CS + Math @ École Polytechnique
Teaching artifical intelligent models to be intelligent :)
I am a passionate about Artificial Intelligence and Machine Learning research. My research interests span computer vision, representation learning, generative models, multimodal learning, and responsible AI. I am driven by the potential of artificial intelligence (AI) to address complex challenges and create impactful solutions. :)
Outside of work, I love traveling to explore new countries and cultures. I'm also deeply committed to education, and I have volunteered in various countries to support those without access to learning opportunities.
about_me.yml
metadata:
name: Bryan C.
title: Research Engineer
origin: China 🇨🇳
nationality: France 🇫🇷
tagline: Engineering the intelligence in AI & Automating with it
travel_into: [Singapore 🇸🇬, Thailand 🇹🇭, Malaysia 🇲🇾, Japan 🇯🇵, China 🇨🇳, Taiwan 🇹🇼, Italy 🇮🇹, Germany 🇩🇪,
France 🇫🇷, Spain 🇪🇸, Sweden 🇸🇪]
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, Azure/GCP/AWS]
- area: Software Engineering
skills: [Python, Git, FastAPI, SQL, JAX, C++, C, Java, TypeScript/JavaScript, conda, uv]
career_highlights:
- role: Data Scientist
company: Iliad Group (Free & Scaleway)
focus: Building production-level GenAI solutions and data pipelines.
- role: Research Engineer
institutions: [École Polytechnique, ENS Ulm, ENSAE/Apple, Télécom Paris, 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
- Gyoza Making
- K-Pop Dancing
- Open Source Contribution
philosophy:
- "Low ego, high impact."
- "Follow what you like, success will follow."
- "Continuous learning & open collaboration."
- "Open source is magical."more?
| Role | Description |
|---|---|
Data Scientist @  (February 24, 2025) |
Generated coverage maps using an image segmentation model in the GeoAI domain, automated anomaly detection (Prophet & SARIMAX), built ETL data pipelines, and created insightful dashboards for directors. |
Research Engineer @  (December 12, 2024) |
Enhanced sparse attention selection mechanisms to boost few-shot classification performance on Vision Language Models. (See Report) |
Research Engineer @  (November 5, 2024) |
Improved alignment between visual and textual embeddings for composed video retrieval by designing a novel loss function and an MLP architecture on the CoVR research paper. (See Paper) |
Research Engineer @  (March 2, 2024) |
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) |
Research Engineer @  (June 8, 2023) |
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 | Skill-Gap Analysis Automation |
|---|---|---|
| Rank: 1st / 178 |
Rank: 2nd / 338  |
Rank: 2nd / 639 |
| 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, OpenAI, FastAPI, Next.js) to trace and visualize AI agent interactions. | Placed 2nd at the MIT Hackathon with a full-stack app (React, FastAPI, Gemini, BeautifulSoup) to automate skill-gap analysis by aggregating multi-source data. |
| Hugging Face Transformers | 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 :)
