Events

Karthyek Murthy & Dai Gengling (Singapore University of Technology and Design) – Presentations by the Aviation Studies Institute

The Data and Business Analytics course provides an introduction to the Engineering Systems and Design (ESD) pillar. It focuses on using industrial or commercial data to identify an opportunity for system improvement and estimating the value of this improvement to the system owner. A signature feature of the course and the primary motivation for the course material comes from a team-based, semester-long project for an external client.

Cyber security and resilience are major challenges in our modern economies; this is why they are top priorities on the agenda of governments, security and defense forces, management of companies and organizations. Hence, the need of a deep understanding of cyber risks to improve resilience. We propose here an analysis of the database of the cyber complaints filed at the French Gendarmerie Nationale. We perform this analysis with a new algorithm developed for non-negative asymmetric heavy-tailed data, which could become a handy tool for applied fields, including operations research. This method gives a good estimation of the full distribution including the tail. Our study confirms the finiteness of the loss expectation, necessary condition for insurability. Finally, we draw the consequences of this model for risk management, compare its results to other standard EVT models, and lay the ground for a classification of attacks based on the fatness of the tail. This is a joint work with M. Dacorogna and N. Debbabi.

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.

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.

Financial institutions (FIs) are leading targets of cyber-attacks. Banks are where the money is, and for cybercriminals, attacking banks offers multiple avenues for profit through extortion, theft, and fraud, while nation-states and hacktivists also target the financial sector for political and ideological leverage. Regulators are taking notice, and raising the bar and their expectations from FIs to ensure customers data and funds are well protected.

Within our session we will discuss on the everchanging cyber landscape, the emerging threats and the challenges they bring to financial insinuations and the people who operates in the Information and Cyber Security space.

Dor will refer to the SUTD graduates program within SCB and how the grads can contribute to the Information Cyber Security program within SCB.

We define a new class of set functions that in addition to being monotone and subadditive, also admit a very limited form of submodularity defined over a permutation of the ground set. We refer to this permutation as a submodular order. We give fast and best possible approximation algorithms for constrained maximization of submodular order functions. Applying this new notion to the problem of assortment optimization, we obtain new and, in some cases, the first constant factor guarantees for constrained assortment optimization in fundamental choice models. We also show an intriguing connection to the maximization of monotone submodular functions in the streaming model, where we recover best known approximation guarantees as a corollary of our results.

Moving goods in a globalized world is not easy but with automation to one integrated platform for full visibility and see better performance is possible. Currently, we leverage on technology to automate our shipment-based tracking on delivery status, respond to immediate adjustments and provide data to our clients. We foresee the future with shift to using robotic forklifts, automated storage systems in warehouses with robots picking and retrieving the goods on the shelves. Manpower equipped with specialized skills is crucial for adoption of these new technologies.
We must still create an identity through experience, commitment and working collaboratively are important; we want to ingrain our mission statement to all staff “People before Profit – Getting it Right First Time, Every Time”.

Motivated by emerging energy-intensive applications in electric vehicle charging and cloud computing, my research has focused on large-scale stochastic deadline scheduling problems under random task arrivals and processing cost. The objective is to minimize the expected sum of stochastic processing cost (due to intermittent renewable generation and fluctuating energy prices) and delay penalty cost (resulting from failures to finish tasks before user-specified deadlines). The hardness in these problems stems from the unknown dynamics of system uncertainties, and the high dimensionality in both the system state and action spaces. This talk overviews our research results on the rigorous establishment of structural optimal policy characterizations under discrete/continuous action spaces and convex/discrete delay penalties. The established optimal policy characterizations do not rely on any probabilistic assumption on the evolution of system uncertainties, and therefore can be naturally integrated into data-driven deep reinforcement learning (DRL) approaches for action space dimensionality reduction without loss of optimality. Numerical results on real-world data show that the proposed approach outperforms state-of-the-art DRL and MPC (model predictive control) based approaches.