In today's world of abundant data and complex decision-making, Machine Learning stands as a cornerstone of innovation, addressing diverse challenges across multiple fields. Our research focuses on leveraging the transformative power of Machine Learning to solve intricate problems and explore new opportunities. By extracting valuable insights from large datasets, Machine Learning has revolutionized decision-making, pattern recognition, and predictive modeling. The key areas of our research focus include:

1. Supervised Learning
2. Unsupervised Learning
3. Representation Learning
4. Reinforcement Learning
5. Deep Learning

Through these areas, we aim to harness Machine Learning's potential to drive innovation and create impactful solutions across various domains.

Biometrics and Human Language Processing are pivotal research fields with significant societal and scientific impact. Our research aims to lead in these areas, harnessing their transformative potential across education, e-security, healthcare and information retrieval. By utilizing advanced algorithms, deep learning, and extensive language resources, we explore biometrics recognition, speech processing, language understanding, sentiment analysis, dialogue systems, and machine translation. Our such research focus includes:

1. Biometrics Recognition
2. Speech Processing
3. Text Mining
4. Sentiment Analysis
5. Machine Translation
6. Language Generation

We also delve into innovative applications like voice assistants and chatbots. Through interdisciplinary collaborations with experts in linguistics, identity science, cognitive science, and industry partners, we aim to advance biometrics and human language processing research. Our goal is to drive breakthroughs that will redefine human-computer interaction and revolutionize human communication in the digital age.

The field of Computer Vision and Graphics has experienced significant advancements, transforming industries and applications by enabling machines to perceive and interpret visual information. As visual data grows in complexity, the demand for sophisticated algorithms require new research initiatives. Our research aims to leverage machine learning, high-performance computing, and advanced sensing technologies, to design novel solutions for image recognition, object detection, scene understanding, virtual reality, and augmented reality.

Our research focuses on:

1. Image and Video Analysis
2. 3D Vision and Reconstruction
3. Graphics and Rendering
4. Computational Photography and Image Processing
5. Human-Computer Interaction (HCI) and User Interfaces

Through interdisciplinary collaborations with computer scientists, mathematicians, engineers, and industry experts, we aim to push the boundaries of computer vision and graphics. Our goal is to contribute to the development of innovative solutions and transformative technologies that will shape the future of visual computing.
 

In today's digital era, the exponential growth of data presents both challenges and opportunities across various sectors. Data technologies and governance have become crucial research areas, focusing on efficient data management and analysis for public benefit. This field addresses the evolving needs of industries, e-governance, and society, emphasizing the transformative power of data to enhance decision-making and unlock valuable insights.

Our research aims to lead in data technologies and governance, by leveraging on advances in data mining and predictive analytics. Our collaboration with experts in computer science, statistics, mathematics, and industry will develop novel methodologies, algorithms, and tools for efficient data storage, retrieval, analysis, and visualization. Our focus includes:

1. Data Acquisition and Storage
2. Data Integration and Preprocessing
3. Big Data Technologies
4. Data Privacy and Governance
5. Bias, Fairness, and Ethical AI

Through these efforts, we aim to address data challenges, drive innovation, and transform industries.

In today's data-rich environment, statistical learning and optimization strategies are crucial for addressing complex challenges across various domains. Our research focuses on leveraging these disciplines to address open research problems and explore new possibilities from the cutting-edge advancements in data science. Emerging optimization methods offer powerful tools for enhancing efficiency, resource allocation, and problem-solving in a diverse context.

Our research areas include:

1. Probability Theory and Mathematical Statistics
2. Statistical Analysis and Uncertainty Quantification
3. Evolutionary Computation
4. Generative and Large Models
5. Time Series Analysis and Online Learning
6. Optimization Methods

Through these efforts, we aim to advance statistical learning and optimization methods, contributing to the next-gen innovative solutions and transformative technologies.

In the context of swiftly advancing AI capabilities, AI integration has become a transformative force, accelerating discoveries and optimizing processes across diverse fields. Our such AI + X initiative seeks to harness AI's potential to advance scientific inquiry in areas such as healthcare, neuroscience, and beyond. The fusion of AI technologies with vast datasets from experiments, simulations, and observations offers unprecedented opportunities to shape the future of society.

These research areas include:

1. Domain-Specific Applications
2. Interdisciplinary Research
3. Neuromorphic Computing
4. Personalized Medicine
5. Financial Analytics
6. Marketing and Social Analysis

Collaborating with academic units at PolyU, global research organizations, and partners, we aim to foster innovation and knowledge exchange, translating AI-driven advancements into practical applications for social good.