About

Geovani RIZK is a highly skilled Research Intern in Machine Learning on Semi-Supervised GANs at Lamsade - CNRS, currently working with the Centre National de la Recherche Scientifique (CNRS). With a Master's degree in Machine Learning from Université Paris Dauphine and over 4 years of relevant experience, Geovani is an expert in the field of research science, machine learning, and data science. Geovani's academic background includes a Bachelor's degree in Computer Science & Data Science, along with 2 years of Bachelor's in Mathematics, Computer Science & Economy, both from Université Paris Dauphine. He has also completed a Master's degree in Machine Learning from the same university. Throughout his academic journey, Geovani has developed a deep understanding of the theoretical and practical aspects of machine learning, which he has applied in his professional career. Geovani has worked with several organizations in the past, including Autorité des Marchés Financiers (AMF) – France, where he worked as a Data Scientist. He has also worked with CNRS - Centre National de la Recherche Scientifique as a Research Intern in Machine Learning on Recommender Systems at Lamsade - CNRS. These experiences have allowed him to develop expertise in various fields, including machine learning, data science, and research science. Geovani's technical skills include research science, machine learning, and data science, making him a valuable asset to any team. He has a strong understanding of various machine learning algorithms and techniques, including semi-supervised GANs, and has experience working with large datasets. Geovani's ability to analyze complex data sets and provide actionable insights has helped him to develop a reputation as a skilled data scientist. Overall, Geovani RIZK is a highly skilled and experienced Research Intern in Machine Learning on Semi-Supervised GANs at Lamsade - CNRS. With a deep understanding of machine learning, data science, and research science, he is well-equipped to tackle complex challenges in these fields.