Upcoming Events

Benefits of Weighted Training in Machine Learning and PDE-based Inverse Problems

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.

How to optimize a power grid

Industrial Problems Seminar

Austin Tuttle (Open Systems International)

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

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.

Lecture: Soledad Villar

Data Science Seminar

Soledad Villar (John Hopkins University)

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)