In full-tensor extensions of 1D interpolation or quadrature rules, the number of nodes will grow exponentially in the dimension—commonly called the “curse of dimensionality.” In this talk, we present an overview of sparse grids, in which the number of nodes grows only polynomially in the dimension. First, we will do an overview of 1D interpolation for…
One of the primary goals of text mining is to group together, organize and summarize textual information. In social network analysis, we typically tie individuals together through social connections — can we connect documents and their contents in a similar fashion? What can social network analysis tell us about the content of a document or…
Students are often given the impression that there is one way to have an academic career, the right way. Reality is much more complicated, but this can also be empowering. We will discuss such things informally, while drawing some examples from my own path: from particle physics at Fermilab and CERN to cancer research at…
The outgoing student chapter president Joey Hart will give a short presentation overviewing SIAM and the opportunities its provides for graduate student members. A discussion time will follow. Pizza will be served.
This will be an informal discussion for graduate students about the opportunities and benefits available through the Society for Industrial and Applied Mathematics (SIAM).
Mathematical modeling is a way for researchers to visualize components of a system and analyze how those components interact. When these models become significantly large, many problems arise. Here are a few: 1. As models increasingly complex with a myriad of parts, it is prudent for the researcher to ask whether all of these…
This will be the first seminar in the SIAM Industry series, which is focused on presenting information about research opportunities in business, industry, and government jobs. Speakers will discuss their career paths and speak on projects they have worked on at their current industrial institution. The DOE Exascale Computing Project (ECP), a collaborative effort between…
This seminar will be an informal panel of some of our fellow graduate students speaking on their experiences with applying to and participating in internships in business, industry, and government. This will be an opportunity for graduate students to hear testimonies about the benefits and types of problems that are addressed when doing an internship…
Machine learning has become widely popular in fields like computer vision, natural language processing, and speech recognition, often performing tasks better than humans. A fundamental building block of many of these algorithms is a neural network known as a multilayer perceptron. In this tutorial we will discuss how to construct these networks and how to…
Kitware develops and supports modeling and simulation platforms that power medical training, planning, and predictive applications for improved patient treatment and outcomes. Our capabilities include whole-body computational physiology models for faster than real-time simulation, surgical planning, and guidance applications, high-fidelity computational fluid dynamics for patient-specific treatment planning, and virtual/augmented reality solutions for immersive training, and…
In this talk, I will present basic Bayesian statistics and multi-source Bayesian data fusion methods. It will start with basic statistical definitions, working through a coin flip problem. From there, I will outline multi-source fusion using several simple methods that require the previous Bayesian background. I will also provide several motivating examples, including one drawn…
This talk will report on my experiences as a Mathematician at Space and Naval Warfare (SPAWAR) Systems Center Atlantic. After an introduction, I will provide an overview of SPAWAR Atlantic, talk in general about my opportunities and experiences as a Mathematics PhD in the government, and then give examples of research projects I support. Finally,…
How much is an idea really worth? What defines success for a product? How can we quantify "better" or “worse”? At Microsoft we have tens of thousands of engineers and data scientists trying to improve products that touch over a billion people worldwide. The data scale is enormous, and we're trying to learn from that…
Convex optimization problems appear naturally across the sciences in fields such as compressed sensing, statistics, machine learning, image processing, and inverse problems. This talk will discuss the theory, applications, and methods of proximal minimization: a wide-reaching subset of convex optimization. Advantages: you do not need your optimization problem to be differentiable (e.g. minimization problems with l-1…
The SIAM student chapter will be hosting an introductory lecture aimed at Undergraduates and Graduate students on national SIAM events, SIAM resources, and upcoming student chapter events. Cookies and coffee will be provided.
Machine learning has become widely popular in fields like computer vision, natural language processing, and speech recognition, often performing tasks better than humans. A fundamental building block of many of these algorithms is a neural network known as a multilayer perceptron. In this tutorial we will discuss how to construct these networks and how to train them using back propagation…
Mike Thompson, the Managing Director and CEO of First Analytics, will be giving a seminar on how machine learning and mathematics are used to handle large data analytics programs at First Analytics.
Kitware researchers Dr. Rachel Clipp and Dr. Matt Brown will talk about their transition from academic research to R&D at a private company as well as some of their active projects. Kitware is a software research and development company with expertise in computer vision, data and analytics, high-performance computing and visualization, medical computing, and software…
Profs. Ilse Ipsen, Ralph Smith, and Mansoor Haider will provide their insight on experiences in academic job hiring. This panel will provide graduate students interested in careers in academia with useful advice on job interviews and the job market in academia.
How do we know that we can trust our machine learning models? How do we detect when an adversary is exploiting our machine learning solutions? How does the explainability of machine learning models help the customers of such models versus helping the adversaries of such models? In this talk we will examine recent issues in Machine…