Assistant Sql Server

VIVEKSAGAR KRISHNAMURTHY RADHAKRISHNA

**********.**@******.*** 470, 16th street NW, Apt# 5017, Atlanta, GA- 30363 747-***-****

EDUCATION

MS Bioinformatics, Georgia Institute of Technology, USA GPA: 4.0 Aug. 2012 - Present (Graduating in Dec. 2013)

BE Biotechnology, PESIT, VTU, Bangalore, India % avg.: 76.83 Oct. 2004 - June 2008

SKILLS

Bioinformatic tools/concepts:

Sequence alignment: samtools, bwa, bowtie, bowtie2, BLAST+, MUMmer, blat, soap2, snap

Variant calling/annotation: GATK, snpEff, bcftools

Data visualisation: IGV, bamview, UCSC genome browser, MAUVE, Hawk-eye, Artemis, samstats

Genome assembly: AMOScmp, Newbler, Cabog, RAY, Velvet, MIRA, SOAP denovo2, SUTTA

Machine learning: Linear regression, Logistic regression, SVM, Neural Network, PCA, K-means clustering

Generic: AMOS package, kent source utilities

Programming and web-development:

Languages: Perl,Python, UNIX shell scripting, C, Matlab, Java, C#, R programming language- Basics

Databases: Oracle, MS SQL Server 2008, MySQL

Web-technologies: HTML, JavaScript, JSP, ASP.NET, AJAX, XML, Web service consumption, Basics of cloud

computing, Basic WCF, J-Query

High performace computing: PBS script creation and HPC usage

PROJECTS UNDERTAKEN/COMPLETED - MS BIOINFORMATICS, GEORGIA INSTITUTE OF TECHNOLOGY

Personalized medicine/ therapeutic optimization: Predicting the best drug/ drug cocktail for a given cancer patient

Created a shell script based automated pipeline to analyse tumor and normal tissue sequences o f patients and

identify the somatic driver mutation for oncogenisis. Used industry standard tools such as bowtie2 for a lignment;

prinseq for preprocessing; GATK for variant calling; snpEff for variant annotation and mutect to compare tumor-

normal pairs

Created a custom implementation of MOCA in python which uses the CCLE and COSMIC cancer cell line and drug

response information to predict the response of patients to a particular drug with respect to the features of the

tumor cell line. The algorithm also gives insights to both drug sensitivity and resistance

Followed industry standard coding conventions with an easy-to-use configuration script

Version 2 of the tool is currently being implemented to suit a distributed computing environment with hadoop

and/or in-memory computing platforms and is expected to speed up the analysis by 10 folds

CMAP (Clinical metagenomic analysis pipeline): Identifying and categorizing exogenous sequences against a host

background

Created a shell script based automated pipeline to identify and characterize non -host sequence in a given

metagenomic clinical sample

The pipeline consisted of a 3-step bowtie runs which would perform the process of host digital subtraction and

retain and characterize pathogenic sequences

As a positive control, the tool was run on various simulated and cell-line derived sequence reads

A bioraptor was created to analyse around 1000 directories of the 1000genome project and results were project on

the Vannberg lab website using some of the latest web technologies

CMAP2, with increased sensitivity and specificity, is currently in the pipeline which would also look at the geo-

spatial distribution of pathogen

Genome assembly of Vibrio vulnificus and Vibrio navarrensis species in collaboration with the Centers for Disease Control

and prevention(CDC), Atlanta

As a part of the core-curriculum of Master's in Bioinformatics at Georgia Technology, was involved in a 5-stage

project where in the primary goal was to check for pathogenicity and speciation of V. navarrensis

Lead the Genome Assembly team and adopted an all vs all comparison strategy to get the best assembly. Splinter,

an assembly pipeline created by the team proved to be better than that of the one used by the CDC

Was a part of the Genome Browser group and created the details page and involved in the UI development

K-mer based clustering/classification of pathogenic bacteria and virus using machine learning approaches

The concept of the uniqueness of the number of a candidate k -mers (8 and 12 for now) for a given organism relative

to another is harnessed

Machine learning algorithms such as neural-network (classification) and PCA-kmeans(clustering) is being

implemented to test the hypothesis

Development and maintenance of Vannberg Lab website

A hybrid html-jsp website was developed for Vannberg lab

PROFESSIONAL EXPERIENCE

Graduate Research Assistant: Georgia Institute of Technology; Adviser: Dr. Fredrik Vannberg Aug. 2012 – Present

- Developing a generic web-based framework for to analyze and visualize global distribution patterns pathogen

Instructor- CS1-1086, Matlab, Higher education opportunity program, NYU-Poly July 2012 - Aug. 2012

- Responsible for syllabus design, content creation, course delivery, grading and proctoring

Teaching Assistant- CS1133, Matlab, Computer Science and Engg. Dept., NYU-Poly Feb. 2012 - May 2012

- Provided assistance with hands-On practical lab, competency assessment and proctoring.

Senior System's Engineer/ Senior Associate- Microsoft Tech. Track, Infosys Ltd., Mysore, India Sep. 2008 - Aug. 2011

- Handling technical training, web application development and Database administration

- Actively involved in Corporate Social Responsibility activities

- Demonstrated the ability to be a team player and was made the lead of the one of the team bui lding activity

- Database administrator for Trainee Performance Dashboard(TPD) which supported trainee performance and analysis

PRESENTATION AND PUBLICATION

Oral Presentation: Model depicting the action of a reverse transcriptase inhibitor and protease inhibitor in HIV infected T –

cells, International Conference on Molecular Mechanisms and Systems Biology, Ma y 2008, PESIT, Bangalore, India

Poster Presentation: Drug effectiveness enhancement in T–cell populations by modified reverse transcriptase – an in-silico

study, International Conference on Systems Biology, August 2008, Gothenburg, Sweden

TECHNICAL TRAINING AND CERTIFICATIONS

Online Stanford Machine Learning course

.NET and JAVA with a GPA of 4.94 (on scale of 5, ~95 percentile)

Microsoft certified technology specialist- 70-433 Microsoft SQL Server 2008, Database Development

REFERENCE

Dr. Fredrik Vannberg; Assistant Professor, School of Biology; Georgia Institute of Technology ; *******.********@*******.******.***

Dr. Jung Choi; Associate Professor; Director of MS Bioinformatics; Georgia Institute of Technology; ****.****@*******.******.***

Ms. Komal Papdeja; Principal, Education and Research, Infosys Ltd., Pune, India; *************@*******.***

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