\n\t| 17:15- 18:00<\/td> | Keynote IV: Deep Learning for Large-Scale Tree Carbon Stock Estimation From Satellite Imagery<\/p> Prof. Dr. Christian Igel<\/p> University of Copenhagen<\/p><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n\n\n\n \n \n\n\n\tWednesday, 26.06.24
\nAI in NRW \u2013 Opportunities and Needs<\/th>\n<\/tr>\n<\/thead>\n | \n\n\t| 09:30-10:00<\/td> | Registration & Welcome Coffee<\/td>\n<\/tr>\n | \n\t| 10:00-10:15<\/td> | Welcome Addresses Rectorate of Bielefeld University
\n
\nProf. Dr. Andrea Kienle
\n<\/p> Head of the Department of Research
\nMKW NRW<\/p><\/td>\n<\/tr>\n | \n\t| 10:15-10:45<\/td> | Overview of AI research in NRW and the NRW Graduate Network DataNinja
\n<\/p> Prof. Dr. Barbara Hammer<\/p> Bielefeld University<\/p><\/td>\n<\/tr>\n | \n\t| 10:45-11:00<\/td> | Short Break<\/td>\n<\/tr>\n | \n\t| 11:00- 12:30<\/td> | DataNinja Spotlights<\/p> Results and Insights from the PhD projects<\/p><\/td>\n<\/tr>\n | \n\t| 12:30- 14:00<\/td> | Lunch Break with Poster Session<\/td>\n<\/tr>\n | \n\t| 14:00- 15:30<\/td> | Highlights KI-Starter Program I<\/p> Prof. Dr. Andrea Horbach:<\/p> Educational Natural Language Processing \u2013 How Language Technology Supports Teachers and Learners<\/p>
\nDr. Robin Chan:<\/p>
Generative Modeling of Object Keypoints for 6D Pose Estimation<\/p>
\nDr. Petra Bevandi\u0107:<\/p>
Leveraging Class Relations for
\nMulti-Dataset Semantic Segmentation<\/p>
\nProf. Dr. Zorah L\u00e4hner:<\/p>
Machine Learning in Between Discrete and Continuous Settings<\/p>
\nDr. France Rose:<\/p>
Missing Data? Keep Calm and Use DISK (Deep Imputation of SKeleton data)<\/p>
\nDr. Friedrich Solowjow:<\/p>
Event-triggered Learning and Similarity of Dynamical Systems<\/p><\/td>\n<\/tr>\n | \n\t| 15:30- 16:30<\/td> | Panel Discussion:
\n“AI – Needs and Risks”<\/p><\/td>\n<\/tr>\n | \n\t| 16:30- 17:00<\/td> | Coffee Break with Poster Session<\/td>\n<\/tr>\n | \n\t| 17:00- 17:45<\/td> | Keynote V: Perspective Taking in Large Language Models
\n<\/p> Prof. Dr. Lucie Flek<\/p> University of Bonn<\/p><\/td>\n<\/tr>\n | \n\t| 17:45- 18:30<\/td> | Keynote VI: LLM-based Argument Quality Improvement
\n<\/p> Prof. Dr. Henning Wachsmuth<\/p> Leibniz University Hannover<\/p><\/td>\n<\/tr>\n | \n\t| from 18:30<\/td> | Conference Dinner
\n
\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n\n\n\n\n \n\n\n\tThursday, 27.06.2024
\nEvaluation and networking “AI made in NRW”<\/th>\n<\/tr>\n<\/thead>\n | \n\n\t| 09:30-10:00<\/td> | Registration & Welcome Coffee
\n<\/td>\n<\/tr>\n | \n\t| 10:00-10:45<\/td> | Keynote VII: Trustworthy AI for Physical Machines: Integrating Machine Learning and Control
\n<\/p> Prof. Dr. Sebastian Trimpe<\/p> RWTH Aachen University<\/p><\/td>\n<\/tr>\n | \n\t| 10:45-11:00<\/td> | Short Break<\/td>\n<\/tr>\n | \n\t| 11:00-12:30<\/td> | Highlights KI-Starter Program II<\/p> Dr. Leonardo Galli:<\/p> Don\u2019t be so Monotone: Relaxing Stochastic Line Search in
\nOver-Parameterized Models<\/p>
\nDr. Daniel Horn:<\/p>
Optimal Data Rotations for Trees and Forests<\/p>
\nProf. Dr. Tanya Braun:<\/p>
What Can Explainability Mean for Probabilistic Inference?<\/p>
\nDr. Kira Maag:<\/p>
Out-of-Distribution Segmentation via Pixel-wise Gradient Uncertainty<\/p>
\nProf. Dr. Benjamin Risse:<\/p>
A Glimpse into Quantifying Machine Intelligence and its Limitations<\/p>
\nDr. Andrew Melnik:<\/p>
Semantic Reasoning and Goal Understanding for Home-Robotics<\/p><\/td>\n<\/tr>\n | \n\t| 12:30-13:30<\/td> | Networking Lunch
\n<\/td>\n<\/tr>\n | \n\t| 13:30-15:00<\/td> | Networking break:
\nSAIL Poster Session
\n<\/td>\n<\/tr>\n | \n\t| 15:00-15:45<\/td> | Keynote VIII: Biological Biases for Learning Robust Robot Behavior: Does Deep Reinforcement Learning Run into the Alignment Problem?<\/p> Prof. Dr. Malte Schilling<\/p> University of M\u00fcnster<\/p><\/td>\n<\/tr>\n | \n\t| 15:45-16:15<\/td> | Coffee Break<\/td>\n<\/tr>\n | \n\t| 16:15-16:45<\/td> | Award Ceremony for Outstanding Poster Presentation
\n<\/td>\n<\/tr>\n | \n\t| 16:45-17:00<\/td> | Closing Remarks & Farewell<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/div>\n\n<\/div><\/div>\n\n\n \n Tuesday, 25.06.2024<\/p> <\/span>\n <\/div><\/div>\n\n <\/p>\n\n\n\n \n\n\n\t| DAY 1: New Developments in Trustworthy AI<\/th> | ![\"\"](\"https:\/\/dataninja.nrw\/wp-content\/uploads\/2024\/03\/DataNinja_N-300x300.png\") <\/th>\n<\/tr>\n<\/thead>\n | \n\n\t
\n13:30-14:20<\/p><\/td>
|
\nRegistration and Welcome Coffee
\n<\/td> | <\/td>\n<\/tr>\n | \n\t
\n14:20-14:30<\/p>
\n<\/td>
|
\nWelcome Address<\/td> | <\/td>\n<\/tr>\n | \n\t
\n14:30-15:15<\/p><\/td>
| ![\"\"](\"https:\/\/dataninja.nrw\/wp-content\/uploads\/2024\/03\/Anand_Supramoney_Photo_Anand_Supramoney-1-300x300.png\")
\n
\nProf. Dr. Anand Subramoney
\n<\/p>
Department of Computer Science, Royal Holloway, University of London<\/p>
\n<\/td> |
\n
\n Keynote I:
\nScalable Architectures for Neuromorphic Machine Learning<\/summary>
\n
\nI will discuss how to design architectures for neuromorphic machine learning from first principles. These architectures take inspiration from biology without being constrained by biological details. Two major themes will be sparsity and asynchrony, and their significant role in scalable neuromorphic systems. I will present recent work from my group on using various forms of sparsity and distributed learning to improve the scalability and efficiency of neuromorphic deep learning models.
\n<\/p>
\n<\/details><\/td>\n<\/tr>\n
\n\t
\n15:15-16:00<\/p><\/td>
| ![\"\"](\"https:\/\/dataninja.nrw\/wp-content\/uploads\/2024\/03\/Kerstin_Bunte_Photo_Magda_Piekorz-3-300x300.png\")
\n
\nProf. Dr. Kerstin Bunte
\n<\/p>
Professor of Machine Learning for Interdisciplinary Data Analysis
\nHead of Intelligent Systems Group, University of Groningen<\/p>
\n <\/td> |
\n
\n Keynote II:
\nScientific Machine Learning for Partially Observed Dynamical Systems<\/summary>
\n Nowadays, most successful machine learning (ML) techniques for the analysis of complex interdisciplinary data use significant amounts of measurements as input to a statistical system. The domain expert knowledge is often only used in data preprocessing. The subsequently trained technique appears as a \u201cblack box\u201d, which is difficult to interpret and rarely allows insight into the underlying natural process. Especially in critical domains such as medicine and engineering, the analysis of dynamic data in the form of sequences and time series is often difficult. Due to natural or cost limitations and ethical considerations data is often irregularly and sparsely sampled and the underlying dynamic system is complex. Therefore, domain experts currently enter a time-consuming and laborious cycle of mechanistic model construction and simulation, often without direct use of the experimental data or the task at hand. We now combine the predictive power of ML and the explanatory power of mechanistic models.
\nTherefore we perform learning in the space of dynamic models that represent the complex underlying natural processes, with potentially very few and limited measurements. We use principles of dimensionality reduction, such as subspace learning, to determine relevant areas in the parameter space of the underlying model as a first step to achieve task-driven model reduction. We furthermore incorporate identifiability analysis for informed posterior construction to improve learning with ill-posed systems caused by data limitations. Findings indicate the possibility of an alternative handling of epistemic uncertainties for scientific machine learning techniques applicable for all linear and classes of non-linear mechanistic models based on Lie symmetries.
\n
\nJoint work of:
\nBunte, Kerstin; Tino, Peter; Oostwal, Elisa; Norden, Janis; Chappell, Michael; Smith, Dave
\n<\/p>
\n<\/details><\/td>\n<\/tr>\n \n\t
\n16:00-16:30<\/p><\/td>
|
\nCoffee Break
\n
\n<\/td> | <\/td>\n<\/tr>\n | \n\t
\n16:30- 17:15<\/p><\/td>
| ![\"\"](\"https:\/\/dataninja.nrw\/wp-content\/uploads\/2024\/03\/Holger_Hoos_Photo_Humboldt-Stiftung-Elbmotion-1-300x300.png\")
\n
\nProf. Dr. Holger Hoos
\n<\/p>
Alexander von Humboldt Professor of Computer Science
\nFaculty of Mathematics, Computer Science and Natural Sciences, RWTH Aachen<\/p><\/td> |
\n
\n Keynote III:
\nHow and Why AI Will Shape the Future of Science and Engineering<\/summary>
\n
\nRecent progress in artificial intelligence has elevated what used to be a highly specialised research area to a topic of public discourse and debate. In this presentation I will discuss why beyond the hype, there are good reasons to be excited, but also concerned about AI. Specifically, I will explain how and why AI will have transformative impact on all sciences and engineering disciplines. Based on my own research on the robustness of neural networks, I will discuss some of the fundamental strengths, weaknesses and limitations of current AI systems. Finally, I will share some thoughts on the most serious risks of deploying these systems quickly and broadly, as well as on what needs to be done in order to manage these risks and to realise the benefits AI can bring.
\n<\/p>
\n<\/details><\/td>\n<\/tr>\n
\n\t
\n17:15- 18:00<\/p><\/td>
| ![\"\"](\"https:\/\/dataninja.nrw\/wp-content\/uploads\/2024\/03\/Christian_Igel_Photo_UNIVERSITY_OF_COPENHAGEN-1-300x300.png\")
\n
\nProf. Dr. Christian Igel
\n<\/p>
Department of Computer Science, University of Copenhagen
\nDirector of the SCIENCE AI Centre, University of Copenhagen<\/p>
\n <\/td> |
\n
\n Keynote IV:
\nDeep Learning for Large-Scale Tree Carbon Stock Estimation From Satellite Imagery<\/summary>
\n
\nTrees play an important role for carbon sequestration, biodiversity, as well as timber and food production. We need a better characterization of woody resources at global scale to understand how they are affected by climate change and human management. Recent advances in satellite remote sensing and machine learning (ML) based computer vision makes this possible. This talk discusses large-scale mapping of individual trees using deep learning applied to high-resolution satellite imagery. The biomass of each tree, and thereby its carbon content, is estimated from the crown size using allometric equations. The parameters of these equations are learned from data. The functional relation is assumed to be non-decreasing. Such monotonicity constraints are powerful regularizers in ML in general. They can support fairness in computer-aided decision making and increase plausibility in data-driven scientific models. This talk introduces a conceptually simple and efficient neural network architecture for monotonic modelling that compares favorable to state-of-the-art alternatives. After this technical excursion, we present an application of our tree monitoring in Rwanda, where it helps quantifying progress of restoration projects and developing a pathway to reach the country\u2019s goal of net zero emissions by 2050.
\n<\/p>
\n<\/details><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n<\/div><\/div>\n\n
\n \n Wednesday, 26.06.24<\/p> <\/span>\n <\/div><\/div>\n\n <\/p>\n\n\n\n \n\n\n\t| DAY 2: AI in NRW \u2013 Opportunities and Needs<\/th> | <\/th>\n<\/tr>\n<\/thead>\n | \n\n\t
\n09:30-10:00<\/p><\/td>
|
\nRegistration &
\nWelcome Coffee
\n<\/td> | <\/td>\n<\/tr>\n | \n\t
\n10:00-10:15<\/p><\/td>
|
\nWelcome Address by Rectorate of Bielefeld University
\n
\nOpening Remarks by Prof. Dr. Andrea Kienle, Head of the Department of Research, Ministry of Culture and Science NRW<\/td> | <\/td>\n<\/tr>\n | \n\t
\n10:15-10:45<\/p><\/td>
| ![\"\"](\"https:\/\/dataninja.nrw\/wp-content\/uploads\/2024\/03\/Barbara_Hammer_photo_Sarah_Jonek-300x300.png\")
\nProf. Dr. Barbara Hammer
\n<\/p>
Professor of Machine Learning
\nHead of Machine Learning Group, Bielefeld University
\n<\/p><\/td> |
\n
\nOverview of AI research in North Rhine-Westphalia and the NRW Graduate Network DataNinja
\n<\/p><\/td>\n<\/tr>\n
| \n\t
\n10:45-11:00<\/p><\/td>
|
\nShort Break
\n
\n<\/td> | <\/td>\n<\/tr>\n | \n\t
\n11:00- 12:30<\/p><\/td>
| <\/td> |
\n
\n
\nDataNinja Spotlights
\n<\/p>
Results and Insights from the PhD projects<\/p><\/td>\n<\/tr>\n | \n\t
\n12:30- 14:00<\/p><\/td>
|
\nLunch Break with Poster Session
\n
\n<\/td> | <\/td> | <\/td>\n<\/tr>\n | \n\t
\n14:00- 15:30<\/p><\/td>
|
\nHighlights KI-Starter Program I<\/p>
\n
\n![\"\"](\"https:\/\/dataninja.nrw\/wp-content\/uploads\/2024\/03\/andreahorbach_photo_Henrik_Schipper-300x300.png\")
\nProf. Dr. Andrea Horbach
\n<\/p>
\nUniversity of Hildesheim
\n<\/p>
\nEducational Natural Language Processing –
\nHow Language Technology Supports
\nTeachers and Learners
\n<\/p>
\n![\"\"](\"https:\/\/dataninja.nrw\/wp-content\/uploads\/2024\/03\/Petra_Bevandic_Photo_Universitat_Zagreb_Sasa_Cetkovic-300x300.png\")
\nDr. Petra Bevandi\u0107
\n<\/p>
\nBielefeld University
\n<\/p>
\nLeveraging Class Relations for
\nMulti-Dataset Semantic Segmentation
\n<\/p>
\n![\"\"](\"https:\/\/dataninja.nrw\/wp-content\/uploads\/2024\/03\/France_Rose_photo_france_rose-300x300.png\")
\nDr. France Rose
\n<\/p>
\nUniversity of Cologne
\n<\/p>
\nMissing data? Keep Calm and Use DISK
\n(Deep Imputation of SKeleton data)
\n<\/p><\/td>
|
\n
\n
\n
\n![\"\"](\"https:\/\/dataninja.nrw\/wp-content\/uploads\/2024\/03\/Robin_Chan_photo_Robin_Chan-300x300.png\")
\n
\nDr. Robin Chan
\n<\/p>
\nBielefeld University \/ TU Berlin
\n<\/p>
\nGenerative Modeling of Object Keypoints for 6D Pose Estimation
\n<\/p>
\n
\n![\"\"](\"https:\/\/dataninja.nrw\/wp-content\/uploads\/2024\/03\/Zorah_Lahner_Photo_Zorah_Lahner-300x300.png\")
\n
\nProf. Dr. Zorah L\u00e4hner
\n<\/p>
\nUniversity of Bonn
\n<\/p>
\nMachine Learning in Between Discrete and Continuous Settings
\n<\/p>
\n![\"\"](\"https:\/\/dataninja.nrw\/wp-content\/uploads\/2024\/03\/Friedrich_Solowjow_Photo_Friedrich_Solowjow-300x300.png\")
\n
\nDr. Friedrich Solowjow
\n<\/p>
\nRWTH Aachen University
\n<\/p>
\nEvent-triggered Learning and Similarity of Dynamical Systems
\n<\/p><\/td>\n<\/tr>\n
| \n\t
\n15:30- 16:30<\/p><\/td>
|
\nPanel Discussion: “AI – Needs and Risks”<\/p>
\n
![\"\"](\"https:\/\/dataninja.nrw\/wp-content\/uploads\/2024\/03\/Klaus_Neumann_Photo_UniBielefeld-300x300.png\") <\/p>
\nProf. Dr. Klaus Neumann
\n<\/p>
Bielefeld University
\n<\/p>
\n ![\"\"](\"https:\/\/dataninja.nrw\/wp-content\/uploads\/2024\/03\/Anja_Moldehn_Photo_CIIT-300x300.png\") <\/p>
\nAnja Moldehn<\/p>
Centrum Industrial IT, Lemgo<\/p>
\n
\n<\/td> |
\n
\n
\n![\"\"](\"https:\/\/dataninja.nrw\/wp-content\/uploads\/2024\/04\/Sebastian_Walter_Photo_Sebastian_Walter-300x300.png\") <\/p>
\nDr. Sebastian Walter
\n<\/p>
weav.ai, Mountain View, CA, USA
\n<\/p>
\n ![\"\"](\"https:\/\/dataninja.nrw\/wp-content\/uploads\/2024\/04\/Roman_Dumitrescu_Photo_Fraunhofer_IEM-300x300.png\") <\/p>
\nProf. Dr.-Ing. Roman Dumitrescu<\/p>
Fraunhofer IEM, Paderborn<\/p>
\n ![\"\"](\"https:\/\/dataninja.nrw\/wp-content\/uploads\/2024\/04\/Felix_Reinhart_Photo_Felix-Reinhart-300x300.png\") <\/p>
\nDr. Felix Reinhart
\n<\/p>
Miele<\/p><\/td> |
\n
\n
\n![\"\"](\"https:\/\/dataninja.nrw\/wp-content\/uploads\/2024\/03\/Daniel_Neider_Photo_TU_Dortmund_Oliver_Dietze-300x300.png\") <\/p>
\nProf. Dr. Daniel Neider
\n<\/p>
TU Dortmund University
\nResearch Center Trustworthy Data Science and Security<\/p>
\n ![\"\"](\"https:\/\/dataninja.nrw\/wp-content\/uploads\/2024\/04\/Julia_Frohlich_Photo_Julia_Frohlich-300x300.png\") <\/p>
\nDr. Julia Fr\u00f6hlich
\n<\/p>
HARTING Technology Group<\/p><\/td>\n<\/tr>\n | \n\t
\n16:30- 17:00<\/p><\/td>
|
\nCoffee Break with Poster Session<\/td> | <\/td> | <\/td>\n<\/tr>\n | \n\t
\n17:00- 17:45<\/p><\/td>
| ![\"\"](\"https:\/\/dataninja.nrw\/wp-content\/uploads\/2024\/03\/Lucie_Flek_Photo_Lucie_Flek-300x300.png\")
\nProf. Dr. Lucie Flek
\n<\/p>
Professor of Data Science & Language Technologies,
\nUniversity of Bonn
\n<\/p><\/td> |
\n
\n Keynote V: Perspective Taking in Large Language Models <\/summary>
\n
\nPerspective-taking, the process of conceptualizing the point of view of another person, remains a challenge for LLMs. Understanding the mental state of others – emotions, beliefs, intentions – is central for the ability to empathize in social interactions. It is also the key to choose the best action to take next.
\nEnhancing perspective-taking capabilities of LLMs can unlock their potential to react better and safer to hints of distress, to engage in a more receptive argumentation, or to target an explanation to an audience. In this talk, I will present our recent perspective-taking experiments, and discuss further opportunities for bringing the human-centered perspectivist paradigm into the LLMs.
\n<\/p>
\n<\/details><\/td>\n<\/tr>\n
\n\t
\n17:45- 18:30<\/p><\/td>
| ![\"\"](\"https:\/\/dataninja.nrw\/wp-content\/uploads\/2024\/03\/Henning_Wachsmuth_Photo_LUH.svg_-300x300.png\")
\nProf. Dr. Henning Wachsmuth
\n<\/p>
Head of the Natural Language Processing Group
\nLeibniz University Hannover
\n<\/p><\/td> |
\n Keynote VI: LLM-based Argument Quality Improvement<\/summary>
\n
\nNatural language processing (NLP) has recently seen a revolutionary breakthrough, due to the impressive capabilities of large language models (LLM). This also affects NLP research on computational argumentation: the computational analysis and synthesis of natural language arguments. While one of the core tasks studied in computational argumentation is the assessment of an argument’s quality, in this talk I look one beyond, namely at how to improve argument quality. Starting from basics of argumentation, I present insights from selected research of my group involving LLMs for improving argument quality. As part of this, I also look at the recent breakthroughs of LLMs and the paradigm shift that comes with them for computational argumentation in particular and for NLP in general.
\n<\/p>
\n<\/details><\/td>\n<\/tr>\n
\n\t
\nfrom 18:30<\/p><\/td>
|
\nConference Dinner
\n
\n<\/td> |
\n<\/td> | <\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n<\/div><\/div>\n\n\n \n Thursday, 27.06.24<\/p> <\/span>\n <\/div><\/div>\n\n <\/p>\n\n\n\n \n\n\n\t| DAY 3: Evaluation and networking “AI made in NRW”<\/th> | <\/th>\n<\/tr>\n<\/thead>\n | \n\n\t
\n09:30-10:00<\/p><\/td>
|
\nRegistration &
\nWelcome Coffee
\n<\/td> | <\/td>\n<\/tr>\n | \n\t
\n10:00-10:45<\/p><\/td>
| ![\"\"](\"https:\/\/dataninja.nrw\/wp-content\/uploads\/2024\/03\/Sebastian_Trimpe_Photo_Junker_MPI-IS-300x300.png\")
\nProf. Dr. Sebastian Trimpe
\n<\/p>
Professor and Head of Institute for Data Science in Mechanical Engineering,
\nRWTH Aachen University
\n<\/p><\/td> |
\n Keynote VII:
\nTrustworthy AI for Physical Machines: Integrating Machine Learning and Control<\/summary>
\n
\nAI promises significant advancements in engineering, enhancing both design and operation processes. Given that engineering focuses on physical machines like vehicles or robots, ensuring trustworthy solutions is crucial. This talk will explore how combining classical control methods with modern machine learning can create reliable algorithms for real-world applications. Specifically, we will discuss some of our recent research on (i) Bayesian optimization for controller learning, (ii) deep reinforcement learning, and (iii) approximate model-predictive control via imitation learning. The effectiveness of the developed algorithms will be demonstrated through experimental results on robotic hardware.
\n<\/p>
\n<\/details><\/td>\n<\/tr>\n
\n\t10:45-11:00<\/p><\/td> | Short Break<\/td> | <\/td> | <\/td>\n<\/tr>\n | \n\t
\n11:00-12:30<\/p><\/td>
|
\nHighlights KI-Starter Program II<\/p>
\n
\n![\"\"](\"https:\/\/dataninja.nrw\/wp-content\/uploads\/2024\/03\/Leonardo_Galli_Photo_Leonardo_Galli-300x300.png\")
\nDr. Leonardo Galli
\n<\/p>
\nLMU Munich
\n<\/p>
\nDon’t be so Monotone: Relaxing Stochastic Line Search in
\nOver-Parameterized Models
\n<\/p>
\n
\n
\n![\"\"](\"https:\/\/dataninja.nrw\/wp-content\/uploads\/2024\/03\/Tanya_Braun_Photo_Gesellschaft_fur_Informatik_eV-300x300.png\")
\nProf. Dr. Tanya Braun
\n<\/p>
\nUniversity M\u00fcnster
\n<\/p>
\nWhat Can Explainability Mean for Probabilistic Inference?
\n<\/p>
\n
\n
\n![\"\"](\"https:\/\/dataninja.nrw\/wp-content\/uploads\/2024\/03\/Benjamin_Risse_Photo_UniMS_Niklas_Arndt.svg_-300x300.png\")
\nProf. Dr. Benjamin Risse
\n<\/p>
\nUniversity M\u00fcnster
\n<\/p>
\nA Glimpse into Quantifying Machine Intelligence and its Limitations
\n<\/p><\/td>
|
\n
\n
\n
\n![\"\"](\"https:\/\/dataninja.nrw\/wp-content\/uploads\/2024\/03\/Daniel_Horn_Photo_Hesham-Elsherif\u200b\u200b-TU-Dortmund-300x300.png\")
\nDr. Daniel Horn
\n<\/p>
\nTU Dortmund
\n<\/p>
\nOptimal Data Rotations for Trees and Forests
\n<\/p>
\n
\n
\n
\n![\"\"](\"https:\/\/dataninja.nrw\/wp-content\/uploads\/2024\/03\/Kira_Maag_Photo_Christian_Kielmann-300x300.png\")
\nDr. Kira Maag
\n<\/p>
\nTU Berlin
\n<\/p>
\nOut-of-Distribution Segmentation via Pixel-wise Gradient Uncertainty
\n<\/p>
\n
\n![\"\"](\"https:\/\/dataninja.nrw\/wp-content\/uploads\/2024\/03\/Andrew_Melnik_Photo_UniUBIE.svg_-300x300.png\")
\nDr. Andrew Melnik
\n<\/p>
\nBielefeld University
\n<\/p>
\nSemantic Reasoning and Goal Understanding for Home-Robotics
\n<\/p><\/td>\n<\/tr>\n
| \n\t
\n12:30-13:30<\/p><\/td>
|
\nNetworking Lunch
\n
\n<\/td> | <\/td> | <\/td>\n<\/tr>\n | \n\t
\n13:30-15:00<\/p><\/td>
|
\nNetworking Break:
\nSAIL Poster Session
\n
\n<\/td> | <\/td> | <\/td>\n<\/tr>\n | \n\t
\n15:00-15:45<\/p><\/td>
| ![\"\"](\"https:\/\/dataninja.nrw\/wp-content\/uploads\/2024\/05\/Malte_Schilling_Photo_Kalle_Kroger_UniMS-300x300.png\")
\nProf. Dr. Malte Schilling
\n<\/p>
Head of the Autonomous Intelligent Systems Group
\nUniversity of M\u00fcnster
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\n Keynote VIII:
\nBiological Biases for Learning Robust Robot Behavior: Does Deep Reinforcement Learning Run into the Alignment Problem?<\/summary>
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\n<\/p>
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\n\t
\n15:45-16.15<\/p>
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\nCoffee Break
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\n16:15-16:45<\/p><\/td>
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\nAward Ceremony for Outstanding Poster Presentation
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\n16:45-17:00<\/p><\/td>
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\nClosing Remarks & Farewell
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\n\n\n\nRegistration<\/h2>\n\n\n\nParticipation is free of charge, however, advance registration is required and will close on May 31st, 2024<\/strong>! Register now!<\/s><\/p>\n\n\n\nRegistration is now closed. We look forward to welcoming all registered participants in Bielefeld! <\/mark><\/p>\n\n\n\nCall for Posters!<\/h2>\n\n\n\nThe deadline for poster submissions has now passed. Thank you to everyone who submitted their work. The review process is currently underway, and poster authors will be informed of the results by May 24th, 23.59h CEST. We look forward to showcasing the selected posters at the conference<\/mark>!<\/p>\n\n\n\nWe are excited to invite researchers, particularly junior researchers<\/strong>, to participate in the poster session at the DataNinja sAIOnARA 2024 Conference. You will have the chance to present your poster on Wednesday, June 26th, from 12.30-14.00h and again from 16:30-17:00h.<\/p>\n\n\n\nWe welcome submissions of current work at any stage of the research process. Submission may also offer conceptual syntheses of existing knowledge to present a cohesive new concept or idea. If you’re exclusively submitting previously published work, make sure to clearly state where your work has appeared before at the beginning of the abstract. While previously published work is acceptable for presentation at the conference, note that the selection committee may decide not to include it in the conference proceedings to prevent double publication and maintain the integrity of academic publishing standards.<\/p>\n\n\n\n Contributions can cover a wide range of topics within the realm of trustworthy AI, including but not limited to:<\/p>\n\n\n\n \n- Ethical considerations in AI development and deployment<\/li>\n\n\n\n
- Transparency and explainability in AI algorithms<\/li>\n\n\n\n
- Fairness and bias mitigation strategies<\/li>\n\n\n\n
- Privacy-preserving AI technologies<\/li>\n\n\n\n
- Robustness and resilience of AI systems against adversarial attacks<\/li>\n\n\n\n
- Accountability and responsibility in AI decision-making<\/li>\n\n\n\n
- Human-centered AI design and interaction<\/li>\n<\/ul>\n\n\n\n
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