About

Brief Summary:

Graduate of MSc Machine Learning at University College London. With experience and interests in Reinforcement Learning and Generative Models. I have also completed courseworks and work experience with NLP (GAN-BERT for semi-supervised learning) and Computer Vision (VAE, Neural Style Transfer).

Before this I was working as an Applied Scientist at NewDay, where I applied Deep Learning (Natural Language Processing) for text augmentation and sentiment analysis. I also iterated over different architectures (CNN, LSTM, BERT) to improve the performance of an incumbent chatbot model. Other projects I worked on include building a churn prediction model and marketing channel analysis using Bayesian models.

I did my bachelor’s degree - BSc Statistics, Economics and Finance (accredited by the Royal Statistical Society) - at University College London.

Career interests:

Thanks to the deep technical skills acquired from my master’s degree in Machine Learning, I am looking forward to working on the implementation and engineering of complex systems similar but not limited to the ones mentioned above (RL and NLP). As such I will be more interested in an Engineering role rather than a pure Research one.

A link to my CV is provided here. Check some of the blog posts I’ve written here.

Featured projects:

• Reinforcement Learning algorithms - such as PPO and DQN.
• Instruction fine-tuned GPT-2 using my own implementation of LoRA with PyTorch and Lightning. Trained in bfloat16 and deployed to a Gradio app with Docker.
• Fine-tuned BERT with LoRA in bfloat16 for text classification - Using PyTorch, Lightning and Hugging Face. Local testing with Flask and Docker and deployed to a Gradio app with Docker.