Data as Artistic Medium PechaKucha Presentations | Data|Art 2025
Автор: DataArt Community
Загружено: 2025-11-12
Просмотров: 36
PechaKucha 1: Data as Artistic Medium brings together artists and researchers exploring how data can be experienced, embodied, and interpreted beyond visualization. Presented at the 2025 Data|Art Symposium, this session highlights new forms of creative and critical engagement with data as material and meaning.
Moderator: Sarah Newman (Principal, metaLAB (at) Harvard)
Presenters:
Gibrann Morgado — Interpreting Earth Through Spectral Data
Artist and full-stack developer from Mexico City, Morgado explores how satellite imagery and remote sensing reveal the unseen systems of our planet.
Sarah Trew — Reimagining the Still Life: Digital Florals in the Age of Data
Curatorial Assistant at the Indianapolis Museum of Art, Trew shares how contemporary artists use digital tools to reimagine the traditional still life through data and new media.
Levin Brinkmann — The Model is the Medium
Research Scientist at the Max Planck Institute for Human Development, Brinkmann examines how models act as artistic and epistemic media—exploring information exchange between humans and machines.
Rebecca Ruige Xu — Preserving Ambiguity in Data-Driven Art
Xu explores how embracing ambiguity in perception, data, and interpretation can enrich data-driven art, opening creative and cognitive spaces rather than closing them.
Data|Art 2025 took place at Harvard University on June 11–12, hosted by Barabási Lab and metaLAB (at) Harvard members Albert-László Barabási, Jeffrey Schnapp, Kim Albrecht, and Sarah Newman. The symposium explored how data configures aesthetic, social, and epistemic structures through art, science, and design.
Follow Data|Art Community for more talks and creative research at the intersection of data, art, and technology.
Instagram: / dataartcommunity
Learn more:
Data|Art 2025 → https://data-art.info
Albert-László Barabási | Barabási Lab → https://barabasi.com
Jeffrey Schnapp, Kim Albrecht | metaLAB (at) Harvard → https://mlml.io
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