Events

Heather VanCura (Oracle) – What Is MySQL, and Why Should You Learn it? and Santosh Panda (Cloudrea) – Building modern databases using object stores

Global supply chains have experienced tremendous disruptions over the last few years resulting in a pursuit of resilience. While there are many ways of creating resilience in an enterprise’s value chain including the more headline grabbing trends of re-shoring, securing critical minerals; there are also companies pushing hard for digitalization of cross border operations to better cushion foreseeable and unforeseeable shocks. The use of big data and technology initiatives is not only limited to the private sector but also in the public sector as well. The speaker will share on some of the big data initiatives being adopted in a seemingly far removed world of commercial trade and supply chains.

Mok Sze Xin (Ernst & Young) – Technology and Data in the World of Commercial Trade and Supply Chains

Greater uncertainty in global trade flows and black swan events, such as COVID-19, have increasingly challenged the current supply chain business model. Digital technologies have been recognized as a key enabler for enabling resilient and responsive supply chains. In this seminar, the speaker will share about the ongoing research program Supply Chain 4.0, which aims to research and co-develop digital and automation technologies with industry members to enable responsive, resilient and secured supply chain management processes to prepare company for the ever volatile, uncertain, complex, and ambiguous (VUCA) global supply chain environment. Details of the three research themes will be shared, which include Supply Chain Connectivity, Data-Driven Optimization and Smart Warehouse Automation for next generation logistics.

Jonathan Tan (Unabiz) – Massive IoT: Tiny Sensors, Low Power Network, Unified Interface

EPD Industry Day

Featured in The Business Times – M1 and SUTD to jointly develop 5G robotics technology – Engineering Product Development (EPD)

Matthias Chin (Cloudsine) – Using Deep Learning to Detect Web Defacement Hackings

Signs of Safety: Qualification Devices and the Consumption of Risky Poultry in China’s ‘Wet’ Markets

The nonparametric maximum likelihood estimation (NPMLE) is a classic and important method to estimate the mixture models from finite observations. The discretization of the infinite dimensional probability measure with a fixed support in the NPMLE leads to a finite dimensional convex optimization problem. Although can be solved by off-the-shelf interior point based solvers, the algorithm does not scale well with the number of grid points and the number of observed data points. In this talk, we propose an efficient and scalable semismooth Newton based augmented Lagrangian method (ALM). By carefully exploring the structure of the ALM subproblem, we show that the computational cost of the generalized Hessian (second order information) is independent of the number of grid points. Extensive numerical experiments are conducted to show the effectiveness of our approach.