🎓 M.S. Biology graduate from IISER Pune
🔬 Aspiring Computational Biologist | 🤖 Budding Data Scientist | 💡 Machine Learning Enthusiast
📍 New Delhi, India | LinkedIn
I'm passionate about combining biology and data science to solve real-world problems. I enjoy working with machine learning models, large biological datasets, and building tools that bridge computation with biology.
I’ve developed deep learning pipelines, analyzed genomic datasets, and worked on protein modeling and classification models. My projects reflect my interest in applying AI in bioinformatics.
Python, PyTorch, CNN, Flask, Docker
- Built a Flask server to classify handwritten digits (MNIST).
- Achieved >90% accuracy and containerized it with Docker.
Python, TensorFlow, Keras, LSTM, NLP
- Built a binary sentiment classifier on the IMDB dataset.
- Designed a custom preprocessing pipeline and trained an LSTM model (>85% accuracy).
Python, Scikit-learn, PCA, SMOTE
- Developed an SVM classifier for cancer subtype prediction from gene expression data.
- Used PCA for dimensionality reduction and SMOTE for class balance.
Python, Pytorch-geometric, GINE network, Deep Learning
- Developed a Graph Isomorphism Network (GINE) to predict drug permeability into the CNS, explicitly modeling chemical bond attributes.
- Implemented Bemis-Murcko Scaffold Splitting on a merged BBBP/B3DB dataset to ensure model generalization across novel chemical families (0.82 F1-score).
- Languages/Tools: Python, Bash, Rust, Java, HTML, CSS and Javascript
- ML/DS: CNN, LSTM, SVM, PCA, SMOTE, Deep Learning Pipelines, Graph Neural Networks (GNNs)
- Bioinformatics: CHiP-seq, RNA-seq, Mass Spec, Genome Annotation, Genome data pipelines
- Libraries: NumPy, Pandas, Scikit-learn, TensorFlow, PyTorch, Pytroch Geometric, Rdkit, Seaborn, Plotly, matplotlib
- Tools: Git, Jupyter, Docker