Teaching Experience
March 2025 - December 2025
Assistant Professor,
Department of Biotechnology,
SRM Institute of Science & Technology,
Tiruchirappalli, Tamil Nadu, India.
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Teaching, mentoring, and evaluation of Undergrad (B.Tech) students.
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Machine learning based predictive analysis of methylation profiles in oral cancer datasets.
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Bioinformatics based analysis of microbial sequencing data obtained from patient samples including QC analysis, gene annotation, prediction of AMR and virulence genes.
April 2023 - December 2023
Assistant Professor,
Department of Bioinformatics,
Marwadi University, Gujarat, India.
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Teaching, mentoring, and evaluation of Undergrad (B.Tech) students.
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Study of differential methylation patterns observed in Oral Cancer patients.
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Conducted IEEE R10-HTC 2023 Workshop on
Computer-aided Drug Designing.
Research Experience
May 2020 - June 2021
Postdoctoral Fellow
Emory University School of Medicine, Atlanta, USA
Analysis of single cell RNA seq data from patients of AML, T-ALL, PDAC, Diabetic Foot Ulcers to find novel biomarkers and/or therapeutic targets.
Feb 2019 - May 2020
Postdoctoral Associate
SUNY Upstate Medical University, Syracuse, USA
Computational analysis of novel genomic features like triple-stranded DNA:RNA hybrids called R-loops from genomic and transcriptomic datasets to investigate their role in cancer initiation & progression, as well as resistance to chemotherapy.
Nov 2012 - Sep 2018
PhD student/candidate
Bose Institute, Kolkata, India
Development of online resources for studying linear motifs mediating protein interactions, including manually curated relational database LMPID, and SVM-based machine learning prediction server LMDIPred.
Education
2019
University of Calcutta | PhD
PhD from the Department of Biophysics, Molecular Biology & Bioinformatics
2010
West Bengal University of Technology | Master's Degree
M.Sc. in Bioinformatics
Skills
& Expertise
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Genomic, Transcriptomic (Bulk and single-cell RNA-seq), Proteomic (Mass-spec), and metagenomics data analysis.
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Biomolecular interaction network and pathway analysis, Time-course and Survival Analysis.
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Machine learning techniques (Support Vector Machines, Neural Networks, Bayesian inference, Decision Trees, Cluster analysis).
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Protein structure modelling and protein-ligand docking.