Events

James Fu (Venti Technologies) – Technical Challenges in Autonomous Driving

SUTD x ESSEC Create4Good Hackathon

Dr. Pang Yang Huei’s Presentation with the Academy of Singapore Teachers

ARISE Program – Validate

In this talk, we will be interested in inverse problems arising in the signal and image processing field. Solving such problems imply in a fist time to formalise the direct problem by understanding the physics behind and in a second time, to solve the associated inverse problem, through a variational formulation, that is, solving an optimization problem. Classical optimization-based approaches consist in, once the optimization problem has been formulated, proposing iterative procedures converging to a solution of the considered inverse problem. More recently, unrolled neural networks have been proposed. They combine optimization and learning, constitute interpretable networks and integrate information about the direct model. We will study and describe such networks for the resolution of two inverse problems: image deconvolution and robust PCA.

Entrepreneurship Capstone Selection Pitch

The GEAR Future City Festival is back! In collaboration with the Building and Construction Authority (BCA), Singapore Management University’s (SMU) Institute of Innovation & Entrepreneurship (IIE), and Singapore University of Technology and Design (SUTD), this Built Environment Innovation Exchange (BEIX) event will feature insightful conversations with thought leaders, discussions on the next big ideas in the industry, and various initiatives we’re taking to nurture emerging talents.

ISDN-SUTD Drone Workshop and Racing March 2024

In physical experiments, the response of a process or system can be affected by three categories of variables: experimental variables or factors to be investigated, observable variables assumed to be fixed, and lurking variables that are unknown or unmeasurable. The experimental variables are often assumed to be independent of other variables with “constant values”. This talk shows the violation of this assumption in a 3D printing experiment and proposes to infer and control the effects of lurking variables through an effect equivalence approach.

The Laboratory for Air Transportation, Infrastructure, and Connected Environments (LATTICE), situated in the Department of Aerospace Engineering at the University of Michigan, Ann Arbor, is focused on identifying and addressing research problems that contribute towards a safer, more efficient, more resilient, as well as user- and equity-oriented air transportation system and other societal-scale infrastructures. In this presentation, I will cover some ongoing and recently completed projects at LATTICE, spanning methods such as stochastic/robust optimization, bandit (exploration/exploitation) strategies, combinatorial programs (e.g., facility location problems, vehicle routing problems), reinforcement learning, and probabilistic privacy, applied to a variety of settings ranging from air traffic management, UAS traffic management, and space systems/infrastructures. The overarching goal of my presentation is to solicit feedback and spur discussions, potentially leading to new and exciting interdisciplinary and international collaborations.