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Administrative data

Sofiane Sarni

sofiane.sarni@epfl.ch +41 21 693 14 04

EPFL SDSC
INN 218 (Bâtiment INN)
Station 14
CH-1015 Lausanne

Selected publications

A Spreadsheet Framework for Visual Exploration of Biomedical Datasets

SARNI, S.; MACIEL, A.; BOULIC, R.; THALMANN, D.
Published in Proceddings of the 18th IEEE Symposium on Computer-Based Medical Systems (CBMS 2005), Dublin, Ireland, June 23-24, 2005 in

Stress Distribution Visualization on Pre- and Post-Operative Virtual Hip

MACIEL, A.; SARNI, S.; BOULIC, R.; THALMANN, D.
Published in 5th Annual Meeting of the International Society for Computer Assisted Orthopaedic Surgery (CAOS 2005), Helsinki, Finland, June 19-22, 2005 in

Evaluation and Visualization of Stress and Strain on Soft Biological Tissues in Contact

SARNI, S.; MACIEL, A.; BOULIC, R.; THALMANN, D.
Published in In. INTERNATIONAL CONFERENCE ON SHAPE MODELING AND APPLICATIONS, 2004, Genova, Italy. Proceedings of International Conference on Shape Modeling and Applications, Los Alamitos, CA IEEE Computer Society Press, 2004. in

Multi-Finger Haptic Rendering of Deformable Objects

MACIEL, A.; SARNI, S.; BUCHWALDER O.; BOULIC, R.; THALMANN, D.
Published in In. Tenth Eurographics Symposium on Virtual Environments (In Cooperation with ACM Siggraph), 2004, Grenoble, France. Eurographics/ACM SIGGRAPH Symposium Proceedings, p.105-111, Eurographics Association, Aire-la-Ville. in

Colored Visualization of Shape Differences between Bones

Lim, I. S.; Sarni, S.; Thalmann, D.
Published in Proceedings of the 16th IEEE Symposium on Computer-Based Medical Systems (CBMS 2003), June 26--27, 2003, New York in

Planar Arrangement of High-dimensional Biomedical Data Sets by Isomap Coordinates

Lim, I. S.; Ciechomski, P. H.; Sarni, S.; Thalmann, D.
Published in Proceedings of the 16th IEEE Symposium on Computer-Based Medical Systems (CBMS 2003), June 26--27, 2003, New York in

Teaching & PhD

Courses

Large-scale data science for real-world data

COM-490

This hands-on course covers tools and methods used by data scientists, from researching solutions to scaling prototypes on Spark clusters. Students engage with the full data engineering and data science pipeline, from data acquisition to extracting insights, applied to real-world problems.