CS + Math @ École Polytechnique
I am Bryan, a Computer Scientist. I am driven by the potential of machine learning to address complex challenges and create impactful solutions. Outside of work, I love traveling to explore new countries and cultures.
cat config/about_me.yml
metadata:
name: Bryan Chen
title: Machine Learning Engineer
location: France 🇫🇷🇪🇺
tagline: Engineering the intelligence in AI & Automating with it.
core_competencies:
-
area: Artificial Intelligence
skills: [Deep Learning, Generative AI, Computer Vision, NLP, LLM, Responsible AI, Agentic AI]
-
area: MLOps & Data Engineering
skills: [CI/CD, Docker, K8s, DVC, MLflow, ETL, Dashboard, GitHub Actions]
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area: Software Engineering
skills: [Python, Git, FastAPI, SQL, JAX, C++, C, Java, TypeScript/JavaScript, conda, uv]
career_highlights:
- role: Machine Learning Engineer
company: Iliad Group (Free & Scaleway)
focus: Building production-level GenAI solutions and data pipelines.
- role: Machine Learning Research Engineer
institutions: [École Polytechnique, ENS Ulm, NUS, CNRS]
focus: Advancing research in VLMs, model compression, and optimization.
- role: Hackathon Winner
achievements:
- "1st/178 @ Inria Challenge (Mean Arterial Pressure Prediction)"
- "2nd/338 @ MIT Hackathon (AI AgentOps Replay)"
personal_interests:
- Traveling & Exploring Cultures
- Languages
- Gyoza Making
- K-Pop Dancing
- Open Source Contribution
philosophy:
- "Strive for Excellence in everything."
- "Low ego, high impact."
- "Find joy in the process and the code."
- "Commit to continuous learning and open collaboration."
wanna know more?
| Role & Company | Description & Key Contributions |
|---|---|
ML Engineer @  (Apr. 2025 - Oct. 2025) |
At Iliad Group (Free / Scaleway) in Paris, I develop an image segmentation model in the GeoAI domain, build ETL data pipelines, and create insightful dashboards for directors and responsibles. |
ML Research Engineer @  (Dec. 2024 - Apr. 2025) |
At CMAP - École Polytechnique, I contributed to research on Improving Vision-Language Models (VLMs) by enhancing sparse attention selection mechanisms to boost few-shot classification performance. (See Report) |
ML Research Engineer @  (Nov. 2024 - Feb. 2025) |
At École Normale Supérieure (ENS) - Ulm, I extended the CoVR research papers by designing a novel loss function and MLP architecture to improve alignment between visual and textual embeddings for composed video retrieval. (See Paper) |
| ML Research Engineer @ (Mar. 2024 - Sep. 2024) |
At the National University of Singapore (NUS), I created an efficient checkpointing fine-tuning scheme for Deep Neural Networks (DNNs) using Delta-LoRA + LC-checkpoint, achieving compression ratios up to 25x on models like ViTs, ResNets, VGGs, AlexNet, and LeNet. (See Code) |
ML Research Engineer @  (Jun. 2023 - Aug. 2023) |
At CNRS in Toulouse, I developed an interactive optimization algorithm for a Constraint Satisfaction Problem (CSP), applying Neural Networks and Decision Trees and engineering techniques to improve IBM's CPLEX solution generation. (See Code) |
| Containerized Youtube Sentiment Analysis | Retrieval-Augmented Generation (RAG) System | HYGENE: Diffusion-based Hypergraph Generation |
|---|---|---|
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| A full MLOps pipeline using Docker, GitHub Actions, DVC, and MLflow. | A RAG system leveraging LangChain, FAISS, and Ollama for inference. | Implemented hypergraph generation with diffusion models (AAAI), a project at Télécom Paris. |
| Groundwater Level Prediction | Neural Graph Generation from Text | Classifier-Free Diffusion Guidance (NeurIPS) |
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| A project with Hi!Paris & Inria to predict groundwater levels, which received an Honorable Mention. | Developed a Neural Graph Generator to create complex graph structures from text at École Polytechnique. | A paper implementation focused on jointly training conditional/unconditional diffusion models. |
| ImageNet-sketch Classification | EuroSAT Image Classification | Predicting Naturalness with Acoustic Indices |
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| Classifying sketch images with EVA-CLIP finetuned with an extra-MLP layer. | Classify geospatial images with ResNet from the EuroSAT dataset. | Used scikit-maad and VGGish to extract acoustic indices and apply ML models to find patterns and predict naturalness. |
| Presentation: Generative Recommender Systems | Presentation: Unifying GANs & Diffusion | |
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| Presented TIGER, a method for generative retrieval of item IDs for recommender systems. | Presented Score GAN & Discriminator Flow, a unified framework for GANs and Diffusion models. |
| Mean Arterial Pressure Prediction | Full-stack Agent-Agnostic Solution |
|---|---|
| Rank: 1st / 178 |
Rank: 2nd / 338  |
| Achieved 1st place at the Inria challenge with a domain adaptation-aware model for Mean Arterial Pressure (MAP) prediction. | Placed 2nd at the MIT Hackathon with a full-stack solution (LangGraph, FastAPI, Next.js) to trace and visualize AI agent interactions. |
| Hugging Face Transformers | Apple/Google (OTT-JAX) | Responsible AI Blog (IP Paris) |
|---|---|---|
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| Updated model cards for the Swin Transformer, Swin V2, Pixtral & ShieldGemma 2 models to improve documentation. | Authored a tutorial on Annealed Sinkhorn for the Optimal Transport Tools (OTT-JAX) library. | Wrote a blog post analyzing the impact of knowledge distillation on model interpretability. |
| Institution | Status & Focus |
|---|---|
 Harvard University |
CS50 Student Completed renowned courses providing a robust foundation in Computer Science and AI (CS50x, CS50P & CS50AI). |
 Stanford University |
Visiting Student Followed world-class courses in Computer Science and Artificial Intelligence. |
I'm always excited to take on new challenges in AI research and application. If you have an interesting project, a research idea, or just want to discuss the latest in tech, let’s connect! I'm open to collaborations and geeking out about all things AI :)
