Data Scientist and Engineer with a strong background in Mathematical Modelling, Computation, and Chemical Engineering. Passionate about applying advanced machine learning techniques to solve complex problems in biosustainability and urban mobility.
- Programming/Tools: Python, Sci-kit, Pandas, R, MATLAB, TensorFlow, PyTorch, C/C++, SQL, Tableau
- Data Science/ML: Deep Learning, Generative AI, NLP, LLMs, Optimization, A/B Testing
- Developed an innovative deep learning model using Python, TensorFlow, and Graph Neural Networks
- Implemented a Relational Graph Convolutional Variational Autoencoder with a Regression Network
- Utilized Google Maps and Mapillary APIs for data extraction and feature engineering
- Developed and trained a custom TensorFlow & Python deep learning generative AI autoencoder (VAE) for time-series analysis
- Achieved over 90% dimensionality reduction with less than 5% error in cell lifespan and metabolism analysis
- Presented research on "Decoding Cell Behavior in Bioreactors: Deep Learning & Latent Space Insights" at a major symposium
- Applied advanced data manipulation techniques using Pandas and machine learning models with scikit-learn
- Implemented clustering, classification, and regression models on large-scale biological datasets
- Implemented LoRA on BERT's attention mechanism
- Fine-tuned a bert-base-cased model on Yelp reviews using LoRA
- Explored parameter-efficient fine-tuning techniques including prefix-tuning
- Data Angels Women in Data Networking Group (Jan 2024 β Present)
- Artificial Intelligence at the IT University of Copenhagen (AITU) Journal Club (Jan 2023 β Present)
- Email: elysiagao@gmail.com
- LinkedIn: Elysia Gao