This seminar series and international symposium is funded by Hong Kong Research Grants Council (RGC) under the Inter-Institutional Development Scheme (UGC/IIDS16/M01/25) and Hong Kong Metropolitan University Research Impact Fund (RIF/2024/1.1).
The use of natural resources by analyzing consumption patterns and identifying inefficiencies, leading to waste reduction and minimizing environmental impact. In energy management, AI can optimize the distribution and consumption of renewable energy sources, contributing to a reduction in carbon emissions. AI and big data are also valuable in monitoring wildlife populations and their habitats, providing critical insights into biodiversity health. By analyzing data from camera traps, acoustic sensors, and satellite imagery, AI can help identify threats to endangered species and inform conservation efforts. Beyond addressing environmental problems, AI and big data can assist in designing smart cities that prioritize sustainability. These technologies can optimize traffic flow, reduce energy consumption, and improve waste management systems, thereby reducing the ecological footprint of cities and enhancing the quality of life for their inhabitants. In the future, AI and big data will be essential tools for policy decision-making, as they can model the impacts of different policy scenarios, helping policymakers make informed decisions. Additionally, AI can support the development of innovative solutions, such as carbon capture technologies and climate-resilient infrastructure, to address the challenges posed by climate change.
Despite the significant advantages of AI and big data, their application in enhancing environmental sustainability in China remains limited. One reason is the reliance on traditional methods, with many individuals hesitant to adopt new technologies due to perceived risks, lack of understanding, or fear of disrupting current operations. Additionally, there is often a lack of awareness among businesses and the public about the potential benefits of AI and big data. Without a clear understanding of how these technologies can add value, organizations and policymakers may be less inclined to invest in and adopt them. To address this gap, our seminar series aims to increase the understanding of AI and big data among students and various stakeholders. We will invite experts to share their ideas and experiences in using these technologies to tackle environmental issues. Following the presentations, discussions will be encouraged to promote the exchange of ideas. We believe that through the success of this seminar series, the understanding of AI and big data among different stakeholders in China will be significantly enhanced.
Main objectives of this seminar series and international symposium:Environmental research is the strategic research area of the Department of Applied Science of the School of Science and Technology, HKMU.
The latest global trend in environmental science is to integrate molecular and cell biology, as well as multi-omics technologies, with traditional environmental science to explain broad environmental phenomena at the molecular and cellular levels, thereby leading to the emerging field of environmental omics, which is interdisciplinary in nature and encompasses environmental science, molecular biology, programming, statistics, and data science. This new frontier emerges because conventional environmental research approaches cannot achieve precise manipulation and regulation of the environment. In contrast, the latest molecular and cellular information can elucidate the mechanisms underlying various environmental phenomena and help identify molecular and cellular targets for the specific control and regulation of environmental processes.
With this concept in mind, the Department of Applied Science of the School of Science and Technology of HKMU established the Platform for Research and Omics Training in Environmental Analysis (PROTEA) to facilitate environmental omics research by providing training and resources to our faculty members, research students, and laboratory staff and by organising seminars, workshops, and symposiums to disseminate knowledge, skills, and knowhow in this emerging field.
Main objectives of this seminar series and international symposium:
The use of natural resources by analyzing consumption patterns and identifying inefficiencies, leading to waste reduction and minimizing environmental impact. In energy management, AI can optimize the distribution and consumption of renewable energy sources, contributing to a reduction in carbon emissions. AI and big data are also valuable in monitoring wildlife populations and their habitats, providing critical insights into biodiversity health. By analyzing data from camera traps, acoustic sensors, and satellite imagery, AI can help identify threats to endangered species and inform conservation efforts. Beyond addressing environmental problems, AI and big data can assist in designing smart cities that prioritize sustainability. These technologies can optimize traffic flow, reduce energy consumption, and improve waste management systems, thereby reducing the ecological footprint of cities and enhancing the quality of life for their inhabitants. In the future, AI and big data will be essential tools for policy decision-making, as they can model the impacts of different policy scenarios, helping policymakers make informed decisions. Additionally, AI can support the development of innovative solutions, such as carbon capture technologies and climate-resilient infrastructure, to address the challenges posed by climate change.
Despite the significant advantages of AI and big data, their application in enhancing environmental sustainability in China remains limited. One reason is the reliance on traditional methods, with many individuals hesitant to adopt new technologies due to perceived risks, lack of understanding, or fear of disrupting current operations. Additionally, there is often a lack of awareness among businesses and the public about the potential benefits of AI and big data. Without a clear understanding of how these technologies can add value, organizations and policymakers may be less inclined to invest in and adopt them. To address this gap, our seminar series aims to increase the understanding of AI and big data among students and various stakeholders. We will invite experts to share their ideas and experiences in using these technologies to tackle environmental issues. Following the presentations, discussions will be encouraged to promote the exchange of ideas. We believe that through the success of this seminar series, the understanding of AI and big data among different stakeholders in China will be significantly enhanced.
Main objectives of this seminar series and international symposium:
Environmental research is the strategic research area of the Department of Applied Science of the School of Science and Technology, HKMU.
The latest global trend in environmental science is to integrate molecular and cell biology, as well as multi-omics technologies, with traditional environmental science to explain broad environmental phenomena at the molecular and cellular levels, thereby leading to the emerging field of environmental omics, which is interdisciplinary in nature and encompasses environmental science, molecular biology, programming, statistics, and data science. This new frontier emerges because conventional environmental research approaches cannot achieve precise manipulation and regulation of the environment. In contrast, the latest molecular and cellular information can elucidate the mechanisms underlying various environmental phenomena and help identify molecular and cellular targets for the specific control and regulation of environmental processes.
With this concept in mind, the Department of Applied Science of the School of Science and Technology of HKMU established the Platform for Research and Omics Training in Environmental Analysis (PROTEA) to facilitate environmental omics research by providing training and resources to our faculty members, research students, and laboratory staff and by organising seminars, workshops, and symposiums to disseminate knowledge, skills, and knowhow in this emerging field.
Main objectives of this seminar series and international symposium:
Principle Investigator | Dr. Livia PAN Min | Assistant Professor, School of Science and Technology |
Co-Principle Investigator | Dr. Steven XU Jingliang | Head of Applied Science cum Associate Professor, School of Science and Technology |
Co-Investigator | Dr. Caroline LAW Man-yee | Founder, Nature In situ Limited |
Principle Investigator | Professor Fred LEE Wang-fat | Associate Dean cum Professor, School of Science and Technology |
Co-Investigator | Dr. Steven XU Jingliang | Head of Applied Science cum Associate Professor, School of Science and Technology |
Dr. CHAN Ping-lung | Assistant Professor, School of Science and Technology | |
Dr. Sophie SHI Ling | Lecturer, School of Science and Technology |
Exploring AI and Big Data Strategies in Environmental Sustainability | Establishment of Platform for Research and Omics Training in Environmental Analysis (PROTEA) in the Department of Applied Science, S&T | ||||
|---|---|---|---|---|---|
Principle Investigator | Dr. Livia PAN Min | Assistant Professor, School of Science and Technology | Professor Fred LEE Wang-fat | Associate Dean cum Professor, School of Science and Technology | |
Co-Investigator | Dr. Steven XU Jingliang | Head of Applied Science cum Associate Professor, School of Science and Technology | Dr. Steven XU Jingliang | Head of Applied Science cum Associate Professor, School of Science and Technology | |
Dr. Caroline LAW Man-yee | Founder, Nature In situ Limited | Dr. CHAN Ping-lung | Assistant Professor, School of Science and Technology | ||
Dr. Sophie SHI Ling | Lecturer, School of Science and Technology | ||||
Chairperson | Professor Fred LEE Wang-fat | Hong Kong Metropolitan University,
Hong Kong, China |
Co-Chairperson | Dr. Steven XU Jingliang | Hong Kong Metropolitan University,
Hong Kong, China |
Secretary | Dr. Livia PAN Min | Hong Kong Metropolitan University,
Hong Kong, China |
Ms. Louise LUK Nga-ying | Hong Kong Metropolitan University,
Hong Kong, China | |
Member | Dr. CHAN Ping-lung | Hong Kong Metropolitan University,
Hong Kong, China |
Dr. Christie NG Ching-man | Hong Kong Metropolitan University,
Hong Kong, China | |
Dr. NG Tsz-wai | Hong Kong Metropolitan University,
Hong Kong, China | |
Dr. Sophie SHI Ling | Hong Kong Metropolitan University,
Hong Kong, China | |
Dr. Thomas LEE Chun-hung | Hong Kong Metropolitan University,
Hong Kong, China | |
Ms. Judy LIN Xinyi | Hong Kong Metropolitan University,
Hong Kong, China | |
Ms. SHAM Yik-tung | Hong Kong Metropolitan University,
Hong Kong, China | |
Ms. Jennifer POON Wing-yan | Hong Kong Metropolitan University,
Hong Kong, China |
