Computing & Intelligence

IHPC strives to create efficient and intelligent technologies through innovative knowledge-driven AI algorithms and advanced computing techniques and platforms. The key research areas include Computational Artificial Intelligence, High Performance Computing and Decentralised Computing, which are motivated by real-world applications with societal and/or economic benefits.

From these research outcomes, we innovate technologies that are used in the real-world through partnering closely with public sector agencies and companies (SMEs, LLEs and MNCs). Some of our capabilities and technologies are as follow: 

About Computing & Intelligence

Computational Artificial Intelligence

IHPC focuses on developing knowledge-driven AI modelling frameworks with inputs from both domain knowledge and data. This is accomplished by leveraging on multi-type models such as physics-based, data-driven, and simulation, leading towards accurate, robust, generalisable, and explainable solutions. 

The main research areas include: 

Physics-based AI
To couple AI with physics and/or other types of scientific knowledge, and/or modelling and simulation outputs, such to leverage their complementary strengths and improve performance of target tasks.

Physics-based AI

Research Topics: 
To make use of different physics knowledge for: 

  • Physics-based Data Augmentation: to augment & enrich the data in both quantity and quality.
  • Physics-based Representation Learning: to learn features with physical meanings from original data.
  • Physics-based Modelling: to build ML models with physics guidance/ incorporation. 

Efficient AI
To improve data and computation efficiency in machine learning.

Research Topics:
  • Data efficiency: to achieve efficient learning from a small amount of labelled data or even no labelled data. We devote to explore on semi-supervised learning/weekly supervised learning, self-supervised learning, transfer learning and unsupervised learning to reduce the data annotation requirement.
  • Computation efficiency: to reduce the computation cost of deep AI models, especially on edge devices. We focus on constraint-aware model compression and energy-efficient learning to compress heavy deep model into a lighter one to meet the requirement with constraints on memory and power consumption.

Efficient AI

Multimodal AI
To achieve better performance by leveraging multimodal inputs.

Research Topics:
  • Learning with Multimodal Representations: to learn the model using multimodal inputs.
  • Learning with Auxiliary Modality: to train the model with auxiliary modality which is available during training but not available to testing.
  • Learning with Domain Knowledge: to learn the model using domain knowledge in the form of graph and physical rules.

Multimodal AI

Autonomous AI
To simplify AI solutions development through automation and autonomy.

Research Topics: 
  • Auto data pre-processing and feature engineering: to automate the data cleaning, feature generation, and feature selection for data sources of domain specific applications.
  • Auto modelling: machine learning (ML) algorithms selection and hyperparameter optimisation; to automate ML pipeline creation and evaluation; to automate multi-model ensemble; to automate modelling with constraints such as time, resource, host device, etc.
  • Lifelong learning: continuous monitoring of data characteristics and model performance; continuous knowledge/experience accumulation through past learning; strategy in triggering auto feature engineering and/or auto-modelling.

Autonomous AI

High Performance Computing / Decentralised Computing

IHPC develops advanced computing techniques on various hardware architectures (e.g. multicore CPU, GPU, FPGA, ASIC) for various applications, especially compute-intensive AI/ML-oriented ones. 

The main research areas include: 
  • Code optimisation for parallel & heterogeneous computing  with modern hardware architectures. 
  • Edge AI computing: to enable ubiquitous AI deployment catering to demanding scenarios and constraints.
  • Quantum computing: to understand quantum computing and quantum-inspired computing paradigms, and apply quantum computing to real-world applications.

High Performance Computing_Dencentralised Computing

Decentralised Computing focuses on data, computation, and intelligence across platforms and organisations, tackling the challenges of performance, scalability, cost-efficiency, privacy-preserving and trustworthy collaborations.

Decentralised Computing

Research areas include: 
  • Distributed computation & system for efficient parallel data processing and adaptive resource provisioning on distributed system and framework.
  • Distributed learning and interoperability on complex data flow, efficient analytics workflow, flexible model deployment on edge, on-premises and cloud platforms.
  • Trusted computing & collaboration to technology convergence of Blockchain, secure multiparty computation and federated learning for trustworthy ecosystem.