Meeting Societal Challenges: Scalable Big Data-driven, AI- enabled Approaches | Liangxiu Han
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Prof. Liangxiu Han has a PhD in Computer Science from Fudan University, Shanghai, P.R. China (2002). Prof. Han is currently a full Professor of Computer Science at the Department of Computing and Mathematics, Faculty of Science and Engineering, Manchester Metropolitan University. Prof. Han is Faculty Lead for AI, Digital and Cyber Physical Systems and Deputy Director of ManMet Crime and Well-Being Big Data Centre.
Join Professor Liangxiu Han from Manchester Metropolitan University for an engaging lecture on addressing societal challenges through a big data-driven approach. In this comprehensive talk, Professor Han delves into scalable learning from big data, emphasizing its importance and applications across various fields.
Lecture Highlights:
Introduction to Big Data:
Overview of the rapid growth of data from different media sources like TikTok and Twitter.
Discussion on the characteristics of big data: volume, variety, and complexity.
Technical Challenges of Big Data:
Filtering and reducing data to gain timely insights and make decisions.
Efficient data processing and analysis technologies.
System architecture considerations, including parallel and distributed computing, grid computing, and cloud computing.
Research and Applications:
Combining fundamental and applied research in big data analytics.
Applying big data technologies in domains such as precision agriculture, healthcare, and smart cities.
Developing lightweight edge computing algorithms for real-world impact.
Big Data Projects and Achievements:
Innovative AI solutions for detecting and screening diseases like Alzheimer’s and heart disease.
Precision agriculture projects utilizing data from drones and robotics to advise farmers on nutrient deficiencies and crop disease detection.
Scalable data products and platforms developed to handle diverse datasets, including satellite, biomedical, and mobile phone data.
Scalable Deep Learning Models:
Development of efficient machine learning and deep learning models.
Parallelization techniques for distributed training of neural networks.
Implementation of a hybrid parallelization approach to improve model performance and scalability.
Future Directions:
Exploration of advanced deep learning models and scalable architecture for efficient big data processing.
Continuous efforts to translate research into practical solutions for societal challenges.
Professor Han’s insightful lecture not only sheds light on the technical aspects of big data but also highlights its potential to drive real-world impact. Whether you are a computing science enthusiast, a researcher, or a professional in the field, this talk offers valuable knowledge and inspiration.
Stay Connected:
For further inquiries or to connect with Professor Han, please email her at the provided contact address.