Michael Thane

Visual Analytics for Behavioral and Biological Data

Previous affiliations: Hokkaido University · Leibniz Institute for Neurobiology

Computer scientist specialising in machine learning, computer vision, and visualization pipelines for complex biological data. Long-term interdisciplinary work includes image-based behavioural tracking, segmentation, feature extraction, high-dimensional analysis, and research software built together with experimental neuroscientists—often centred on Drosophila behavioural experiments.

Dissertation topic: Structure Detection in High-Dimensional Data with Applications in Neurobiology.

German (native) · English (fluent) · Japanese (intermediate).

Demos Publications Contact
Portrait of Michael Thane.

Demos

Interactive applications and video walkthroughs of visual analytics systems for exploring research data.

  • Screenshot of CatNetVis.

    CatNetVis

    Semantic Visual Exploration of Categorical High-Dimensional Data

    CatNetVis is an interactive visual analytics system for exploring categorical high-dimensional data through semantic network representations. It supports the discovery of relations between categorical attributes, helping users identify meaningful structures, clusters, and associations in complex data sets.

    Outcome: EuroVis Short Paper 2023

    Visual Analytics · Categorical Data · Network Visualization · High-Dimensional Data

  • Screenshot of KumaQ.

    KumaQ

    Spatiotemporal Visualization of Wildlife Sightings in Hokkaido

    KumaQ is an interactive map-based visualization system for exploring wildlife sightings across Hokkaido. It combines spatial, temporal, and contextual information to support the analysis of sighting patterns, risk areas, seasonal dynamics, and route-related exposure.

    Outcome: Research prototype for environmental visual analytics

    Environmental Visualization · Spatiotemporal Data · Wildlife Sightings · Leaflet · Hokkaido

  • RelationExplorer interface.

    RelationExplorer

    Discovering Relations in Mixed High-Dimensional Behavioral Data

    RelationExplorer is an interactive visual analytics system for discovering relations in mixed high-dimensional behavioral data. It combines type-aware relation measures, coordinated views, clustering, and interactive filtering to help researchers identify meaningful patterns across numerical and categorical attributes.

    Outcome: VMV 2025 research system

    Visual Analytics · Mixed Data · Relation Discovery · Behavioral Data · Drosophila

Publications

Peer-reviewed articles and refereed proceedings (published only). Citation counts and the full bibliography are on Google Scholar and ORCID.

  1. Kolms, J., Blum, K. M., Thane, M., Kurczveil, T., & Lehmann, D. J. (2026). Energy Optimized Green Light Assist in Varying Traffic Scenarios Using Reinforcement Learning. In SUMO Conference.

  2. Thane, M., Blum, K. M., & Lehmann, D. J. (2025). Uncovering Relations in High-Dimensional Behavioral Data of Drosophila melanogaster. In VMV — Vision, Modeling and Visualization.

    doi.org/10.2312/vmv.20251234

  3. Bormann, A., Körner, M. B., Dahse, A.-K., Gläß, S., Irmer, J., Lede, V., Alenfelder, J., Lehmann, J., Hall, D. C. N., Thane, M., Schleyer, M., Kostenis, E., Schöneberg, T., Bigl, M., Langenhan, T., Ljaschenko, D., & Scholz, N. (2025). Intron retention of an adhesion GPCR generates 1TM isoforms required for 7TM-GPCR function. Cell Reports, 44(1), 115078.

    doi.org/10.1016/j.celrep.2024.115078

  4. Thane, M., Blum, K. M., & Lehmann, D. J. (2023). CatNetVis: Semantic Visual Exploration of Categorical High-Dimensional Data with Force-Directed Graph Layouts. In EuroVis Short Papers.

    doi.org/10.2312/evs.20231049

  5. Thane, M., Paisios, E., Stöter, T., Krüger, A.-R., Gläß, S., Dahse, A.-K., Scholz, N., Gerber, B., Lehmann, D. J., & Schleyer, M. (2023). High-resolution analysis of individual Drosophila melanogaster larvae uncovers individual variability in locomotion and its neurogenetic modulation. Open Biology, 13(4), 220357.

    doi.org/10.1098/rsob.220308

  6. Thane, M., Viswanathan, V., Meyer, T. C., Paisios, E., & Schleyer, M. (2019). Modulations of microbehaviour by associative memory strength in Drosophila larvae. PLOS ONE, 14(10), e0222676.

    doi.org/10.1371/journal.pone.0224154

Presentations

  • CatNetVis: Semantic Visual Exploration of Categorical High-Dimensional Data. EuroVis 2023, Leipzig, Germany.
  • Uncovering Relations in High-Dimensional Behavioral Data. VMV, 2025.

Contact

For collaborations or general inquiries, please write by email.