I am an ML Scientist at NASA with 5+ years of expertise in AI/ML algorithm design and deployment. I excel in developing advanced machine learning and neural network models for processing and analyzing images and time series data. I work closely with engineers and scientists across the organization to deliver impactful and innovative solutions in space and engineering technologies.

I love how machine learning has leveraged our life. Over the past four years after my PhD, I had this great opportunity to work on a variety of exciting projects ranging from deploying and training ML models, analyzing telescope datasets to visializating the findings and reporting them to technical and non-technical audiance. This way I share the joy of having insights driven from the data with others.

• Led research programs to develop and deploy ML/DL models techniques with ML publications

TelescopeML PyPI Software: 📦
Key developer; Implimenting CNN models, enhancing the analysis of telescope data for scientists. Including:

• 📄 User-Friendly Documenattion
• published paper in Journal of Open Source Software

• Raised $1.7 million for data processing and analysis from US space missions.
• Experienced in simplifying complex problems and collaborating with cross-functional teams to leverage products.
• Holds a Ph.D. with a dissertation utilizing advanced statistical models and developed Python codes.

Project	Techniques	Data Type	Preview
COVID-19 Reddit Post Classifier Developed a binary classification model to categorize Reddit posts related to the COVID-19 pandemic, benchmarking various machine learning algorithms.	XGBoost, SVM, Random Forest, kNN, Feature Engineering, Hyperparameter Tuning, Cross-Validation	Text Data, NLP, Social Media Posts
DNA Sequencing Classification System Implemented a multi-class classification system to accurately identify and label seven DNA gene families using machine learning techniques.	Random Forest, Deep Neural Networks (DNN), Genetic Algorithms, Feature Selection, Model Evaluation	Genomic Data, DNA Sequences, Bioinformatics
Housing Price Prediction Model Developed a regression model to predict housing prices, utilizing various feature engineering techniques to enhance model accuracy.	Linear Regression, Feature Engineering, Data Preprocessing, Model Evaluation, Statistical Analysis	Tabular Data, Real Estate Data, Housing Market
American Sign Language Alphabet Classifier Built a convolutional neural network (CNN) model to classify and recognize American Sign Language (ASL) alphabet gestures from images.	CNN, Transfer Learning, Data Augmentation, Model Fine-tuning	Image Data, Gesture Recognition
Automated Wafer Defect Detection and Pattern Recognition Using Deep Learning Developed a deep learning-based framework for automated defect detection and pattern recognition in semiconductor wafer images, achieving 98% accuracy and 93% precision.	AutoEncoder CNNs, PyTorch, Data Augmentation, Bayesian Optimization	High-resolution Wafer Images, Semiconductor Data