Past Events

Industrial Problems Seminar

Natalia Alexandrov (NASA Langley Research Center)

Registration is required to access the Zoom webinar.

Data Science Seminar

March Boedihardjo (ETH Zürich)

Registration is required to access the Zoom webinar.

Industrial Problems Seminar

Luke Jacobsen (Medtronic), Jeff Lande (Medtronic)

Data Science Seminar

Mauro Maggioni (Johns Hopkins University)

You may attend the talk either in person in Walter 402 or register via Zoom. Registration is required to access the Zoom webinar.

Title: Two estimation problems for dynamical systems: linear systems on graphs, and interacting particle systems

Abstract: We are interested in problems where certain key parameters of a dynamical system need to be estimated from observations of trajectories of the dynamical systems. In this talk I will discuss two problems of this type.

The first one is the following: suppose we have a linear dynamical systems on a graph, represented by a matrix A. For example, A may be a random walk on the graph. Suppose we observe some entries of A, some entries of A^2, …, some entries of A^T, for some time T, and wish to estimate A. We are interested in the regime when the number of entries observed at each time is small relative to the total number of entries of A. When T=1 and A is low-rank, this is a matrix completion problem. When T>1, the problem is interesting also in the case when A is not low rank, as one may hope that sampling at multiple times can compensate for the small number of entries observed at each time. We develop conditions that ensure that this estimation problem is well-posted, introduce a procedure for estimating A by reducing the problem to the matrix completion of a low-rank structured block-Hankel matrix, obtain results that capture at least some of trade-offs between sampling in space and time, and finally show that this estimator can be constructed by a fast algorithm that provably locally converges quadratically to A. We verify this numerically on a variety of examples. This is joint work with C. Kuemmerle and S. Tang.

The second problem is when the dynamical system is nonlinear, and models a set of interacting agents. These systems are ubiquitous in science, from modeling of particles in Physics to prey-predator and colony models in Biology, to opinion dynamics in social sciences. Oftentimes the laws of interactions between the agents are quite simple, for example they depend only on pairwise interactions, and only on pairwise distance in each interaction. We consider the following inference problem for a system of interacting particles or agents: given only observed trajectories of the agents in the system, can we learn what the laws of interactions are? We would like to do this without assuming any particular form for the interaction laws, i.e. they might be “any” function of pairwise distances. We discuss when this problem is well-posed, we construct estimators for the interaction kernels with provably good statistically and computational properties, and discuss extensions to second-order systems, more general interaction kernels, and stochastic systems. We measure empirically the performance of our techniques on various examples, that include extensions to agent systems with different types of agents, second-order systems, families of systems with parametric interaction kernels, and settings where the interaction kernels depend on unknown variables. We also conduct numerical experiments to test the large time behavior of these systems, especially in the cases where they exhibit emergent behavior. This is joint work with F. Lu, J. Feng, P. Martin, J.Miller, S. Tang and M. Zhong.

Data Science Seminar

Pei-Chun Su (Duke University)

You may attend the talk either in person in Walter 402 or register via Zoom. Registration is required to access the Zoom webinar.

Abstract

High dimensional noisy dataset is commonly encountered in many scientific fields, and a critical step in data analysis is denoising. Under the white noise assumption, optimal shrinkage has been well-developed and widely applied to many problems. However, in practice, noise is usually colored and dependent, and the algorithm needs modification. We introduce a novel fully data-driven optimal shrinkage algorithm when the noise satisfies the separable covariance structure. The novelty involves a precise rank estimation and an accurate imputation strategy. In addition to showing theoretical supports under the random matrix framework, we show the performance of our algorithm in simulated datasets and apply the algorithm to extract fetal electrocardiogram from the benchmark trans-abdominal maternal electrocardiogram, which is a special single-channel blind source separation challenge.

Industrial Problems Seminar

Cora Brown (Bridge Financial Technology)

You may attend the talk either in person in Walter 402 or register via Zoom. Registration is required to access the Zoom webinar.

Abstract

In this talk I will discuss my early career as a Data Scientist and Software Engineer. The skills necessary for these two types of roles overlap and complement each other. Drawing on my experiences in both fields, I will share some of the skills I’ve found valuable in each position and why I’ve chosen to follow this path. I will focus on the ways in which developing solid software skills have made me a better Data Scientist. Finally, I will describe some of the specific problems I’ve worked on as a Data Scientist and Software Engineer and how a background in mathematics can aid in solving these problems.

Data Science Seminar

Soledad Villar (John Hopkins University)

Abstract

In this talk we will give an overview of the enormous progress in the last few years, by several research groups, in designing machine learning methods that respect the fundamental symmetries and coordinate freedoms of physical law. Some of these frameworks make use of irreducible representations, some make use of high-order tensor objects, and some apply symmetry-enforcing constraints. Different physical laws obey different combinations of fundamental symmetries, but a large fraction (possibly all) of classical physics is equivariant to translation, rotation, reflection (parity), boost (relativity), units scalings, and permutations. We show that it is simple to parameterize universally approximating polynomial functions that are equivariant under these symmetries, or under the Euclidean, Lorentz, and Poincare groups, at any dimensionality d. The key observation is that nonlinear O(d)-equivariant (and related-group-equivariant) functions can be universally expressed in terms of a lightweight collection of (dimensionless) scalars -- scalar products and scalar contractions of the scalar, vector, and tensor inputs. We complement our theory with numerical examples that show that the scalar-based method is simple, efficient, and scalable, and mention ongoing work on cosmology simulations. 

Industrial Problems Seminar

Austin Tuttle (Open Systems International)

Slides

Abstract

There are more choices for a career than data scientist!

I will talk about what it is like to work as a software developer, my experience in industry, and how I got here.

Power systems are getting increasingly complex(distributed generation, more interconnections) and automated(more measurements and remote controls). Sifting through all this new data is a very complex problem and with the large diversity of power systems around the world there are many problems that can arise.

We will discuss some of the mathematics that shows up when managing a power grid. And discuss several problems that we solve. At a high level these relate to:

1. How do you provide situation awareness to a grid operator
2. Can you efficiently detect violations and resolve them with minimal intervention
3. Utilize grid components to minimize power losses while maintaining grid stability
4. Detect an outage, restore customers, locate the cause, and dispatch crews.

Data Science Seminar

Yunan Yang (ETH Zürich)

You may attend the talk either in person in Walter 402 or register via Zoom. Registration is required to access the Zoom webinar.

Many models in machine learning and PDE-based inverse problems exhibit intrinsic spectral properties, which have been used to explain the generalization capacity and the ill-posedness of such problems. In this talk, we discuss weighted training for computational learning and inversion with noisy data. The highlight of the proposed framework is that we allow weighting in both the parameter space and the data space. The weighting scheme encodes both a priori knowledge of the object to be learned and a strategy to weight the contribution of training data in the loss function. We demonstrate that appropriate weighting from prior knowledge can improve the generalization capability of the learned model in both machine learning and PDE-based inverse problems.

Data Science Seminar

Tolga Birdal (Imperial College London)

Registration is required to access the Zoom webinar.

Abstract

Stochastic differential equations have lied at the heart of Bayesian inference even before being popularized by the recent diffusion models. Different discretizations corresponding to different MCMC implementations have been useful in sampling from non-convex posteriors. Through a series of papers, Tolga and friends have demonstrated that this family of methods are well applicable to the geometric problems arising in 3D computer vision. Before inviting the rest of this community to geometric diffusion models, Tolga will share his perspectives on two topics: (i) foundational tool of Riemannian MCMC methods for geometric inference and (ii) applications in probabilistic multi view pose estimation as well as inference of combinatorial entities such as correspondences. If time permits, Tolga will continue with his explorations in optimal-transport driven non-parametric methods for inference on Riemannian manifolds. Relevant papers include:

Bayesian Pose Graph Optimization [NeurIPS 2018]

Pobabilistic Permutation Synchronization [CVPR 2019 Honorable Mention]

Synchronizing Probability Measures on Rotations [CVPR 2020]