I train foundational models — from massive-scale distributed pre-training to custom post-training pipelines, synthetic data generation, and efficient deployment at both datacenter and local scale.
I train foundational models. Previously, I was an AI Researcher at Maincode, where I helped build Australia’s first homegrown LLMs — including the Matilda family and Maincoder-1B. Most of my day involves massive-scale distributed training, spinning up custom post-training pipelines, generating synthetic data, and keeping GPUs alive across W&B and Grafana dashboards.
Before that, I worked on vision-language models and multimodal architectures at Rising Sun Pictures, and earlier on medical image segmentation during my undergraduate research. I’m interested in making models reason better, code faster, and run efficiently — whether on a cluster or local hardware.
Here are a few technologies I've been working with recently:While pursuing my Masters at the UoA, my primary focus was to further my understanding and expertise in GenAI like Diffusion Models and NLP for generative applications along with managing my coursework. I was also an academic tutor for the following courses:
I also actively interned at:
Along with that I also reviewed papers for the following journals:
In conjunction with my academic coursework, I actively engaged in projects involving CNNs and GANs. I contributed to the development of tools aimed at facilitating early diagnosis, while also providing mentorship to fellow students in their respective projects.
I also published a few papers during my time at UIET. Some of these are:
The rest can be found on my Google Scholar page.
