I was able to benefit from a first professional experience with Inria-Saclay in collaboration with the French Navy on Lidar signal and image processing.
Graduated from a Research Master in Mathematics - specialized in stochastic analysis and applications to finance and an M1 in Quantitative Methods - specialized in mathematical economics and econometrics.
Directly accepted in the second year in Artificial Intelligence and Data System Master at Paris Dauphine University and I finished my fourth optional internship oriented to 3D Medical Image Processing at QuantaCell Lab.
I have also followed intensive training in collaboration with the University of Linnaeus, the University of Paris 13 and the University of Strasbourg.
Lidar Signal/Image Processing: BatyNet, DANAE++, Kalman Filter:
Instance Segment on Medical Images: GANS, MasK-RCNN, U-Net.. :
Optimization and AWS Deployment of Video Denoising and Resolution Augmentation Models on Python/C++: Autoencoder, ViDeNN:
Development and Deployment of a Micro-service Oriented to Minimization of Phone Operator Expenses Through ML on Python, AWS: RNN (LSTM), M.O.Optimization, M.LReg, Time-Series. :
Development of a Predictive Bank/Sovereign Default Model on Matlab/R/C++:AR, ARIMA, SARIMAX, DSGE, VFI, EDM, 2EDM.
The objective of this Master of Mathematics and Computer Science also called Big-Data (IASD) is to offer a solid knowledge in Applied Mathematics as well as in Artificial Intelligence in order to cover all the problems of data processing and analysis. massive that can be found in business.
This training takes place over two years during which two major themes are taught:
The Quantitative Economics mention aims to train economists at the best level, open and responsive to the plurality of
questions and challenges facing the contemporary economy,
while having mastery of quantitative tools to address them in terms of data given in particular the developments in the world of decision-makers,
both public and private, brought about by the Big Data .
This Master also allows me to:
The goal of this Master is to present the usual continuous-time stochastic processes and to allow students to deepen in directions such as: finance, reliability, Monte-Carlo methods, links between probabilities and partial differential equations :
The Mathematics license allows me to acquire in-depth disciplinary skills in Mathematics (analysis, algebra, probability, etc.), knowing how to organize mathematical reasoning and write rigorously. The development of relational and organizational skills are also at the heart of the training.
Platypus is a framework for evolutionary computing in Python with a focus on multiobjective evolutionary algorithms (MOEAs). It differs from existing optimization libraries, including PyGMO, Inspyred, DEAP, and Scipy, by providing optimization algorithms and analysis tools for multiobjective optimization. It currently supports NSGA-II, NSGA-III, MOEA/D, IBEA, Epsilon-MOEA, SPEA2, GDE3, OMOPSO, SMPSO, and Epsilon-NSGA-II.
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Master's Practical work project in Spark [without using ML.Lib]
Implementation: On Dauphine Private Cluster.
Project: The project related to the current situation: .Link. It's a competition to detect the presence or not of a mask on an image.
View ProjectFast SGD with Adagrad and Momentum, comparisons with basic implementations (batch, SGD, minibatch) (RDD, DataFrames, DataSet).
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