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Some of the project highlights of the Department are listed below:

Artificial Intelligence and Machine Learning (AIML)

How Serendipity Improves User Satisfaction with Recommendations? A Large-Scale User Evaluation
Staff Dr. CHEN, Li
  • To identify the causal relationships from novelty, unexpectedness, relevance, and timeliness to serendipity, and from serendipity to user satisfaction and purchase intention.
  • To reveal the moderating effects of user curiosity on the relationships from novelty to serendipity and from serendipity to satisfaction.
  • To compare four algorithms for recommending e-commerce products in terms of user perceptions.

Macro environment and non-company news on stock market performance: A comparative study of media sentiment, public engagement, and prediction models
Staff Dr. CHEN, Li
  • To analyze the news coverage by Hong Kong media in past 20 years, and illustrate the longitudinal patterns of news sentiment and related public engagement on interactive media outlets.
  • To explore and discover several useful non-company specific and macro environment indicators from the news patterns that are significantly correlated with market (Hang Seng Index and sub-indices) and specific stock price fluctuations.More
  • To compare predictability and reliability of the found non-company specific news indicators and that of existing company specific tools in financial practices.
  • To develop accurate and efficient computational methods for market indicators identification and media text analysis.

User Evaluation on Sentiment-based Recommendation Explanations
Staff Dr. CHEN, Li
  • To investigate how review information could be exploited to generate explanations for multiple recommendations especially in high-investment product domains (i.e., digital camera and laptop).
  • To identify the exact effect of sentiment-based explanations on improving users’ decision effectiveness and system perceptions.More
  • To investigate how users view information and compare products on the sentiment-based explanation interface.

Detecting the Tipping Points from Inflammation to Tumorigenesis Using A Multi-level Network Modeling Approach
Staff Dr. CHEUNG, William Kwok Wai
  • To detect tipping points by identifying “dynamical network biomarker” at molecular network level by analysing the ‘omics’ data.
  • To identify potential networks associated with tipping points by constructing multi-level regulatory networks from heterogeneous data sources and performing structural analysis on evolving networksMore
  • To conduct biological experiments for validating our findings of potential networks associated with the tipping points.

Joint Embedding of Sequential Data and Knowledge Graphs with application to Predictive Analytics in Healthcare
Staff Dr. CHEUNG, William Kwok Wai
  • To develop methodologies that can learn representations simultaneously from heterogeneous clinical events in the EHR data and the medical knowledge graphs for patient outcome prediction with enhanced interpretability.
  • To develop an interpretable framework to jointly infer the correspondence among different modalities in the heterogeneous EHR data and generate high-throughput phenotype candidates.

Learning Discriminative Manifolds from Multiple Data Modalities for Multimedia Content Understanding
Staff Dr. LIU, Yang
Objectives To discover the intrinsic structure of multimedia data with multiple modalities for multimedia content understanding
  • For multi-label multimedia data, learn the correlation between different labels while preserving the discriminative information.
  • For multi-modal multimedia data, learn the consistency between different modalities while preserving the discriminative information.

Spatio-Temporal Data Analytics for Disease Transmission Modeling: Diffusion Networks, Underlying Factors, and Partial Observations
Staff Prof. LIU, Jiming
  • To infer the structures of latent diffusion networks for active surveillance
  • To reveal the impact of underlying factors on geographic variations for epidemic prevention
  • To derive the epidemic risk map based on partial observations for disease control

Towards Epidemiological Intelligence – Machine Learning and Data-Driven Modeling for Disease Surveillance and Control
Staff Prof. LIU, Jiming
Objectives To tackle the challenges in infectious disease surveillance and control, via developing and deploying novel AI methods and data-driven models
  • To infer the underlying diffusion networks of infectious diseases
  • To assess the transmission risks of infectious diseases in different regions
  • To learn and deploy optimal strategies for intervention under limited anti-disease resources

Project in Different Research Areas:

Artificial Intelligence and Machine Learning (AIML) Big Data and Data Management (BDDM) Computer Vision and Pattern Recognition (CVPR) Distributed Systems and Networking (DSAN)
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Department of Computer Science, Hong Kong Baptist University
Hong Kong Baptist University