Hi there and welcome! I am currently a Postdoctoral Fellow at the Inria Grenoble-Rhône-Alpes where I am very fortunate to be mentored by Senior Researcher Florence Forbes, Senior Lecturer Hien Duy Nguyen, and Associate Researcher Julyan Arbel. I finished my Ph.D. Degree in Statistics and Data Science at Normandie Univ, UNICAEN, CNRS, LMNO, Caen, France in December 2021 where I am very fortunate to be advised by Professor Faicel Chamroukhi and Senior Lecturer Hien Duy Nguyen. During my Ph.D. research, I am also very fortunate to collaborate with Professor Geoff McLachlan focusing in mixture models. I received a Visiting PhD Fellowship at the Inria Grenoble-Rhône-Alpes Research Centre, working with Senior Researcher Florence Forbes and Associate Researcher Julyan Arbel in the Statify team under a project LANDER (from September 2020 to January 2021).
A central theme of my research focuses on Data Science, at the interface of:
Ph.D. in Statistics and Data Science, 2018-2021
Université de Caen Normandie, France
M.S. in Applied Mathematics, 2017-2018
Université d'Orléans, France
B.S. Honors Program in Mathematics and Computer Science, 2013-2017
Vietnam National University-Ho Chi Minh Univeristy of Science, Vietnam
Hours of lectures: 39.
Accomplishment: 19 French courses from Newcomer to Advanced Course based on level B2-C1 of the Common European Framework of Reference for Languages (CEFR).
Description: Learn French online help you to strengthen your French skills, whatever your motivation to learn. Select a guided learning path based on your skill level, or choose a work-, travel- or culture-focused course.
Niveau acquis: A1+ selon le Cadre européen commun de référence pour les langues (CECR).
Description: Le Soutien linguistique en français est un programme semestriel de cours du soir d’apprentissage de la langue française et de soutien linguistique en français sur objectifs universitaires. Il est principalement proposé aux étudiants et enseignants-chercheurs inscrits à l’université de Caen Normandie en complément de leurs formations disciplinaires et ou de leurs recherches en laboratoire. Le programme est également ouvert aux particuliers dont les activités professionnelles et ou la situation familiale ne seraient pas compatibles avec les emplois du temps de nos formations intensives de type DUEF. Les stagiaires doivent être titulaires d’un diplôme ou titre donnant accès à l’enseignement supérieur en France.
Thesis title: Model selection and approximation in high-dimensional mixtures of experts models$:$ from theory to practice.
Instructor: Andrew Ng. (Stanford University)
There are 5 Courses in this Specialization:
Neural Networks and Deep Learning (Grade: 100%).
Improving Deep Neural Networks: Hyperparameter tuning (Grade: 100%).
Regularization and Optimization, Structuring Machine Learning Projects (Grade: 98.3%).
Convolutional Neural Networks (Grade: 98.9%).
Sequence Models (Grade: 100%).
Description: The Deep Learning Specialization is a foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology. In this Specialization, you will build and train neural network architectures such as Convolutional Neural Networks, Recurrent Neural Networks, LSTMs, Transformers, and learn how to make them better with strategies such as Dropout, BatchNorm, Xavier/He initialization, and more. Get ready to master theoretical concepts and their industry applications using Python and TensorFlow and tackle real-world cases such as speech recognition, music synthesis, chatbots, machine translation, natural language processing, and more. AI is transforming many industries. The Deep Learning Specialization provides a pathway for you to take the definitive step in the world of AI by helping you gain the knowledge and skills to level up your career. Along the way, you will also get career advice from deep learning experts from industry and academia.
Overall Band Score: $7.0/9.0$.
CEFR Level: C1.
Description: IELTS – the International English Language Testing System – is the world’s most popular English language test. It is developed by some of the world’s leading experts in language assessment and evaluates all of your English skills — reading, writing, listening and speaking. The test reflects how you’ll use English to study, work and live in an English speaking environment. You can take the test at any of our official test centres across the world.
Total Score: $795/990$ (Listening: $410/495$ + Reading: $385/495$).
Description: The TOEIC Listening and Reading Test measures listening and reading skills for beginner to advanced levels of English.
Instructor: Andrew Ng. (Stanford University).
Description: Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. More importantly, you’ll learn about not only the theoretical underpinnings of learning, but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems. Finally, you’ll learn about some of Silicon Valley’s best practices in innovation as it pertains to machine learning and AI. This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. The course will also draw from numerous case studies and applications, so that you’ll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas.
(i) Supervised learning (parametric or non-parametric algorithms, support vector machines, kernels, neural networks).
(ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning).
(iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI).