Dr. Saikishore Kalloori Saikishore is a Lecturer in the Department of Computer Science at ETH Zurich, specializing in areas such as Recommender Systems, Federated Learning, and Augmented Reality Applications. His research focuses on developing privacy-aware collaborative systems, improving news and advertising recommendations, and enhancing user interaction through advanced AR interfaces. His work integrates machine learning techniques with real-world applications, addressing challenges like cold-start recommendations and cross-silo data collaboration. Recent contributions explore ethical AI in news curation, AR-driven tourism experiences, and federated learning frameworks for corporate environments. Notable projects include the EB-NeRD news recommendation dataset and cross-silo federated learning architectures. His research emphasizes balancing algorithmic accuracy with user-centric design principles, particularly in sequential task guidance systems and occlusion effects in AR interfaces. Professional activities include organizing RecSys Challenges (2022-2024) focused on practical recommendation problems like fashion purchase prediction and privacy-preserving ad systems. He maintains active collaborations with industry partners to bridge academic research and real-world deployment.
جستوجوی استاد و پژوهشگر
استاد راهنما، استاد دانشگاه یا پژوهشگر مناسب برای مسیر پژوهشیتان را پیدا کنید و با او ارتباط بگیرید.
نمایش ۹۱-۱۰۵ از ۱۴۷ استاد راهنما
اطلاعات این پروفایلها از صفحات عمومی دانشگاهها جمعآوری شده و ممکن است بهروز نباشد.
Anjany Sekuboyina is a Postdoctoral Researcher at ETH Zurich's Department of Quantitative Biomedicine, focusing on medical image analysis using machine learning. Her work emphasizes deployable solutions for hospitals, including probabilistic ML, generative models, and relational ML/graph-based approaches. She co-developed the VerSe dataset, a large-scale CT spine segmentation benchmark, and contributed to projects like MedShapeNet and GenerateCT. Research Interests: Medical Image Segmentation (e.g., vertebrae, spine, and vascular structures) Generative Models for Medical Imaging Synthesis Relational Machine Learning and Graph Neural Networks Automated Clinical Workflow Integration Labs/Teams: Bjoern Menze Team at ETH Zurich. Active contributor to open-source repositories like VerSe (234 stars) , focusing on medical imaging challenges and datasets.
Grégory Mermoud is an Associate Professor at HES-SO Valais-Wallis, School of Engineering, specializing in Machine Learning, Artificial Intelligence, and large-scale distributed systems. He teaches computer networks, machine learning, and distributed computing. His research focuses on the intersection of ML, simulation, software systems, and data science. Affiliations: HES-SO Valais-Wallis (current), Cisco Systems (former Distinguished Engineer) Gregory holds a BSC in Computer Science and Communication Systems from HES-SO Valais-Wallis. With over a decade of industry experience at Cisco, he led teams developing flagship enterprise products and holds over 200 U.S. patents. His work spans network optimization, predictive modeling, and application-driven routing. Research Interests: Machine Learning applications in networking, Quality of Experience (QoE) prediction, network path optimization, and adaptive systems. His contributions include groundbreaking work on AI-driven network monitoring, QoE prediction models, and SD-WAN optimization. Advising & Grants: Oversaw recruitment of over 50 engineers at Cisco, many from HES-SO. His research has been supported by industry collaborations and academic grants. Labs/Teams: Engaged in collaborative projects with Cisco’s R&D teams and academic institutions, focusing on network intelligence and distributed systems innovation.
Philip Treleaven is a Professor at the Department of Computer Science , University College London (UCL). His work bridges Artificial Intelligence , Blockchain Technology , and Financial Computing , with a focus on DeFi , Tokenization , and Algorithm Auditing . He leads research on AI governance , privacy-preserving data , and financial market automation . Key Research Areas AI Ethics & Governance Decentralized Finance (DeFi) Tokenization & Web3 Cybersecurity & Emerging Threats Notable Collaborations Adriano Koshiyama Jeremy Barnett Rem Sadykhov Lukasz Szpruch Nick Firoozye Giles Pavey Recent Publications "Data Assets: Tokenization and Valuation" (2023) "DeTEcT: Dynamic and Probabilistic Parameters Extension" (2024) "Optimising Large Language Models" (2025) His research drives cross-disciplinary innovation , impacting finance , law , and data governance through applied machine learning and blockchain solutions . Current projects include DeTEcT (Decentralized Token Economy Theory) and Federated Computing Infrastructure .
Gregor Kasieczka is a Professor at the University of Hamburg, affiliated with the Faculty of Mathematics and Natural Sciences and the Department of Physics. His research focuses on applying machine learning techniques to high-energy physics, particularly for LHC event generation, detector simulations, and jet reconstruction. He collaborates extensively with physicists and computer scientists globally. Research Trends: His recent work explores equivariant neural networks for particle jets, invertible models for detector simulations, and uncertainty quantification in ML-driven calibration. He also investigates generative models (GANs) to amplify event samples and improve resolution beyond traditional methods. Collaborations: Gregor works with prominent researchers like Tilman Plehn, Benjamin Nachman, and Jesse Thaler, contributing to interdisciplinary advancements in ML applications for particle physics. Labs & Teams: He leads the Gruppe Kasieczka at the University of Hamburg, specializing in machine learning for experimental particle physics and detector-level simulations.
Miles Timpeمشاهده پروفایل
پژوهشگر
Miles Timpe is a computational astrophysicist and space sustainability researcher based at the University of Zurich (UZH), where he works in the Department of Astrophysics and contributes to the Institute for Computational Science (ICS). His research bridges extrasolar and solar system domains, focusing on exoplanet/exomoon formation, sustainable spaceflight, and physics-guided machine learning algorithms. PhD in Astrophysics (University of Zurich, 2020) Developed numerical tools for N-body simulations and SPH methods Active in Academic Spaceflight Initiative of Switzerland (ARIS) Timpe's work emphasizes computational astrophysics, orbital dynamics, and machine learning integration. He has pioneered physics-aware algorithms to predict giant impact outcomes while advancing simulation techniques for planet formation. His expertise also includes anomaly detection in financial datasets and space traffic management automation. Publications highlight his contributions to exoplanet secular evolution (2013), machine learning applications in collision modeling (2020), and blockchain-based research verification systems (2019). He received the Discipline Astrobiology Fellowship (2025-2026) and has presented at AI/ML in Science conferences. Timpe contributes to open science initiatives and safety-critical flight software development for ARIS Space's PICCARD rocket. His broader interests encompass environmental stewardship in space, satellite formation, and preventing Kessler syndrome through innovative collision avoidance strategies.
Prof. Torsten Hoefler is a Full Professor at the Department of Computer Science, ETH Zürich. His research focuses on High-Performance Computing (HPC), parallel systems, networking, and AI-infrastructure. He leads projects on scalable interconnects, network topology design, and cloud computing benchmarks like SeBS. His work bridges theoretical foundations with practical implementations in distributed systems. Research Interests : High-Performance Computing & Networking Parallel Algorithms & Architectures AI Infrastructure & Distributed Systems Chiplet Interconnects & Topology Optimization Key Contributions : Developed tools like ATLAHS (AI/HPC network simulation) Advanced RDMA-based communication protocols (SDR-RDMA) Benchmarks for serverless computing (SeBS-Flow) His recent articles (2025) emphasize adaptive networks, low-precision AI models, and energy-efficient HPC systems. He also explores ethical computing and sustainability in supercomputing through initiatives like Core Hours & Carbon Credits.
Deborah Sulemمشاهده پروفایل
استادیار
- Data Science
- Statistics
- Machine Learning
- +۷ مورد دیگر
Deborah Sulem is an Assistant Professor (tenure-track) of Data Science in the Faculty of Informatics at the Università della Svizzera italiana (USI). She leads the Statistical Science Laboratory and is affiliated with the Institute of Computing (CI). Her research focuses on graphs/networks, Bayesian inference, high-dimensional statistics, explainability, and fairness in machine learning. Education: Ph.D. in Statistics, University of Oxford (2023), supervised by Judith Rousseau, Mihai Cucuringu, Xiaowen Dong, and Vincent Rivoirard Postdoctoral Researcher at Barcelona School of Economics and Universitat Pompeu Fabra (with David Rossell and Gabor Lugosi) Joined USI in 2024 Research Interests: Combines statistical theory with modern machine learning, emphasizing graph deep learning, point processes, and nonparametric methods. Her work addresses challenges in high-dimensional data analysis and ethical AI practices like model explainability and fairness. Grants & Labs: Leads the Statistical Science Laboratory Focuses on interdisciplinary projects involving dynamic networks and Bayesian model selection Contact: Email | Office: East Campus, Sector D, D5.18 (P5)
- Real Estate Management
- Digital Transformation in Real Estate
- Sustainable Urban Development
- +۳ مورد دیگر
Markus Schmidiger is a Lecturer at the Lucerne School of Business, part of the Lucerne University of Applied Sciences and Arts. He is affiliated with the Institute of Financial Services Zug (IFZ) and the Competence Center Real Estate (CC Real Estate). His academic work focuses on real estate management, digital transformation in construction, and sustainable urban development. Schmidiger also leads the MAS Real Estate Management program and contributes to industry research through projects like the Digitalisierungsbarometer for real estate. Education: PhD in Economics (HSG), Master in NLP (Society of NLP), Corporate Real Estate Manager (ebs) Research Areas: Real estate digitalization, circular economy in construction, machine learning for property valuation Projects: NISMO (Spatial ML for real estate), Digitalisierungsbarometer, sustainable development in Uri canton, smart city initiatives His industry mandates include roles on the boards of Utilita Foundation (non-profit housing investments) and Seraina Investment Foundation's Investment Committee, alongside serving on AST Migros Pension Fund's real estate committee. Schmidiger actively engages in media commentary and professional networking through his Immobilienblog and LinkedIn profile.
Michael Bronstein is a Professor at Imperial College London, holding the Chair in Machine Learning and Pattern Recognition. He also serves as Head of Graph Learning Research at Twitter and leads ML research in Project CETI, a TED Audacious Prize-winning collaboration focused on understanding sperm whale communication. Professor Bronstein's research centers on Geometric Deep Learning , which aims to provide a unifying mathematical framework for neural network architectures like CNNs, RNNs, GNNs, and Transformers. His work spans multiple domains including: Computer vision Social science Biology Drug design Protein folding His groundbreaking research has led to applications in diverse fields, from understanding complex biological structures to developing new drug discovery methodologies. Bronstein approaches geometry in deep learning following the spirit of Felix Klein's Erlangen Programme, seeking unifying principles across disparate neural network architectures. Professor Bronstein has received numerous prestigious awards and honors: Royal Society Wolfson Research Merit Award Royal Academy of Engineering Silver Medal Five ERC grants Two Google Faculty Research Awards Two Amazon AWS ML Research Awards Member of the Academia Europaea Fellow of IEEE, IAPR, BCS, and ELLIS ACM Distinguished Speaker World Economic Forum Young Scientist Beyond academia, Bronstein is a serial entrepreneur who has founded multiple successful startups: Novafora Invision (acquired by Intel in 2012) Videocites Fabula AI (acquired by Twitter in 2019) He previously served as Principal Engineer at Intel Perceptual Computing and was a key developer of the Intel RealSense technology.
Emmanuel Abbé is a Full Professor at the École Polytechnique Fédérale de Lausanne (EPFL) , holding the Chair of Mathematical Data Science jointly under the School of Basic Sciences (SB) and School of Computer and Communication Sciences (IC) . His research focuses on the mathematical foundations of data science, combining probability, statistics, and discrete mathematics to advance machine learning and information theory. Research Interests : Community detection and stochastic block models Information theory and coding Deep learning theory and generalization bounds Graph-based data analysis and spectral methods Neural network regularization and optimization Mathematical aspects of AI and ML Scientific Awards : Foundation Latsis International Prize Bell Labs Prize NSF CAREER Award Google Faculty Research Award Walter Curtis Johnson Prize von Neumann Fellowship IEEE Information Theory Society Paper Award Simons-NSF Mathematics of Deep Learning Collaborative Research Award Advising and Grants : Prof. Abbé supervises multiple PhD students including Lotfi Jandaghi Aryo, Pushkin Denys, and Senouf Ortal Yona. His research is supported by grants from the Swiss National Science Foundation and other international bodies.
Michal Friedmanمشاهده پروفایل
استادیار
- Systems
- Concurrent Computing
- Programming Languages
- +۳ مورد دیگر
Michal Friedman is an Assistant Professor at the Department of Computer Science, ETH Zurich, specializing in systems, concurrent computing, programming languages, and sustainable computing. He leads research on designing system fundamentals across software and hardware to enhance performance and efficiency in next-generation computing platforms. Prior to ETH, he completed a postdoc at the same institution and earned his Ph.D. from the Technion under Prof. Erez Petrank, focusing on concurrent data structures for non-volatile memories. Education: Ph.D. in Computer Science (Technion, advised by Erez Petrank), BSc Summa Cum Laude (Technion). Awards include the Eric and Wendy Schmidt Postdoctoral Award (2022), Blavatnik Prize (2021), and Azrieli Fellowship (2018–2021). His work spans persistent memory systems, concurrent algorithms, and energy-efficient computing. Key contributions include PCcheck (ML checkpointing), Dirigent (serverless orchestration), and foundational research on non-volatile memory correctness conditions. He has authored over 15 papers in top conferences like ASPLOS, SOSP, and VLDB, and serves on program committees for systems and programming languages venues.
Matteo Berlucchi is an entrepreneur, mentor, speaker, and CEO with extensive experience in digital businesses, artificial intelligence, and start-up ecosystems. He currently serves as an Entrepreneur in Residence at ETH Zurich's Chair of Entrepreneurship and is a guest lecturer at Imperial College Business School, ETH Zurich, University College London (UCL), and Goldsmiths. His professional roles include senior executive, angel investor, and board director. His expertise spans digital strategy, scaling businesses, AI/ML applications, digital health, e-commerce, and SaaS platforms. He actively contributes to conferences (e.g., FT, World Bank) and think tanks, focusing on AI, digital transformation, and start-up financing. Berlucchi leads mentoring activities at ETH Zurich and advises start-ups through his ventures. His current companies include Your.MD , showcased at Slush 2016 and featured in a KPMG interview on AI in healthcare.
Zhilu Laiمشاهده پروفایل
استادیار
- Physics-Informed Machine Learning
- Structural Health Monitoring
- Computer Vision Applications
- +۵ مورد دیگر
Zhilu Lai is an Assistant Professor at The Hong Kong University of Science and Technology (Guangzhou). He previously worked as a Postdoctoral Researcher at ETH Zurich's Chair of Structural Mechanics and Monitoring (2018-2020) and as a Senior Assistant with the Dynamic Mobile Sensing Platform team at the Singapore-ETH Centre (2020-2022). His research bridges physics-based modeling and machine learning. Research Interests Physics-informed machine learning for dynamical systems Computer vision in structural monitoring Variational inference with physical constraints Hybrid frameworks for structural health monitoring (SHM) Energy-preserving neural architectures Applications in bridge monitoring and wind turbine dynamics Scientific Contributions Developed symplectic encoder frameworks for nonlinear dynamics modeling Proposed physics-guided Deep Markov Models (PgDMM) for hysteretic systems Integrated neural ODEs with physics-based eigenmodes for high-dimensional SHM Created robust algorithms for bridge weigh-in-motion and influence surface identification
Georg Kocurمشاهده پروفایل
پژوهشگر
Georg Kocur is a Researcher at the Institute of General Mechanics, RWTH Aachen University. He holds a Diploma in Civil Engineering from Bergische Universität Wuppertal and a Ph.D. from ETH Zurich (IBK), focusing on non-destructive testing of structural concrete. His research emphasizes experimental mechanics, elastic wave propagation, and acoustic emission localization in complex materials. Previously, he served as a post-doctoral researcher at MIT’s Laboratory for Infrastructure Science and Sustainability and worked as a development engineer at Leviat (HALFEN). Key research interests include structural health monitoring, wave propagation modeling, and the application of machine learning in acoustic sensing. His work bridges civil engineering, materials science, and computational methods to enhance infrastructure safety and reliability. Publications highlight advancements in acoustic source localization, time reverse modeling, and numerical simulations for defect mapping in concrete and pipework. His contributions address challenges in sensor array optimization, signal processing, and algorithmic development for industrial and academic applications. Kocur’s career combines academic research with industry innovation, focusing on practical solutions for structural analysis and non-destructive testing in civil infrastructure.