Events

The rapid development and widespread adoption of mobile devices, sensors, and IoT have led to the generation of vast volumes of multi-source, high-dimensional data within the broader framework of smart cities, including transportation, logistics, e-commerce, healthcare, etc. Consequently, numerous data-driven methods have been developed and implemented to address research challenges related to the design and operations of these systems. In this talk, we briefly discuss several research cases on the applications of data-driven methods in smart cities, include: (1) Descriptive methods for mobile transaction digits distribution and crowd-sourcing food delivery; (2) Predictive methods for ICU patient evaluation; (3). Prescriptive method for multi-objective matching in ride-sourcing. Through these cases, we showcase the diverse applications of data-driven methods in addressing some key challenges in smart cities.

Data placement is one of the fundamental allocation problems in storage-capable distributed systems, such as web caches and peer-to-peer networks. Motivated by such applications, we consider the non-metric data placement problem and develop distributed algorithms for computing or approximating its optimal solutions. In this problem, the goal is to store copies of the data points among cache-capacitated servers to minimize overall data storage and clients’ access costs. We first show that the non-metric data placement problem is inapproximable up to a logarithmic factor. We then provide a game-theoretic decomposition of the objective function and show that natural Glauber dynamics will converge to an optimal global solution with polynomial mixing time for a certain range of noise parameters. Moreover, we provide another auction-based distributed algorithm, which allows us to approximate the optimal solution with a theoretical performance guarantee. The proposed algorithms not only provide good performance guarantees but also will enable the system to operate in a distributed manner, hence reducing the computational load and improving the robustness to failures.

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.

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).

It has long been suggested that the biological brain operates at some critical point between two different phases, possibly order and chaos, to maximize the information processing power. Investigating the same hypothesis on the ‘artificial’ brains, i.e. the modern computer vision models, we find that they exhibits the same pattern, i.e. highest test accuracy or lowest test loss at the edge of chaos. A theoretical investigation demonstrates that, the best performance is attributed to the maximal metastable states/periodic cycle length near the edge of chaos, where each metastable state can represent an information point. Applied on a very simple network equivalent of the SK spin glass model and Fashion MNIST dataset, we illustrate a simple and principled training method that can achieve both high accuracy and prevent fitting noisy labels automatically.

Master of Architecture Information Session for Intake 2024