 2022, HiPEAC 2022) Professor Guerrieri actively participates in international research projects including the DyReCte project (2019-2021) on dynamically reconfigurable cryptoengines for nano-satellites. He currently chairs the Onboard Computing topic for the Swiss consortium CHEESE affiliated with NASA SSERVI and collaborates extensively with industry partners including AMD-Xilinx, NVIDIA, Arm, NASA, and CERN, as well as academic institutions like ETH Zurich and University of Geneva. His current research focuses on developing next-generation EDA tools and reconfigurable computing platforms for both terrestrial and space applications. His laboratory work centers around FPGA-based prototyping and validation, with specialized facilities for space applications testing. Professor Guerrieri leads a research team that includes Andres Upegui, Quentin Berthet, Laurent Gantel, and Gabriel Da Silva Marques, focusing on practical implementations of reconfigurable architectures for security and space applications.
Ali Ramezani-Kebrya is currently an Associate Professor (with tenure) in Computer Science at the University of Oslo. Previously, he was a postdoctoral fellow at the Laboratory for Information and Inference Systems (LIONS) at EPFL and the Vector Institute in Canada. His research focuses on large-scale and distributed machine learning, optimization, privacy/security, reinforcement learning, and communication/networking aspects of machine learning algorithms. He holds a Ph.D. from the University of Toronto. Key research interests include developing robust federated learning frameworks, addressing label shift in distributed systems, and improving the generalization capabilities of stochastic gradient descent methods. His work bridges theoretical foundations with practical applications in distributed optimization and privacy-preserving machine learning. Recipient of the NSERC Postdoctoral Fellowship (equivalent to NSF fellowship in the US) His publications emphasize advancements in distributed learning systems, robust optimization techniques, and theoretical guarantees for federated learning under covariate shifts. Current research trends explore Nash equilibrium-based approaches for robustness and communication-efficient algorithms in distributed settings.
Prof. Alberto S. Cattaneo is a faculty member at the University of Zurich, affiliated with the Department of Mathematics. He holds the rank of Professor and has been actively involved in teaching and research since at least 1998. His research interests span mathematical physics, differential geometry, topology, and interdisciplinary areas like computational biology, genomics, and digital forensics. He has developed courses on topics such as field theory, quantum mechanics, and differential manifolds, reflecting his expertise in theoretical and applied mathematics. Prof. Cattaneo has contributed to numerous publications, including works on distributed genomic analysis, sensor pattern noise (PNU) in forensics, and algorithm optimization for big data frameworks like Hadoop and Spark. His work bridges pure mathematics with applications in bioinformatics and cybersecurity. Though no awards are explicitly mentioned, his extensive publication record and teaching roles highlight his academic standing. He maintains an active presence through courses and research collaborations, with no indication of part-time roles or retirement.
Olaf Schenk is a Professor at the Institute of Computing within the Faculty of Informatics at Università della Svizzera italiana (USI), Switzerland. He serves as Director of the Institute of Computing and Co-Director of the Master in Computational Science. He is also an adjunct member of the Computer Systems Institute at USI. PhD in Information Technology and Electrical Engineering, ETH Zurich (2001) Venia Legendi in Mathematics and Computer Science, University of Basel (2009) Applied Mathematics, Karlsruhe Institute of Technology (KIT), Germany His research focuses on high-performance computing , computational science and engineering , and applied algorithms for extreme-scale simulations. He bridges computer science with scientific computing needs, particularly in parallel algorithms , sparse solvers , graph analytics , and manycore architectures . His work emphasizes scalable software tools and programming models for emerging HPC systems. The 15 most recent publications reflect a consistent focus on sparse matrix computations , parallel and task-based algorithms , graph partitioning , and performance optimization for heterogeneous and manycore systems. Keywords span high-performance computing, numerical linear algebra, and large-scale data analysis, showing strong integration of theoretical algorithm design with practical implementation. Olaf Schenk has received several prestigious honors: Elected Fellow, Society for Industrial and Applied Mathematics (SIAM) Senior Member, IEEE and ACM SIAM Supercomputing Prize 2023 IBM Faculty Award Two Leadership Computing Awards from the U.S. Department of Energy He has held leadership roles as Chair, Vice Chair, and Program Director of the SIAM Activity Group on Supercomputing. He serves as Associate Editor for ACM Transactions on Mathematical Software and on the editorial board of SIAM Journal on Scientific Computing . He has participated in over 60 international program committees, including top-tier conferences such as SC, IPDPS, and IEEE CSE. He advises PhD and Master’s students in computational science and leads research projects funded by national and international agencies. He is also the Founder & Director of Panua Technologies Sagl, focusing on high-end software for simulation and optimization. His research group at USI works on next-generation computing tools for extreme-scale scientific simulations, with ongoing work in adaptive algorithms, resilience, and hybrid CPU-GPU computing. He leads collaborative projects with institutions in Europe and the U.S., aiming to develop scalable, robust, and efficient software for future exascale systems.
Julian Charles Shillcock is a Lecturer and computational modeling specialist at École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the School of Life Sciences and the Lashuel Lab. He also serves as a Scientist in the UPDALPE group (Prof. Dal Peraro Group) and teaches in EPFL’s SSV (Teaching) and EDNE (Doctoral Education) sections. His work bridges biophysics, computational modeling, and cellular dynamics. PhD in Physics from Simon Fraser University (1996) Group Leader at Max Planck Institute of Colloids and Interfaces (2000-2005) Associate Professor at University of Southern Denmark (2005-2011) Blue Brain Project member since 2011 Shillcock’s research focuses on biomolecular condensates , membrane dynamics , and computational cell biology . He develops mesoscale simulation methods like Dissipative Particle Dynamics to study vesicle fusion , neuronal morphology , and neurodegenerative disease mechanisms . Recent work includes POETS computing platforms for accelerating simulations and Shiga toxin clustering on membranes. His publications (2022-2024) reveal trends in soft matter physics , computational neuroscience , and biomolecular condensate structure . Key collaborations include Imperial College London and University of Southampton on the POETS project. Scientific recognition includes: 2021 Polysphère prize for Best Teacher in Life Sciences He has advised PhD student Lida Kanari in computational morphology and contributed to neocortical microcircuit reconstruction . His research spans computational biophysics , toxin entry mechanisms , and novel computational platforms for life sciences education.
Rafael Pereira Pires is a Lecturer and researcher at École polytechnique fédérale de Lausanne (EPFL) , affiliated with the Scalable Computing Systems Laboratory (SACS) and IC-SIN units. His research focuses on systems solutions at the intersection of privacy, efficiency, and machine learning in distributed environments. Education PhD in Computer Science (2019, University of Neuchâtel, Switzerland) Professional Master in Mechatronics (2014, IFSC, Brazil) Master in Computer Science (2009, UFSC, Brazil) His work explores privacy-preserving decentralized learning , trusted execution environments , and resource-efficient distributed systems . Recent publications address techniques like model fragmentation, approximate caching, and secure aggregation in decentralized learning contexts. Key trends in his 2023-2025 publications include: Advancements in federated learning and Mixture-of-Experts (MoE) models Applications of Trusted Execution Environments (SGX) to decentralized systems Optimization techniques for energy-aware and low-cost learning Scientific recognition includes the 2019 Léon Du Pasquier et Louis Perrier award for his PhD thesis. He has contributed to open-source tools like DecentralizePy and served as reviewer/PC member for top conferences including NeurIPS , Middleware , and ICDCS .
Jérôme Yerly serves as a Research Staff Scientist at the Translational MR Imaging Section of the Center for Biomedical Imaging (CIBM), jointly affiliated with Lausanne University Hospital (CHUV) and the University of Lausanne (UNIL). His work focuses on translating advanced MRI methodologies into clinical diagnostics and therapeutic assessment. His academic credentials include: Bachelor in Electronic Engineering from University of Applied Sciences of Western Switzerland, Fribourg (2004) MSc and PhD in Electrical and Computer Engineering from University of Calgary Dr. Yerly specializes in developing nonlinear reconstruction techniques to enhance cardiac and neuroimaging applications. His research leverages compressed sensing and parallel imaging to accelerate scan times while improving spatial and temporal resolution. Current projects target coronary artery disease assessment through coronary endothelial function imaging, extending his doctoral work on stroke neuroimaging where he pioneered sparse acquisition strategies for rapid MRI. No information is documented regarding student supervision or research grant funding. He operates within CHUV's Department of Diagnostic Radiology and Interventional Radiology as part of CIBM's collaborative network, which integrates École Polytechnique Fédérale de Lausanne (EPFL), University of Lausanne, CHUV, and Geneva University Hospitals to advance biomedical imaging innovation.
Umberto Michelucci is a Professor of Scientific Machine Learning at Lucerne University of Applied Sciences and Arts (HSLU), Switzerland. He holds a PhD in Machine Learning applied to Physics and has over 20 years of industry experience. He is the Subject Head of Applied Data Intelligence in Continuing and Executive Education, Head of Certificates in Machine Learning/Data Engineering, and founder of TOELT LLC and the AI Center of Excellence at Helsana Versicherung AG. His research focuses on machine learning applications in science, astrophysics, uncertainty quantification, and sensor technology. Education PhD in Machine Learning applied to Physics (Portsmouth University) Master in Theoretical Physics (University of Florence) Postgraduate Certificate in Higher Education (Open University, UK) Research Interests Michelucci’s work bridges machine learning and scientific disciplines. Key areas include: Machine learning for astrophysics (INAF collaborations) Uncertainty analysis in high-stakes ML systems Deep learning for optical sensing (e.g., olive oil quality analysis) Foundational mathematical concepts for ML in science Awards & Recognition World’s Top 2% Scientists (Stanford List) Google Developer Expert in Machine Learning AI Global Ambassador (2022) TOP AI Influencer in Switzerland (2021) Grants & Collaborations He collaborates with institutions like INAF (Italy) and NVIDIA/Google, leading projects on AI for agrifood, medical imaging, and astrophysics. His work includes $multi-million industry partnerships and EU-funded research. Labs & Teams Director of the TOELT AI Lab and oversees HSLU’s Applied Data Intelligence programs. Active in open-source initiatives and global AI standardization efforts.
Christoph Grunau is a Researcher at ETH Zürich's Theoretical Computer Science department, affiliated with the Professorship for Computer Science. His work focuses on distributed computing, parallel algorithms, graph theory, and network decomposition. He has contributed to advancements in scalable MPC (Massively Parallel Computing) algorithms, efficient parallel derandomization techniques, and deterministic network decomposition methods. His research emphasizes algorithmic efficiency, theoretical guarantees, and applications in distributed systems, quantum computing, and dynamic graph problems. Key contributions include work on graph orientation, dynamic coloring algorithms, and clustering techniques such as k-center and k-means++. His publications span topics like shortest path algorithms with negative edge weights, probabilistic methods for algorithm analysis, and distributed symmetry breaking in sparse graphs. Grunau's research bridges foundational theory with practical distributed computing challenges, addressing scalability and efficiency in both classical and emerging computational frameworks.
Feiran Zhao is a Researcher at the Institute of Automatic Control, part of the Department of Mechanical and Process Engineering at ETH Zürich. He holds a B.S. in Control Science and Engineering from Harbin Institute of Technology (2018) and a Ph.D. from Tsinghua University (2024). His research focuses on data-driven control, adaptive control, reinforcement learning, and their applications in engineering systems. Zhao is currently a postdoc under Prof. Florian Dorfler at ETH's Automatic Control Lab. Research interests span topics like policy optimization for LQR systems, quantized feedback control, and model predictive control acceleration. His work bridges machine learning and classical control theory, with applications in robotics, power systems, and aerospace engineering. His publications (2019–2025) explore theoretical foundations of policy gradient methods, convergence analysis, and practical implementations in autonomous systems. Though no awards are explicitly listed, his active research in high-impact areas suggests potential recognition. As part of the Automatic Control Lab, Zhao collaborates on projects involving data-enabled control strategies and real-world system applications. No student advisees are currently listed.
Qi Tang is a Research Fellow in the Department of Environmental Science at the University of Basel and concurrently serves as Coordinator of the Swiss Water Earth Systems PhD School at the University of Neuchâtel. His expertise spans hydrogeology, data assimilation, and Earth system modeling. He holds a PhD in Hydrogeology from RWTH Aachen University (2017) and has held postdoctoral positions at institutions including the Alfred Wegener Institute (Germany), the University of Basel, and the Chinese Academy of Sciences. Education: PhD in Hydrogeology, RWTH Aachen University, Germany (2012–2017) MSc in Hydrology and Water Resources, Beijing Normal University (2009–2012) BSc in Applied Mathematics, China Agriculture University (2005–2009) Research Interests: Qi Tang focuses on advancing coupled Earth system models through data assimilation techniques. His work integrates hydrological, oceanographic, and climatic processes to improve predictive accuracy. Key areas include river-aquifer interaction dynamics, satellite data integration in ocean-atmosphere models, and cloud computing for real-time water resource management. His research bridges theoretical modeling with practical applications in environmental monitoring and climate prediction. Publications: His articles emphasize data-driven approaches to environmental systems. Recent work highlights coupled model improvements using satellite data (e.g., ocean-atmosphere interactions), ensemble Kalman filtering for flood simulations, and Bayesian networks for precipitation modeling. These studies underscore his expertise in both computational methods and field applications. Advising & Grants: While no formal advisees are listed, his postdoctoral roles suggest involvement in mentoring junior researchers. No specific grants are mentioned in the provided texts. Labs/Teams: Affiliated with the Hydrogeological Processes group at the Center for Hydrogeology and Geothermal Energy (CHYN), University of Neuchâtel. This group specializes in geothermal energy, hydrochemistry, and stochastic hydrogeology.
Matteo Biagiola is a Researcher in the Faculty of Informatics at Università della Svizzera italiana (USI), Lugano, Switzerland, and a PostDoctoral researcher at the University of St. Gallen (HSG). He specializes in software testing, particularly test generation for Web applications, deep reinforcement learning systems, and autonomous driving software. His work focuses on enhancing AI robustness through testing and improving software testing via AI techniques. Biagiola holds a Ph.D. from Università degli Studi di Genova (Italy) in collaboration with Fondazione Bruno Kessler, Trento. He has conducted postdoctoral research at USI on the Precrime ERC Advanced Grant project under Paolo Tonella. His research tools include μPRL (mutation testing for RL agents), STILE (web test parallelization), and GenBo (boundary state generation for autonomous systems). Education: Ph.D.: Università degli Studi di Genova / Fondazione Bruno Kessler (2016–2020) M.Sc.: Università Politecnica delle Marche (2014–2016) Affiliations: PostDoc & Scientific Collaborator: University of St. Gallen / USI (2025–present) PostDoc: USI (2020–2025) Visiting Ph.D. Student: University of British Columbia (2018) His research interests span AI-driven testing tools, autonomous system validation, and simulation-based testing. He has received the Distinguished Paper Award at ICST 2025 and serves on program committees for major conferences like FSE, ICSE, and ICST. He co-organizes workshops like DeepTest (co-located with ICSE) and the Cyber-Physical Systems tool competition (SBFT).
Popescu-Belis Andrei serves as an Associate Professor at the University of Applied Sciences and Arts Western Switzerland (HES-SO), specifically within the Institute of Information and Communication Technologies (IICT) at HEIG-VD campus in Yverdon-les-Bains. His academic work spans teaching computer science fundamentals and advanced natural language processing concepts across both undergraduate and graduate programs. His research interests focus on Natural Language Processing , Machine Translation , and Human-Machine Dialogue systems, with particular emphasis on low-resource language scenarios. His work addresses challenges in speech-to-speech translation, subword tokenization, neural machine translation optimization, and creative applications like poem generation. Recent publications through 2025 demonstrate his ongoing contributions to the field, particularly in multilingual NMT training schedules and low-resource language processing pipelines. Analysis of his 15 most recent publications reveals consistent focus on practical solutions for language barriers, with increasing attention to reinforcement learning applications and creative language generation. His work on GPoeT demonstrates the intersection of traditional literary forms with modern language models, while his research on low-resource translation pipelines addresses real-world community interpreting needs. He has led significant research projects including Digital Lyric (2019-2020), a collaboration with UNIL (University of Lausanne) to develop systems for aiding poem generation for a public exhibition at Morges Castle. This project demonstrates his interdisciplinary approach bridging computational linguistics with creative applications. His academic contributions include: Development of specialized training pipelines for low-resource neural machine translation Innovative approaches to speech-to-speech translation for community interpreting Novel methods for constrained language model applications in creative writing Significant contributions to understanding subword tokenization mechanics Popescu-Belis maintains active research collaborations across institutions, as evidenced by his work with UNIL and various international conference publications. His teaching portfolio includes courses in natural language processing, data preparation, unsupervised machine learning, and computer science fundamentals, demonstrating his commitment to both theoretical and practical aspects of the field.
