I'm a Computational Plasma-Physics & Materials Science Researcher. Educational Background: BS Physics (Computational Physics), BS Mathematics (Applied-Mathematics and Scientific Computing), MS Physics (Computational Physics). Minor: Computer Science. Interests: Data Science, Data Engeering, Statistical Modelling, ML .
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
My career goal is to become a skilled data scientist, leveraging my strong foundation in physics, mathematics, and computer science to solve complex, data-driven problems. I have expertise in machine learning, statistical analysis, and computational modeling, enableing me to analyse and interpret diverse datasets. I am passionate about applying data science techniques to gain insight from large datasets and drive impactful decisions.
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