Upcoming Events

Equivariant machine learning

Data Science Seminar

Soledad Villar (John Hopkins University)

Register to join via Zoom

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. 

Data Science to Software Engineering and Back Again

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.

Optimal shrinkage of singular values under noise with separable covariance & its application to fetal ECG analysis

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.

Lecture: Mauro Maggioni

Data Science Seminar

Mauro Maggioni (Johns Hopkins University)

Lecture: Luke Jacobsen and Jeff Lande

Industrial Problems Seminar

Luke Jacobsen (Medtronic), Jeff Lande (Medtronic)

Lecture: Meng-Yu (Jennifer) Kuo

Data Science Seminar

Meng-Yu (Jennifer) Kuo (University of Minnesota, Twin Cities)

Lecture: Natalia Alexandrov

Industrial Problems Seminar

Natalia Alexandrov (NASA Langley Research Center)

Lecture: Brittany Baker

Industrial Problems Seminar

Brittany Baker (The Hartford)

Lecture: Yuxin Chen

Data Science Seminar

Yuxin Chen (University of Pennsylvania)

Lecture: Brittan Farmer

Industrial Problems Seminar

Brittan Farmer (The Boeing Company)