Blog

A currated reading + watch list for beginners to get started with RNA modelling and desing.

An overview of papers on efficient Graph Neural Networks and scalable Graph Representation Learning for real-world applications.

We characterize recent advances in deep learning for routing problems via a unified Neural Combinatorial Optimization pipeline and provide new directions to stimulate future research. (Also published at the inaugural ICLR 2022 Blog Track)

A repository of high-quality resources that helped me understand the ML/AI PhD Application process and craft my application better.

Exploring the connection between Transformer models such as GPT and BERT for Natural Language Processing, and Graph Neural Networks. (80,000+ readers on The Gradient, featured in Probabilistic ML textbook and classes at Stanford, Cambridge, Oxford.)

My Valedictorian speech at the NTU Convocation 2019

Can Neural Networks learn to solve NP-hard optimization problems in scheduling, transportation, and supply chain directly without human handcrafting?

An introduction to deep learning on graphs and geometric data with Graph Neural Networks.

The legendary Rationalism versus Empiricism debate for building Artificially Intelligent systems.

From machine learning research to real-world systems handling over € 200 Million Euro in annual sales pipeline.