Bangalore, India
I still remember learning about the β-globin gene in school and how a single nucleotide change could lead to sickle cell anemia. That moment made me realize how biological outcomes are encoded in data at the molecular level. This fascination naturally led me to bioinformatics, a field that lies at the intersection of biology, computation, and data analysis, where such genetic information can be analyzed computationally. While bioinformatics remains my primary focus, my training in biotechnology has given me interdisciplinary exposure to microbiology, plant biotechnology, and biochemical engineering, broadening my understanding of biological systems without diverting from my computational focus. These experiences have strengthened my perspective and reinforced my motivation to pursue data-driven biological research. My aim is to apply bioinformatics to generate insights that support accessible, evidence-based healthcare solutions and contribute meaningfully to community-oriented biomedical research.
Bachelors — Biotechnology, Dayananda Sagar College of Engineering, Bangalore, India. Nov 2021 - June 2025
Clinical Research Laboratory (CRL), KIMS, Oct 2025 - Present
Bioinformatics Research Intern
Indian Institute of Science, Jan 2025 - May 2025
Research Data Intern
Biocenter, Oct 2023 - Dec 2023
Anlytical Research Intern
KIMS Hospital and Research Centre, Sep 2022 - Dec 2022
Clinical Research Intern
Programming: Python, BioPython, R, SQL, UNIX/LINUX shell script, Nextflow, HPC (SLURM), Docker, Conda
Molecular Modeling: Molecular docking, Protein–ligand interactions, Dimerization, MD trajectory analysisRMSD / RMSF analysis, Binding pocket visualization, Clustering techniques
Bioinformatics: Variant Calling, QC, Trimmomatic, GATK, BWA, AlphaFold, PyMOL, ClustalW, GATK, Sequence alignment, Phylogenetic analysis, Pipeline development, protein/nucleotide secondary and tertiary structure prediction and validation
Clinical & Laboratory Practices: Aseptic techniques, Microbial culture & identification, Automated pathogen detection (VITEK®-2), Immunoassays (ELISA, HIV Spot, HCV Spot), Clinical sample processing, Biosafety compliance (GCP, SOP, cGMP), Diagnostic data recording & management
Analytical Techniques: High-pressure reactor, HPLC, GC-MS, Centrifuge, Chromatography, Tubular Furnace, Rotary Evaporator, ICP-OES
Bacterial strain Taxonomy for Genomis Surveillance: Offered by European Centre for Disease Prevention and Control, Oct 2025
An advanced course focused on the theoretical and practical aspects of bacterial strain taxonomy in the context of genomic surveillance. The program emphasized the use of whole-genome sequencing data for accurate strain classification, nomenclature, and typing, supporting standardized pathogen identification across laboratories and surveillance networks. Key topics included interpretation of phylogenetic relationships, strain relatedness, and evolutionary patterns to inform outbreak investigation and public health monitoring. The course also highlighted the importance of data quality, harmonized analytical workflows, and reproducible methodologies for reliable genomic surveillance. Through case-based examples, the training demonstrated how taxonomy-driven genomic analysis supports antimicrobial resistance tracking, epidemiological studies, and evidence-based public health decision-making at national and international levels.
Next Generation Sequencing (NGS) - Applications and Data Analysis: Offered by Dayananda Sagar College of Engineering, Nov 2024
Comprehensive training program focused on the principles, applications, and analytical workflows of next-generation sequencing technologies. The course covered major NGS platforms and sequencing strategies, along with end-to-end data analysis pipelines including quality control, read preprocessing, alignment, variant detection, and functional interpretation. Emphasis was placed on handling large-scale sequencing datasets and understanding experimental design considerations for genomics, transcriptomics, and pathogen sequencing studies. The training also addressed best practices for data quality assessment, reproducible analysis, and interpretation of sequencing results in biological and clinical contexts. Through applied examples, the course demonstrated how NGS-driven data analysis supports research in areas such as genomic surveillance, disease diagnostics, and biomedical discovery.
Molecular Dynamic Simulation using GROMACS: Offered by Dayananda Sagar College of Engineering, Feb 2024
Completed a specialized training focused on performing molecular dynamics (MD) simulations using the GROMACS software package. The course covered the theoretical foundations of molecular dynamics, including force fields, system solvation, energy minimization, equilibration, and production runs. Practical components emphasized setting up biomolecular systems, selecting simulation parameters, and executing MD simulations on Linux-based environments. The training also included post-simulation trajectory analysis, such as RMSD, RMSF, and interaction stability assessment, to evaluate structural behavior over time. Emphasis was placed on interpreting simulation results in the context of protein stability and protein–ligand interactions. Through hands-on exercises, the course demonstrated how MD simulations using GROMACS support structure-based drug discovery and biomolecular research.
Linux Commands and Bash Scripting, Jan 2024
This course focused on effective use of Linux operating systems and Bash scripting for computational and data-intensive workflows. The course covered essential Linux commands for file system navigation, file manipulation, process management, and environment configuration. Emphasis was placed on writing Bash scripts to automate repetitive tasks, manage software dependencies, and streamline data processing pipelines. The training included working with shell utilities for text processing, job control, and basic system monitoring, as well as handling permissions and directory structures in multi-user environments. Through hands-on exercises, the course demonstrated how Linux command-line proficiency and Bash scripting are applied in high-performance computing environments and bioinformatics workflows to improve efficiency, reproducibility, and scalability of data analysis tasks.