Dr. Pooja R.

Education

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  Great Learning

  2022 - 2022

  Certificate of Python for Machine Learning Machine Learning
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  The Johns Hopkins University School of Medicine

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  Genomic Data Science Specialization #datascience #statistics #Python # R Programming #Biostatictics#Command line Programming

  -> conceptual ideas of normalization, exploratory analysis, linear modeling, testing, and multiple testing that arise over and over in genomic studies.->preprocessing, linear modeling, and batch effects.-> modeling non-continuous outcomes (like binary or count data), hypothesis testing, and multiple hypothesis testing.-> general pipelines people use to analyze specific data types like RNA-seq, GWAS, ChIP-Seq, and DNA Methylation studies.

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  The University of Texas at Austin

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  Post Graduate Program in Data Science and Business Analytics
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  University of Massachusetts Dartmouth

  2017 - 2017

  RESEARCHER Electrical and Computer Engineering Researchers
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  NCU (THE NORTHCAP UNIVERSITY)

  2009 - 2011

  Master’s Degree Information Technology I

  Activities and Societies: • “Performance Comparison of VANET Routing Protocols” 7th IEEE Conference WiCOM-2011.- ISBN: 979-1-4244-6251-3. • “Studying behavior of VANET in under various traffic scenario”, International Journal of Computer Science Issue- 2011. July 2011 Issue (Volume 8, Issue 4): Paper IJCSI-2011-8-4-290. '' • “Self Tuning of Oracle Database Using SQL Scripts” in International Journal in IJCSI, 4 Issue 2011, ISSN (Online) 1694-0814. SPECIALIZATION IN SOFTWARE ENGINEERING

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  National Institute of Technology Puducherry

  2014 - 2019

  Doctor of Philosophy (Ph.D.) COMPUTER SCIENCE AND ENGINEERING

  1. Robust feedforward and recurrent neural network based dynamic weighted combination models for software reliability prediction, In Applied Soft Computing, 22, 629--637. (ISSN: 1568-4946),2014.2. Neural network for software reliability analysis of dynamically weighted NHPP growth models with imperfect debugging, in Software Testing Verification and Reliability, 28(4), e1663, Article ID: 15015060, DOI: 10.1002/stvr.1663, 2018.3. A neuro-particle swarm optimization logistic model fitting… Show more 1. Robust feedforward and recurrent neural network based dynamic weighted combination models for software reliability prediction, In Applied Soft Computing, 22, 629--637. (ISSN: 1568-4946),2014.2. Neural network for software reliability analysis of dynamically weighted NHPP growth models with imperfect debugging, in Software Testing Verification and Reliability, 28(4), e1663, Article ID: 15015060, DOI: 10.1002/stvr.1663, 2018.3. A neuro-particle swarm optimization logistic model fitting algorithm for software reliability analysis, Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, DOI.org/10.1177/1748006X19844784,2019. 4. Entropy based enhanced PSO on multi-objective software reliability modeling for optimal testing resources allocation, in Software Testing Verification and Reliability, DOI: 10.1002/stvr.1765, 2020. Show less