Timothée WRIGHT

Education

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  TELECOM Nancy

  2019 - 2022

  Engineer Diploma Artificial Intelligence and Big Data

  Data visualization, and data analysis (principal component analysis, linear discriminant analysis...) with R and PythonData mining, indexing with Lucene, Spark, Hadoop, MongoDB, ElasticsearchDatabase modelling, MySQLParallel algorithms with MPI and OpenMPSearch algorithms (MCTS, MinMax)Project management, managementProbability model, statistics, optimization.Systems, networks and image processing.

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  IUT Robert SCHUMAN

  2017 - 2019

  Computer Science

  Web, PHP, Laravel, JavascriptOOP, Java, C#, Design PatternsSystem, Memory Management, segmentation, threadProject ManagementDatabase Management, SQL

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  Université du Québec à Chicoutimi

  2019 - 2019

  Baccalauréat Computer Science

  University exchangeAlgorithm courses: Complexity, dynamic algorithms (Floyd, chain matrix multiplication...), divide and conquer algorithms, Las Vegas and Monte Carlo algorithms, probabilistic algorithms (Sherwood, numerical algo), etc.Advanced web technology: TCP, UDP, Sockets, Addressing and routing, Laravel, SymfonyData structure and C++Video game design course

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  Université de Sherbrooke

  2021 - 2022

  Master in artificial intelligence Computer Science

  Neural networks: SVM, bagging, boosting, classical neural networks, convolutional networks, deep learning models (VGG, ResNet, UNet), recurrent neural networks, generative modelsDescriptive data analysis: clustering, association rule extraction, statistical description, similarity measures, anomaly detectionMath for AI: Bayesian and Markovian networks, maximum a posteriori inference and simulated annealing, Monte Carlo methods, time series and latent variable… Show more Neural networks: SVM, bagging, boosting, classical neural networks, convolutional networks, deep learning models (VGG, ResNet, UNet), recurrent neural networks, generative modelsDescriptive data analysis: clustering, association rule extraction, statistical description, similarity measures, anomaly detectionMath for AI: Bayesian and Markovian networks, maximum a posteriori inference and simulated annealing, Monte Carlo methods, time series and latent variable models.Database modelling: Distributed database design. Data warehousing and data mining. Translation of an object-oriented model into a relational model. Show less