Applied mathematics: Recent submissions
Now showing items 120 of 54

This Brain Is a Mess: Inference, Random Graphs, and Biophysics to Disentangle Neuronal Networks
At first glance, the neuronal network seems like a tangled web in many areas throughout the nervous system. Often, our best guess is that such “messy” connections are close to random, while obeying certain statistical ... 
Uncertainty Quantification Problems in Tsunami Modeling and Reduced Order Models for Hyperbolic Partial Differential Equations
In this thesis, we consider an uncertainty quantification (UQ) problem that arises from tsunami modeling, namely the probabilistic tsunami hazard assessment (PTHA) problem. The goal of PTHA is to compute the probability ... 
High order shock capturing methods with compact stencils for use with adaptive mesh refinement and mapped grids
This thesis focuses on several developments toward creating a high order shock capturing method that can be used on mapped grids with blockstructured adaptive mesh refinement (AMR). The discontinuous Galerkin (DG) method ... 
On neural encoding: its estimation, application, and development
The spiking activity of neurons encodes information about sensory stimuli and about planned or executed motor outputs. An important problem in computational neuroscience is the development of predictive models that describe ... 
Irreversibility in Stochastic Dynamic Models and Efficient Bayesian Inference
This thesis is the summary of an excursion around the topic of reversibility. We start the journal from a classical mechanical view of the “time reversal symmetry”: we look into the details to track the movements of all ... 
The stability and instabilities of stationary solutions to the nonlinear Schroedinger equation and the sineGordon equation
I present an analysis of the stability spectrum of all stationary elliptictype solutions to the focusing Nonlinear Schroedinger equation and the sineGordon equation. An analytical expression for the spectrum is given. ... 
Energy and Charge Transfer in Open Plasmonic Systems
Coherent and collective charge oscillations in metal nanoparticles (MNPs), known as localized surface plasmons, offer unprecedented control and enhancement of optical processes on the nanoscale. Since their discovery in ... 
Data assimilation problems in glaciology
Rising sea levels due to mass loss from Greenland and Antarctica threaten to inun date coastal areas the world over. For the purposes of urban planning and hazard mitigation, policy makers would like to know how much ... 
Multiscale modeling of paracrine PDGFdriven glioma growth and invasion
The most common primary brain tumor in adults, glioma claims thousands of lives each year. Despite efforts to improve survival rates, the standard of care has remain unchanged for more than a decade. Recent research has ... 
On computing shape: a study of the neural processes concerning naturalistic boundary conformation within the ventral visual pathway
The perception of shape is a remarkable computation, solved rapidly by the brain to extract boundary features within natural scenes while being robust against many visual obstacles. Interestingly, while observers typically ... 
Stochastic Modeling of Reversible Biochemical ReactionDiffusion Systems and HighResolution ShockCapturing Methods for Fluid Interfaces
My thesis contains two parts, both of which are motivated by biological problems. One is on stochastic reactiondiffusion for biochemical systems and the other on shockcapturing methods for fluid interfaces. In both parts, ... 
Machine learning and data decompositions for complex networked dynamical systems
Machine learning has become part of our daily lives. Its applications include personalized advertisements, stock price predictions, and selfdriving cars. The goal of this thesis is to study ways to apply machine learning ... 
Dimensionality hyperreduction and machine learning for dynamical systems with varying parameters
This work demonstrates methods for hyper reduction and efficient computation of solutions of dynamical systems using optimization and machine learning techniques. We consider nonlinear partial differential equations that ... 
Dynamic, convex, and robust optimization with Bayesian learning for responseguided dosing
Medical treatment commonly involves the administration of drug doses at multiple timepoints. Intuitively, the higher the doses, the higher the likelihood of disease control as well as the risk of adverse effects and of ... 
Variability in Modified Estimators of VaR and ES
Modified ValueatRisk (mVaR) and Modified Expected Shortfall (mES) are risk estimators that can be calculated without modelling the distribution of asset returns. These modifided estimators use skewness and kurtosis ... 
Multiscale Modeling of Esophageal Adenocarcinoma
Over the past three to four decades, esophageal adenocarcinoma (EAC) incidence has increased dramatically in the Western world due to causes that are not well understood. Current screening strategies for early detection ... 
Interface Problems using the Fokas Method
Interface problems for partial differential equations are initial boundary value problems for which the solution of an equation in one domain prescribes boundary conditions for the equations in adjacent domains. These ... 
Coordinated neural activity: Mechanistic origins and impact on stimulus coding
How does the activity of populations of neurons encode the signals they receive? Since neurons in vivo are inherently variable, each fixed input to a population will elicit not a deterministic response, but rather a ... 
Closing the Loop: Optimal Stimulation of Neuronal Networks via Adaptive Control Algorithms
The Caenorhabditis elegans (C. elegans) worm is a wellstudied biological organism model. The nervous system of C. elegans is particularly appealing to study, since it is a tractable fully functional neuronal network for ... 
Integrating DataDriven Methods in Nonlinear Dynamical Systems: Control, Sparsity and Machine Learning
The goal of my thesis is to provide a theoretical demonstration of how dimension reduc tion, control and machine learning techniques can be applied to optimize the performance of complex nonlinear systems. Specifically, ...