Machine Learning

Years of experience in building artificial minds led me to believe that these AIs may end up seeming more ‘human’ than we currently imagine them to be.

A thought dump on mRNA vaccines and the future of computational biology

Machine learning models merely amplify our biases - not eliminate them.

Whether you’re only here for the hype or genuinely interested in the field, you’re in for a wild ride.

A minimal implementation of Flash Attention 1 & 2 in just ~350 lines of CUDA code. This is still a work-in-progress, but the ultimate goal is to implement the various variations of Hyperbolic Attention in CUDA.

A C++ implementation of Meta’s Llama2 generative large-language model. I also optimized the original C implementation by Karpathy by adding parallelization on the multi-head attention layer.

Expedock Assistant is a chatbot that allows you to ask questions about your shipments and get answers in real time. It’s like having a personal assistant that knows everything about your business, shipments and industry.

Expedock’s AutoML Library – fit a model, run batch inference, and get explanations in one line of code each.

Booking demand prediction for Grab’s Southeast Asia operations. The project involves spatio-temporal forecasting, anomaly detection, and econometric modeling.

[Technical Report for CVPR’s 2nd MMFM Challenge] This report presents Multimodal Structured Generation, a general framework which constrains the output logits of frozen Multimodal Foundation Models to force them to reason before responding with structured outputs that downstream APIs can parse and use. This approach achieved the second highest score in the hidden test set for Phase 2 and third highest overall in the 2nd Multimodal Foundation Models Challenge hosted by the Computer Vision and Pattern Recognition (CVPR) conference.

[Preprint - Accepted @ IEEE 7th International Conference on Multimedia Information Processing and Retrieval (MIPR) 2024] This paper presents Retrieval Augmented Structured Generation (RASG), a novel general framework for Business Document Information Extraction that achieves state of the art (SOTA) results on both Key-Information Extraction (KIE) and Line Items Recognition (LIR).