Jillur Quddus is a computational mathematician, polyglot software engineer and published author specialising in the design of novel mathematical models and the engineering of innovative, high-performance, ethical and secure artificial intelligence (AI) systems. He is also the founder of HyperLearning AI, which he started with the aim of ensuring that AI delivers positive and sustainable economic, environmental and social change.
Jillur's core areas of expertise include discrete mathematics (number theory, graph theory and computational complexity theory) and graph neural networks (GNNs), which he has applied to the design of real-time neural networks for embedded smart devices, emerging infectious disease modelling, preventative healthcare, and combatting Dark Web-enabled crime. He has deep experience of working within central government, healthcare and law enforcement, and has worked extensively across the world, including in Japan, Singapore, Hong Kong, Malaysia, Australia, New Zealand and the United Kingdom.
Core Skills
- Mathematical Modelling
Computational Complexity Theory • Number Theory • Graph Theory • Statistical Learning • Machine Learning • Deep Learning • Graph Neural Networks (GNNs) - Software Engineering
Python • Java • JavaScript • SQL • Gremlin • Shell Scripting - Distributed Computing and ML Frameworks
Apache Spark • Dask • PyTorch • TensorFlow • Apache TinkerPop • Elasticsearch
Selected Experience
- Joint Biosecurity Centre - Lead Data Scientist
Jillur led a multidisciplinary team of globally leading mathematicians, epidemiologists, AI research fellows and technologists responsible for building pioneering emerging infectious disease modelling systems. - Government (UK) - Lead Artificial Intelligence Engineer
Jillur led a specialised team of intelligence officers, cryptographers, analysts and subject matter experts dedicated to combatting Dark Web-enabled child sexual exploitation and abuse (CSEA) through the forensic analysis of anonymous, decentralised networks and cryptographic protocols. - Local Government (UK) - Lead Data Scientist & Software Engineer
Jillur designed and built an award-winning artificial intelligence system capable of autonomously detecting, classifying and prioritising cases of potholes and fly-tipping in real time. Powered by a bespoke deep convolutional neural architecture and software interface, the system processes images at 45fps with less than 25ms latency - delivering up to 40 detection events per second - and integrates seamlessly with a custom Android mobile application and GPS location services.
Publications and Books
- Machine Learning with Apache Spark
Uncover patterns, derive actionable insights and learn from big data using MLlib. Amazon • Waterstones • Google Play
Languages
- English - Native
- Japanese 日本語 - Advanced (日本語能力試験 JLPT N2)
- Chinese Cantonese 廣東話 - Beginner