Chinedu Eleh
(PhD Candidate, DMS)
This talk, part of the DSSA Seminar Series, delves into the essential tools for launching your data science journey. We'll explore how to set up Python, the workhorse programming language of data science, and leverage GitHub for version control and collaboration. We'll also unpack the benefits of using Trello to effectively manage your data science projects, from task organization to tracking progress. Whether you're a seasoned data scientist or just starting out, this talk equips you with the foundational knowledge to streamline your workflow and tackle data science projects efficiently. Program Link
Mathematics is a fascinating subject used to understand functions' properties and behavior. From simple precalculus to challenging graduate-level courses, there is an intricate web of functions to explore. Unfortunately, functions that arise from real life problems are elusive, hard to characterize and can often only be approximated. In this talk, we will discuss practical methods used to uncover valuable functions in a variety of applications. Program Link
Mathematics is a fascinating subject used to understand functions' properties and behavior. From simple precalculus to challenging graduate-level courses, there is an intricate web of functions to explore. Unfortunately, functions that arise from real life problems are elusive, hard to characterize and can often only be approximated. In this talk, we will discuss practical methods used to uncover valuable functions in a variety of applications. Program Link
Partial differential equation models often involve space-dependent parameters, such as diffusion coefficients and advection fields, that cannot be measured explicitly and are therefore uncertain. Midpoint (MP), spatial averaging (SA), shape function (SF) and series expan- sion (SE) methods have long existed in the stochastic simulation com- munity and are well known for their computational hurdles. In this work, we compute efficient, spatially adaptive, lower-dimensional ap- proximations of these fields, using transport maps. Such parsimonious representations of the parameter space would greatly improve the effi- ciency of the resulting stochastic simulations, allow for more targeted use of reduced order models, and aid in the related design of interven- tions. Numerical examples demonstrate our theoretical results. Program Link