Experience

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  DSS Takara Bio India Pvt Ltd

  • Bengaluru, Karnataka, India

  • Staff Scientist Manager, Bioinformatics

    • Sep 2023 - Present
    • Bengaluru, Karnataka, India

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  Canadian National Microbiology Laboratory

  • Winnipeg, Canada Area

  • Postdoctoral Researcher

    • Jan 2020 - Jun 2023
    • Winnipeg, Canada Area

    1. Developing python-based machine learning probabilistic model for microbial genome assembly quality evaluation. 2. Developing automated pipeline (using Snakemake automated workflow management system) for fast retrieval of NCBI assembly metadata from command line API queries and subsequent integration of prokaryotic assembly and annotation attributes for improved prediction of assembly quality. 3. Collaborating with a team of scientists and software engineers to generate informed decisions and recommendations on micro-organism species specific assembly, annotation and visualization strategies.

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  University of Calgary

  • Canada
  • Higher Education
  • 700 & Above Employee

  • Bioinformatician

    • May 2019 - Jan 2020

    Developing genome sequencing pipelines for processing raw sequencing reads, generating alignments and identifying genomic/structural variants, gene fusions and tumor mutational burden for better identification, detection and analysis of tumors implicated in different types of cancers

  • Graduate Research Assistant

    • Sep 2013 - Apr 2019

    1. Developed a Random Forest integrated classification framework (named TAIGA) to distinguish between damaging and neutral human genetic variations. Achieved best performance amongst state-of the-art methods in classification of rare-diseases (> 99% accuracy) and other evaluations.2. Validated TAIGA against monogenic rare diseases in collaboration from CARE for RARE http://care4rare.ca/. Achieved reduction in causal gene candidate search space to 124 out of ~30,000 candidate genes. Performance improvement was > 17X over the next best method.3. Performed analysis and interpretation of ~ 1 million variants from TCGA and FireBrowse databases’ whole exome sequencing data for 13 cancer types. This work led to identifying association of germline variants in natural killer cells with cancer risk4. Designed a randomized-exclusion-visualization based approach to ascertain feature importance in Machine Learning algorithms (named TREXvis).5. Served as primary computer lab instructor and secondary teaching instructor for Bioinformatics (MDSC401) and Interdisciplinary Research Approaches (MDSC308).

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  The University of Toledo

  • Toledo, Ohio Area

  • Graduate Research Assistant

    • Aug 2011 - Aug 2013
    • Toledo, Ohio Area

    1. Identified genome-wide biased gene conversion instances from large-scale human genomes database (1000 Genomes database ~ 1 TB).2. Designed matrix algorithms of human genome evolution under the influx of mutations and recombinations (Genetic Algorithm) to simulate and determine important factors for population survival.3. Analysed 40,000 non-coding genomic fragments (introns) across dicotyledonous plant species to identify functionally important regions such as transfer RNAs.4. Served as primary computer lab instructor and secondary teaching instructor for Bioinformatics Computation (BIPG610), Statistical Methods I (PUBH5320) and Applications of Bioinformatics (BIPG640).

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  Eduzone Education and Career Services

  • Kolkata Area, India

  • Teaching Faculty

    • Aug 2009 - Jul 2011
    • Kolkata Area, India

    Worked as Mathematics and English instructor for preparing students for worldwide standardized testing services.

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  Indian Institute of Technology, Kharagpur

  • Kharagpur Area, India

  • Undergraduate Research Assistant

    • Aug 2007 - Aug 2009
    • Kharagpur Area, India

    Designed web interface enabled database of biomaterials classifying them based on their uses in stem cells, tissue culture and scaffolds.

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  University of Queensland, Brisbane, Australia

  • Brisbane, Australia

  • Undergraduate Visiting Researcher

    • May 2007 - Aug 2007
    • Brisbane, Australia

    Performed comparative evaluation of efficiency and quality of phylogenetic methods (Distance based, Maximum Parsimony based, Maximum Likelihood based and Bayesian) using different sizes of molecular sequence datasets