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X-ORIGINAL-URL:https://ddsa.dk
X-WR-CALDESC:Events for DDSA
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BEGIN:VEVENT
DTSTART;TZID=Europe/Copenhagen:20260609T083000
DTEND;TZID=Europe/Copenhagen:20260609T133000
DTSTAMP:20260603T142459Z
CREATED:20260603T142459Z
LAST-MODIFIED:20260603T142459Z
UID:10001994-1780993800-1781011800@ddsa.dk
SUMMARY:Generative AI for Assessment and Feedback in Higher Education (HEGenAI): Reviews and Empirical Studies from Five Countries
DESCRIPTION:Objectives\nThe events have two main objectives:\nFirst\, to bring together data scientists\, EdTech companies\, PhD students\, study leaders\, and administrative\, legal\, and AI systems roles to understand the complexity of developing and implementing Generative AI in education; second\, to strengthen the problem space in\nwhich data science solutions can be defined\, tested\, and anchored more firmly in Denmark. \nSecond\, turning Denmark into a meeting point where data-driven educational research\, AI\, pedagogy\, and institutional practice intersect. \nIt gathers Danish and international researchers\, educators\, and students around a wide application area—higher education—while building bridges to broader agendas in AI\, digitalisation\, and democracy. In this way\, the event does not only share knowledge; it helps position Denmark as a natural hub for interdisciplinary collaboration\, international talent attraction\, and future data science initiatives with societal impact. \nProgram: https://drive.google.com/file/d/14vb8-uxl-bwC34d13HBmyMeSzexQ5PtH/view?usp=drive_link\nFormat: In-person or online participation\nCost: DKK 160 for in-person participation. Online participation is free.\nRegistration deadline: June 4 at Noon
URL:https://ddsa.dk/event/generative-ai-for-assessment-and-feedback-in-higher-education-hegenai-reviews-and-empirical-studies-from-five-countries/
LOCATION:Danmarks Tekniske Universitet\, Anker Engelundsvej 101\, Kongens Lyngby\, 2800\, Denmark
CATEGORIES:DDSA-Funded Event,Other Events
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BEGIN:VEVENT
DTSTART;TZID=Europe/Copenhagen:20260120T130000
DTEND;TZID=Europe/Copenhagen:20260123T160000
DTSTAMP:20251209T100856Z
CREATED:20251209T100856Z
LAST-MODIFIED:20251209T100856Z
UID:10001780-1768914000-1769184000@ddsa.dk
SUMMARY:PhD Seasonal School 2026 on AI Alignment\, safety and security: Applications in Mental Health and Human-Centered Systems
DESCRIPTION:The A2S2 Seasonal School provides a technically grounded introduction to AI alignment\, safety and security\, focusing on the whys and hows of making AI systems robust\, appropriate\, and aligned with human or application goals. While mental health is used as one motivating high-stakes application\, the methods\, concepts and evaluation frameworks generalize to a wide range of human-centered and safety-critical domains. The seasonal school is aimed to bring together 35-40 PhD\, advanced masters students and early-career researchers working in data science\, computer science\, statistics\, cognitive science\, psychology\, and health informatics with an interest in AI alignment\, mental health\, and trustworthy AI. The school provides an interdisciplinary environment with participants from both Danish universities and international institutions across Europe. We aim to form a diverse cohort spanning technical\, health\, and social sciences\, and we particularly encourage applications from underrepresented groups.
URL:https://ddsa.dk/event/phd-seasonal-school-2026-on-ai-alignment-safety-and-security-applications-in-mental-health-and-human-centered-systems/
CATEGORIES:Other Events,PhD Course
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20250811
DTEND;VALUE=DATE:20250816
DTSTAMP:20250505T103857Z
CREATED:20250505T103857Z
LAST-MODIFIED:20250505T103857Z
UID:10001612-1754870400-1755302399@ddsa.dk
SUMMARY:Ph. D. school on multi-modal learning
DESCRIPTION:Multi-modal learning is an innovative field in machine learning that focuses on integrating and leveraging data from multiple diverse sources or modalities. This approach aims to create more robust\, accurate\, and comprehensive models by combining information from different types of data\, such as images\, text\, audio\, and numerical data. \nIn this summer school\, we will explore multi-modal learning with a particular emphasis on integrating images data with other modalities. We have invited a group of speakers that are specialist in learning from multiple data sources. The course will examine real-world applications across various disciplines\, including healthcare (integrating medical scans with patient records) and biology (combining genetic information with visual data of insects)\, as well as theoretical developments in combining images with e.g. text or video. \nBy learning to harness the power of multiple data modalities\, researchers can generate new insights and tackle complex problems that single-modality approaches may struggle to solve. This course will provide participants with the theoretical foundations and practical skills needed to apply multi-modal learning techniques in their own research domains. \nA key component of the summer school will be hands-on\, group-based project work. Participants will engage in a programming challenge that applies multi-modal learning techniques to a real-world problem. The format of a challenge will encourage collaboration\, creativity\, and critical thinking in the practical application of multi-modal learning concepts.
URL:https://ddsa.dk/event/ph-d-school-on-multi-modal-learning/
LOCATION:Hotel Kobæk Strand\, Kobækvej 85\, Skælskør\, 4230\, Denmark
CATEGORIES:DDSA-Funded Event,PhD Course
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20240812
DTEND;VALUE=DATE:20240817
DTSTAMP:20240410T104415Z
CREATED:20240410T104415Z
LAST-MODIFIED:20240410T104415Z
UID:10001057-1723420800-1723852799@ddsa.dk
SUMMARY:Summer school on biomedical image analysis – from acquisition to fairness and bias
DESCRIPTION:Biomedical image analysis is a broad topic that covers a variety of disciplines in modern computer science\, AI Research\, medical sciences and biology. In this summer school\, we aim at given the participant a good overview of the current state-of-the-art in several of the fundamental topics used in modern biomedical image analysis. \nBiomedical image analysis typically starts with the acquisition of data. This can for example be large 3D volume data from high-energy scanners\, human motion data from optical or infrared sensors\, cell images from microscopes or standard historical photos of species. We have invited a group of speakers that are specialist in several of these techniques. \nA common issue with complex data is how to represent the data in a compact way but where the information is still preserved. Currently\, there is a large research interest in implicit representation of 3D shapes for machine learning. These representation and other suitable ways of handling complex biomedical data is also presented at the summer school. \nWhile much modern image analysis is based on large deep neural networks\, there is still a need for knowing about core methods like advanced 3D morphological analysis and statistics of shapes. There will be expert speakers in this field. \nWith the modern data driven approaches it can be very hard to judge the fairness and biases of the models. We will have a session on these complex topic. \nThe course will include group based project work\, where the participants make a programming project relating their research to the summer school’s topics. It will be in the form of a challenge.
URL:https://ddsa.dk/event/summer-school-on-biomedical-image-analysis-from-acquisition-to-fairness-and-bias/
LOCATION:Hotel Christiansminde\, Christiansmindevej 16\, Svendborg\, 5700\, Denmark
CATEGORIES:PhD Course
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20240417
DTEND;VALUE=DATE:20240420
DTSTAMP:20240209T143826Z
CREATED:20240209T143826Z
LAST-MODIFIED:20240209T143826Z
UID:10000368-1713312000-1713571199@ddsa.dk
SUMMARY:Crash Course on Functional Data Analysis and Machine Learning of Complex Data: Techniques and Applications
DESCRIPTION:In an era of explosive growth in complex data\, our course is your bridge to a deeper understanding of Functional Data Analysis (FDA)and Machine Learning (ML). \nThe main challenge today isn’t data availability but rather our ability to interpret it. Join our growing community of professionals applying ML to tackle complex data challenges. \nThis crash course highlights key developments at the intersection of FDA and ML\, with a focus on handling complex functional data. Moreover\, as a student\, you have the opportunity to earn 3 ECTS credits by submitting a report at the end of the workshop. Don’t miss this chance to unlock new insights and skills in data analysis.
URL:https://ddsa.dk/event/crash-course-on-functional-data-analysis-and-machine-learning-of-complex-data-techniques-and-applications/
LOCATION:DTU\, Building 303A aud. 42\, DTU Lyngby Campus\, Lyngby\, Denmark
CATEGORIES:DDSA-Funded Event,PhD Course
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