Scholar
Nathanaël Beau, PhD
Google Scholar ID: K3TIGhsAAAAJ
Ansys, Inc.
Citations & Impact
Citations
41
H-index
3
i10-index
2
Publications
5
Co-authors
3
list available
Publications
Browse publications on Google Scholar (top-right) ↗
Resume (English only)
Academic Achievements
- Authored multiple publications in top-tier NLP conferences; developed a Retrieval-Augmented Generation (RAG) model that outperforms leading solutions like Mistral and Copilot on code-assist datasets; contributed to the creation of a new dataset for fine-tuning, evaluating, and enhancing NL-to-Python code generation with comprehensive unit test coverage; involved in several projects including Code Insight Dataset, BertranX, and grammarBERT.
Research Experience
- PhD Candidate in NLP at Onepoint, conducted doctoral research on 'Generating Python code from a Natural Language description' as part of the CIFRE program. Developed and trained the RETROcode model, a RAG model for code assistance, achieving competitive results with top LLMs such as Codex and Mistral. Created the BertranX model for syntactic validity in code generation and led a team to develop the CodeInsight dataset.
Education
- Recent Ph.D. graduate with a solid foundation in both academic and industry environments, bridging technical complexity with strategic vision. Ph.D. research focused on advancing code generation techniques from natural language descriptions, designing innovative neural architectures to ensure syntactic code validity while leveraging insights from linguistics and computing.
Background
- A Ph.D. researcher focusing on code generation and neural network architectures, blending academic research with practical engineering approaches in AI and NLP.
Miscellany
- Eager to continue exploring areas such as LLM memorization, alternative training strategies for long-term objective alignment, and scalable model solutions.
