Events

Billio Jeverson and Vieri Vincent (SHIELD) – Applied Information Retrieval

Congratulations to Dr Massimiliano Colla and his project team, Team EverGreen with e-Parali, who won first place at the Global Challenge Lab 2022, hosted by Imperial College London.

This virtual entrepreneurship program brings together students and recent university graduates from across the world to form international teams that generate solutions to achieve the United Nations Sustainable Development Goals. This year’s event focused on Goal 13, Climate Action.

Click here to read on.

Zhang Hao (4Paradigm Technology) – Parallel Computing in Machine Learning Database: A Case Study of OpenMLDB

Francesco Gelli (PayPal) – Applied Information Retrieval

The July-August issue of Society of Chemical Industry did a feature on the counterfeiting industry. The article included case studies of research done by EPD Associate Prof Joel Yang on tiny QR codes and SMT Assistant Prof Liu Xiaogang’s research on phosphorescence for security inks. To read more, click here: https://www.soci.org/chemistry-and-industry/cni-data/2022/7-8/security-details

Eugene Lim (GOVTECH) – Hacking the Synology NAS: Reversing and Fuzzing Hardened

Hengky Wirawan, Cheng Yue (Dyson Singapore) – Dyson Manufacturing Landscape

Algorithmic recourse aims to recommend an informative feedback to overturn an unfavourable machine learning decision. We introduce in this paper the Bayesian recourse, a model-agnostic recourse that minimizes the posterior probability odds ratio. Further, we present its min-max robust counterpart with the goal of hedging against future changes in the machine learning model parameters. The robust counterpart explicitly takes into account possible perturbations of the data in a Gaussian mixture ambiguity set prescribed using the optimal transport (Wasserstein) distance. We show that the resulting worst-case objective function can be decomposed into solving a series of two-dimensional optimization subproblems, and the min-max recourse finding problem is thus amenable to a gradient descent algorithm. Contrary to existing methods for generating the robust recourse, the robust Bayesian recourse does not require a linear approximation step. The numerical experiment demonstrates the effectiveness of our proposed robust Bayesian recourse facing model shifts.

Tay Jun Jie (HDB) – Looking Beyond the Nuts and Bolts of AI

Enterprise Singapore awarded Dr. Teo Tee Hui with the EnterpriseSG-SSC/SAC Commendation Award for his contributions to the Singapore Standardisation Programme. Congratulations!