Datacast
Episode 24: From Actuarial Science to Machine Learning with Mael Fabien
Episode Summary
Maël is a Data Scientist at Anasen, a Y-Combinator Startup in Paris where he works on the automation of data exploration and feature extraction for textual data. He majored in Actuarial Science at the University of Lausanne and did a second Master in Data Science at Telecom ParisTech Engineering School. He is also a content writer for several blogs and a freelance Machine Learning instructor for 2 boot camps in Paris and Dakar. He is especially interested in applications of Machine Learning in the medical field.
Episode Notes
Show Notes:
- (2:08) Mael recalled his experience getting a Bachelor of Science Degree in Economics from HEC Lausanne in Switzerland.
- (4:47) Mael discussed his experience co-founding Wanago, which is the world’s first van acquisition and conversion crowdfunding platform.
- (9:48) Mael talked about his decision to pursue a Master’s degree in Actuarial Science, also at HEC Lausanne.
- (11:51) Mael talked about his teaching assistantships experience for courses in Corporate and Public Finance.
- (13:30) Mael talked about his 6-month internship at Vaudoise Assurances, in which he focused on an individual non-life product pricing.
- (16:26) Mael gave his insights on the state of adopting new tools in the actuarial science space.
- (18:12) Mael briefly went over his decision to do a Post Master’s program in Big Data at Telecom Paris, which focuses on statistics, machine learning, deep learning, reinforcement learning, and programming.
- (20:51) Mael explained the end-to-end process of a deep learning research project for the French employment center on multi-modal emotion recognition, where his team delivered state-of-the-art models in text, sound, and video processing for sentiment analysis (check out the GitHub repo).
- (26:12) Mael talked about his 6-month part-time internship doing Natural Language Processing for Veamly, a productivity app for engineers.
- (28:58) Mael talked about his involvement with VIVADATA, a specialized AI programming school in Paris, as a machine learning instructor.
- (34:18) Mael discussed his current responsibilities at Anasen, a Paris-based startup backed by Y Combinator back in 2017.
- (38:12) Mael talked about his interest in machine learning for healthcare, and his goal to pursue a Ph.D. degree.
- (40:00) Mael provided a neat summary on current state of data engineering technologies, referring to his list of in-depth Data Engineering Articles.
- (42:36) Mael discussed his NoSQL Big Data Project, in which he built a Cassandra architecture for the GDELT database.
- (47:38) Mael talked about his generic process of writing technical content (check out his Machine Learning Tutorials GitHub Repo).
- (52:50) Mael discussed 2 machine learning projects that I personally found to be very interesting: (1) a Language Recognition App built using Markov Chains and likelihood decoding algorithms, and (2) the Data Visualization of French traffic accidents database built with D3, Python, Flask, and Altair.
- (56:13) Mael discussed his resources to learn deep learning (check out his Deep Learning articles on the theory of deep learning, different architectures of deep neural networks, and the applications in Natural Language Processing / Computer Vision).
- (57:33) Mael mentioned 2 impressive computer vision projects that he did: (1) a series of face classification algorithms using deep learning architectures, and (2) face detection algorithms using OpenCV.
- (59:47) Mael moved on to talk about his NLP project fsText, a few-shot learning text classification library on GitHub, using pre-trained embeddings and Siamese networks.
- (01:03:09) Mael went over applications of Reinforcement Learning that he is excited about (check out his recent Reinforcement Learning Articles).
- (01:05:14) Mael shared his advice for people who want to get into freelance technical writing.
- (01:06:47) Mael shared his thoughts on the tech and data community in Paris.
- (01:07:49) Closing segment.
His Contact Info:
His Recommended Resources: