***************************PARAMETRES*************************** IDFORMATION = 0 IDMENTION = 52 URL-DPT = http://fst-informatique.univ-lyon1.fr RUBRIQUE-NEWS = 1342529038915 ADE-ID = @DEFAUT ACRONYME = Master DISS DESCRIPTION = Site de la formation Data and Intelligence for Smart Systems TITRE-LONG = Master DISS KEYWORDS = formation,informatique,Master,Lyon1,UCBL,FST,diplôme,formation, enseignement, SRS THEME = *****************************ENTETE***************************** ## DISS (master Data and Intelligence for Smart Systems) ******************************MENU****************************** #### Cursus [General Information][intro] [Syllabus ][ues] [Research Internship][stage] [Contacts][nom9] #### Current year [Latest news][actus] [Timetable M2][Emploi du temps M110] #### Future students [Prerequisites][conseils] [Application][Prérequis11] ******************************PAGE****************************** ### General Information The International Master DISS (Data and Intelligence for Smart Systems) is a research-oriented master preparing the next generation of researchers, professionals and scientific leaders, while giving priority to excellence in research and most recent technological advances in a handful of computer science subfields. It benefits from the international collaborations and rich networking of the researchers participating in the Master. It leverages the [Arqus European University Alliance](https://www.arqus-alliance.eu/) by targeting research universities abroad as well as Erasmus+ agreements between the Computer Science Department at Lyon 1 University and other European research universities. **Context.** Smart systems are data-driven and intelligent systems that are capable of executing complex tasks of processing and control during which massive data is collected, processed and analyzed for both analysis and prediction. Data is inherently heterogeneous, including structured/unstructured data, processes and images. This data needs to be prepared, processed and modeled in order to feed analytical and inference methods for smart systems. The inherent tasks of these systems involve several computational challenges, such as the storage and processing of streaming, sensor and IoT data, symbolic and statistical learning on top of these data and its processes, as well as the modeling and deployment of services and processes leveraging these data. The training programme of the Master DISS revolves around the smart integration and processing of the data (including all kinds of data, from structured to graph-based data and images, ranging from static to evolving data) as well as the inference and Artificial Intelligence processes on top of these data for enabling analysis, prediction and forecast. Data-driven and intelligent systems represent the pillars of Industry 4.0 and Digital Transformation with many concrete applications in various domains such as healthcare, education, energy efficiency, transportation and climate change. The master DISS has two main objectives: * Prepare the students to a PhD thesis and later to research jobs in both academia and industry; * Train the students to research work and methodology, while increasing their knowledge in cross-disciplinary fields in Computer Science (i.e. Data Management, Graphics and Visual Computing, Artificial Intelligence and Service-oriented Computing) Notice that starting from year 2022 only students having completed the first year of Master (M1) in Computer Science can be admitted in the Master cursus (covering the second year of Master). The master offers a well-balanced syllabus, consisting of the following competences and skills: * Detailed knowledge of the components of Big Data processing systems spanning heterogeneous data. * Study of the Machine Learning techniques and applications, by focusing on unsupervised and supervised learning. * Study of service science and business processes, focusing on cognitive and AI-driven services and applications to blockchain. * Study of image understanding and geometric feature extraction, pattern recognition problems and denoising techniques. * Capability to formulate a research question, build the related work relative to a research problem and design a solution for the research problem at hand. * ******************************PAGE****************************** ### Syllabus The master includes four mandatory modules (each of 6ECTS) as follows: #### **M2 DISS Data and Intelligence for Smart Systems :** ##### **Bloc 1, 24 ects :** * Data Processing and Analytics, 6 ects In many sectors of the society, spanning from business to finance, from e-commerce to telecommunication, from scientific research to advanced engineering, successful systems must leverage the availability of large volumes of data through the ability to efficiently perform complex analytics in order to extract significant information. In this course, the students will learn the data processing techniques including analytical and transactional processing for both structured and unstructured data. Data processing and analytics techniques apply to a wide range of data-oriented paradigms in the Big Data landscape. Techniques include query processing and data preparation techniques and algorithms allowing to handle complex massive datasets as input to subsequent data intelligence tasks. During the course, the students will be the opportunity to use and experiment with existing data processing systems and to get familiar with the latest research advances on this topic. * Fundamentals of Image Processing and Interpretation, 6 ects This course introduces high-level problems that require image understanding and geometric feature extraction. Students will learn about the problems of extracting depth information from a monocular or binocular system, image denoising and restoration techniques, inpainting and pattern recognition problems. Different mathematical models will be useful for this task, based on the spatial or temporal domain as well as on the statistical analysis of data. Part of the course will be devoted to the use of existing libraries and to reading of research papers. * Machine Learning Techniques and Applications, 6 ects This course prepares students to understand fundamentals of Machine Learning and existing techniques and their applications. It considers different approaches and techniques (regression models, clustering methods, supervised/unsupervised learning, clustering, neural networks/deep learning, reinforcement learning). * Smart Service Science, 6 ects This module focuses on Service Science as follows: 1) Service Science Definition and Theories 2) Service Innovation and Smart Services (Cloud Services (XaaS, BaaS, SaaS, PaaS, IaaS), Blockchain as a Service, Cognitive Services, Uncertain Services, Machine Learning as a Service), 3) Use cases as appropriate. ##### **Bloc 1, (choice of 2 modules) 6 ects :** * Big Graph Processing Systems, 3 ects This module will focus on: advanced graph processing; analytical systems and their languages leading to optimized and cost-based query execution. Recent advances in complex graph query processing will be presented along with evaluation metrics for efficient graph query processing and analytics. * Blockchain as a Service, 3 ects This module will focus on design of blockchain systems as a service. This course uses the MOOCS developed in the European Projects BLISS and CHAISE with European Certifications The learning units are: (U1) Blockchain essentials for ICT professionals, (U2) Blockchain platforms, (U3) Communicating the merits, challenges and implications of blockchain technology, (U4) Practical design and development of blockchain application. * Distributed Artificial Intelligence and Multi-agent Systems, 3 ects This course prepares students to develop Distributed Artificial Systems based on the following paradigms: the multi-agents paradigm, including hybrid approaches and multi-agent based modeling and simulation, multi-agents learning, ..). * Fundamentals of 3D Shape Modeling, 3 ects This course introduces the main models used to model 3D shapes and worlds. These more or less accurate models (point clouds, meshes, parametric shapes) are accompanied by special techniques for their construction, editing, but also for matching and shape recognition problems. We will also address the problems raised by the generation of large digital worlds. * Robotic Modeling and Control, 3 ects (in collaboration with ECL Lyon) TBA ##### **Bloc 2, 9 ects :** * Internship Training and Soft Skills Part 1, 6 ects This course will let students become familiar with the research methodology and let them acquire the skills needed for the research internship. It will also let them acquire the necessary skills to conduct a successful investigation of the related work, the writing of a research problem statement and prepare the outline of a scientific paper. * Internship Training and Soft Skills Part 2, 3 ects This course will let students become familiar with the study of the research literature as well as the learning of the research methodology. This will let them acquire the necessary skills to be prepared to the research internship. ##### **Bloc 3, 21 ects :** * Internship (in a research lab), 21 ects This research internship will let the students work on a research topic of their choice and develop research skills in order to be prepared for advanced studies (e.g. PhD programs). ******************************PAGE****************************** ### Research Internship The research internship lasts at least 5 months and must be pursued in a company or in a research lab. During the internship, you will focus on topics proposed by the internship advisor on research and development. At the end of the internship and by a deadline communicated by us, you will have to upload the thesis on Tomuss (link communicated via email). The thesis is at most 20 pages long (not including bibliographic references) and must be in English. The thesis defense will happen in September of the following year at a date communicated by email. The presentation will last at most 15 minutes with an additional slot for Q&A of 5 minutes. Then, a discussion among the internship advisors and the academic tutor will follow to decide the grade. ******************************PAGE****************************** ### Contacts **Director of the Master program DISS :** Prof. Angela Bonifati (angela.bonifati@univ-lyon1.fr) **Administrative staff (for students) :** Computer Science Department (scolarite.informatique@univ-lyon1.fr) Teachers (Main contacts for each course in the first period): **Course DPA** - Data Processing and Analytics, 6 ects Prof. Angela Bonifati (angela.bonifati@univ-lyon1.fr) **Course FIP** - Fundamentals of Image Processing, 6 ects Prof. Raphaelle Chaine (raphaelle.chaine@univ-lyon1.fr) **Course MLT** - Machine Learning Techniques and Applications, 6 ects Prof. Remy Cazabet (remy.cazabet@univ-lyon1.fr) **Course SSS** - Smart Service Science, 6 ects Prof. Parisa Ghodous-Shariat (parisa.ghodous-shariat-torbaghan@univ-lyon1.fr ) **Course INT1** - Internship Training and Soft Skills 1, 6 ects Prof. Andrea Mauri (andrea.mauri@univ-lyon1.fr ) ******************************PAGE****************************** ### NEWS The courses will start on Monday September 4, 2023 according to the timetable (see the menu on the left - will be updated starting from end of August). A kick-off meeting will take place on Monday September 4, 2023 at 09:45AM in the Building Quai 43 Room 104 (1st floor) - Campus La Doua for welcoming the newly enrolled students. In the academic year 2022/2023, we hosted several distinguished invited speakers in the Data Processing and Analytics course: - Yannis Papakonstantinou (Databricks & UCSD, USA) with a talk entitled "SQL in accessing and cleaning semistructured data" - Christopher Rost (Univ. of Leipzig & Scads.AI, Germany) with a talk entitled "Scalable Analysis of Temporal Property Graphs" - Matteo Lissandrini (Aalborg Univ., Denmark) with a talk entitled "Graph-powered Big Data Exploration" - Renzo Angles (University of Talca, Chile) with a talk entitled "Graph Query Languages" We expect several other distinguished invited speakers in the 2023/2024 edition. Stay tuned! ******************************PAGE****************************** # Week Schedule The weekly schedule is also available on the official website of the university (https://adelb.univ-lyon1.fr/). Search for DISS as a keyword to find the schedule. &ADEVIEW(0-1-2-3-4,36405) [Lien vers ADE](https://adelb.univ-lyon1.fr/direct/index.jsp?projectId=2&ShowPianoWeeks=true&displayConfName=DOUA_CHBIO&showTree=false&resources=36405&days=0,1,2,3,4) ******************************PAGE****************************** ### Pre-requisites * Having completed the first year of Master in Computer Science (with acquired basic knowledge of Databases, Machine Learning, Image Processing and/or Service-oriented Computing) * ******************************PAGE****************************** Students coming from outside Europe must apply to this Master through the Etudes en France portal. The applications are usually in January/February. European students (from outside France) are invited to send an application to the Director of the Master program DISS Angela Bonifati (angela.bonifati@univ-lyon1.fr) Please include in one ZIP file the following documents: * your recent CV * your M1 diploma or enrolment certificate with list of exams and corresponding grades * a letter of motivation in which you explain why you are interested in this Master * two academic references (from professors in your previous M1 school) Other resources: * After contacting the Director of the Master, both European and French students will have to submit an application to e-candidat N.B. you can create a valid account starting from March and submit your application until end of May) * Other useful information: Practical Guide for foreign students