Yannick Le Cacheux
Verified Expert in Engineering
Deep Learning Scientist and Developer
Yannick is a developer who holds two master's degrees and a PhD in machine learning. He has successfully carried out many ambitious data science projects for Fortune Global 500 companies as well as research institutions. Yannick has also authored several articles in international scientific journals, teaches machine learning in graduate classes, and is a co-author of a deep learning book to be published by the end of the year.
Portfolio
Experience
Availability
Preferred Environment
Linux, PyCharm, Jupyter Notebook
The most amazing...
...image classifier I have developed uses text descriptions of classes instead of images during the training of the model.
Work Experience
Machine Learning Lecturer
CentraleSupélec
- Created and taught the machine learning class for the master's degree in artificial intelligence.
- Co-created and taught the deep learning class for the master's degree in data sciences and business analytics.
- Supervised student projects and graded exams in machine learning in several other graduate classes.
Lead Data Scientist
Saint-Gobain Group
- Managed a team of 10 data scientists, providing technical guidance and expertise.
- Overviewed and contributed to models for the optimization of the sales force's customer portfolio and store distribution.
- Overviewed and contributed to models for sales forecast, prediction of supplier lead time, and optimization of store inventory.
- Overviewed and contributed to models for client segmentation and profiling, business lifecycle detection, and estimation of clients' sales potential.
Senior Data Scientist
L'Oreal
- Developed models to detect and evaluate clinical signs of aging (wrinkles, dark circles, etc.) from multispectral photos.
- Created a fast and lightweight method to estimate the 3D shape of a face from single- and multi-view pictures based on statistical shape modeling.
- Integrated the developed models on iOS with Core ML to deploy them in Lancôme points of sale.
Deep Learning Scientist
Commissariat à l'Energie Atomique (CEA)
- Developed cutting-edge models to outperform all existing approaches in "multimodal" tasks involving both images and text in natural language.
- Published several scientific articles in international peer-reviewed journals and conferences.
- Contributed to the writing of a deep learning book as a co-author.
Data Scientist
AXA Group
- Led the data science team in the deduplication and client knowledge project to identify links and patterns among millions of clients in multiple databases.
- Designed predictive algorithms deployed in lead management information systems to optimize prospect conversion.
- Developed a model analyzing and redirecting requests from incoming emails from clients.
- Designed data science tests and conducted technical interviews.
Data Science Consultant
CGI Business Consulting
- Developed big data and analytics products for large-scale unsupervised data visualization and clustering.
- Supervised the deployment of large-scale flight-tracking software related to the passenger name record European directive.
- Benchmarked many existing NoSQL databases and cloud platforms.
Analyst
Goldman Sachs
- Developed a tool to monitor activities on the futures trading platform as close to real-time as possible.
- Analyzed past anomalies to predict most likely future malfunctions on trading platforms.
- Tracked and fixed bugs on trading platforms using Jira.
Experience
Generative AI for Creative Professionals
Customers' Portfolio Optimization
3D Face Estimation
Zero-shot Image Classifier
https://tinyurl.com/ICCV2019-lecacheuxZero-shot classifiers can be useful when one does not have training data for all classes.
I was the lead scientist on this research project aiming to improve existing zero-shot classifiers. I, together with two other deep learning researchers, showed that most existing approaches lacked desirable theoretical properties. More specifically, the usual loss functions do not enable the model to capture certain intra-class and inter-class structures. We provided novel theoretical results and developed a new model capable of outperforming all previous existing models.
Our proposed approach was published and presented at the 2019 International Conference on Computer Vision in Seoul, Korea.
Deduplication and Client Knowledge
At first glance, this is neither very difficult nor exciting: if two people have the same name and address, they are the same person. Problem solved. Except homonyms exist, addresses change, typos are made, and databases can be messy. And with millions of clients, a brute-force pairwise comparison is not an option.
Hence the need for clever predictive models to efficiently and accurately identify duplicates. I was in charge of the data science team designing the models and assessing associated risks.
The regularly updated information provided by these models is now a central component in many internal information systems.
The project was presented at the 2016 Viva Technology show in Paris.
Skills
Languages
Python, Java, SQL, Scala, Groovy, R
Frameworks
Apache Spark, Hadoop, Spark
Libraries/APIs
PyTorch, Spark ML, TensorFlow, Scikit-learn, Pandas, OpenCV
Paradigms
Data Science
Other
Deep Learning, Computer Vision, Natural Language Processing (NLP), Machine Learning, Artificial Intelligence (AI), Deep Neural Networks, GPT, Generative Pre-trained Transformers (GPT), Generative Adversarial Networks (GANs), Operations Research, Applied Mathematics, Web Development
Tools
PyCharm, Apache Impala, Tableau
Platforms
Jupyter Notebook, Linux, Amazon Web Services (AWS), Azure, Google Cloud Platform (GCP)
Storage
Google Cloud, Apache Hive, MongoDB, Cassandra, Elasticsearch
Education
PhD in Deep Learning
Hautes Etudes Sorbonne Arts et Métiers - Paris, France
Master's Degree in Machine Learning
Georgia Institute of Technology - Atlanta, GA, United States
Master's Degree in Computer Science
Institut Mines Telecom Atlantique - Nantes, France
Certifications
Certificate in Data Science and Engineering with Apache Spark
edX
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