Technical Sharing Session on AI Robustness

Tianyu Pang is a Senior Research Scientist at Sea AI Lab. He received his Ph.D. degree of Computer Science in 2022 from Tsinghua University, advised by Prof. Jun Zhu. He received his B.S. degree of Mathematics and Physics in 2017 from Tsinghua University, and he was a visiting student researcher at Stanford University (2020) and Carnegie Mellon University (2016).

Tianyu's research interests span the areas of machine learning, including Trustworthy AI and Generative Models. He has published over 40 papers on top-tier conferences and journals including ICML/NeurIPS/ICLR and CVPR/ICCV/ECCV/TPAMI. His published papers have been selected for Spotlight and/or Oral presentations several times, and these papers have received over 8,000 citations (according to Google Scholar). He has won first places in AI safety/security competitions, such as the NeurIPS Adversarial Attacks and Defense Competition (2017), GeekPwn Worldwide Cyber Security Contest (2018, 2019), and Inclusion | A-tech Contest (2020).

Tianyu is a recipient of several awards including the Microsoft Research Asia Fellowship, Baidu Scholarship, NVIDIA Pioneering Research Award, Zhong Shimo Scholarship, Schlumberger Scholarship, WAIC Rising Star Award, CAAI Outstanding Doctoral Dissertation Award.

Seok Min leads the technical research team in Infocomm Media Development (IMDA), driving initiatives in trust-related technologies, such as AI testing and digital watermarking. She began her career in cybersecurity research and has more recently focused on AI governance testing (AI Verify) and the evaluation of generative models (Project Moonshot).

As a data scientist in the Data Science and AI Division (DSAID) at GovTech, Shaun works closely with government agencies to develop and implement best practices in their data science, AI, and MLOps work.

During his time as the data science team lead at MOM, he worked on a variety of projects relating to foreign workforce policy and labour data quality.

Now at GovTech, he focuses on developing MLOps best practices and working on Responsible AI for the whole-of-government.

Jessica holds a B.A. Economics from the University of Pennsylvania and a M.Sc. Statistics from the University of Chicago. She has led a variety of data science projects at the Ministry of Trade and Industry, focusing on network analytics, machine learning causal inference and policy evaluation.

At GovTech, she has developed computer vision platforms and algorithms for explainability, and is part of growing efforts to improve testing and responsible AI across government AI applications.