📢 The programme of our new lecture series »Cooperative Methodologies: Studying Sensory Media & A
| Source: Mastodon | Original article
The University of Siegen has published the full programme for its summer lecture series “Cooperative Methodologies: Studying Sensory Media & AI”. The eight‑session series, running from late June to early August, will be delivered both on campus and via WebEx, with registration open through the university’s SFB 1187 portal. Organisers have assembled a roster that mixes AI researchers, media scholars, and sensory‑technology experts from Germany, Scandinavia and beyond, including a keynote by Prof. Anja Müller (TU Dresden) on multimodal perception and a panel with representatives from the Nordic AI Lab on ethical data handling in immersive media.
The series matters because it tackles a convergence that is still fragmented in academic and industry circles: the use of artificial intelligence to analyse, generate and interact with sensory‑rich media such as VR, AR, haptic interfaces and bio‑feedback systems. By foregrounding cooperative research methods, the programme promises to produce reproducible workflows and open‑source toolkits that could accelerate the deployment of AI‑driven media in education, entertainment and health‑care. For the Nordic AI community, the event offers a rare chance to engage with German partners on standards for multimodal datasets and to explore joint funding opportunities under the EU’s Horizon Europe framework.
Watch next for the series’ opening lecture on 28 June, which will be streamed live and archived for later viewing. Organisers have pledged to publish selected papers in the Lecture Notes in Networks and Systems (LNNS) volume, providing a citable outlet for early results. A follow‑up workshop in September, co‑hosted by the University of Helsinki’s interdisciplinary AI programme, is already being planned, signalling that the Siegen series could become a recurring hub for cross‑border collaboration on sensory AI. Registration closes on 20 May, and spots are expected to fill quickly.
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