{"id":999,"date":"2023-03-08T12:30:19","date_gmt":"2023-03-08T12:30:19","guid":{"rendered":"https:\/\/dataninja.nrw\/?page_id=999"},"modified":"2023-06-20T15:01:45","modified_gmt":"2023-06-20T15:01:45","slug":"dataninja-spring-school-2023","status":"publish","type":"page","link":"https:\/\/dataninja.nrw\/?page_id=999","title":{"rendered":"DataNinja Spring School 2023"},"content":{"rendered":"\n<h2 class=\"wp-block-heading\">DataNinja Spring School 2023<\/h2>\n\n\n\n<div class=\"event-container\">\n    <p class=\"title\">Spring School Information<\/p>\n    <span class=\"dashicons dashicons-calendar-alt\"><\/span>\n    <b>8<sup>th<\/sup> to 10<sup>th<\/sup> of May 2023<\/b>, Hybrid Event; <span style=\"color : red\">All slots for in-person attendance are already booked out.<\/span> We welcome interested participants to joint virtually. Virtual registration remains open until shortly before the event. There will be a poster session on 09<sup>th<\/sup> of May at 15:00 PM.<br>\n    <span class=\"dashicons dashicons-post-status\"><\/span>\n    <b>DataNinja Spring School 2023<\/b><br>\n    <span class=\"dashicons dashicons-groups\"><\/span>\n    Organizers:\n    <a href=\"https:\/\/www.cit-ec.de\/en\/tcs\/barbara-hammer\">Barbara Hammer<\/a>,\n    <a href=\"https:\/\/ekvv.uni-\nbielefeld.de\/pers_publ\/publ\/PersonDetail.jsp?personId=173944650 \">Ulrike Kuhl<\/a><br>\n<\/div>\n\n\n\n<div class=\"wp-block-buttons is-content-justification-center is-layout-flex wp-container-core-buttons-layout-1 wp-block-buttons-is-layout-flex\">\n<div class=\"wp-block-button\"><a class=\"wp-block-button__link wp-element-button\" href=\"#registration\"><s>Register Now<\/s><\/a><\/div>\n\n\n\n<div class=\"wp-block-button\"><a class=\"wp-block-button__link wp-element-button\" href=\"#important-dates\">Important Dates<\/a><\/div>\n\n\n\n<div class=\"wp-block-button\"><a class=\"wp-block-button__link wp-element-button\" href=\"https:\/\/dataninja.nrw\/?page_id=1142\">Online Proceedings<\/a><\/div>\n<\/div>\n\n\n\n<p>We are excited to invite you to the 2nd Data-NInJA Spring School, which will take place from May 8th to May 10th, 2023 in hybrid mode. This year&#8217;s theme is &#8220;Trustworthy AI: Building Safe and Reliable Solutions&#8221;, and we have a fantastic line-up of speakers who will be sharing their insights on reinforcement learning, the social and ethical impact of Artificial Intelligence, with a special focus on sustainability, and causal inference in Machine Learning.<\/p>\n\n\n\n<p>The Data-NInJA Spring School is aimed at PhD students, master students and interested researchers from the broad area of Artificial Intelligence and Machine Learning. At this event, participants will learn about latest trends and developments in trustworthy AI from leading experts in the field)<\/p>\n\n\n\n<ul>\n<li>Prof. Virgina Dignum (Ume\u00e5 University, Sweden)<\/li>\n\n\n\n<li>Prof. Ann Now\u00e9 (Vrije Universiteit Brussel, Belgium)<\/li>\n\n\n\n<li>Prof. Christian Igel (University of Copenhagen, Denmark)<\/li>\n\n\n\n<li>Dr. Martin Riedmiller (Google DeepMind)<\/li>\n\n\n\n<li>Prof. Jonas Peters (ETH Zurich)<\/li>\n<\/ul>\n\n\n\n<p>Lectures will be complemented by tutorial style talks, geared towards providing hands-on experience and application or transfer of methods to own tasks and problems. In addition, participants will have the opportunity to showcase their own work in a virtual poster session. This is a wonderful chance for students, researchers, and practitioners to share their ideas, get feedback, and network with others in the field. We strongly encourage all attendees to consider presenting a poster.<\/p>\n\n\n\n<table id=\"tablepress-2\" class=\"tablepress tablepress-id-2\">\n<thead>\n<tr class=\"row-1 odd\">\n\t<th class=\"column-1\"><\/th><th class=\"column-2\">Monday, 08.05.2023<\/th><th class=\"column-3\">Tuesday, 09.05.2023<\/th><th class=\"column-4\">Wednesday, 10.05.2023<\/th>\n<\/tr>\n<\/thead>\n<tbody class=\"row-hover\">\n<tr class=\"row-2 even\">\n\t<td class=\"column-1\">9:00 &#8211; 10:30<\/td><td class=\"column-2\"><b>Tutorial: Multi-armed bandits<\/b><br \/>\nDr. Viktor Bengs, <br \/>\nLMU Munich<\/td><td class=\"column-3\"><b>1, 2, 3: Deep Learning for Semantic Segmentation<\/b><br \/>\nProf. Christian Igel, <br \/>\nUniversity of Copenhagen<\/td><td class=\"column-4\"><b>Data-efficient RL Agents &#8211; how to build and why they matter<\/b><br \/>\nDr. Martin Riedmiller, <br \/>\nDeepMind<\/td>\n<\/tr>\n<tr class=\"row-3 odd\">\n\t<td class=\"column-1\">10:30<\/td><td class=\"column-2\"><em>Coffee Break<\/em><\/td><td class=\"column-3\"><em>Coffee Break<\/em><\/td><td class=\"column-4\"><em>Coffee Break<\/em><\/td>\n<\/tr>\n<tr class=\"row-4 even\">\n\t<td class=\"column-1\">11:00 &#8211; 12:30<\/td><td class=\"column-2\"><b>Causality<\/b><br \/>\nProf. Jonas Peters, <br \/>\nETH Zurich<\/td><td class=\"column-3\"><b>Responsible AI: From Principles To Action<\/b><br \/>\nProf. Virgina Dignum, <br \/>\nUme\u00e5 University<\/td><td class=\"column-4\"><b>A Brief Tutorial on Geometric Methods in Robot Learning<\/b><br \/>\nDr. No\u00e9mie Jaquier, <br \/>\nKarlsruhe Institute of Technology<\/td>\n<\/tr>\n<tr class=\"row-5 odd\">\n\t<td class=\"column-1\">12:30<\/td><td class=\"column-2\"><em>Break<\/em><\/td><td class=\"column-3\"><em>Break<\/em><\/td><td class=\"column-4\"><em>Break<\/em><\/td>\n<\/tr>\n<tr class=\"row-6 even\">\n\t<td class=\"column-1\">15:00 &#8211; 16.30<\/td><td class=\"column-2\"><b>Reinforcement learning in ChatGPT, Robotics, and AI agents<\/b><br \/>\nDr. Andrew Melnik, <br \/>\nKI-Starter, Uni Bielefeld<\/td><td class=\"column-3\">Virtual poster session, <br \/>\ngather.town<\/td><td class=\"column-4\"><\/td>\n<\/tr>\n<tr class=\"row-7 odd\">\n\t<td class=\"column-1\">16:30<\/td><td class=\"column-2\"><em>Break<\/em><\/td><td class=\"column-3\"><em>Break<\/em><\/td><td class=\"column-4\"><\/td>\n<\/tr>\n<tr class=\"row-8 even\">\n\t<td class=\"column-1\">17:00 &#8211; 18.30<\/td><td class=\"column-2\"><b>Aspects of Trustworthy Reinforcement Learning<\/b><br \/>\nProf. Ann Now\u00e9, <br \/>\nVrije Universiteit Brussels<\/td><td class=\"column-3\"><b>Biosignal-Adaptive Cognitive Systems<\/b><br \/>\n<i>SAIL Lecture<\/i><br \/>\nProf. Tanja Schultz, University of Bremen<\/td><td class=\"column-4\"><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<!-- #tablepress-2 from cache -->\n\n\n<h3 class=\"wp-block-heading\">Details on the Lectures<\/h3>\n\n\n\n<h4 class=\"wp-block-heading\">Monday, 08.05.2023<\/h4>\n\n\n<div class=\"wp-block-ub-content-toggle\" id=\"ub-content-toggle-9be131d9-297c-4a6d-9aa0-b1685791fd1a\" data-mobilecollapse=\"true\" data-desktopcollapse=\"true\">\n<div class=\"wp-block-ub-content-toggle-accordion\" id=\"ub-content-toggle-panel-block-\">\n                <div class=\"wp-block-ub-content-toggle-accordion-title-wrap\"\" aria-controls=\"ub-content-toggle-panel-0-9be131d9-297c-4a6d-9aa0-b1685791fd1a\" tabindex=\"0\">\n                    <p class=\"wp-block-ub-content-toggle-accordion-title ub-content-toggle-title-9be131d9-297c-4a6d-9aa0-b1685791fd1a\"><strong>Tutorial: Multi-armed bandits<\/strong>, Viktor Bengs &#8211; 9:00<\/p><div class=\"wp-block-ub-content-toggle-accordion-toggle-wrap right\"><span class=\"wp-block-ub-content-toggle-accordion-state-indicator wp-block-ub-chevron-down\"><\/span>\n                    <\/div><\/div><div role=\"region\" aria-expanded=\"false\" class=\"wp-block-ub-content-toggle-accordion-content-wrap ub-hide\" id=\"ub-content-toggle-panel-0-9be131d9-297c-4a6d-9aa0-b1685791fd1a\">\n\n<p><strong>Lecturer: <\/strong><a rel=\"noreferrer noopener\" href=\"https:\/\/www.kiml.ifi.lmu.de\/people\/postdocs\/bengs\/index.html\" data-type=\"URL\" data-id=\"https:\/\/www.kiml.ifi.lmu.de\/people\/postdocs\/bengs\/index.html\" target=\"_blank\">Dr. Viktor Bengs<\/a> is a postdoctoral researcher at LMU Munich. His research focuses on the development of theoretically sound algorithms for sequential decision tasks with weakly supervised feedback, e.g., preference information or censored observations, with automated algorithm configuration being the main application area. His recent research covers bandit algorithms, explainable AI, and the study of fundamental properties for quantifying uncertainty.<\/p>\n\n\n\n<p><strong>Summary:<\/strong> This tutorial provides an overview of multi-armed bandits, an intensively studied problem class in the realm of machine learning, in which a learner is facing a sequential decision-making problem under uncertainty. A decision (action) corresponds to making a choice between a finite set of specific choice alternatives (objects, items, etc.), also called arms in reference to the metaphor of gambling machines in casinos. After each decision to choose a particular arm, the learner receives some form of feedback \u2013 typically a numerical reward \u2013 determined by a feedback mechanism of the chosen arm. The learner is not aware of the arms\u2019 feedback mechanisms and consequently tries to learn these in the course of time by performing actions according to its learning strategy. The concrete design of its learning strategy depends essentially on two main components of the learning setting: the assumptions on the feedback mechanisms and the learning task. <\/p>\n\n\n\n<p>In this tutorial, we will have a special focus on the classical learning setting, where the feedback mechanism is of stochastic nature and the learning task is to minimize regret. Accordingly, the tutorial begins by introducing the basic terminology and concepts of multi-armed bandits, including the exploration-exploitation tradeoff and regret minimization. Moreover, different examples of real-world applications of multi-armed bandits, such as online advertising, personalized recommendations, and clinical trials will be provided. In the next step, the different types of bandit algorithms and the idea for their design will be presented. In addition to the theory, we will develop small code examples for the algorithms in the tutorial. As an outlook, we will consider further assumptions about feedback mechanisms and the learning task. <\/p>\n\n\n\n<p>By the end of the tutorial, the participants will have a solid understanding of the theory and practice of multi-armed bandits and be able to apply these techniques to their own problems.<\/p>\n\n<\/div><\/div>\n\n<div class=\"wp-block-ub-content-toggle-accordion\" id=\"ub-content-toggle-panel-block-\">\n                <div class=\"wp-block-ub-content-toggle-accordion-title-wrap\"\" aria-controls=\"ub-content-toggle-panel-1-9be131d9-297c-4a6d-9aa0-b1685791fd1a\" tabindex=\"0\">\n                    <p class=\"wp-block-ub-content-toggle-accordion-title ub-content-toggle-title-9be131d9-297c-4a6d-9aa0-b1685791fd1a\"><strong>Causality<\/strong>, Prof. Jonas Peters &#8211; 11:00<\/p><div class=\"wp-block-ub-content-toggle-accordion-toggle-wrap right\"><span class=\"wp-block-ub-content-toggle-accordion-state-indicator wp-block-ub-chevron-down\"><\/span>\n                    <\/div><\/div><div role=\"region\" aria-expanded=\"false\" class=\"wp-block-ub-content-toggle-accordion-content-wrap ub-hide\" id=\"ub-content-toggle-panel-1-9be131d9-297c-4a6d-9aa0-b1685791fd1a\">\n\n<p><strong>Lecturer:<\/strong> <a rel=\"noreferrer noopener\" href=\"http:\/\/web.math.ku.dk\/~peters\/\" data-type=\"URL\" data-id=\"http:\/\/web.math.ku.dk\/~peters\/\" target=\"_blank\">Jonas Peters<\/a> is Professor of Statistics at the University of Copenhagen. His work focuses mainly on causal inference, trying to learn causal structures either from purely observational data or from a combination of observational and interventional data. His work covers both theoretical and methodological contributions, relating to areas like high-dimensional statistics, computational statistics or graphical models.<\/p>\n\n\n\n<p><strong>Summary:<\/strong> In science, we often want to understand how a system reacts under interventions (e.g., under gene knock-out experiments or a change of policy). These questions go beyond statistical dependences and can therefore not be answered by standard regression or classification techniques. In this tutorial we will learn about the powerful language of causality and recent developments in the field. No prior knowledge about causality is required.<\/p>\n\n<\/div><\/div>\n\n<div class=\"wp-block-ub-content-toggle-accordion\" id=\"ub-content-toggle-panel-block-\">\n                <div class=\"wp-block-ub-content-toggle-accordion-title-wrap\"\" aria-controls=\"ub-content-toggle-panel-2-9be131d9-297c-4a6d-9aa0-b1685791fd1a\" tabindex=\"0\">\n                    <p class=\"wp-block-ub-content-toggle-accordion-title ub-content-toggle-title-9be131d9-297c-4a6d-9aa0-b1685791fd1a\"><strong>Reinforcement learning in ChatGPT, Robotics, and AI agents<\/strong>, Dr. Andrew Melnik &#8211; 15:00<\/p><div class=\"wp-block-ub-content-toggle-accordion-toggle-wrap right\"><span class=\"wp-block-ub-content-toggle-accordion-state-indicator wp-block-ub-chevron-down\"><\/span>\n                    <\/div><\/div><div role=\"region\" aria-expanded=\"false\" class=\"wp-block-ub-content-toggle-accordion-content-wrap ub-hide\" id=\"ub-content-toggle-panel-2-9be131d9-297c-4a6d-9aa0-b1685791fd1a\">\n\n<p><strong>Lecturer:<\/strong> <a rel=\"noreferrer noopener\" href=\"https:\/\/ni.www.techfak.uni-bielefeld.de\/people\/anmelnik\" data-type=\"URL\" data-id=\"https:\/\/ni.www.techfak.uni-bielefeld.de\/people\/anmelnik\" target=\"_blank\">Dr. Andrew Melnik<\/a> is an AI Researcher at Bielefeld University, focusing on integrating Artificial Intelligence, Machine Learning,and Robotics. In 2021, Andrew received one of the coveted AI-Starter grants for his project &#8220;Learning to Plan with Deep Neural Networks\u201c.<\/p>\n\n\n\n<p><strong>Summary:<\/strong> Reinforcement learning is a powerful technique used in various fields of artificial intelligence, including natural language processing, robotics, and agent-based systems. I will introduce the basic concepts of reinforcement learning, including the Q-learning algorithm, discuss how reinforcement learning is used in ChatGPT, applications in game playing, explore the role of reinforcement learning in robotics for navigation, control, and manipulation. Overall, I will provide a comprehensive overview of the current state-of-the-art in reinforcement learning and highlight future research directions in this exciting field.<\/p>\n\n<\/div><\/div>\n\n<div class=\"wp-block-ub-content-toggle-accordion\" id=\"ub-content-toggle-panel-block-\">\n                <div class=\"wp-block-ub-content-toggle-accordion-title-wrap\"\" aria-controls=\"ub-content-toggle-panel-3-9be131d9-297c-4a6d-9aa0-b1685791fd1a\" tabindex=\"0\">\n                    <p class=\"wp-block-ub-content-toggle-accordion-title ub-content-toggle-title-9be131d9-297c-4a6d-9aa0-b1685791fd1a\"><strong>Aspects of Trustworthy Reinforcement Learning<\/strong>, Prof. Ann Now\u00e9 &#8211; 17:00<\/p><div class=\"wp-block-ub-content-toggle-accordion-toggle-wrap right\"><span class=\"wp-block-ub-content-toggle-accordion-state-indicator wp-block-ub-chevron-down\"><\/span>\n                    <\/div><\/div><div role=\"region\" aria-expanded=\"false\" class=\"wp-block-ub-content-toggle-accordion-content-wrap ub-hide\" id=\"ub-content-toggle-panel-3-9be131d9-297c-4a6d-9aa0-b1685791fd1a\">\n\n<p><strong>Lecturer:<\/strong> <a rel=\"noreferrer noopener\" href=\"https:\/\/ai.vub.ac.be\/team\/ann- nowe\/?utm_source=www.google.com&amp;utm_medium=organic&amp;utm_campaign=Google&amp;referrer- analytics=1\" data-type=\"URL\" data-id=\"https:\/\/ai.vub.ac.be\/team\/ann- nowe\/?utm_source=www.google.com&amp;utm_medium=organic&amp;utm_campaign=Google&amp;referrer- analytics=1\" target=\"_blank\">Ann Now\u00e9<\/a>, director of the AI-Lab at Vrije Universiteit Brussels (VUB), is a professor both in the Computer Science Department of the faculty of Sciences as well as in the Computer Science group of the Engineering faculty at VUB. Prof. Now\u00e9 is well-known for her work in reinforcement learning, supporting the development of smarter and safer intelligent agents.<\/p>\n\n<\/div><\/div>\n<\/div>\n\n\n<h4 class=\"wp-block-heading\">Tuesday, 09.05.2023<\/h4>\n\n\n<div class=\"wp-block-ub-content-toggle\" id=\"ub-content-toggle-e8b6fd3c-14d2-472f-8c4c-a2ebcf354e8d\" data-mobilecollapse=\"true\" data-desktopcollapse=\"true\">\n<div class=\"wp-block-ub-content-toggle-accordion\" id=\"ub-content-toggle-panel-block-\">\n                <div class=\"wp-block-ub-content-toggle-accordion-title-wrap\"\" aria-controls=\"ub-content-toggle-panel-0-e8b6fd3c-14d2-472f-8c4c-a2ebcf354e8d\" tabindex=\"0\">\n                    <p class=\"wp-block-ub-content-toggle-accordion-title ub-content-toggle-title-e8b6fd3c-14d2-472f-8c4c-a2ebcf354e8d\"><strong><strong>1, 2, 3: Deep Learning for Semantic Segmentation<\/strong>, <\/strong>Prof. Christian Igel &#8211; 9:00<\/p><div class=\"wp-block-ub-content-toggle-accordion-toggle-wrap right\"><span class=\"wp-block-ub-content-toggle-accordion-state-indicator wp-block-ub-chevron-down\"><\/span>\n                    <\/div><\/div><div role=\"region\" aria-expanded=\"false\" class=\"wp-block-ub-content-toggle-accordion-content-wrap ub-hide\" id=\"ub-content-toggle-panel-0-e8b6fd3c-14d2-472f-8c4c-a2ebcf354e8d\">\n\n<p><strong>Lecturer:<\/strong> <a rel=\"noreferrer noopener\" href=\"https:\/\/christian-igel.github.io\" data-type=\"URL\" data-id=\"https:\/\/christian-igel.github.io\" target=\"_blank\">Christian Igel<\/a> is a professor at the Department of Computer Science at the University of   Copenhagen, and director of the SCIENCE AI Centre. His main research area is Machine Learning, with particular focus on the theory, application, and efficient implementation of deep neural networks, kernel-based methods, multi-objective optimization, reinforcement learning, and ensemble methods. With his strong commitment towards cross-disciplinary collaborations, he develops applications of machine learning that  help achieve the sustainable development goals.<\/p>\n\n\n\n<p><strong>Summary:<\/strong> Machine learning can help to reach sustainable development goals. In this talk, we will consider deep learning for semantic segmentation in the areas \u201cGood Health and Well-being\u201d, \u201cLife on Land\u201d, and \u201cClimate Action\u201d.&nbsp;<\/p>\n\n\n\n<p>Semantic image segmentation algorithms provide a classification of each pixel or voxel of the input, and their basic concepts can be adapted for time series analysis. We will discuss fully convolutional neural networks for segmentation of 1-, 2- and 3-dimensional data with application examples from remote sensing and medical data analysis.<\/p>\n\n<\/div><\/div>\n\n<div class=\"wp-block-ub-content-toggle-accordion\" id=\"ub-content-toggle-panel-block-\">\n                <div class=\"wp-block-ub-content-toggle-accordion-title-wrap\"\" aria-controls=\"ub-content-toggle-panel-1-e8b6fd3c-14d2-472f-8c4c-a2ebcf354e8d\" tabindex=\"0\">\n                    <p class=\"wp-block-ub-content-toggle-accordion-title ub-content-toggle-title-e8b6fd3c-14d2-472f-8c4c-a2ebcf354e8d\"><strong>Responsible AI: From Principles To Action<\/strong>, Prof. Virginia Dignum &#8211; 11:00<\/p><div class=\"wp-block-ub-content-toggle-accordion-toggle-wrap right\"><span class=\"wp-block-ub-content-toggle-accordion-state-indicator wp-block-ub-chevron-down\"><\/span>\n                    <\/div><\/div><div role=\"region\" aria-expanded=\"false\" class=\"wp-block-ub-content-toggle-accordion-content-wrap ub-hide\" id=\"ub-content-toggle-panel-1-e8b6fd3c-14d2-472f-8c4c-a2ebcf354e8d\">\n\n<p><strong>Lecturer:<\/strong> <a rel=\"noreferrer noopener\" href=\"https:\/\/people.cs.umu.se\/virginia\/\" target=\"_blank\">Prof. Virginia Dignum<\/a> leads the Social and Ethical Artificial Intelligence group at Ume\u00e5 University. Her research focuses on the complex interconnections and interdependencies between people,  organizations, and technology. Thus, she works towards responsible AI through value-sensitive designs of intelligent systems, and multi-agent organisations. As one of the leading experts in AI and its social and ethical impact, she acts as a scientific advisor in several international initiatives on policy and strategy guidelines for AI research and applications.<\/p>\n\n\n\n<p><strong>Summary:<\/strong> Every day we see news about advances and the societal impact of AI. AI is changing the way we work, live, and solve challenges but concerns about fairness, transparency or privacy are also growing.<\/p>\n\n\n\n<p>Ensuring AI ethics is more than designing systems whose result can be trusted. It is about the way we<br>design them, why we design them, and who is involved in designing them. In order to develop and use<br>AI responsibly, we need to work towards technical, societal, institutional and legal methods and tools<br>which provide concrete support to AI practitioners, as well as awareness and training to enable<br>participation of all, to ensure the alignment of AI systems with our societies\u2019 principles and values.<\/p>\n\n<\/div><\/div>\n\n<div class=\"wp-block-ub-content-toggle-accordion\" id=\"ub-content-toggle-panel-block-\">\n                <div class=\"wp-block-ub-content-toggle-accordion-title-wrap\"\" aria-controls=\"ub-content-toggle-panel-2-e8b6fd3c-14d2-472f-8c4c-a2ebcf354e8d\" tabindex=\"0\">\n                    <p class=\"wp-block-ub-content-toggle-accordion-title ub-content-toggle-title-e8b6fd3c-14d2-472f-8c4c-a2ebcf354e8d\"><strong>Biosignal-Adaptive Cognitive Systems<\/strong>, Prof. Tanja Schultz &#8211; 17:00<\/p><div class=\"wp-block-ub-content-toggle-accordion-toggle-wrap right\"><span class=\"wp-block-ub-content-toggle-accordion-state-indicator wp-block-ub-chevron-down\"><\/span>\n                    <\/div><\/div><div role=\"region\" aria-expanded=\"false\" class=\"wp-block-ub-content-toggle-accordion-content-wrap ub-hide\" id=\"ub-content-toggle-panel-2-e8b6fd3c-14d2-472f-8c4c-a2ebcf354e8d\">\n\n<p><em>SAIL Lecture and Networking Event<\/em><\/p>\n\n\n\n<p><strong>Lecturer:<\/strong> <a rel=\"noreferrer noopener\" href=\"https:\/\/www.uni-bremen.de\/csl\/institut\/direktorin\" target=\"_blank\">Tanja Schultz<\/a> is professor for Cognitive Systems of the Faculty of Mathematics &amp; Computer Science at the University of Bremen. She is the spokesperson of the University Bremen high-profile area \u201cMinds, Media, Machines\u201d and the DFG Research Unit \u201cLifespan AI: From longitudinal data to lifespan inference in health\u201d. Prof. Schultz is a recognized scholar in the field of multilingual speech recognition and cognitive technical systems, where she combines machine learning methods with innovations in biosignal processing to create technologies such as in &#8220;Silent Speech Communication&#8221; and &#8220;Brain-to-Speech&#8221;.<\/p>\n\n\n\n<p><strong>Summary:<\/strong> In my talk, I will describe technical cognitive systems that automatically adapt to users\u2019 needs by interpreting their biosignals. Human behavior includes physical, mental, and social actions that emit a range of biosignals which can be captured by a variety of sensors. The processing and interpretation of such biosignals provides an inside perspective on human physical and mental activities, complementing the traditional approach of merely observing human behavior. As great strides have been made in recent years in integrating sensor technologies into ubiquitous devices and in machine learning methods for processing and learning from data, I argue that the time has come to harness the full spectrum of biosignals to understand user needs. I will present illustrative cases ranging from silent and imagined speech interfaces that convert myographic and neural signals directly into audible speech, to interpretation of human attention and decision making from multimodal biosignals.<\/p>\n\n<\/div><\/div>\n<\/div>\n\n\n<h4 class=\"wp-block-heading\">Wednesday, 10.05.2023<\/h4>\n\n\n<div class=\"wp-block-ub-content-toggle\" id=\"ub-content-toggle-08d0b53a-3948-4c81-86a9-a526d4e09fbb\" data-mobilecollapse=\"true\" data-desktopcollapse=\"true\">\n<div class=\"wp-block-ub-content-toggle-accordion\" id=\"ub-content-toggle-panel-block-\">\n                <div class=\"wp-block-ub-content-toggle-accordion-title-wrap\"\" aria-controls=\"ub-content-toggle-panel-0-08d0b53a-3948-4c81-86a9-a526d4e09fbb\" tabindex=\"0\">\n                    <p class=\"wp-block-ub-content-toggle-accordion-title ub-content-toggle-title-08d0b53a-3948-4c81-86a9-a526d4e09fbb\"><strong><strong>Data-efficient RL Agents &#8211; how to build and why they matter<\/strong>, <\/strong>Dr. Martin Riedmiller &#8211; 9:00<\/p><div class=\"wp-block-ub-content-toggle-accordion-toggle-wrap right\"><span class=\"wp-block-ub-content-toggle-accordion-state-indicator wp-block-ub-chevron-down\"><\/span>\n                    <\/div><\/div><div role=\"region\" aria-expanded=\"false\" class=\"wp-block-ub-content-toggle-accordion-content-wrap ub-hide\" id=\"ub-content-toggle-panel-0-08d0b53a-3948-4c81-86a9-a526d4e09fbb\">\n\n<p><strong>Lecturer:<\/strong> <a rel=\"noreferrer noopener\" href=\"https:\/\/sites.google.com\/view\/riedmiller\/home\" target=\"_blank\">Dr. Martin Riedmiller<\/a>, research scientist and former professor for machine learning, is the team lead at DeepMind. His core scientific interest are intelligent machines learn new things autonomously from scratch. His work particularly focuses on neural networks and their ability to store and generalize information.<\/p>\n\n\n\n<p><strong>Summary:<\/strong> Intelligence is the ability to efficiently and effectively generate knowledge out of experience\u2019 &#8211; guided by this hypothesis, we investigate algorithms and agent architectures that can autonomously learn with minimal interactions and from minimal prior knowledge. I will discuss the \u2018collect &amp; infer\u2019 principle that provides the blueprint for an agent architecture of interacting learning processes and give concrete examples of their implementation. The learning behavior will be shown on several examples in the field of control and robotics.<\/p>\n\n<\/div><\/div>\n\n<div class=\"wp-block-ub-content-toggle-accordion\" id=\"ub-content-toggle-panel-block-\">\n                <div class=\"wp-block-ub-content-toggle-accordion-title-wrap\"\" aria-controls=\"ub-content-toggle-panel-1-08d0b53a-3948-4c81-86a9-a526d4e09fbb\" tabindex=\"0\">\n                    <p class=\"wp-block-ub-content-toggle-accordion-title ub-content-toggle-title-08d0b53a-3948-4c81-86a9-a526d4e09fbb\"><strong>A Brief Tutorial on Geometric Methods in Robot Learning<\/strong>, Dr. No\u00e9mie Jacquier &#8211; 11:00<\/p><div class=\"wp-block-ub-content-toggle-accordion-toggle-wrap right\"><span class=\"wp-block-ub-content-toggle-accordion-state-indicator wp-block-ub-chevron-down\"><\/span>\n                    <\/div><\/div><div role=\"region\" aria-expanded=\"false\" class=\"wp-block-ub-content-toggle-accordion-content-wrap ub-hide\" id=\"ub-content-toggle-panel-1-08d0b53a-3948-4c81-86a9-a526d4e09fbb\">\n\n<p><strong>Lecturer:<\/strong> <a rel=\"noreferrer noopener\" href=\"https:\/\/njaquier.ch\" target=\"_blank\">Dr. No\u00e9mie Jacquie<\/a>r is a postdoctoral researcher in the HighPerformance Humanoid Technologies Lab (H\u00b2T) at the Karlsruhe Institute of Technology (KIT). Her work is centered around providing robots with efficient and reliable learning capabilities by combining robot learning and control with differential geometry. Specifically, she develops models that efficiently integrate geometric information encapsulated in data.<\/p>\n\n\n\n<p><strong>Summary:<\/strong> To be deployed in our everyday life, robots must display outstanding learning and adaptation capabilities allowing them to act, react, and continuously learn in unstructured dynamic environments. A key component in both data-driven learning and adaptation is how robots may exploit explicit (e.g., domain knowledge) or implicit (e.g., learned) structures arising in the collected data. Domain knowledge and data structures in robotics can be viewed through the lens of geometry: Different variables have specific geometric characteristics, collected data may lie on curved spaces, and various problems can be naturally formulated from a geometric perspective. In this context, differential geometry, or more specifically Lie groups and Riemannian manifold theories provide appropriate methods to cope with the geometry of non-Euclidean spaces. Although geometric methods have been successfully applied to robotics from early on, they only gained interest recently in robot and machine learning. In this brief tutorial, I will provide an introduction on geometric methods in machine and robot learning, and showcase the benefits of these methods in various robotics applications.<\/p>\n\n<\/div><\/div>\n<\/div>\n\n\n<hr class=\"wp-block-separator has-css-opacity is-style-wide\"\/>\n\n\n\n<h3 class=\"wp-block-heading\">Registration<\/h3>\n\n\n\n<p>All slots for in-person attendance are already <strong>booked out<\/strong>. We welcome interested participants to joint virtually. Advance registration is required via the following link:<\/p>\n\n\n\n<p><a href=\"https:\/\/uni-bielefeld.zoom.us\/webinar\/register\/WN_FxCqf89gR9i6NtawCzQHaA\" target=\"_blank\" rel=\"noreferrer noopener\">https:\/\/uni-bielefeld.zoom.us\/webinar\/register\/WN_FxCqf89gR9i6NtawCzQHaA<\/a><\/p>\n\n\n\n<p>At registration, you will need to indicate whether you plan to submit a poster.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Important Dates<\/h3>\n\n\n\n<ul>\n<li>until <strong>April 14 th , 2023, 00.00h CET<\/strong>: Abstract submission deadline for the virtual poster presentation and the KAI Travel Grant<\/li>\n\n\n\n<li>until <strong>April 21st , 2023, 00.00h CET<\/strong>: Poster acceptance notification<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Poster Session<\/h3>\n\n\n\n<p>The virtual poster session will be held in gather.town. We look forward to hearing about your current work, be it planned, just finished, or already published. To qualify for the the poster session (and the chance to win the 2023 DataNinja Poster Prize), please<\/p>\n\n\n\n<ol start=\"1\">\n<li>indicate that you want to present a poster during the registration process, and<\/li>\n\n\n\n<li>submit a short extended abstract of up to two pages to <a href=\"mailto:contact@dataninja.nrw\">contact@dataninja.nrw<\/a>, <strong>until April 14th , 2023, 00:00h CET<\/strong> (note: for female in-person attendees who would like to apply for the KAI Travel Grant, also attach a CV, more information below)<\/li>\n<\/ol>\n\n\n\n<p>Contributions will be reviewed and selected by the organizers. All workshop contributions will appear as online proceedings on our webpage.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Kunoichi in AI (KAI) Travel Grant<\/h3>\n\n\n\n<p>The Kunoichi in AI (KAI) Travel Grant is a program designed to support female attendees who wish to participate in person in the Data-NInJA Spring School. The grant provides financial assistance to cover travel and accommodation expenses, allowing more female up-and-coming scientisits to attend and benefit from this event.<\/p>\n\n\n\n<p>To be eligible for the grant, applicants must identify as female and submit a poster for presentation at the Spring School. In addition, they must have a demonstrated interest or background in AI. Registration to the Spring School is mandatory for application.<\/p>\n\n\n\n<p>To apply fort he KAI Travel Grant, please<\/p>\n\n\n\n<ol>\n<li>specify that you \u201chereby apply for the KAI Travel Grant\u201c within the body of the mail submitting the extended poster abstract<\/li>\n\n\n\n<li>also submit a CV together with the extended poster abstract<\/li>\n<\/ol>\n\n\n\n<p>The organization committee reviews and evaluates each application based on several criteria. The KAI Travel Grant is an excellent opportunity for women to network with other professionals, learn about the latest developments in trustworthy AI, and gain exposure to new ideas and perspectives in their field.<\/p>\n\n\n\n<p>Recipients of the (KAI) Travel Grant will be notified shortly before the event.<\/p>\n\n\n\n<p>Expenses for travel and accommodation will be reimbursed (after the event) according to the specifications of the LRKG NRW.<\/p>\n\n\n\n<style>\n#page-site-header > .overlay, .page-header {\n    display: none;\n}\n<\/style>\n\n\n\n<p><\/p>\n\n\n\n<p><\/p>\n","protected":false},"excerpt":{"rendered":"<p>DataNinja Spring School 2023 Spring School Information 8th to 10th of May 2023, Hybrid Event; All slots for in-person attendance [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":1075,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"ub_ctt_via":"","footnotes":""},"featured_image_src":"https:\/\/dataninja.nrw\/wp-content\/uploads\/2023\/04\/SpringSchool2023_HeaderOfPage-rescaled.png","_links":{"self":[{"href":"https:\/\/dataninja.nrw\/index.php?rest_route=\/wp\/v2\/pages\/999"}],"collection":[{"href":"https:\/\/dataninja.nrw\/index.php?rest_route=\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/dataninja.nrw\/index.php?rest_route=\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/dataninja.nrw\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/dataninja.nrw\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=999"}],"version-history":[{"count":41,"href":"https:\/\/dataninja.nrw\/index.php?rest_route=\/wp\/v2\/pages\/999\/revisions"}],"predecessor-version":[{"id":1858,"href":"https:\/\/dataninja.nrw\/index.php?rest_route=\/wp\/v2\/pages\/999\/revisions\/1858"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/dataninja.nrw\/index.php?rest_route=\/wp\/v2\/media\/1075"}],"wp:attachment":[{"href":"https:\/\/dataninja.nrw\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=999"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}