Shui Yu

University of Technology Sydney,

 Title: High Quality Paper Writing – persuasive writing with evidence

Abstract: High quality publication is an important metric to most of us (faculty members, especially PhD students). Many passionate young researchers struggle to find the right way to achieve their success in publication. In this talk, we would like to discuss this from different perspectives as an editor, a reviewer, and an author: the key is persuasive writing with evidence. We humbly hope the talk will shed light for ambitious hard-working young researchers.

Bio: Shui Yu is a Professor of School of Computer Science, University of Technology Sydney, Australia. His research interest includes Security and Privacy, Network Science, Big Data, and Mathematical Modelling. He has published four monographs and edited two books, more than 400 technical papers with an h-index of 63. He is currently serving a number of prestigious editorial boards, including IEEE Communications Surveys and Tutorials and IEEE Communications Magazine. He is a Senior Member of IEEE, a member of AAAS and ACM, a Distinguished Lecturer of IEEE Communication Society, and an elected member of the Board of Governor of IEEE Vehicular Technology Society.


Tung DAO


 Title: Building Better Models Faster: A Charming Tool for Versioning Data and Experiments

Abstract: Machine Learning is experimental in nature. We try various features, algorithms, modeling techniques, and parameter configurations in order to find the best solution for the problem as quickly as possible. The challenge is keeping track of what worked and what didn’t, as well as preserving reproducibility while maximizing code reusability. This is where MLOps comes to play an essential role in Machine Learning systems development. This tutorial equips ML researchers and practitioners, who want to automate and operate their ML products, with a lightweight and interoperable MLOps tool to spend less time manually tracking results, version datasets and experiments with reproducibility, and tune hyperparameters automatically.

Bio: Tung Dao is a Senior ML Platform Engineer at Shopee (Singapore) where he is focusing on developing a distributed training framework and orchestrating model deployment at the production scale. He is also an MLOps Lead at AICOVIDVN, a community project collaborated with Vietnam Government Agencies, where he architects AWS-based ML systems for a cough-based COVID-19 detection application. Previously, he was a Machine Learning Engineer at Dell Technologies where he focused on building Deep Learning models for cloud-based Computer Vision applications. Tung graduated in Computer Engineering from Hanoi University of Science and Technology and received a Research Scholarship to research about Computer Vision at Nanyang Technological University (Singapore). He has experience as a consultant in designing ML systems for different private companies in Singapore and Vietnam. He is also the Founder of an AI blog ( and the Admin of the MLOps Vietnam community.