In the era of unprecedented data availability and complex decision-making processes, the fields of Machine Learning and Optimization have emerged as crucial pillars for driving innovation and addressing multifaceted challenges across various domains. Our research in Machine Learning and Optimization aims to harness the transformative potential of these disciplines to tackle intricate problems and unlock new possibilities. Machine Learning, with its ability to extract meaningful insights from vast datasets, has revolutionized decision-making, pattern recognition, and predictive modeling. Optimization, on the other hand, provides powerful tools for maximizing efficiency, resource allocation, and problem-solving in diverse contexts. By integrating these two fields, we seek to push the boundaries of knowledge and advance the frontiers of research and application. Our research endeavors will encompass developing novel machine learning algorithms and optimization techniques to address complex problems with real-world impact. Additionally, we will emphasize the translation of research findings into practical solutions, working closely with industry partners and policymakers to drive innovation and create a positive societal impact. In the long run, our research will drive innovations, fostering interdisciplinary collaborations, and equipping future generations with the skills and knowledge needed to tackle the most pressing challenges of our time.

In today's digital age, the world is generating an unprecedented amount of data, presenting both challenges and opportunities across various sectors. The field of Big Data Analytics and Management has emerged as a critical area of research, focusing on the efficient management, analysis, and utilization of vast and complex datasets. This research domain plays a pivotal role in addressing the evolving needs of industries, governments, and society at large. The importance of harnessing the power of data cannot be overstated, as it has the potential to drive innovation, enhance decision-making processes, and unlock valuable insights that can shape the future. Our research in Big Data Analytics and Management aims to be at the forefront of this transformative field, contributing to cutting-edge research and fostering advancements in data management, data mining, machine learning, and predictive analytics. Through collaboration with experts in computer science, statistics, mathematics, and industry partners, we seek to develop novel methodologies, algorithms, and tools for efficient data storage, retrieval, analysis, and visualization. Furthermore, we will explore multidisciplinary applications of big data analytics, including but not limited to healthcare, finance, cybersecurity, social sciences, and environmental studies. In the future, we hope to address the challenges posed by the exponential growth of data and leveraging its potential to drive innovation, inform decision-making, and transform industries.

The field of Computer Vision and Graphics has witnessed remarkable advancements in recent years, revolutionizing various industries and applications. Traditional approaches and research in computer vision have paved the way for significant breakthroughs, enabling machines to perceive and understand visual information. However, with the growing complexity of visual data and the need for more sophisticated algorithms, new research avenues have emerged. In this era of big data and data-driven methodologies, there is a tremendous opportunity to explore the untapped potential of computer vision and graphics. By harnessing the power of machine learning, high-performance computing, and cutting-edge sensing technologies, we can unlock novel solutions for image recognition, object detection, scene understanding, virtual reality, and augmented reality. Our research in Computer Vision and Graphics aims to revolutionize this dynamic field, fostering interdisciplinary collaborations among computer scientists, mathematicians, engineers, and industry experts. Through our collective efforts, we aim to push the boundaries of computer vision and graphics, and ultimately contribute to the development of innovative applications and transformative technologies that will shape the future of visual computing.

Speech and Natural Language Processing (NLP) have emerged as vital fields of research with profound implications for human communication, technology, and society. The ability to comprehend and process spoken and written language has transformative potential across various domains, including education, healthcare, customer service, and information retrieval. The rapid advancement of machine learning, artificial intelligence, and linguistic models has revolutionized the way we interact with and harness the power of language. Within this context, our research in Speech and Natural Language Processing aims to be at the forefront of cutting-edge research and innovation. By leveraging state-of-the-art algorithms, deep learning techniques, and large-scale language resources, we will explore diverse aspects of speech recognition, natural language understanding, sentiment analysis, dialogue systems, and machine translation. Additionally, we will investigate novel applications in voice assistants, chatbots, and language generation, etc. Through interdisciplinary collaborations with experts in linguistics, computer science, cognitive science, and industry partners, we aspire to push the boundaries of speech and NLP research, fostering breakthroughs that will shape the future of human-computer interaction and revolutionize communication in the digital age.

In the rapidly evolving landscape of scientific research, the integration of AI has emerged as a powerful tool with immense potential to accelerate discoveries, optimize processes, and unlock new frontiers in knowledge. Our research in AI for Science aims to leverage the transformative capabilities of AI to revolutionize scientific inquiry across diverse domains. The advent of AI technologies, combined with vast amounts of data generated from experiments, simulations, and observations, presents unprecedented opportunities for breakthroughs in scientific research. We envision a multidisciplinary approach, bringing together experts in computer science, mathematics, physics, chemistry, biology, and other scientific disciplines to explore the synergistic interactions between AI and scientific domains. Our research endeavors will span a wide range of applications, including but not limited to data-driven discovery, predictive modeling, optimization of experimental design, analysis of complex systems, and automation of scientific workflows. By integrating AI techniques such as machine learning, deep learning, and natural language processing, we aim to enhance scientific understanding, expedite knowledge acquisition, and facilitate the development of novel hypotheses. Through collaborations with other academic units at PolyU, local and overseas research organizations, and industry partners, we will foster an environment of innovation and knowledge exchange, promoting the translation of AI-driven scientific advancements into practical applications and real-world solutions. In the long run, we position ourselves at the forefront of AI-driven scientific research, driving innovation, and harnessing the potential of AI to accelerate scientific discovery and shape the future of scientific inquiry.

The field of healthcare stands at the forefront of societal priorities, with the continuous pursuit of improving patient outcomes, enhancing healthcare delivery, and addressing complex medical challenges. In recent years, there has been an exponential growth in the availability of healthcare data, coupled with remarkable advancements in artificial intelligence (AI) technologies. This convergence presents unprecedented opportunities for the transformation of healthcare through the application of AI techniques. Our research in AI for Healthcare aims to harness the power of data-driven approaches, machine learning, and predictive modeling to revolutionize healthcare practices. By leveraging interdisciplinary collaborations between experts in medicine, healthcare, computer science, data analytics, and bioinformatics, we seek to propel research and innovation in this dynamic field. The primary objectives of our research include the development of AI-driven tools for early disease detection, personalized treatment recommendations, medical imaging analysis, patient monitoring, and healthcare resource optimization. We also aspire to explore the ethical implications of AI in healthcare, ensuring the responsible and transparent implementation of these technologies. Through close partnerships with healthcare providers, industry leaders, and policymakers, we strive to translate our research findings into practical solutions that will improve patient care, enhance healthcare systems, and ultimately contribute to the well-being of individuals and communities. In the long run, our research will lead the way in spearheading AI-driven advancements in healthcare, serving as a driving force for innovation and actively tackling the challenges present within the healthcare landscape.