[Event at CIG] Engineer or postdoctoral research position in France (Nantes) - Analysis of time series in robotic surgery

[Christine Sinoquet]

  *Engineer or post-doctoral research position **
  **Analysis of time series in robotic surgery*

**

*Key****words**: machine learning on time series, clustering, 
classification, average time series*


    *Context*

Surgical robotics is now widely used with, for instance, more than 5000 
Da Vinci systems and one million procedures performed worldwide. Surgery 
is a complex activity, in a very small anatomical volume, and with a lot 
of variability between patients and between surgeons. The global 
objective of the two-year SPARS (Sequential Pattern Analysis in Robotic 
Surgery: Understanding Surgery) project led by the MediCIS team (LTSI 
(1), Inserm, Rennes 1 University) is to develop data analysis approaches 
being able to provide a better understanding of the surgical practice, 
from complex surgical data. The approaches will be developed thanks to 
the complementary skills available in the project’s consortium, 
including time series analysis. In this consortium, the IRISA laboratory 
(Rennes and Vannes) is calling for applications for a post-doctoral 
research position (duration two years) on time series analysis.

(1) Laboratoire Traitement du Signal et de l'Image


    *Missions*

In the SPARS project context, a trajectory compiles information on the 
3D location of the tip of a surgical instrument at the hands of the 
surgeon, at a constant frequency. The candidate will be mostly involved 
in one of the three workpackages of the SPARS project. *A first task* 
will focus on clustering and classification for such trajectories. 
Various *practical objectives* are pursued, including the generation of 
a model corresponding to a cluster or a class, the characterization of 
operating modes specific to a type of patient or a type of surgeon, the 
provision of advice to practitioners in the case of robotic surgeries 
that are not or not very well documented, the identification of the 
level of expertise of a practitioner, the prediction of the surgical 
procedure to be chosen according to the type of patient. These 
investigations will use dissimilarity measures based on temporal 
alignment, as DTW [SC71] or elastic kernels as proposed in [CVB07], 
[CB17] and [M19a]. This task will also address co-clustering for 
trajectories. The investigations will focus on how to combine time 
series with other types of data for a co-clustering purpose, using 
either deep learning [XCZ19] if enough data is available, symbolic 
representation [BBC15] or latent block [BLN20] models that all need to 
be adapted to the specificity of kinematics data.

Once a cluster or a class is obtained, another task will be to compute 
an average trajectory from a set of trajectories. The *practical 
objectives* will be the following: highlight deviations from the average 
trajectory that are potentially interpretable (as characteristics of the 
practitioner, or of the patient, for example) ; identify the best 
operating mode to young practitioners or trainees if it is possible to 
correlate the operating mode with clinical results. Intuitively, on the 
graphical representation of a time series, variability related to 
temporality (phase) concerns the abscissa axis, and variability related 
to shape concerns the ordinate axis. To compute a consensus trajectory, 
*the second task* of the package will examine how to extract the 
atemporal form and the variable component related to temporality, 
assuming that this atemporal form may be interpreted as an approximation 
of the consensus. The problem of shape and phase separation has been 
studied in [PZ16], [SSV10] and [M19a]. The second task will examine how 
to improve the preliminary work in [M19b], notably by proposing other 
kernels.


    *Requirements for this position*

Doctorate in computer science, applied mathematics and computer science, 
or mathematics, with a specialization in machine learning and the 
following requirements:

- theoretical skills and experience in probability / statistics, applied 
mathematics, machine learning,

- strong knowledge and solid experience in temporal data analysis,

- publications in major conferences or journals in the field,

- mastery of data manipulation, relying on machine learning libraries,

- programming experience, good programming skills (notably in Python) 
and technical ability to manage a code development project,

- ability to work in a team, and report on the progress of work.


Some knowledge in deep learning will be a plus.

The personal qualities expected are mostly autonomy and interest in 
interdisciplinarity (health), as well as writing skills (both in French 
and English). Fluency in French will be a plus.


    *Work environment*

*Location**:***Institut de Recherche en informatique et Systèmes 
Aléatoires (IRISA), Université de Rennes 1 - Campus Beaulieu, 263 Av. 
Général Leclerc, 35000 Rennes

*Duration**:* 24 months – Applications will be accepted until the 
position is filled (*for recruitment by 1 December 2022 at the latest*)

*Host team**:* LINKMEDIA

The successful candidate will work with four academic researchers from 
IRISA / Rennes / LINKMEDIA team (Simon Malinowski, Associate Professor 
in Computer Science), IRISA / Vannes / EXPRESSION team (Pierre-François 
Marteau, Full Professor in Computer Science), LS2N (2) / Nantes / DUKe 
team (Christine Sinoquet, Associate Professor with French Accreditation 
to supervise Research (HdR)) and INSERM / Rennes / LTSI MediCIS team 
(Pierre Jannin, Directeur de recherche INSERM, HdR). The successful 
candidate will collaborate with the partners in the project, among which 
the other post-doctoral fellow involved in the project and the project 
partners experts in surgery and in surgical data analysis.

(2) Laboratoire des Sciences du Numérique de Nantes : UMR CNRS 6004

*
*

*Income:***2160,26 euros before taxes monthly


    *How to apply?
    *

Documents to be provided :

- detailed Curriculum Vitae including a complete list of publications,

- letter of motivation indicating the candidate’s research interests and 
achievements to date,

- a selection of publications,

- the PhD thesis manuscript,

- Master 2 marks (with rank and number of students in the year)

- letters of recommendation for the current year,

- contact details of two referees (at least) with whom the candidate has 
worked (first name, surname, status, institution (give details of 
acronyms if applicable), city, e-mail address, telephone number)


Questions or application files (*zip archive only*) should be sent to 
the four contact persons below:


simon.malinowski at irisa.fr

christine.sinoquet at univ-nantes.fr

pierre-francois.marteau at univ-ubs.fr

pierre.jannin at univ-rennes1.fr (SPARS project leader)


Simon Malinowksihttp://people.irisa.fr/Simon.Malinowski/

Christine Sinoquet https://christinesinoquet.wixsite.com/christinesinoquet

Pierre-François Marteau https://people.irisa.fr/Pierre-Francois.Marteau/

Pierre Jannin https://medicis.univ-rennes1.fr/members/pierre.jannin/index

*
*


    Bibliographical references

[BBC15] A. Bondu, M. Boullé, A. Cornuéjols (2015) Symbolic 
representation of time series: a hierarchical coclustering 
formalization. In : International Workshop on Advanced Analysis and 
Learning on Temporal Data, pp. 3-16.

[BLN20] R. Boutalbi, L. Labiod, M. Nadif (2020) Tensor latent block 
model for co-clustering. International Journal of Data Science and 
Analytics, 1-15.

[CVB07] M. Cuturi, J.-P. Vert, O. Birkenes, T. Matsui (2007) A kernel 
for time series based on global alignments. In: IEEE International 
Conference on Acoustics, Seepch and Signal Processing, ICAPPS, vol. 2, 
pp. II–413–II–416.

[CB17] M. Cuturi, M. Blondel (2017) Soft-DTW: a differentiable loss 
function for time-series. In: International Conference on Machine 
Learning (ICML), 894-903.

[M19a] P.-F. Marteau (2019) Times series averaging and denoising from a 
probabilistic perspective on time-elastic kernels. International Journal 
of Applied Mathematics and Computer Science, 29 (2), 375-392.

[M19b] P.-F. Marteau (2019) On the separation of shape and temporal 
patterns in time series. Application to signature authentication. 
https://hal.archives-ouvertes.fr/hal-02373531v2.

[PZ16] V. M. Panaretos, Y. Zemel (2016) Amplitude and phase variation of 
point processes. The Annals of Statistics, 44(2), 771-812.

[SC71] H. Sakoe, S. Chiba (1971) A dynamic programming approach to 
continuous speech recognition. In: ICA, Paper 20 CI3.

[SSV10] L. M. Sangalli, P. Secchi, S. Vantini, and V. Vitelli (2010) 
k-mean alignment for curve clustering. Computational Statistics and Data 
Analysis, 54(5), 1219-1233.

[XCZ19] D. Xu, W. Cheng, B. Zong et al. (2019) Deep co-clustering. In: 
SIAM International Conference on Data Mining (SDM), 414-422.


Christine Sinoquet
http://christinesinoquet.wixsite.com/christinesinoquet
Associate Professor with French Habilitation to Supervise Research 
(2014), Qualified for Full Professor Activities (2015, 2020)
Head of the  Master Mention of Bioinformatics of the University of Nantes
Head of DUKe Research Group (Data / User / Knowledge) - LS2N 
(Laboratoire des Sciences du Numérique de Nantes - Laboratory of Digital 
Science of Nantes)  / UMR CNRS 6004, https://www.ls2n.fr/