Resume

• 2008

  Doctor of Philosophy (Ph.D.)

  Appplied Mathematics

  University of Houston

  3.96/4.0

• 2007

  Master's Degree

  Mathematics

  University of Houston

  3.97/4.0

• 2004

  Master’s Degree

  Mathematics

  Delhi University

• UH Math High School Contest

  Volunteer

  FIRST

  Medical Imaging

  Mathematical Modeling

  R

  Research

  Signal Processing

  Data Analysis

  Statistics

  Data Science

  Applied Mathematics

  Deep Learning

  Machine Learning

  Python

  Image Analysis

  Numerical Analysis

  Matlab

  LaTeX

  Mathematica

  C++

  Algorithms

  Image Processing

  Genetic deletion of fibroblast growth factor 14 recapitulates phenotypic alterations underlying cognitive impairment associated with schizophrenia

  +11 more

  Fernanda Laezza

  Demetrio Labate

  Cognitive processing is highly dependent on the functional integrity of gamma-amino-butyric acid (GABA) interneurons in the brain. These cells regulate excitability and synaptic plasticity of principal neurons balancing the excitatory/inhibitory tone of cortical networks. Reduced function of parvalbumin (PV) interneurons and disruption of GABAergic synapses in the cortical circuitry result in desynchronized network activity associated with cognitive impairment across many psychiatric disorders

  including schizophrenia. However

  the mechanisms underlying these complex phenotypes are still poorly understood. Here we show that in animal models

  genetic deletion of fibroblast growth factor 14 (Fgf14)

  a regulator of neuronal excitability and synaptic transmission

  leads to loss of PV interneurons in the CA1 hippocampal region

  a critical area for cognitive function. Strikingly

  this cellular phenotype associates with decreased expression of glutamic acid decarboxylase 67 (GAD67) and vesicular GABA transporter (VGAT) and also coincides with disrupted CA1 inhibitory circuitry

  reduced in vivo gamma frequency oscillations and impaired working memory. Bioinformatics analysis of schizophrenia transcriptomics revealed functional co-clustering of FGF14 and genes enriched within the GABAergic pathway along with correlatively decreased expression of FGF14

  PVALB

  GAD67 and VGAT in the disease context. These results indicate that Fgf14−/− mice recapitulate salient molecular

  cellular

  functional and behavioral features associated with human cognitive impairment

  and FGF14 loss of function might be associated with the biology of complex brain disorders such as schizophrenia.

  Genetic deletion of fibroblast growth factor 14 recapitulates phenotypic alterations underlying cognitive impairment associated with schizophrenia

  Demetrio Labate

  Manos Papadakis

  Fernanda Laezza

  Pooran Negi

  The spatial organization of neurites

  the thin processes (i.e.

  dendrites and axons) that stem from a neuron’s soma

  conveys structural information required for proper brain function. The alignment

  direction and overall geometry of neurites in the brain are subject to continuous remodeling in response to healthy and noxious stimuli. In the developing brain

  during neurogenesis or in neuroregeneration

  these structural changes are indicators of the ability of neurons to establish axon-to-dendrite connections that can ultimately develop into functional synapses. Enabling a proper quantification of this structural remodeling would facilitate the identification of new phenotypic criteria to classify developmental stages and further our understanding of brain function. However

  adequate algorithms to accurately and reliably quantify neurite orientation and alignment are still lacking. To fill this gap

  we introduce a novel algorithm that relies on multiscale directional filters designed to measure local neurites orientation over multiple scales. This innovative approach allows us to discriminate the physical orientation of neurites from finer scale phenomena associated with local irregularities and noise. Building on this multiscale framework

  we also introduce a notion of alignment score that we apply to quantify the degree of spatial organization of neurites in tissue and cultured neurons. Numerical codes were implemented in Python and released open source and freely available to the scientific community.

  Multiscale Analysis of Neurite Orientation and Spatial Organization in Neuronal Images

  Bernhard Bodmann

  This paper investigates the performance of frames for the linear

  redundant encoding of vectors when consecutive frame coefficients are lost due to the occurrence of random burst errors. We assume that the distribution of bursts is invariant under cyclic shifts and that the burst-length statistics are known. In analogy with rate-distortion theory

  we wish to find frames of a given size

  which minimize the mean-square reconstruction error for the encoding of vectors in a complex finite-dimensional Hilbert space. We obtain an upper bound for the mean-square reconstruction error for a given Parseval frame and in the case of cyclic Parseval frames

  we find a family of frames which minimizes this upper bound. Under certain conditions

  these minimizers are identical to complex Bose-Chaudhuri-Hocquenghem codes discussed in the literature. The accuracy of our upper bounds for the mean-square error is substantiated by complementary lower bounds. All estimates are based on convexity arguments and a discrete rearrangement inequality.

  Burst erasures and the mean square error for cyclic Parseval frames

  Bernhard Bodmann

  The objective of this paper is to study the performance of fusion frames for packet encodings in the presence of erasures. These frames encode a vector in a Hilbert space in terms of its components in subspaces

  which can be identified with packets of linear coefficients. We evaluate the fusion frame performance under some statistical assumption on the vector to be transmitted

  when part of the packets is transmitted perfectly and another part is lost in an adversarial

  deterministic manner. The performance is measured by the mean-squared Euclidean norm of the reconstruction error when averaged over the transmission of all unit vectors. Our main result is that a random selection of fusion frames performs nearly as well as previously known optimal bounds for the error

  characterized by optimal packings of subspaces

  which are known not to exist in all dimensions.

  Random fusion frames for loss-insensitive packet encoding

  Despite the significant advances in the development of automated image analysis algorithms for the detection and extraction of neuronal structures

  current software tools still have numerous limitations when it comes to the detection and analysis of dendritic spines. The problem is especially challenging in in vivo imaging

  where the difficulty of extracting morphometric properties of spines is compounded by lower image resolution and contrast levels native to two-photon laser microscopy. To address this challenge

  we introduce a new computational framework for the automated detection and quantitative analysis of dendritic spines in vivo multi-photon imaging. This framework includes: (i) a novel preprocessing algorithm enhancing spines in a way that they are included in the binarized volume produced during the segmentation of foreground from background; (ii) the mathematical foundation of this algorithm

  and (iii) an algorithm for the detection of spine locations in reference to centerline trace and separating them from the branches to whom spines are attached to. This framework enables the computation of a wide range of geometric features such as spine length

  spatial distribution and spine volume in a high-throughput fashion. We illustrate our approach for the automated extraction of dendritic spine features in time-series multi-photon images of layer 5 cortical excitatory neurons from the mouse visual cortex.

  Automated 3-D Detection of Dendritic Spines from In Vivo Two-Photon Image Stacks

  Pankaj

  Singh

  Texas A&M Health Science Center

  University of Houston

  MD Anderson Cancer Center

  Houston

  Texas Area

  Associate Research Scientist

  Texas A&M Health Science Center

  Graduate Research Assistant

  Houston

  Texas Area

  University of Houston

  Postdoctoral Fellow

  Houston

  Texas Area

  University of Houston

  Houston

  Texas Area

  Performing image analysis pertaining to cellular images using Python

  R

  ImageJ and Matlab; developing and applying computational procedures to interpret image derived measurements like cell size

  count

  etc. to study problems related to melanoma cancer

  Research Scientist

  MD Anderson Cancer Center

  Secretary

  University of Houston SIAM Student Chapter

  University of Houston