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- Email: mvenkataraman@ph.iitr.ac.in
- Github: VIGNESHinZONE
Integerated Master of Science, Physics (2017-2022)
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Built an end-to-end face recognition system used for attendance monitoring connected that could be connected to Vido Surveillance Camera or WebCam. Deployed the full pipeline in AWS server using Flask and also provided a RestAPI for local prototyping. Connected the final application to Tableau for further visualization.
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In the Later half, we expanded the system to include Anamoly Detection with the model using unsupervised training (Multi-instance Learning).
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Integrated the Jax framework into Deepchem codebase which interacts with the existing modules suited for made for bio-informatics and analytics.
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Revamp the CI/CD pipeline for the organisation into three separate setups (also followed by libraries like Huggingface, Atari, etc ) due to the dependency inconsistencies with Tensorflow and other libraries, etc.
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Built a general framework for solving differential equations using Neural Networks with the help of the Jax framework built during the same period inspired by Physics Informed Neural Networks.
- Collaborated with Prof. Venkat's group, on developing new Drug Discovery methods using existing Molecular Scaffolds that already follow the underlying chemistry. So far have we have been researching the existing RL-based approach and Molecular Transformer to combine different groups of scaffolds as a Reaction Prediction task. Conduct weekly meetings, discussing the progress and potential research ideas.We are currently working with both graph and string representation of Molecules, SMILES.
- Built an ensemble ML model to predict potential inhibitors of the SARS coronavirus protease molecule. The ensemble was built on models trained using Graph Neural Networks, Neural Networks, Tree and Boosting methods. Was able to achieve an average AUC-score - 0.76 and average PRC-AUC - 0.301 on 5 folds. Were able to predict 12 potential drugs after docking.
- Applying deep learning techniques on pharmacore fingerprints for Alzheimer's disease with Biomolecules from PubChem dataset and drugs from the Drugbank dataset. Had to work with a highly imbalanced dataset and applied focal loss function. Obtained a 0.89 F1 score on Test data and predicted 107 potential drugs for further testing.
DeepChem PR
- The DeepChem project is an open source python library that provides tools for drug discovery, materials science, quantum chemistry, and biology. I had implemented the Crystal graph model for Adsorption Datasets following, "Lattice Convolutional Neural Network Modeling of Adsorbate Coverage Effects" by Lym et al.
DGL-LifeSci PR
- The DGL-Lifesci is an open source python library for deep learning on graphs with models specific for Molecular and Biological graphs. I implemented a model which benchmarks on the MOLNET dataset, "Path-Augmented Graph Transformer Network" by Chen et al.
- Python
- C++
- HTML
- CSS
- Javascript
- NN Packages - Pytorch, JAX, Keras, Deepchem, Scikit Learn
- ML Utilites - Pandas, Numpy, Mathplotlib
- Databse - SQL
- Front End WebD - React, Sass
- Platforms & Others - Git, Docker, SLURM
- English
- Hindi
- Tamil