I'm a Computational Plasma-Physics & Materials Science Researcher. Educational Background: BS Physics (Computational Physics), BS Mathematics (Applied-Mathematics and Scientific Computing), MS Physics (Materials Physics), MS Mathematics (Applied-Mathematics), Minor: Computer Science. Research Interests:Theorerical ML & AI in Materials Discovery .
Using An Open-Source Software Package (Basilisk) To Develop AMR Codes For Multi-Dimensional and Multi-Species Laser Plasma Simulations in Very-High Vaccum and Pressure environments.
Utilizing An Open-Source ML Package (CDVAE) To Train Deep Neural Networks On A Dataset of >415K Stable and Semi-Stable Crystal Structures To Predict Novel Materials with Targeted Physical Structures and Physical/Chemical Properties
Using Convolutional Neural Networks To Guide Plasma Plume Parameters in Materials Synthesis and Characterisation Process:Plasma Laser Deposition
I want to leverage my extensive academic background in Physics, Mathematics, and Computer Science, to become a leading specialist in Machine Learning (ML) and Artificial Intelligence (AI). My primary objective is to develop innovative ML and AI tools that significantly enhance the efficiency and accuracy of scientific discovery, particularly in the fields of Computational Chemistry and Materials Science.
Used Python For Automation, Post-Process Analysis, and Custom Files Generation
Developing a Plasma-Physics Fluid Dynamics Simulation Using An Opensource Software Package Based on C Programming Language
Developing a Monte Carlo Method Based Simulation Model for 1D Laser-Plasma Simulations
Numpy, Pandas, Scikit-Learn, TensorFlow, PyTorch, Seaborn, Keras, Matplotlib
KNN, CNN, DNN, LSTM, Logistic Regression, Random Forrest, SVM, Decision Tree, Naive Bayes, Clustering
MySQL, PostgreSQL, MongoDB
A Few Extracurriculars & Honors From My College Career (Not an Exhaustive List)
UAB International Scholarship, Joseph F. Volker International Scholarship, Departmental Scholarship: Mathematics, Physics, Computer Science