I am an NSF postdoctoral fellow at the Mathematical Biosciences Institute in Columbus Ohio. I am generally interested in using statistical mechanics to understand biology, and formal methods for dimensionality reduction in dynamical systems. I am also interested in applications of the probabalistic method in number theory.
My previous work has been primarily on inverse problems using methods of statistical physics with applications to imaging in biology. I paid particular attention to ion and oxygen homeostasis in the brain, with the goal of gaining an understanding of a phenomenon known as cortical spreading depression.From 2008-2012, I was a graduate student in the UCLA Department of Biomathematics (Department of Mathematical and Computational Biology), an applied mathematics program with emphasis on biological applications. My graduate advisors were Tom Chou, KC Brennan, and Van Savage. Additionally, I previously had completed an MS in Statistics under the tutelage of Rick Schoenberg, and Jan de Leeuw. To find out about my current research projects, come and pay me a visit, or check my calendar to see if I am visiting someplace near you. Below I have summarized some of the research projects for which I have submitted papers or have had papers published.
Spreading depression is a wave of depolarization and subsequent depression that travels in gray matter in the brain. It involves drastic changes in ionic balances and brain metabolism. Previously, the connection between these two aspects of this phenomenon were not clear. Using a lumped vascular model that is controlled by extracellular ion concentrations, and a model for oxygen use and delivery, we examined the connection.
Inverse problems often require the use of "regularization" tricks, particularly when they are ill-posed or ill-conditioned. Recent work has elucidated the connection between the commonly used techniques of Tikhonov regularization and Bayesian Gaussian random fields. This interpretation of regularization suggests that one should use regularizing terms that are consistent with a-priori knowledge of the desired field. The resulting method, using the path-integral-formulation of random fields, is amenable to deterministic perturbative approximation. Such an approach is desirable compared to standard computational methods of inversion which typically involve the use of Markov-Chan Monte-Carlo methods.
Usually, when seeking to identify objects in images, one knows the general shapes of the objects that one is looking for. When this knowledge is available as a set of possible templates, a natural technique for representation of said knowledge is through kernel density estimation. With the prior knowledge, one can frame segmentation as Bayesian inference, resulting in an energy minimization problem that is nonlinear. This nonlinearity can be ameliorated through the use of majorization minimization to find an algorithm such that image segmentations can be found quickly using graph cuts.
This project extended particle filtering, a stochastic variant of the Kalman filter, to the tracking of moving interfaces in image sequences. The fast marching method is used as a model for the evolution of the interface, and Gaussian random fields are used to estimate and predict the future speed and position of the interface for use in regularization.
Spreading depression is a large metabolic event, where the dicharge of many ions into the extracellular space requires the large use of ATP and hence oxygen for recovery. Furthermore, blood flow in the brain is disrupted as the vascular system reacts to local increases in extracellular potassium. This study investigated these changes using second-derivative optical spectroscopy. Second-derivative spectroscopy shows changes in blood oxygenation level because of spectroscopic signatures of oxy- and deoxy- hemoglobin are different. Furthermore, both spectra have high curvature, so second derivative spectroscopy is able to control for background absorbers while maintaining the blood signal. In this study, we also found a prolonged second phase of imbalance lasting approximately an hour and starting approximately five minutes after the initial spreading depression event. This phase of the phenomenon is poorly understood and may be highly relevant to brain disorder.
Modeling ambulance dispatch events as a spatial-temporal point-process, one is able to identify clustering in ambulance response time. To some degree, this clustering is expected since ambulances are a finite resource. If a large amount of EMS calls are concentrated in a single area, it adversely affects the ability of the system to respond to future calls, at least while the current ambulances are still busy servicing previous calls. Point-process modeling allows one to identify areas that are performing well with adversely high loads, and areas that are more vulnerable to extraordinary emergencies.