Events

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.

Assistant Professor Ang Yee Sin’s research team members have won two awards in the International Symposium on Advanced Functional Materials (ISAFM2). The symposium is organised by Monash University Malaysia.

SMT Senior Research Assistant, Tan Chuin Wei has won the Champion award for the Modelling category (best presentation).
SMT visiting student, Su Tong (funded by CSC) has won the best poster design award.

Congratulations to both Chuin Wei and Su Tong for their hard work and achievements!

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.

Assistant Professor Liu Xiaogang’s PhD student, Wu Xia, presented the research work titled “Molecular Design of Dual-Emission Rhodamine Analogs” in the 4th Asian Conference on Chemosensors & Imaging Probes (Asian-ChIP 2023), and she won the best oral presentation award.

This is a big achievement and a testament to her hard work, dedication, and talent. Congratulations to Wu Xia!

Congratulations to Associate Professor Wu Lin’s graduate student in winning the best student paper award

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”.

Archifuture Design Challenge 2023

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.

Clustering is a fundamental problem in unsupervised learning. In this talk, we will introduce a convex clustering model, which can be regarded as a convex relaxation to the K-means with some favorable properties. We will establish sufficient conditions for the perfect recovery guarantee of the general weighted convex clustering model, which also improves existing theoretical results of the convex clustering model with uniform weights. In addition, we will introduce a highly efficient algorithm and a dimension reduction technique for solving the convex clustering model. Extensive numerical results will also demonstrate the superior performance of the convex clustering model and the proposed algorithms.