Events

James Chong (The Tesseract, NCS Group) – “How to Sell a Better Mousetrap” – How Advanced Analytics and Generative AI is changing the world and how to convince people to use it

According to a UN report from 2022, time is running out to limit temperature rises to 1.5C by 2050. Around $3.5 trillion climate investment per year is needed to build the net zero economy by 2050. This investment is possible only with significant contribution from the private financial sector. This is specifically the case in emerging markets, where public investments aren’t growing as quickly as needed to meet climate targets. There are a quite a few issues related to data disclosures, transparency and complexities, which needs to be tackled in order to efficiently manage the private sustainable investment need. Data Science and Innovation can help tackle these issues and assist the community is making quick and accurate investment decisions.

Pye Sone Kyaw (Government Technology Agency) – Leveraging AI/ML for Transformative Solutions in Diverse Industries

Data is now unanimously considered a key firm asset for enabling better operational decisions. However, data-driven decisions can inadvertently expose private data, leaving firms vulnerable to unforeseen danger. How to manage data security risks by protecting data from being inferred from observable decisions thus becomes an important question. In this paper, we focus on data security in supply chains due to their data-intensive nature. Specifically, we examine a data-driven contextual newsvendor problem. To quantify and ensure data security, we adopt the notion of differential privacy, a mathematically rigorous measure of data security that limits an attacker’s inference accuracy. Employing convolution smoothing and noise injection, we propose several differentially private algorithms that provably guarantee both data security and asymptotic optimality with (near) optimal rates. In the non-asymptotic regime, we further identify three drivers of the cost of data security; namely, dataset size, context, and number of products. This finding suggests that gathering more data, collecting detailed context, and pooling data from multiple products can lower data security cost. Lastly, we examine the impact of a newsvendor’s private algorithms on supply chain partners. We discover additional distortion to the demand signaling process and lower profit share for an upstream supplier.

Andre Sirimanne & Arun Chandrasekaran (Ollion) – Navigating the Data Analytics Frontier: From Concepts to Applications

DH Asia Webinar Series: “Computational Methods and Intangible Cultural Heritage” by Dr. Miguel Escobar Varela

Assessing license applications submitted by Financial Institutions can be a long and manual process with stringent requirements to be met to ensure Singapore remains a secure and stable financial hub. How might we enable a smooth and comprehensive process for Financial Institutions to transact with Monetary Authority of Singapore so that applications can be processed more efficiently? With the launch of eLicensing application, the application process has now become more streamlined with automated processes and built-in validations for forms.

It has long been suggested that the biological brain operates at some critical point between two different phases, possibly order and chaos, to maximize the information processing power. Investigating the same hypothesis on the ‘artificial’ brains, i.e. the modern computer vision models, we find that they exhibits the same pattern, i.e. highest test accuracy or lowest test loss at the edge of chaos. A theoretical investigation demonstrates that, the best performance is attributed to the maximal metastable states/periodic cycle length near the edge of chaos, where each metastable state can represent an information point. Applied on a very simple network equivalent of the SK spin glass model and Fashion MNIST dataset, we illustrate a simple and principled training method that can achieve both high accuracy and prevent fitting noisy labels automatically.

DH Asia Webinar Series: “The Diaries of the Soviet Ambassador in Pyongyang: Data-Specific Network Approaches to North Korean History Studies” by Dr. Donghyun Woo

We consider the diffusion of two alternatives in social networks using a game-theoretic approach. Each individual plays a coordination game with its neighbors repeatedly and decides which to adopt. As products are used in conjunction with others and through repeated interactions, individuals are more interested in their long-term benefits and tend to show trust to others to maximize their long-term utility by choosing a suboptimal option with respect to instantaneous payoff. To capture such trust behavior, we deploy limited-trust equilibrium (LTE) in diffusion process. We analyze the convergence of emerging dynamics to equilibrium points using mean-field approximation and study the equilibrium state and the convergence rate of diffusion using absorption probability and expected absorption time of a reduced-size absorbing Markov chain. We also show that the diffusion model on LTE under the best-response strategy can be converted to the well-known linear threshold model. Simulation results show that when agents behave trustworthy, their long-term utility will increase significantly compared to the case when they are solely self-interested. Moreover, the Markov chain analysis provides a good estimate of convergence properties over random networks.