MS in Bioinformatics & Bachelor's in Pharmacy
Bioinformatics Researcher with 5+ years of experience specializing in chromatin conformation analysis (Hi-C, HiChIP, ChIA-PET) and deep learning for genomics. Developed Chr3D chromatin analysis framework and built open-source implementations of 20+ SOTA models including AlphaFold, CLIP, and diffusion architectures.
Analyzed 20+ TB of chromatin conformation capture (3C) data. Developed Chr3D, a standardized Python framework for comprehensive 3C technique analysis. Built transformer-based deep learning model for Hi-C coverage enhancement. Contributing to manuscript on chromatin 3D structure prediction methods.
Trained PointMAE model from scratch on 8+ H100 Nvidia GPU cluster. Fine-tuned deep learning models for 3D spatial embeddings in radio signal propagation. Developed CLIP-based multimodal models and diffusion architectures for 3D scene understanding.
Secured ₹1,20,000 SSIP Hub grant for synthesis and formulation targeting TNBC. Designed lead molecule library using RDKit and performed molecular docking on BRCA proteins. Conducted SAR analysis and wet lab characterization including HPLC and TLC.
Reimplemented 20+ SOTA deep learning papers from scratch including AlphaFold 2/3, ESM-2 (650M parameters), and DNABERT. Created detailed documentation with architecture visualizations. Trained models at scale on 8+ H100 GPU Nvidia Brev cluster.
Built no-code bioinformatics platform with drag-and-drop graph-based interface. Engineered dependency resolution system supporting R, Python, and Bash workflows. Implemented LangChain-powered LLM integration for automated code generation.
Developed Chr3D, a comprehensive Python framework for chromatin interactions analysis with integrated statistical methods. Manuscript in preparation for expected submission March 2026.
Interactive 3D visualization of CLIP embeddings showing how the model organizes visual and textual concepts in a shared embedding space. Explore 100 classes with 3000 image and text embeddings. View on GitHub
Click on dots it will show you the embeddings of the class and images representing that embeddings
Exploring evolutionary changes at the molecular level with an emphasis on genetic and protein modifications, including practical bioinformatics applications in phylogenetics and molecular clock theory.
An introduction to computational drug discovery techniques, covering key methods like molecular docking, QSAR, and ligand-based virtual screening. Ideal for bioinformaticians entering pharmacoinformatics.
A deep dive into the role of scRNA-seq in cancer studies, focusing on its applications for tumor heterogeneity and precision medicine.
Overview of efficient methods for acquiring datasets in bioinformatics, including resources like NCBI, GenBank, and EMBL, with a focus on open-source data for large-scale analyses.
Explains the EADock algorithm and its role in docking small molecules into protein active sites using multiobjective evolutionary optimization, highlighting its utility in structure-based drug design.
A guide to using the TeachOpenCADD library for computational drug design in Python, covering core techniques like molecular modeling, docking, and cheminformatics applications.