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X-WR-CALNAME:MediaFutures
X-ORIGINAL-URL:https://mediafutures.no
X-WR-CALDESC:Events for MediaFutures
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TZID:Europe/Oslo
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BEGIN:VEVENT
DTSTART;TZID=Europe/Oslo:20250403T153000
DTEND;TZID=Europe/Oslo:20250403T163000
DTSTAMP:20260510T213459
CREATED:20250321T152101Z
LAST-MODIFIED:20250402T133519Z
UID:20540-1743694200-1743697800@mediafutures.no
SUMMARY:Mid-term evaluation of PhD candidate Bilal Mahmood
DESCRIPTION:On Thursday\, April 3rd\, PhD candidate Bilal Mahmood will have his mid-term evaluation. \nMain supervisor: Mehdi Elahi \nCo-Supervisor: Samia Touileb \nExternal Evaluator: Sole Pera
URL:https://mediafutures.no/event/mid-term-evaluation-of-phd-candidate-bilal-mahmood/
LOCATION:SFI MediaFutures\, MCB
CATEGORIES:Events,WP2 User Modeling, Personalisation & Engagement
ATTACH;FMTTYPE=image/png:https://mediafutures.no/wp-content/uploads/bilal.png
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BEGIN:VEVENT
DTSTART;TZID=Europe/Oslo:20250404T091500
DTEND;TZID=Europe/Oslo:20250404T101500
DTSTAMP:20260510T213459
CREATED:20250321T154003Z
LAST-MODIFIED:20250402T123002Z
UID:20553-1743758100-1743761700@mediafutures.no
SUMMARY:Mid-term evaluation of PhD candidate Jia-Hua Jeng
DESCRIPTION:On Friday\, April 4\, PhD candidate Jia-Hua Jeng will have his mid-term evaluation. \nMain supervisor: Christoph Trattner \nCo-Supervisors: Erik Knudsen\, Alain Starke \nExternal Evaluator: Sole Pera
URL:https://mediafutures.no/event/mid-term-evaluation-of-phd-candidate-jia-hua-jeng/
LOCATION:SFI MediaFutures\, MCB
CATEGORIES:Events,WP2 User Modeling, Personalisation & Engagement
ATTACH;FMTTYPE=image/png:https://mediafutures.no/wp-content/uploads/jeng.png
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BEGIN:VEVENT
DTSTART;TZID=Europe/Oslo:20250404T104500
DTEND;TZID=Europe/Oslo:20250404T130000
DTSTAMP:20260510T213459
CREATED:20250210T133947Z
LAST-MODIFIED:20250403T073320Z
UID:20232-1743763500-1743771600@mediafutures.no
SUMMARY:Talks on Responsible Recommender Systems
DESCRIPTION:April 4\, MediaFutures sets the stage for a joined event featuring a series of talks\, including contributions from Sole Pera (TU Delft) and the PhD candidates Lukas Wegmeth and Tobias Vente (University of Siegen). \n\n10:45 – 11:45 | Sole Pera on “Into the AI-Known: How Search and Recommender Systems Shape Children’s Online Experiences & the Path to Safer Information Access”\n12:00 – 13:00 | Lukas Wegmeth & Tobias Vente\, “Green Recommender Systems – Minimizing Carbon Footprint for Sustainable Personalization”\n\nSole Pera\, Associate Professor @ Delft University of Technology \nTime: 10:45 – 11:45 \nTitle: “Into the AI-Known: How Search and Recommender Systems Shape Children’s Online Experiences & the Path to Safer Information Access” \nAbstract: In the AI era\, search and recommendation systems increasingly shape how children perceive the world and interact with digital media. However\,  these systems are seldom designed with young users in mind\, leading to exposure to content that is misleading\, inappropriate\, or simply not aligned with their needs. While AI has the potential to improve online information access\, ensuring safe\, effective\, and age-appropriate digital experiences remains a challenge. In this talk\, we will explore how mainstream search and recommendation algorithms influence what children encounter online. Along the way\, we review current research\, discuss the challenges and opportunities in building safer digital environments\, and consider the ethical implications of designing AI-driven information access systems that prioritize children’s rights and well-being. \nDr. Maria Soledad Pera is an Associate Professor at the Web Information Systems group of the Faculty of Electrical Engineering\, Mathematics and Computer Science (EWI)\, Delft University of Technology. Sole’s main area of expertise is in Information Retrieval with emphasis on non-traditional population. \nLukas Wegmeth & Tobias Vente\, PhD candidates at University of Siegen \nTime: 12:00 – 13:00 \nTitle: “Green Recommender Systems – Minimizing Carbon Footprint for Sustainable Personalization” \nAbstract: Did you know one recommender systems research paper emits ~3\,300 kgCO₂e — the same as one person flying from New York to Melbourne? This talk unveils the urgent need for Green Recommender Systems and delivers actionable guidelines to achieve them. We quantify the carbon footprint of training and inference\, comparing deep learning and traditional algorithms. The goal? High-performance recommender systems that don’t cost the Earth. Discover how to minimize the carbon footprint while maintaining performance through energy-aware design\, efficient hardware\, and transparent reporting. This is a call to action: by rethinking how we design and measure recommenders\, we can pioneer sustainable AI that benefits both users and the planet. \nLukas Wegmeth is a Ph.D. Student of the Intelligent Systems Group at the University of Siegen. Before joining the ISG he completed his bachelor’s and master’s degree in Medical Computer Science at the University of Siegen. During his time as a graduate student\, Lukas set his focus on the topic of Machine Learning and collaborated with different chairs of the University of Siegen to work on and release scientific research papers in the field. Lukas is currently analysing recommender systems from an energy efficiency context\, measuring amongst other power consumptions. \nTobias is a joint Ph.D. candidate at the ISG – Intelligent Systems Group (University of Siegen) in Siegen\, Germany and the ADReM – Adrem Data Lab (University of Antwerp) in Antwerp\, Belgium\, working on model selection and automation in recommender systems. Generally\, his research interests revolve around Recommender Systems\, specifically applying ideas from AutoML (Automated Machine Learning) to information retrieval and recommender systems. \nTo follow the talks online\, follow us on zoom: \nhttps://uib.zoom.us/j/65989683261?pwd=DCf1f1O3qFTMoltYavbf9e4h0m8qTy.1
URL:https://mediafutures.no/event/responsible-recommender-systems/
LOCATION:SFI MediaFutures\, MCB
CATEGORIES:Events
ATTACH;FMTTYPE=image/png:https://mediafutures.no/wp-content/uploads/Frame-76-1.png
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DTSTART;TZID=Europe/Oslo:20250404T140000
DTEND;TZID=Europe/Oslo:20250404T150000
DTSTAMP:20260510T213459
CREATED:20250321T152847Z
LAST-MODIFIED:20250402T133219Z
UID:20549-1743775200-1743778800@mediafutures.no
SUMMARY:Mid-term evaluation of PhD candidate Khadiga Seddik
DESCRIPTION:On Friday\, April 4th\, PhD candidate Khadiga Seddik will have her mid-term evaluation. \nMain supervisor: Erik Knudsen \nCo-Supervisor: Damian Trilling\, Alain Starke (Step-in) \nExternal Evaluator: Sole Pera \n\nTitle: The Influence of News Recommender technology on Shaping Selective Exposure and Sharing. \nAbstract:  \nDespite the benefits the news recommenders provide\, overly personalized news recommendations and too much exposure to like-minded news can pose a threat to democracy by leading to filter bubble\, echo-chambers\, and political polarization. These negative consequences are not given\, but they could depend on conditions and factors under which news recommenders amplify or reduce selective exposure. Many studies argue that news recommenders can be programmed to promote factors that reduce selective exposure because they are programmed by human beings\, and they are dependent on the decisions surrounding the implementation and design of the technology. However\, programming recommender systems to shape selective exposure is not a straightforward task\, as we don’t know which factors the recommenders should be designed to promote\, as well as how the recommenders should promote them. In this research project I investigate the heavily debated consequences of news recommender technologies\, selective exposure and selective of like-minded news. The aim is to shift the scholarly attention from uncovering whether the current recommenders amplify or reduce selective exposure to understanding the conditions under which recommender systems do so\, given that they are designed for that purpose. By doing so\, we shift the responsibility for the democratic implications of recommenders from the technology itself to the decisions surrounding the implementation and design. \nThe project is a part of a larger project\, the NEWSREC project (https://www.newsrec.ai). The main objective of NEWSREC project is to study\, understand\, and assess the precise conditions under which algorithmic news recommenders have positive or negative effects on the democratic role of the news media by focusing on both the input side and the output side of news recommenders.
URL:https://mediafutures.no/event/mid-term-evaluation-of-phd-candidate-khadiga-seddik/
LOCATION:SFI MediaFutures\, MCB
CATEGORIES:Events,WP1 Understanding Media Experiences,WP2 User Modeling, Personalisation & Engagement
ATTACH;FMTTYPE=image/png:https://mediafutures.no/wp-content/uploads/khadiga.png
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