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Prof. Jiming LIU

Jiming

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Chair Professor, Department of Computer Science

  • COMPUTATIONAL MEDICINE, DATA ECONOMY and ETHICAL AND THEORETICAL AI

Jiming Liu is Chair Professor in Computer Science (04/2010-present), and was previously Associate Vice-President (Research) (05/2017-12/2019), at Hong Kong Baptist University. He received his M.Eng. and Ph.D. degrees in Electrical Engineering from McGill University (Centre for Intelligent Machines) in Canada. His current research interests include Data Analytics and Machine Learning, Complex Networks and Systems, Web Intelligence (WI), Autonomy-Oriented Computing (AOC) Paradigms, and AI-Enabled Epidemiology and Practice. He is a Fellow of the IEEE. Prof. Liu has served as Editor-in-Chief of Web Intelligence Journal (IOS), and Associate Editor of IEEE Transactions on Cybernetics, IEEE Transactions on Knowledge and Data Engineering, Data and Information Analytics (AIMS), and Computational Intelligence (Wiley), among others. He was Chair of IEEE Computer Society Technical Committee on Intelligent Informatics (TCII).

 

Project Highlights

 

The proposed Interactively- and Integratively-connected Deep Recurrent Neural Network (I2DRNN) model.
Multi-scale dependency among various regions on three real-world predictive spatio-temporal analytics tasks (disease prediction, climate forecast, and traffic prediction) learned by I2DRNN.

1. Demystifying deep learning: An information-theoretic framework

 

Deep learning has achieved incredible success in various challenging predictive spatiotemporal analytics (PSTA) tasks. However, given a specific PSTA task, how to appropriately determine the desired architecture of a deep learning model remains a mystery. To demystify the power of deep learning for PSTA in a theoretically sound and explainable way, we provide a comprehensive framework for deep learning model design and information-theoretic analysis. In so doing, we tackle an important open question in deep learning, that is, how to theoretically determine the necessary and sufficient configurations of a deep learning model, with respect to given learning datasets.

 

Measurement of the intensity of social contacts among seven age-groups (G1: 0-6; G2: 7-14; G3: 15-17; G4: 18-22; G5: 23-44; G6: 45-64; and G7: 65 or above) in four major settings: (A) households; (B) schools; (C) workplaces; and (D) public/community.
Predictions on the trends of disease infection and transmission risks associated with different work resumption plans based on the social contact patterns and reported cases.

2. How COVID-19 transmits: A social-contact characterization

 

COVID-19 has spread to 6 continents. Gaining a deeper understanding of what may have happened could potentially inform mitigation strategies in disease-emerging countries. This work addresses how COVID-19 spreads among different populations and the corresponding impacts on disease control, e.g., with/without interventions. We approach this question by examining age-specific social contact-based transmissions. The results show that the social contact-based analysis can readily explain the underlying disease transmission patterns and associated potential risks. This work has uncovered the ins and outs of COVID-19 outbreaks, which could have practical implications to mitigation strategy planning in various countries and regions. (Please view the related output here)

 

Media Coverage

 

  1. New HKBU-led study unveils COVID-19 transmission patterns and safety-conscious reopening plans
  2. A new chapter of HKBU COMP AI-enabled programme on proactive risk prevention and early case discovery for malaria elimination in the China-Myanmar border has begun
  3. Deployment of HKBU AI tools for malaria control and prevention in Greater Mekong Subregion (GMS) and Southeast Asia (SEA) countries

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