Events

Hank Tang & Denghui Yao (LightSpeed Studios Singapore) – How to Develop a Video Game in Real Life? – from the industry perspective

Nguyen Huu-Hai (Standard Chartered) – Functional Programming in Financial Markets

Robin Foe & Tham Hon Hoe (OCBC Bank) – OCBC Cloud Native Journey

HASS Colloquium Series: Supplying Colors and en-Route Science: German Synthetic Dyestuffs in Modern China in the Early 20th Century by Ms. Lejie Zeng

Decision trees are important both as interpretable models, amenable to high-stakes decision-making, and as building blocks of ensemble methods such as random forests and gradient boosting. Their statistical properties, however, are not yet well understood. In particular, it is unclear why there is a prediction performance gap between them and powerful but uninterpretable machine learning methods. In this talk, we discuss how to bridge this gap partially via Hierarchical Shrinkage (HS), a post-hoc algorithm which regularizes the tree not by altering its structure, but by shrinking the prediction over each leaf toward the sample means over each of its ancestors. Furthermore, we discuss generalization lower bounds that reveal some of the inductive biases of tree-based methods, and how HS helps to overcome some of it.

Kendrik Tan & Selvi Yang (NCS Group) – Unboxing UX Design: The Art and Science

We introduce a framework to formally connect causal inference with tensor completion. In particular, we represent the various potential outcomes (i.e., counterfactuals) of interest through a tensor. The key theoretical results presented are: (i) Formal identification results establishing under what missingness patterns, latent confounding, and structure on the tensor is recovery of unobserved potential outcomes possible. (ii) Introducing novel estimators to recover these unobserved potential outcomes and proving they are finite-sample consistent and asymptotically normal. Through this analysis, we show how to generalize synthetic controls to product counterfactuals under treatment, and how to do entry-wise estimation and inference for matrix completion with missing not at random (MNAR) data. The efficacy of our framework is shown on high-impact applications. These include working with: (i) TaurRx Therapeutics to identify patient sub-populations where their therapy was effective. (ii) Uber Technologies on evaluating the impact of driver engagement policies without running an A/B test. (iii) The Poverty Action Lab at MIT to make personalized policy recommendations to improve childhood immunization rates across villages in Haryana, India.

Yethoven Tukimin (Meta) – Building Immersive Future of Work

Women In Engineering – EPD Lecture Series in partnership with The James Dyson Foundation

DH Asia Webinar Series: New Horizon of Digital Humanities? Computational Social Science and its Application by Dr. Hsuanlei Shao