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I work at the intersection of biology, bioinformatics, data science, machine learning, and scientific software. My focus is on building practical computational tools that help turn biological questions into transparent and reproducible workflows. Here, you will find projects spanning phage biology, CRISPR, microbial genomics, structural bioinformatics, bioprocess modelling, and AI/ML for biotechnology. |
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I am an M.Sc. Biotechnology student specialization in Industrial Biotechnology at Gujarat Biotechnology University working at the intersection of biology, data science, bioinformatics, and research software.
My work focuses on turning biological questions into transparent, reproducible, and practical computational workflows. I am especially interested in microbial genomics, phage biology, antimicrobial research, genome engineering, structural bioinformatics, and machine-learning applications in biotechnology.
Current direction: using data and biology together to build tools that are scientifically useful, interpretable, and reproducible.
- π¦ Phage & microbial bioinformatics - phage annotation, endolysin discovery, sequence analysis, protein representations, and host prediction
- 𧬠Genome engineering - CRISPR guide design, multi-gene targeting, validation, and sequence provenance
- 𧫠Bioprocess data science - soft sensors, fault-inclusive modelling, uncertainty, and out-of-distribution analysis
- π¬ Structural bioinformatics - protein structure analysis, molecular docking, and computational modelling
- π οΈ Scientific software - Python-based tools, Streamlit applications, reproducible pipelines, and research dashboards
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Research-oriented SpCas9 workflow for identifying a single guide RNA that can target multiple homologous plant genes, with mismatch-aware consensus design, exon/segment-safe scanning, validation, and sequence provenance.
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Guide-design and validation workbench for OpenCRISPR-1-compatible NGG targets, including both-strand scanning, guide-expression analysis, local specificity review, and reproducible sequence provenance.
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Machine-learning soft sensor for estimating penicillin concentration from simulated fermentation batches, with normal-only and fault-inclusive models, batch-wise validation, uncertainty estimation, and OOD detection.
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General-purpose SpCas9/CRISPRi design workbench with NCBI/Ensembl sequence retrieval, transparent guide ranking, local off-target screening, and TSS-aware CRISPRi support.
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Core development
Data science & machine learning
Bioinformatics & scientific apps
Web, testing & reproducibility
| Area | Details |
|---|---|
| π Education | M.Sc. Industrial Biotechnology, Gujarat Biotechnology University |
| π National rank | GAT-B 2025 - All India Rank 62; DBT-supported graduate fellowship |
| π¬ Research experience | Structure-based study of human Ξ²-cardiac myosin inhibitors at IIT Roorkee |
| π Recognition | Best Student of the Year Award (2022-24), MBSI with HiMedia Laboratories |
I aim to keep research tools:
- Biologically grounded - scientific assumptions and limitations are stated clearly
- Reproducible - versions, provenance, benchmark inputs, and tests are retained
- Interpretable - model or heuristic outputs are presented with appropriate context
- Usable - researchers can run workflows through practical interfaces and export results
- Open to validation - computational predictions are treated as evidence to investigate, not substitutes for experimental confirmation
- π AMS Venom Diagnostic Dashboard - interactive simulation and visualization of antigen-scFv diagnostic signals
- 𧬠Basit Hub (code) - custom research start page with a 3D protein of the day, database search across PubMed, UniProt and PDB, and quick links for genomics, docking and ML
I am interested in research collaborations where biotechnology, computational biology, bioinformatics, machine learning, and practical scientific software come together.
Thank you for visiting my profile
Biotechnology Γ AI/ML for Biological Systems Γ Computational Biology Γ Bioinformatics