Peter Naylor

Nationality: English email: [email protected] City of residence: Villejuif Website: https://peterjacknaylor.github.io/ Full driving licence Work Experience

from August Reviewer for Transactions on Medical Imaging and International Symposium on Biomedical 2017 - Now : Imaging, IEEE.

November 2015 - PhD in Bio-informatic under the supervision of T. Walter at the Centre for Computational December 2019 : Biology, MINES Paristech, Institut Curie and INSERM. Cellular Phenotyping for cancer tissues. In particular: computer vision and machine learning applying in the field of histopathology in order to better understand and quantify breast cancer.

June - October Internship at the Center of Computationnal biology under the supervision of T. Walter, MINES 2015 : Paristech, Institut Curie and INSERM. Non-mitotic state classifier for live-imaging. In particular, applied to very large datasets such as the MitoCheck project. Application of mathematical morphology and machine learning to biological data.

October 2015 : Tutor at ENSAE ParisTech in C++. Tutoring C++ to 1st year of master ENSAE students.

December 2014 : Assigned a mission by ENSAE Junior Etude for Vinci-Autoroute. Create a specialized software in order to help extract from large data sets valuable information and smaller databases so that they can be analyzed easier. These datasets had to be preprocessed and our part of the french Census of Population since 1964.

2014-2015 : Tutor at the University Paris-Sorbonne, Paris 4, in statistics. Tutoring statistics to 2nd years students in undergraduate sociology studies.

Summer 2014 : Internship in computational statistics in a CREST laboratory. Emphasis on Monte Carlo methods and on the ”Nested Sampling” algorithm and its application to ”fixed likelihood” models. Supervised by N. CHOPIN and C. ROBERT.

2013-2014 : Applied statistics memoir. Memoir on the theme ”Predicting football results in the French league”, based on statistics. Team work in Descriptive Statistics and data analysis. Use of a logistic model and analysis of model fitting for predicting possible outcomes.

Summer 2013 : Internship as an English teacher in China. One month internship in a primary school as an English teacher in Qingdao, China.

2009 to present : Mathematics , computing and Physics tutor. Tutored to under graduate level in general mathematics, physics and computing. Particularly students in intensive undergraduate programs. Education

Statistical Engineering Diploma at ENSAE. Majoring in the datascience module: Statistics and 2012-2015 : statistical learning, Paris, France. Ecole Nationale de la Statistique et de l’Administration Economique. Final Year Specialization: Supervised/Unsupervised/On-line machine learning; bayesian statistics; biostatistics (via machine learning techniques, statistics for epidemiology and molecular biology); bootstrap; times series; econometrics; computational statistics and tools for handling large databases, such as distributed computing with Hadoop Pig/Apache Spark-Scala.

Masters (Research), TSI (Traitement Statistique de l’Information), with University Paris-Dauphine 2014-2015 : and ENSAE ParisTech, Paris, France. Entitled Statistical processing of information. Main courses: Non parametric bayesian; semi and non-parametric econometrics; additional courses of statistics; bayesian studies and machine learning/data mining.

∗ 2010-2012 : MPSI-MP at Lycée Champollion, Grenoble, France. Intense undergraduate studies in mathematics, physics, engineering science and computer science as preparation to a national competitive exam for entrance into the Grandes Ecoles (French higher education establishments).

Baccalauréat, (A levels/18+ exam), Scientific Series with European option, with Merit. Lycée 2010 : Charles Baudelaire, Annecy, France. Specializing in Mathematics with European option. Publications and talks

Introduction to machine learning and image analysis at Lycée Les Feuillades, Lunel, France during January 2020 : the science week. Scientific talk to a french high school audiance in english.

Predicting Residual Cancer Burden in a triple negative breast cancer cohort. In International April 2019 : Symposium on Biomedical Imaging (ISBI’19), Venice, Italy, by Peter Naylor, Joseph Boyd Marick Laé, Fabien Reyal and Thomas Walter. Conference publication and poster in Histopathology Machine Learning.

Segmentation of Nuclei in Histopathology Images by deep regression of the distance map. In August 2018 : IEEE transactions on medical imaging, by Peter Naylor, Marick Laé, Fabien Reyal and Thomas Walter. Journal publication.

August 2018 : Teaching assistant at the DS3 (Data Science Summer School) 2018. For the In-depth Tutorial with Practical Session Speakers.

April 2018 : Finished in the top 6% of the 2018 Data Science Bowl out of 3 634 teams. Computer science online challenge.

January 2018 : Invited speaker at the Young Statisticians and Probabilists (YSP) day in Paris. Oral session.

August 2017 : Best poster award among 110 posters at DS3 2017.

June 2017 : Invited speaker at the 4th Mini-Symposium on Bioimage Informatics, Rennes. Oral session.

Nuclei Segmentation in Histopathology Images using Deep Neural Networks. In International April 2017 : Symposium on Biomedical Imaging (ISBI’17), Melbourne, Australia, by Peter Naylor, Marick Laé, Fabien Reyal and Thomas Walter. Conference publication and oral session in Histopathology Machine Learning.

Internal seminar on Deep Learning at the Center of Mathematical Morphology lab of Mines January 2017 : Paristech, Fontainebleau, France. Oral session.

August 2016 : Machine Learning Summer School, Arequipa. Poster Session. Languages

English: Mother tongue French: Language of schooling German: Conversational and written skills Spanish: Conversational Mandarin: Conversational and written skills Computer Skills

Advanced Knowledge: Linux, Bash, R, C++, Matlab and Python (Pandas, scikit-image, scikit-learn, tensorflow, keras, caffe). Intermediate Knowledge: Hadoop Pig, Apache Spark, Scala, SQL, vba, Mapple, Caml, Stata and SAS Analytics. Text Editors: LATEX and Pack Office. Summer schools

August 2017 : DS3: Data Science Summer School in Polytechnique, Paris, France. Deep Learning, Graphical Models and Bandits were the main topics.

August 2016 : Machine Learning Summer School, Arequipa, Peru. Wide range of topics in machine learning, kernel methods, deep learning, bayesian statistics, non parametrics bayesian statistics, reninforcement learning and optimization.

September 2015 : MLPM: Machine Learning for Personalized Medicine Summer School, Manchester, England Wide range of topics of applied mathematical concepts such as machine learning and statistical testing applied to themes such as single cell data and personnalized treatments. Interests and Extra curricular Activities

2012-2013 : Member of ENSAE Junior Etudes. Student led Council. Part of the administration and in charge of maintaining the computer system.

2012-2014 : Head of the Baby-foot Club and member of the bar team. In charge of organizing student events within the school and overall coordinator.

2014 : Hash Code tournament by Google France. Programming tournament organized by Google on the topic of the shortest path problem in road networks.

May - June 2015 : Member of the organizing staff of the Data Science Game. 1st edition of the games, general organizing and in charge of the website design. May - June 2016 : Member of the organizing staff of the Data Science Game. 2nd edition of the games.

Volley-ball, Badminton, Squash, music (cello) and travelling.