Events

Sven Janusch (Ubisoft Singapore) – Introduction to fluid simulations based on the Wave Equation

DH Asia Webinar Series: “Locating Digital Archives for An Indian Digital Commons” by Dr. Maya Dodd

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

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.

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.

Eng Wei Koo (Keysight) – Sky to Lab – design challenges and testing considerations leading to successful deployments

Seow Chun Yong (Ensign InfoSecurity) – Hosting Applications on the Cloud and Cloud Security Challenges

Progressively applying Gaussian noise transforms complex data distributions to approximately Gaussian. Reversing this dynamic defines a generative model. When the forward noising process is given by a Stochastic Differential Equation (SDE), Song et al. (2021) demonstrate how the time inhomogeneous drift of the associated reverse-time SDE may be estimated using score-matching. A limitation of this approach is that the forward-time SDE must be run for a sufficiently long time for the final distribution to be approximately Gaussian. In contrast, solving the Schrödinger Bridge problem (SB), i.e. an entropy-regularized optimal transport problem on path spaces, yields diffusions which generate samples from the data distribution in finite time. We present Diffusion SB (DSB), an original approximation of the Iterative Proportional Fitting (IPF) procedure to solve the SB problem, and provide theoretical analysis along with generative modeling experiments. The first DSB iteration recovers the methodology proposed by Song et al. (2021), with the flexibility of using shorter time intervals, as subsequent DSB iterations reduce the discrepancy between the final-time marginal of the forward (resp. backward) SDE with respect to the prior (resp. data) distribution. Beyond generative modeling, DSB offers a widely applicable computational optimal transport tool as the continuous state-space analogue of the popular Sinkhorn algorithm (Cuturi, 2013).

HASS Colloquium Series: Achieving the digital equity for students with special educational needs by Dr. Sin Kuen Fung Kenneth