Graph Theory and Deep Learning Enthusiast

Hello, Welcome to my page! I probably won’t update this website often but It’s a good slice into what I do on the day-to-day. Although my major is in CS/Math, I also have a keen interest in protein folding and protein dynamics; particularly, the concept of high-dimensional graph representations of proteins. I have a good deal of experience working with and reading about different neural network architectures, and I will give a graphic below:

The % describes how I feel my general level of experience with each, as well as my knowledge thereof. All of my experience deploying the models have been in python, largely through google colab, but with some work on the ASU Supercomputer as well. More specifically, much of my previous work has focused on CNN’s and VAE’s for image generation and vector space generation, and the work on GNN’s, Transformers, and PINN’s relates to my work with De Novo Peptide design. To see more about that project, click here

Here is a similar graphic for language proficiency:

Education:

In progress:

• Bachelor’s in Computer Science
• Master’s in Computer Science
• Bachelor’s in Mathematics

Completed:

• N/A 😓

Papers/Textbooks I am reading currently:

• Protein Discovery with Discrete Walk-Jump Sampling
• Monte Carlo Methods (specifically chapter 11)
• Beyond Weisfeiler-Lehman: A Quantitative Framework for GNN Expressiveness
• Spherical Bessel Functions

Papers I have read:

• Semi-Supervised Classification with Graph Convolutional Networks
• De novo design of protein structure and function with RFdiffusion
• SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networks
• MAPE-PPI: Towards Effective and Efficient Protein-Protein Interaction Prediction via Microenvironment-Aware Protein Embedding
• Generative Adversarial Nets
• Atomic context-conditioned protein sequence design using LigandMPNN
• Robust deep learning–based protein sequence design using ProteinMPNN
• Deep Learning Model for Efficient Protein–Ligand Docking with Implicit Side-Chain Flexibility
• GAABind: a geometry-aware attention-based network for accurate protein–ligand binding pose and binding affinity prediction
• Attention Is All You Need (The Classic, of course)
• Highly accurate protein structure prediction with AlphaFold
• BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
• Towards Predicting Equilibrium Distributions for Molecular Systems with Deep Learning
• Generative Modeling of Molecular Dynamics Trajectories
• Auto-Encoding Variational Bayes (Another Timeless Classic)
• Combining Bayesian optimization with sequence- or structure-based strategies for optimization of peptide-binding protein
• Evolutionary-scale prediction of atomic-level protein structure with a language model

Previous Positions

• ASU iGEM Team Leader
• Research Assistant/Software Developer @ BioProteanLab

Current Position

• Research Assistant/Machine Learning Engineer @Singharoy Lab in Biodesign Institute

What’s in my Repository?

• Alot of old projects, some are for school, almost all of them have no README (smh). I will probably go back and work through it though.
• Mostly, my crown jewel is OpenBind, a fully end-to-end generative deep learning workflow for the development of therapeutics. Although there aren’t any experimentally validated results (TBD!), I am still trying to make the code as useable and easy to understand as possible.