Nina Miolane

I am a Ph.D candidate on Geometric Statistics for Medical Image Analysis, at Inria Asclepios Team and Stanford University, under the supervision of Xavier Pennec and Susan Holmes.

Contact information

  • Addresses:
Inria, Asclepios Project-Team, Office F224, Bâtiment Fermat, 2004 route des lucioles, 06902 Sophia-Antipolis, France

Stanford University, Department of Statistics, Sequoia Hall, 390 Serra Mall, CA 94305, United States
  • Emails: nina.miolane at inria.fr/ nmiolane at stanford.edu
  • Phones: + 33 6 33 55 62 02/ +1 650 656 6678

Publications


Journals

  • Miolane, N., Holmes, S., Pennec, X.: Topologically constrained template estimation via Morse-Smale complices allows to control its statistical consistency. (Submitted, 2016).
  • Miolane, N., Holmes, S., Pennec, X.: Template organ shape estimation in Computational Anatomy: Correcting an asymptotic bias (Submitted, 2016).
  • Miolane, N., Pennec, X.: Computing bi-invariant pseudo-metrics on Lie groups for consistent statistics. Entropy, 17(4), 1850–1881 (Apr. 2015). https://hal.inria.fr/hal-01133922
  • Darmante, H., Bugnas, B., Dompsure, R.B.D., Barresi, L., Miolane, N., Pennec, X., de Peretti, F., Bronsard, N.: Analyse biometrique de l'anneau pelvien en 3 dimensions – a propos de 100 scanners. Revue de Chirurgie Orthopedique et Traumatologique 100 (7, Supplement), S241 – (Nov. 2014). http://www.sciencedirect.com/science/article/pii/S187705171400327X.
Conferences
  • Miolane, N., Pennec, X., Holmes, S.: Miolane, N., Pennec, X., Holmes, S.: Quantifying and correcting the inconsistency of the mean shape estimation (Submitted, 2016).
  • Miolane, N., Pennec, X., Holmes, S.: Toward a unified geometric Bayesian framework for template estimation in Computational Anatomy. World Meeting of the International Society for Bayesian Analysis (June 2016). (Young Researcher Travel Award).
  • Miolane, N., Pennec, X.: Biased estimators on Quotient spaces. 2nd International Conference on Geometric Sciences of Information (Oct. 2015). https://hal.inria.fr/hal-01203805 (Oral presentation).
  • Miolane, N., Pennec, X.: A survey of mathematical structures for extending 2D neurogeometry to 3D image processing. Medical Computer Vision Workshop, 18th International Conference on Medical Image Computing and Computer Assisted Intervention (Oct. 2015). https://hal.inria.fr/hal-01203518
  • Miolane, N., Pennec, X.: Statistics on Lie groups : A need to go beyond the pseudo-Riemannian framework. 34th International Workshop on Bayesian Inference and Maximum Entropy Methods (Sept. 2014). https://hal.inria.fr/hal-0109151 (Oral presentation)

Others

  • Miolane, N.: Statistics on Lie groups: can we obtain a consistent framework with pseudo-Riemannian metrics? Workshop on Geometrical Models in Vision, Institut Poincare, Paris (Oct. 2014) http://gmvision.lsis.org/slides/miolane.pdf (Poster).
  • Miolane, N., Khanal, B.: Statistics on Lie groups for Computational Anatomy. Video for the Educational Challenge of the 17th International Conference on Medical Image Computing and Computer Assisted Intervention, MIT Boston (Sept. 2014) http://www.miccai.org/edu/videos.html#mec2014winners (1st Popular Prize).
  • Miolane, N.: Defining a mean on Lie groups. Master Thesis. Imperial College London and INRIA Asclepios Team (Nov. 2013) https://hal.inria.fr/hal-00938320

Research interests

  • Applied Statistics and Machine learning,
  • Riemannian, pseudo-Riemannian and sub-Riemannian geometry,
  • (Organ) shape analysis,
  • (Medical) Image processing,
  • (Medical) Image Analysis.

Reviewer

  • NeuroImage (2014)
  • Journal of Mathematical Imaging and Vision (2015)
  • NIPS conference (2016)

Awards

  • Young Researcher Travel Award, International Society for Bayesian Analysis (2016).
  • Ranked 1st/7, Inria@SiliconValley Postdoctoral Fellowship (2016)
  • First Prize at MICCAI Educational Challenge, MIT, Boston (2014).
  • Applied Sciences Visiting Researcher Fellowship, France-Stanford Center for Interdisciplinary Studies (2014).
  • Ranked 1st/50, PhD Scholarship, INRIA S-Cordi (2013)

Background

  • MS. in Theoretical Physics "Quantum Fields and Fundamental Forces". Imperial College London, UK.
  • "Diplome d'Ingénieur" and MS. in Mathematics and Theoretical Physics. Ecole Polytechnique, France.

Useful links

  • Github: https://github.com/ninamiolane/
  • LinkedIn: www.linkedin.com/in/ninamiolane
  • Dblp: http://dblp.uni-trier.de/pers/hd/m/Miolane:Nina
  • Asclepios Team, INRIA: https://team.inria.fr/asclepios/
  • Department of Statistics, Stanford University: https://statistics.stanford.edu/
  • Susan Holmes Lab: http://statweb.stanford.edu/~susan/LabIndex.html