In-Country Events (Vietnam)

• Online presentation via Zoom
• 17 August 2022 (1:30pm-5:50pm), Singapore Standard Time
• 17 August 2022 (12:30pm-4.50pm), Vietnam Standard Time

• Nanyang Technological University, Singapore
• Temasek Foundation, Singapore

• Ho Chi Minh City University of Technology ( https://oisp.hcmut.edu.vn/en/)
• VinUniversity (https://vinuni.edu.vn/)
• AI companies in Vietnam.

• Asst. Prof. Luu Anh Tuan, School of Computer and Engineering, Nanyang Technological University, Singapore.
• Email: [email protected]

Various approaches using different ML were reported for social bot detection. Recently, the emerging Graph Neural Network (GNN) has been increasingly attracting much attention due to its suitable nature to handle social media data. However, when handling large-scale data, this technique expectedly suffers from high computational complexity. To address this issue, we propose SOBOG (SOcial BOt detection using Graph neural networks), which breaks the original huge graph into account-based closure subgraphs for more efficient processing. Experimenting with real Twitter benchmarking datasets, our model gained promising performance, as compared to other related works.

Bio: Dr. Quan Thanh Tho is an Associate Professor in the Faculty of Computer Science and Engineering, Ho Chi Minh City University of Technology (HCMUT), Vietnam. He received his B.Eng. degree in Information Technology from HCMUT in 1998 and received Ph.D degree in 2006 from Nanyang Technological University, Singapore. His current research interests include formal methods, program analysis/verification, the Semantic Web, machine learning/data mining and intelligent systems. Currently, he is the Vice Dean for Academic Affairs of the Faculty Committee.

Abstract: The talk will first give an overview of the research on recommendation systems done by the team of the speaker. Then the rest of the talk will focus on the data issues in recommendation systems. We take an in-depth look at a fundamental but often neglected aspect of the evaluation procedure, i.e. the datasets themselves. To do so, we adopt a systematic and comprehensive approach to understand the datasets used for implicit feedback based top-K recommendation. We look at the characteristics of these datasets to understand their similarities and differences. Finally, we conduct an empirical study to determine whether the choice of datasets used for evaluation can influence the observations and/or conclusions obtained. Our findings suggest that greater attention needs to be paid to the selection process of datasets used for evaluating recommender systems in order to improve the robustness of the obtained results.

Bio: Professor Cong Gao obtained his PhD in Computer Science from the National University of Singapore (NUS) in 2004. He received both his Master of Engineering and Bachelor of Engineering degrees in Information Systems from Tianjin University, China in 1999 and 1996 respectively. Professor Cong was a Postdoctoral Research Fellow with the University of Edinburgh from 2004 to 2006 and a Researcher with Microsoft Research Asia from 2006 to 2008. He joined the Department of Computer Science in Aalborg University, Denmark, as an Assistant Professor in 2008. He became an Assistant Professor with the School of Computer Science and Engineering (SCSE) in 2010 and was promoted to Associate Professor with Tenure in 2015 and to Professor in 2018. He has a courtesy appointment with the School of Physical and Mathematical Sciences (SPMS) since May 2016.

Professor Cong`s research in data science, including data management and data mining, has made significant impact in the areas of geospatial and textual data management, point of interest recommendation and user preference modelling, and mining social networks and social media. Professor Cong has been an Associate Editor of ACM Transactions on Database Systems, the most prestigious journal for data management, since 2016. He is also currently in the editorial board of Proceeding of Very Large Database (PVLDB) and International Journal of Social Network Mining. He was an Associate Editor of PVLDB from 2013 to 2014. He has been involved in organising several conferences as the PC Vice Chair, PC Co-Chair, and Publication Chair. Notably, he is the PC Vice Chair for IEEE International Conference on Data Engineering.

Abstract: Sensing, understanding and synthesizing the neural avatars intuitively have been a long-pursuing goal of computer vision and graphics, with extensive real-life applications. It is at the core of embodied intelligence. In this talk, I will discuss our work in intuitive neural avatars from four different aspects: versatile perception, generalizable understanding, intuitive generation and creative animation. Our approach has shown its effectiveness and generalizability on a wide range of tasks.

Bio: Prof. Ziwei Liu is a Nanyang Assistant Professor at School of Computer Science and Engineering (SCSE) in Nanyang Technological University, with MMLab@NTU. Previously, he was a research fellow (2018-2020) in CUHK (with Prof. Dahua Lin) and a post-doc researcher (2017-2018) in UC Berkeley (with Prof. Stella X. Yu). His research interests include computer vision, machine learning and computer graphics.

Ziwei received his Ph.D. (2013-2017) from CUHK / Multimedia Lab, advised by Prof. Xiaoou Tang and Prof. Xiaogang Wang. He is fortunate to have internships at Microsoft Research and Google Research. His works include Burst Denoising, CelebA, DeepFashion, Fashion Landmarks, DeepMRF, Voxel Flow, Long Tail, Compound Domain, and Wildlife Conservation. His works have been transferred to products, including Microsoft Pix, SenseGo, and Google Clips.

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• Email: [email protected]