Raphael Lenain
Verified Expert in Engineering
Deep Learning Developer
Raphael graduated with a master’s degree in computational and mathematical engineering from Stanford with a specialization in Deep Learning research. He has worked as first engineer in two seed stage London startups, defining their research and Machine Learning (ML) engineering culture. His specializations are in speech and Natural Language Processing (NLP). Raphael has published papers at top ML conferences such as ICML and INTERSPEECH and is the main author on a widely adopted Python package.
Portfolio
Experience
Availability
Preferred Environment
Visual Studio Code (VS Code), Vim Text Editor, Python, MacOS, Linux, PyTorch, Amazon Web Services (AWS), Google Cloud Platform (GCP)
The most amazing...
...project I’ve led was the development of a Python package which fully automated data preprocessing, training, and validation of ML models.
Work Experience
Research Software Engineer
Samsung
- Researched and developed applications of Federated Learning (FL) and Knowledge Distillation (KD) to Automatic Speech Recognition (ASR) models.
- Assisted with maintenance of internal preprocessing, training, and testing pipeline of Automatic Speech Recognition (ASR) models.
- Attended brainstorming and research meetings, discussing latest trends and state of the art in Automatic Speech Recognition (ASR) and Federated Learning (FL).
Research Engineer
Novoic Ltd
- Researched Natural Language Processing (NLP) and speech Deep Learning technology applications to medical data and published papers at top conferences (ICML, INTERSPEECH).
- Developed an internal package which fully automated preprocessing, training and validation of Natural Language Processing (NLP) and speech Deep Learning models.
- Developed a widely adopted (over 350 stars) open-source package, surfboard, and published an accompanying paper at an ML conference (INTERSPEECH).
- Participated in the decision and management of company operations, sitting on C-suite meetings helping guide the technical roadmap and stressing technical requirements.
- Built a Google Cloud Platform (GCP) based serverless app which recorded speech on the phone and triggered a Deep Learning pipeline to predict disease status.
Machine Learning Engineer
Papercup Technologies Ltd
- Built internal tooling to automate the research, development and deployment cycles of text-to-speech synthesis systems.
- Led research and co-authored text-to-speech synthesis research papers published at a top ML conference (INTERSPEECH).
- Mentored an intern through a research summer internship. Resulted in their project published at a top ML conference (INTERSPEECH) and being awarded best project amongs a class of over 200 students.
- Assisted C-suite members in organizing company operations and deciding on a technical roadmap.
Experience
Learning De-identified Representations of Prosody from Raw Audio
http://proceedings.mlr.press/v139/weston21a/weston21a.pdfRealtalk: Automated Preprocessing, Training, and Validation of Machine Learning
COSCO: Continuous Style Control of Text-to-Speech Synthesis
Skills
Other
Deep Learning, Machine Learning, Natural Language Processing (NLP), Text to Speech (TTS), GPT, Generative Pre-trained Transformers (GPT), Software Development, Federated Learning, Automatic Speech Recognition (ASR), Speech Recognition, Artificial Intelligence (AI), Voice Recognition, API Integration, Hugging Face, Mathematics, Research
Languages
Python
Libraries/APIs
PyTorch, TensorFlow
Tools
Vim Text Editor, Mathematica
Platforms
Visual Studio Code (VS Code), MacOS, Linux, Amazon Web Services (AWS), Google Cloud Platform (GCP)
Paradigms
Agile, Data Science
Education
Master's Degree in Computational and Mathematical Engineering
Stanford University - Stanford, CA
Bachelor's Degree in Mathematics
Imperial College London - London, UK
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