Postdoctoral Associate

Stony Brook University

Nov 2016 – Present
Develop and implement methods for the analysis of next-generation sequencing data, machine learning and the quantitative analysis of biological systems. Participate and assist in writing grants, preparing manuscripts for publication in scientific journals, and present research at scientific conferences.

Research Associate

The Joint School of Nanoscience and Nanoengineering

Jun 2016 – Nov 2016
Concentrate on the design of mathematical models and develop software applications to study the interaction of nanostructures with biological organisms in environmental and medical applications involving the brain and cardiovascular system.


Doctor of Philosophy (Ph.D.)

Computational Neuroscience

Aug 2012 – May 2016

Bachelor of Science (B.S.)

Physics (Cum Laude)

Aug 2008 – May 2012

Research Publications


The Relationship Between Nernst Equilibrium Variability and The Multifractality of Interspike Intervals in The Hippocampus

The Journal of Computational Neuroscience

April 2017
Spatiotemporal patterns of action potentials are considered to be closely related to information processing in the brain. Auto-generating neurons contributing to these processing tasks are known to cause multifractal behavior in the inter-spike intervals of the output action potentials. In this paper we define a novel relationship between this multifractality and the adaptive Nernst equilibrium in hippocampal neurons. Using this relationship we are able to differentiate between various drugs at varying dosages. Conventional methods limit their ability to account for cellular charge depletion by not including these adaptive Nernst equilibria. Our results provide a new theoretical approach for measuring the effects which drugs have on single-cell dynamics.

Non-invasive evaluation of cardiac repolarization in mice exposed to single-wall carbon nanotubes and ceria nanoparticles via intratracheal instillation

Environmental Science: Nano

March 2016
We present results obtained from electrocardiogram (ECG) measurements performed on mice exposed to single-walled carbon nanotubes and ceria nanoparticles through instillation. From these non-invasive ECG measurements, QT and RR intervals were obtained at various times after exposure and used to compute a novel metric for evaluating cardiac signal propagation stability, the reserve of refractoriness (RoR). It is demonstrated that while the isolated QT and RR intervals are essentially uncorrelated with histological data from hematoxylin and eosin stains of control and exposed tissue samples, the RoR is sensitive to cardiovascular effects from the exposure to nanoparticles.

Bursting Regimes in a Reaction-Diffusion System with Action Potential-Dependent Equilibrium

Plos One

March 2015
The equilibrium Nernst potential plays a critical role in neural cell dynamics. A common approximation used in studying electrical dynamics of excitable cells is that the ionic concentrations inside and outside the cell membranes act as charge reservoirs and remain effectively constant during excitation events. Research into brain electrical activity suggests that relaxing this assumption may provide a better understanding of normal and pathophysiological functioning of the brain. In this paper we explore time-dependent ionic concentrations by allowing the ion-specific Nernst potentials to vary with developing transmembrane potential. As a specific implementation, we incorporate the potential-dependent Nernst shift into a one-dimensional Morris-Lecar reaction-diffusion model. Our main findings result from a region in parameter space where self-sustaining oscillations occur without external forcing. Studying the system close to the bifurcation boundary, we explore the vulnerability of the system with respect to external stimulations which disrupt these oscillations and send the system to a stable equilibrium. We also present results for an extended, one-dimensional cable of excitable tissue tuned to this parameter regime and stimulated, giving rise to complex spatiotemporal pattern formation. Potential applications to the emergence of neuronal bursting in similar two-variable systems and to pathophysiological seizure-like activity are discussed.

Multiple Mating But Not Recombination Causes Quantitative Increase in Offspring Genetic Diversity for Varying Genetic Architectures

Plos One

October 2012
Explaining the evolution of sex and recombination is particularly intriguing for some species of eusocial insects because they display exceptionally high mating frequencies and genomic recombination rates. Explanations for both phenomena are based on the notion that both increase colony genetic diversity, with demonstrated benefits for colony disease resistance and division of labor. However, the relative contributions of mating number and recombination rate to colony genetic diversity have never been simultaneously assessed. Our study simulates colonies, assuming different mating numbers, recombination rates, and genetic architectures, to assess their worker genotypic diversity. The number of loci has a strong negative effect on genotypic diversity when the allelic effects are inversely scaled to locus number. In contrast, dominance, epistasis, lethal effects, or limiting the allelic diversity at each locus does not significantly affect the model outcomes. Mating number increases colony genotypic variance and lowers variation among colonies with quickly diminishing returns. Genomic recombination rate does not affect intra- and inter-colonial genotypic variance, regardless of mating frequency and genetic architecture. Recombination slightly increases the genotypic range of colonies and more strongly the number of workers with unique allele combinations across all loci. Overall, our study contradicts the argument that the exceptionally high recombination rates cause a quantitative increase in offspring genotypic diversity across one generation. Alternative explanations for the evolution of high recombination rates in social insects are therefore needed. Short-term benefits are central to most explanations of the evolution of multiple mating and high recombination rates in social insects but our results also apply to other species.