ÌÒÉ«ÊÓƵ

Subekshya Bidari, Department of Applied Mathematics, ÌÒÉ«ÊÓƵEvidence accumulation models of social foragingForaging is often modeled as a sequence of patch-leaving decisions. The distribution of food in the environment is

Anthony Kearsley, Mathematical Analysis and Modeling Group, National Institute of Standards and Technology (NIST)Control of inward solidification in CryobiologyFor many years, mathematical models that predict a cell’s response to encroachingÌý

Erin Ellefsen and Lindsey Wong, Department of Applied Mathematics, ÌÒÉ«ÊÓƵLyndsey’s Title: Mathematical Models of Wealth Distribution Through an Amenities-Based Theory Erin’s Title: Efficiently finding Equilibrium Solutions of

Nick Barendregt, Department of Applied Mathematics, ÌÒÉ«ÊÓƵAdaptive Decision Rules are Optimal in Simple EnvironmentsDecision-making in uncertain environments often requires adaptive forms of evidence accumulation, but less is

Sabina Altus, Department of Applied Mathematics, ÌÒÉ«ÊÓƵMobility Informed Regional Modeling of the COVID-19 Pandemic in ColoradoThe trajectory of the COVID-19 pandemic has varied widely by region, and an understanding of these

Dan Messenger, Department of Applied Mathematics, ÌÒÉ«ÊÓƵWeak-Form Sparse Identification of Nonlinear Dynamics with Applications to Cell MigrationThe weak-form sparse identification of nonlinear dynamics (WSINDy) algorithm for

David Bortz and Nancy Rodriguez, Department of Applied Mathematics, ÌÒÉ«ÊÓƵOverview of Math Biology Research in the Applied Math DepartmentDavid Bortz will give an overview of the math bio group here in the Applied Math

Tahra Eissa, Department of Applied Mathematics, ÌÒÉ«ÊÓƵNormative decision asymmetries with symmetric priors but asymmetric evidenceDecisions based on rare events are challenging because rare events alone can be both

David Stearns, Department of Applied Mathematics, ÌÒÉ«ÊÓƵDynamics and Analysis of Territorial AnimalsThe ways animals form territories, interact with members of their own social group, and interact with members of other

John Nardini, Department of Applied Mathematics, ÌÒÉ«ÊÓƵData-driven modeling for noisy biological data and agent-based ModelsI will consider the problem of inferring the dynamics underlying biological data using two case