Teaching artifical intelligent models to be intelligent :)
I am a passionate about Artificial Intelligence and Machine Learning research. My research interests span ai for science, 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.
identity:
origin: China
nationality: France
tagline: Engineering the intelligence in AI & Automating with it
travel_into: [Singapore, Thailand, Malaysia, Japan, China, Taiwan,
France, Spain, Italy, Germany, Sweden]
passions:
culture: 🌍 Traveling & Exploring Cultures
activity: 🥟 Gyoza Making & Eating
sport: 💃 K-Pop Dancing
love: ❤️ Girlfriend Caring
gaming: 💥 Counter Strike
philosophy:
ego_check: "Low ego, high impact."
success: "Follow what you like, success will follow."
open_source: "Open source is magical."
resilience: "Failure is a form of success." 🌱 ourtub: seeing knowledge, not just searching for it
🤖 jaxlaxy: your compass for the jax multiverse
🤖 sweepkit: rust-powered blazing-fast cli tool to scan & clean unused dev dependencies
🤖 torchOCR: pytorch library for end-to-end optical character recognition
🤖 courant: cozy reminders for developers to drink water, rest eyes, and stretch
🔖 huggingscience: partial differential equations (pde) importance
🔖 responsible ai: impact of knowledge distillation on model interpretability
🔖 ml4sci: physics-guided machine learning on gravitational lensing
🔖 graph generation: neural graph generation with text conditioning
🔖 listen to the wild: predicting naturalness with acoustic indices
🔖 vlm few-shot performance improvement: enhance sparse attention selection mechanism
🔖 hygene: diffusion-based hypergraph generation method
🔖 cfdg: review of classifier-free diffusion guidance
🔖 unifying gan & diffusion: score gan and discriminator flow, a unified framework
🔖 tiger: generative retrieval of item IDs for recommender systems
more?
| Role | Description |
|---|---|
Research 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. |
| PyTorch / torchtitan | Hugging Face Transformers | OTT-JAX | Responsible AI Blog (IP Paris) |
|---|---|---|---|
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Added missing --model.flavor=debugmodel_flex_attn flags for Llama 3 FlexAttention in the compiler toolkit documentation. |
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 :)
