Modern AI models achieve impressive performance but require enormous amounts of energy when trained and run on conventional GPU hardware. A promising alternative is analog in-memory computing, where neural network computations are performed directly inside memory devices such as resistive crossbar arrays. This approach can dramatically improve the efficiency of AI systems, but it also … full description “Training Neural Networks for Analog AI Hardware (available)”
Project List
Exploring optimization algorithms for deep sequence models (available)
Deep sequence models such as recurrent neural networks (RNNs) are a key type of architecture in modern deep learning, particularly for processing sequential data such as language text, speech, video, and time series data. RNNs have loops that allow information to persist and be passed from one step to the next, enabling them to effectively … full description “Exploring optimization algorithms for deep sequence models (available)”
Implementation of biologically inspired efficient deep learning models (available)
As deep learning models continue to grow in size and complexity to tackle increasingly difficult tasks, the need for efficient and scalable models becomes ever more important. Extremely large language models like GPT-4 require massive computational resources and expensive hardware to train and run. This makes them impractical to deploy at scale in many real-world … full description “Implementation of biologically inspired efficient deep learning models (available)”
AI-powered COVID-19 detection via cough sounds (available)
Respiratory diseases such as COVID-19 are known to physically damage our airway and lungs, which in turn alters the produced respiratory sounds (e.g., cough, breadth). During the pandemic, cough classification has emerged as an accessible, low-cost, and environmentally friendly COVID-19 screening alternative, needing only a smartphone to collect and process cough samples. However, audio processing … full description “AI-powered COVID-19 detection via cough sounds (available)”
Predicting and Explaining Drug Toxicity with Graph Neural Networks (ongoing)
This student research project focuses on predicting and explaining drug toxicity using advanced machine learning techniques, specifically Graph Neural Networks (GNNs). Graph Neural Networks are a modern, powerful class of machine learning models widely used across diverse fields, from chemistry to social networks. However, one of the major challenges with traditional GNNs is their “black-box” … full description “Predicting and Explaining Drug Toxicity with Graph Neural Networks (ongoing)”
Teaching Small LLMs to Reason (ongoing)
Large Language Models (LLMs) such as GPT4, are a game-changer for AI. Equipped with hundreds of billions of parameters, and trained on vast amounts of textual data totalling hundreds of terabytes, these models have revolutionised operations across numerous domains. But despite their considerable capabilities, their sheer size often means that they require substantial computational resources … full description “Teaching Small LLMs to Reason (ongoing)”