Roger Wattenhofer: Catalogue data in Spring Semester 2023 |
| Name | Prof. Dr. Roger Wattenhofer |
| Field | Distributed Computing |
| Address | Inst. f. Techn. Informatik u. K. ETH Zürich, ETZ G 96 Gloriastrasse 35 8092 Zürich SWITZERLAND |
| Telephone | +41 44 632 63 12 |
| wattenhofer@ethz.ch | |
| URL | http://www.disco.ethz.ch |
| Department | Information Technology and Electrical Engineering |
| Relationship | Full Professor |
| Number | Title | ECTS | Hours | Lecturers | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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| 227-0085-59L | Projekte & Seminare: Hands-On Deep Learning   Course can only be registered for once. A repeatedly registration in a later semester is not chargeable. | 2 credits | 2P | R. Wattenhofer | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | The category of "Laboratory Courses, Projects, Seminars" includes courses and laboratories in various formats designed to impart practical knowledge and skills. Moreover, these classes encourage independent experimentation and design, allow for explorative learning and teach the methodology of project work. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | The objective of this P&S is to expose students to both common and cutting-edge neural architectures and to build intuition about their inner working by the means of examples. Students learn about various network structures as building blocks and use them to solve worked examples and course challenges. After attending this course, students will be familiar with multi-layer perceptrons, convolutional neural networks, recurrent neural networks, transformer encoders, graph convolutional/isomorphism/attention networks, and autoencoders. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | This P&S introduces deep learning through the PyTorch framework in a series of hands-on examples, exploring topics in computer vision, natural language processing, graph neural networks, and representation learning. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lecture notes | Python Notebooks will be distributed to students before every session. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 227-0558-00L | Principles of Distributed Computing | 7 credits | 2V + 2U + 2A | R. Wattenhofer | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | We study the fundamental issues underlying the design of distributed systems: communication, coordination, fault-tolerance, locality, parallelism, self-organization, symmetry breaking, synchronization, uncertainty. We explore essential algorithmic ideas and lower bound techniques. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | Distributed computing is essential in modern computing and communications systems. Examples are on the one hand large-scale networks such as the Internet, and on the other hand multiprocessors such as your new multi-core laptop. This course introduces the principles of distributed computing, emphasizing the fundamental issues underlying the design of distributed systems and networks: communication, coordination, fault-tolerance, locality, parallelism, self-organization, symmetry breaking, synchronization, uncertainty. We explore essential algorithmic ideas and lower bound techniques, basically the "pearls" of distributed computing. We will cover a fresh topic every week. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | Distributed computing models and paradigms, e.g. message passing, shared memory, synchronous vs. asynchronous systems, time and message complexity, peer-to-peer systems, small-world networks, social networks, sorting networks, wireless communication, and self-organizing systems. Distributed algorithms, e.g. leader election, coloring, covering, packing, decomposition, spanning trees, mutual exclusion, store and collect, arrow, ivy, synchronizers, diameter, all-pairs-shortest-path, wake-up, and lower bounds | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lecture notes | Available. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | Lecture Notes By Roger Wattenhofer. These lecture notes are taught at about a dozen different universities through the world. Mastering Distributed Algorithms Roger Wattenhofer Inverted Forest Publishing, 2020. ISBN 979-8628688267 Distributed Computing: Fundamentals, Simulations and Advanced Topics Hagit Attiya, Jennifer Welch. McGraw-Hill Publishing, 1998, ISBN 0-07-709352 6 Introduction to Algorithms Thomas Cormen, Charles Leiserson, Ronald Rivest. The MIT Press, 1998, ISBN 0-262-53091-0 oder 0-262-03141-8 Disseminatin of Information in Communication Networks Juraj Hromkovic, Ralf Klasing, Andrzej Pelc, Peter Ruzicka, Walter Unger. Springer-Verlag, Berlin Heidelberg, 2005, ISBN 3-540-00846-2 Introduction to Parallel Algorithms and Architectures: Arrays, Trees, Hypercubes Frank Thomson Leighton. Morgan Kaufmann Publishers Inc., San Francisco, CA, 1991, ISBN 1-55860-117-1 Distributed Computing: A Locality-Sensitive Approach David Peleg. Society for Industrial and Applied Mathematics (SIAM), 2000, ISBN 0-89871-464-8 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | Course pre-requisites: Interest in algorithmic problems. (No particular course needed.) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Competencies |
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| 227-0559-00L | Seminar in Deep Neural Networks Number of participants limited to 25. | 2 credits | 2S | R. Wattenhofer | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | In this seminar participating students present and discuss recent research papers in the area of deep neural networks. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | We aim at giving the students an in depth view on the current advances in the area by discussing recent papers as well as discussing current issues and difficulties surrounding deep neural networks. The students will learn to read, evaluate and challenge research papers, prepare coherent scientific presentations and lead a discussion on their topic. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | The seminar will cover a range of research directions, with a focus on Graph Neural Networks, Algorithmic Learning, Reinforcement Learning and Natural Language Processing. It will be structured in blocks with each focus area being briefly introduced before presenting and discussing recent research papers. Papers will be allocated to the students based on their preferences. For more information see www.disco.ethz.ch/courses.html. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lecture notes | Slides of presentations will be made available. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | The paper selection can be found on www.disco.ethz.ch/courses.html. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | It is expected that students have prior knowledge and interest in machine and deep learning, for instance by having attended appropriate courses. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 252-0817-00L | Distributed Systems Laboratory | 10 credits | 9P | G. Alonso, T. Hoefler, A. Klimovic, T. Roscoe, R. Wattenhofer, C. Zhang | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | This course involves the participation in a substantial development and/or evaluation project involving distributed systems technology. There are projects available in a wide range of areas: from web services to ubiquitous computing including as well wireless networks, ad-hoc networks, and distributed application on mobile phones. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | Students acquire practical knowledge about technologies from the area of distributed systems. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | This course involves the participation in a substantial development and/or evaluation project involving distributed systems technology. There are projects available in a wide range of areas: from web services to ubiquitous computing including as well wireless networks, ad-hoc networks, and distributed application on mobile phones. The objecte of the project is for the students to gain hands-on-experience with real products and the latest technology in distributed systems. There is no lecture associated to the course. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 363-1153-00L | Decentralized Finance and the Future of Money | 3 credits | 2V | B. J. Bergmann, H. Gersbach, R. Wattenhofer | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | DLT is emerging for a disruption of our current financial infrastructure. As such, Decentralized Finance (DeFi) seeks to combine open-source, peer to peer building blocks into sophisticated products using blockchain technology, seeking to disintermediate and decentralize the traditional financial service industry. This lecture will combine insights on DLT with recent applications from finance. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | At it’s core, DeFi aims to provide financial products and services on blockchain technologies. The combination of decentralized, smart-contract-based business logic solutions with a blockchain-based settlement layer facilitates the creation of financial services in a decentralized way. Traditional, functional roles of trusted third-party such as brokerage firms, banks, are replaced by smart contracts which fulfill the functions automatically. The goal if this lecture is to let you understand, - The building blocks of Distributed Ledger Technology (DLT) - Some basic applications like smart contracts, tokens, decentralized autonomous organisations (DAOs) - Limitations and concepts for overcoming centralized financial systems - Recent advances on Central Bank Digital Currencies and other applications in DeFi - The business logic behind a decentralized applications (DApps) - How a DLT project is run within a larger organization and in the start-up context The lecture will cover also guest speakers from companies, start-ups, and agencies. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | After a one-hour introduction session on the first day, the lecture will be split into six 4h sessions. Each 4h Session will be held as a workshop session, covering some theoretical and technological insights as well as insights on recent applications. Each session will involve guest speakers from industry, start-ups, agencies. The focus of each session will be on the discussion part. You will be asked to prepare yourself (watch a video, read a paper, etc) for each session. Session 1: Intro to Blockchain, Focus on Exchanges, Transaction Ordering Session 2: Smart Contracts; Focus on Programming, Attacks Session 3: Decentralized Governance, DAOs and Applications Session 4: Central Bank Digital Currencies, recent advances, and approaches Session 5 & 6: DeFi applications, legal aspects, challenges, opportunities & risk in the corporate context The lecture is targeted to students across ETH with an interest in DLT. No specific coding experience is required. During the course you will follow step by step examples. For passing the course you will take online quizzes, selected exercises, and a short exam during the class. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lecture notes | There will lecture slides to each section shared in advanced to each session. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | Selected readings and books are presented in each session. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | The course is opened to students from all backgrounds. Some experience with quantitative disciplines such as probability and statistics, however, is useful but not mandatory. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 364-1058-00L | Risk Center Seminar Series | 0 credits | 2S | H. Schernberg, D. Basin, A. Bommier, D. N. Bresch, S. Brusoni, L.‑E. Cederman, P. Cheridito, F. Corman, H. Gersbach, C. Hölscher, K. Paterson, G. Sansavini, B. Stojadinovic, B. Sudret, J. Teichmann, R. Wattenhofer, U. A. Weidmann, S. Wiemer, R. Zenklusen | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | In this series of seminars, invited speakers discuss various topics in the area of risk modelling, governance of complex socio-economic systems, managing risks and crises, and building resilience. Students, PhD students, post-docs, faculty and individuals outside ETH are welcome. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | Participants gain insights in a broad range of risk- and resilience-related topics. They expand their knowledge of the field and deepen their understanding of the complexity of our social, economic and engineered systems. For young researchers in particular, the seminars offer an opportunity to learn academic presentation skills and to network with an interdisciplinary scientific audience. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | Academic presentations from ETH faculty as well as external researchers. Each seminar is followed by a Q&A session and (when permitted) a networking Apéro. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Lecture notes | The sessions are recorded whenever possible and posted on the ETH Risk Center webpage. If available, presentation slides are shared as well. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Literature | Each speaker will provide a literature review. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | In most cases, a quantitative background is required. Depending on the topic, field-specific knowledge may be required. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Competencies |
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