جستجوی استاد راهنمای دانشگاهی

• Online learning
• Optimization theory
• Bandits
• +۲ مورد دیگر

Tommaso Cesari serves as an Assistant Professor in the EECS department at the University of Ottawa, cross-appointed with the Department of Mathematics and Statistics. He currently teaches Machine Learning (CSI 4145) and Introduction to Computing II (ITI 1121 A), courses he has instructed since 2024 and 2023 respectively. His research centers on theoretical machine learning with applications in market design and operational systems. Key interests include online learning frameworks for bandit problems, pricing mechanisms, bilateral trade optimization, and spacecraft operations scheduling. This work bridges algorithmic theory with real-world economic systems, emphasizing regret minimization under adversarial conditions and smoothed analysis. Analysis of his 2023-2025 publications reveals a cohesive trajectory in applying online learning to bilateral trade mechanisms (appearing in 7 of 15 recent papers), auction theory, and brokerage models. His work consistently targets top venues including JMLR, NeurIPS, and STOC, with growing emphasis on fairness constraints and multi-platform systems in 2024-2025 outputs. Professor Cesari actively recruits Master's and Ph.D. students for research in his domains; prospective candidates may apply through the university portal. His collaborative publications with prominent researchers like Nicolò Cesa-Bianchi indicate strong engagement in the theoretical machine learning community.

• Predictive Maintenance
• Machine Learning
• Fault Detection
• +۵ مورد دیگر

Dr. Lilach Goren Huber is a Senior Lecturer and R&D Projects Leader at the ZHAW School of Engineering, specializing in predictive maintenance and data-driven solutions for industrial systems. She leads multiple projects focusing on AI applications in anomaly detection (e.g., wind turbines, solar power plants), physics-informed machine learning for sensor error correction, and fault prognostics under data scarcity. Her work bridges academic research with industrial implementation, emphasizing scalable deep learning frameworks for commercial fleets and energy infrastructure. Research interests include predictive maintenance strategies, machine learning for industrial IoT, and hybrid prognostics combining domain knowledge with AI. She has published extensively in journals like International Journal of Prognostics and Health Management and Journal of Big Data , with a focus on renewable energy systems, SCADA data analysis, and cross-domain transfer learning. Current Projects: Physics-informed ML for elastomer sensors, end-to-end fault prognostics for power grids, and hybrid prognostics research. Past Contributions: Developed decision support systems for laser cutting machines, optimized hydroelectric maintenance schedules, and created risk-based frameworks for Swiss national road safety equipment. She actively contributes to the Expert Group Smart Maintenance and serves as a project leader in the ZHAW-PARC initiative. Her work addresses technical challenges like data contamination, sensor calibration errors, and real-world implementation barriers in industrial AI adoption.

• Computer Architecture
• Distributed Systems
• Cloud Computing
• +۳ مورد دیگر

Javier Picorel is a former researcher at École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the Parallel Systems Architecture Lab (Parsa). His work focuses on computer architecture, distributed systems, and cloud computing, with an emphasis on optimizing storage systems, near-data processing, and high-performance computing. Picorel has contributed to advancements in scalable serverless computing, secure data center architectures, and accelerator design. His research interests include storage-class memory hierarchies, virtual machine performance optimization, and software-defined systems. Key projects include BlockNDP for block-storage acceleration, IndiLog for stateful serverless systems, and SPARTA for accelerator address translation. He has collaborated extensively with the Parsa group and published widely in top conferences such as ISCA, MICRO, and PACT.

• Database Systems
• Storage Systems
• Hardware Acceleration
• +۳ مورد دیگر

Alberto Lerner is a Senior Researcher at the Department of Informatics within the Interfaculty Informatics Department at the University of Fribourg. His research focuses on advancing database systems, storage architectures, and hardware-software co-design. He holds a strong interest in computational storage, network-accelerated query processing, and the integration of modern hardware technologies like CXL into database engines. Lerner's work emphasizes performance optimization and scalable solutions in data-intensive computing environments. His research interests include database architecture, storage co-design, hardware acceleration, and network-driven computing. Recent publications highlight innovations in point cloud data processing, reprogrammable storage devices, and software-defined controllers for NAND flash systems. Lerner's articles reflect a trend toward leveraging modern hardware advancements to enhance database and storage performance. He has contributed to frameworks like BABOL and Data Pipes, advancing declarative control over data movement and network-based graph mining.

Ghislain Fournyمشاهده پروفایل

مدرس

• Quantum Mechanics
• Game Theory
• Database Systems
• +۶ مورد دیگر

Ghislain Fourny is a Lecturer and senior scientist in the Department of Computer Science at ETH Zurich. His research spans quantum foundations, game theory, and database systems. He develops deterministic models merging game theory into quantum physics, proposing non-Nashian solution concepts to address quantum paradoxes. He co-designed the JSONiq query language and leads the RumbleDB project for large-scale data querying. A recipient of multiple teaching awards including the Golden Owl and ETH Medal, he advises numerous students on topics ranging from quantum theory to database systems. His work bridges theoretical physics, computer science, and mathematics, aiming to reconcile determinism with quantum mechanics through game-theoretic models. Education : Holds a Master's in Quantum Information (2007) and a PhD in Computer Science (2011). His early career included pioneering work on XBRL standards and XQuery implementations. Research Themes : Non-Nashian game theory: Fixpoint equilibria avoiding Grandfather paradoxes, Pareto optimality. Quantum foundations: Deterministic models challenging free-choice assumption, spacetime games integrating special relativity. Database systems: JSONiq language design, RumbleDB for scalable querying of heterogeneous data. Teaching Contributions : Courses include Big Data for Engineers, Information Retrieval, and Information Systems for Engineers. His open-access Big Data textbook is widely used in universities. Recognition : Over 20 awards including the 2023 VIS Teaching Award, ETH Medal (2000), and multiple top ranks in international mathematics competitions since 1993.

• Machine Learning
• Visual Analytics
• Artificial Intelligence
• +۳ مورد دیگر

Thilo Spinner is a Researcher affiliated with the Department of Computer Science at ETH Zürich, contributing to the Professorship for Computer Science. His work focuses on visual analytics, explainable AI, and machine learning interpretability. He has developed tools like iNNspector for deep model debugging and explAIner for interactive machine learning frameworks. His research spans topics such as uncertainty-aware dimensionality reduction, language model explainability, and pandemic data visualization (e.g., Coronavis for Covid-19 tracking). Key contributions include frameworks for model interpretability, real-time parameter optimization, and emergency response analysis tools like NEAT. His articles highlight innovations in visualizing complex AI systems and addressing challenges in model transparency and pandemic management. Education details are not explicitly provided in the text. His research trends emphasize bridging technical AI systems with human-understandable insights through visualization and interactive tools. He has explored diverse applications from healthcare crisis analysis to neural network debugging.

Adrienne Grêt-Regameyمشاهده پروفایل

استاد

• Landscape Planning
• Environmental Science
• Ecosystem Services
• +۵ مورد دیگر

Adrienne Grêt-Regamey serves as Full Professor at ETH Zurich's Department of Civil, Environmental and Geomatic Engineering, where she heads the Institute for Spatial and Landscape Development. Her academic career spans over 15 years at one of Europe's leading technical universities, with significant leadership roles including Head of the Engagement Platform for Future Cities Lab Global since 2021. Her research focuses on the complex interactions between human activities and landscape evolution across temporal and spatial scales. Specializing in landscape planning and environmental science, she investigates land-use decision models through forecasting and backcasting approaches. A key innovation in her work involves developing state-of-the-art 3D visualizations and auralizations within her laboratory to understand public perception of landscape changes and create decision support tools for participatory planning processes. Her methodology emphasizes iterative design-science collaboration to develop place-specific solutions that balance human well-being with environmental sustainability. Analysis of her recent publications reveals a strong trend toward interdisciplinary research integrating spatial analysis, ecosystem services assessment, and participatory decision-making. Her work increasingly addresses urgent global challenges including renewable energy transitions, climate adaptation strategies, and urban-rural dynamics, often employing advanced computational methods like machine learning and immersive virtual reality environments. Dandelion Entrepreneurship Award (2023) Most Influential Female Award in Land System Science (2022) ERC Starting Grant for GLOBESCAPE project (2017) Swiss National Science Foundation Transdisciplinary Award (2013) ETH Silver Medals for both Master and PhD theses Grêt-Regamey actively contributes to scientific governance as Member of the Swiss Science Council (since 2024) and Advisory Committee of the Wyss Academy for Nature. She serves as Associate Editor for Landscape and Urban Planning and Editor for disP - The Planning Review. Her research program has secured significant funding including an ERC Starting Grant and multiple Swiss National Science Foundation projects, with current work focusing on integrating design and land system science to foster place-making in peri-urban landscapes. Her laboratory at ETH Zurich specializes in developing immersive 3D environments for landscape assessment and planning, with recent work extending into therapeutic applications of virtual reality. She leads the Future Cities Lab Global engagement platform, facilitating knowledge exchange between researchers, practitioners, and policymakers on sustainable urban development challenges worldwide.

Kyoung-Min Kimمشاهده پروفایل

پژوهشگر

• Large Language Models (LLMs)
• Database Systems
• Inference Optimization
• +۳ مورد دیگر

Kyoung-Min Kim is a Researcher at the Data-Intensive Applications and Systems Laboratory (DIAS) within the College of Computing and Communication Sciences at École Polytechnique Fédérale de Lausanne (EPFL). His work intersects large language models (LLMs) and database systems, focusing on improving inference efficiency and reliability. Email: kyoung-min.kim@epfl.ch Office: BC 224, EPFL, Station 14, Lausanne Research Interests: Recent publications highlight his expertise in: LLM inference optimization GPU resource management Cost modeling for AI systems Scheduling algorithms for scalable inference Preemption strategies reducing computational costs Constraint satisfaction frameworks for system performance Key Contributions: Kim co-developed INFERMAX , an analytical framework comparing LLM inference schedulers. His work demonstrates 30% GPU cost reduction through preemption techniques.

Manuel Gilمشاهده پروفایل

پژوهشگر

• Bioinformatics
• Evolutionary Genomics
• Computational Biology
• +۳ مورد دیگر

Manuel Gil is a Researcher at the Zurich University of Applied Sciences (ZHAW), affiliated with the School of Life Sciences and Facility Management and the Institute of Computational Life Sciences. His work focuses on computational methods in bioinformatics, including ancestral sequence reconstruction, multiple sequence alignment, and semantic integration of biological databases. He leads projects such as computational literature-based drug discovery for natural products and the development of tools like ProPIP and ARPIP. His research interests span evolutionary genomics, bioinformatics software development, and semantic querying of biomedical data. Notable contributions include advancing indel-aware phylogenetic analysis and enabling semantic access to federated bioinformatics databases through projects like Bio-SODA. Publications emphasize methodological advancements in sequence alignment, ancestral reconstruction, and knowledge graph applications in biomedicine. Collaborations include work with institutions like the SIB Swiss Institute of Bioinformatics and involvement in international conferences on text and data mining.

Antonio Carzanigaمشاهده پروفایل

استاد

• Distributed Systems
• Software Engineering
• Content-Based Networking
• +۷ مورد دیگر

Antonio Carzaniga is a Full Professor and founding member of the Faculty of Informatics at Università della Svizzera italiana (USI), where he has been active since 2004. Previously, he served as an Assistant Research Professor at the University of Colorado at Boulder from 2001 to 2007. He holds a Ph.D. in Computer Science and a Bachelor’s degree in Electronic Engineering from Politecnico di Milano. Full Professor, Faculty of Informatics, Università della Svizzera italiana (2004–Present) Assistant Research Professor, Department of Computer Science, University of Colorado at Boulder (2001–2007) Ph.D. in Computer Science, Politecnico di Milano Bachelor’s in Electronic Engineering, Politecnico di Milano His research spans distributed systems and software engineering, with a strong focus on content-based addressing networks, publish/subscribe systems, middleware, software fault tolerance, and verification. He has pioneered work in information-centric networking and developed the Siena project, a scalable publish/subscribe service. His recent work extends into programmable networks, GPU-accelerated matching, and performance annotations for cloud systems. The 15 most recent publications highlight a consistent trajectory in scalable, high-performance networking and adaptive software systems. Key themes include content-based communication, packet subscriptions, information-centric networking, and leveraging redundancy for fault tolerance and testing. His work bridges theoretical foundations with practical implementations, often involving system-level software and performance evaluation. Best Paper Award, ACM SIGCOMM Workshop on Information-Centric Networking (ICN'13) Carzaniga has advised multiple graduate students, including Michele Papalini, Koorosh Khazaei, and Daniele Rogora, and has collaborated on funded research projects in distributed systems and networking. He has contributed to software development through projects like the Siena Fast Forwarding engine and the Synthetic Workload Generator. His service includes organizing workshops and contributing to major conferences in software engineering and computer systems. He leads research initiatives such as Siena and Content-Based Networking, focusing on scalable, decentralized communication infrastructures. His lab has developed key tools for evaluating publish/subscribe performance and implementing high-speed forwarding algorithms.

• Natural Language Processing
• Text Analysis
• Speech Processing
• +۴ مورد دیگر

Mark Cieliebak is a Professor for Speech and Text Processing at the Zurich University of Applied Sciences (ZHAW), School of Engineering, where he leads the Natural Language Processing (NLP) research group at the Centre for Artificial Intelligence. He has held this position since December 2018, following prior roles as a lecturer at ZHAW (2012-2018). His educational background includes a Dr. sc. techn. in Computer Science from ETH Zürich (1999-2003) and a Diplom in Computer Science from Universität Dortmund (1990-1999). His research focuses on Natural Language Processing , Text and Speech Analysis , and Machine Learning applications . Primary domains include dialogue systems evaluation, low-resource speech recognition (particularly Swiss German), automated text generation assessment, and NLP for social media analysis. He also explores educational applications through flipped classroom methodologies and machine learning operations. Cieliebak's publications (2020-2024) demonstrate strong emphasis on NLP evaluation frameworks, speech corpus development for dialects, and generative AI validation. Recurring themes include robustness testing, bias mitigation in automated metrics, and multilingual processing challenges. His work frequently involves large-scale dataset creation and ensemble learning techniques. Scientific Awards: SwissNLP Award 2023 Best Paper Award - Honorable Mention at EMNLP 2020 ZHAW Teaching Award 2015 He leads numerous research projects including: Unified Model for Text Generation Evaluation End-to-End Swiss German Speech Translation Holistic Analysis of Misinformation in Social Networks Automated News Processing (AutoNews) Chatbot Development for Language Learners He co-founded SpinningBytes AG (2015-present) and maintains extensive industry collaborations. As head of the NLP research group, he oversees doctoral candidates and master's students, though specific advisee names aren't listed.

Andrea Rosàمشاهده پروفایل

پژوهشگر

• Dynamic Program Analysis
• Managed Languages and Runtimes
• Dynamic Compilers
• +۷ مورد دیگر

Andrea Rosà is a Researcher and Lecturer at the Faculty of Informatics of Università della Svizzera italiana (USI) in Lugano, Switzerland. He is a member of the Dynamic Analysis Group (DAG), where he conducts research on managed language runtimes, dynamic program analysis, and performance optimization of parallel systems. Andrea earned his PhD in Informatics from USI in 2018 under the supervision of Prof. Walter Binder. Prior to that, he completed both his MSc and BSc in Computer Science Engineering at Politecnico di Milano. His research centers on dynamic program analysis, managed language runtimes (especially the Java Virtual Machine), dynamic compilers, benchmarking, concurrency and parallelism, and big-data analytics. He has developed tools such as AkkaProf for profiling actor-based applications and contributed to the Renaissance benchmarking suite. His recent work investigates task granularity, Java streams, vectorization, and compiler-level profiling. His most recent publications (2023–2025) explore JVM performance, Java Vector API, native-image startup, and adaptive threading, published in leading venues such as TOPLAS, PLDI, CGO, and OOPSLA. The articles collectively reflect a strong focus on empirical systems research, performance engineering, and tool development for modern JVM-based applications. Andrea collaborates extensively with researchers including Matteo Basso, Walter Binder, Eduardo Rosales, and Lydia Y. Chen. He has co-authored over 50 publications and contributed to multiple open-source projects and benchmarking frameworks. He advises no known students but actively contributes to academic service, including organizing and editing workshop proceedings. He is based at the East Campus, Sector D, Office D5.10, Via la Santa 1, Lugano-Viganello, Switzerland.

• Bayesian Probabilistic Models
• Machine Learning
• Optimization
• +۳ مورد دیگر

Dr. Thomas Oskar Weinmann is the Head of the Research Focus Area Scientific Computing & Algorithmics at ZHAW School of Engineering, Zurich University of Applied Sciences (ZHAW). His research interests include Bayesian probabilistic models, machine learning, optimization, and visual computing. He holds a PhD in Mathematics from ETH Zürich (2003–2007) and completed a CAS in Visual Computing at ETH Zürich in 2011. Education: PhD in Mathematics, ETH Zürich (2003–2007) CAS Visual Computing, ETH Zürich (2011) His current research projects include: Smart Acquisition for Ultra-High field NMR Spectroscopy (Deputy project leader, ongoing) Raman for Process Analytics (Team member, ongoing) Target Recognition using Artificial Intelligence (TRAI) (Project leader, ongoing) Completed projects include machine learning applications in NMR spectroscopy, varroa mite counting in honeybee colonies, football motion data analysis, and NoSQL database systems. His publications span Bayesian spectral analysis and query optimization in NoSQL systems.

• Databases and Algorithms
• Data Integration
• Natural Language Processing
• +۳ مورد دیگر

Professor Elzbieta Pustulka is a faculty member at FHNW University's School of Business, affiliated with the Institute for Business Informatics. Her research centers on databases, NLP, and AI-driven business process optimization. Research explores database pedagogy through gamification (SQL Scrolls), NoSQL implementations for ERPs, and automated information extraction techniques. Recent work emphasizes CI/CD practices in database development and educational game design for programming concepts. She supervises graduate students in projects spanning database indexing, logistics simulations, and AI-driven reporting systems. No scientific awards are noted.

Tilmann Zäschkeمشاهده پروفایل

پژوهشگر

• Computer Science
• Database Systems
• Data Structures
• +۴ مورد دیگر

Dr. Tilmann Zäschke is a researcher at the Professorship for Computer Science at ETH Zürich, Switzerland. His work focuses on database systems, data structures, and agile software development, particularly in the context of object-oriented databases and multidimensional indexing. Current affiliation: ETH Zürich Research areas: Database systems, data structures, schema evolution, agile development His research explores optimizing spatial data indexing through structures like the PH-tree, improving conceptual data models iteratively, and enabling agile development practices in object databases. Key trends in his publications include advancements in multidimensional indexing, graph data processing, and schema evolution techniques. Dr. Zäschke has contributed to academic literature on topics such as hypercube traversal algorithms, benchmarking graph data management, and adaptive model-driven systems development.