3 week internship  - 40 hours per week (Sophomore Year Summer)

Worked on research projects with Dr. Ajay Kalra, Ph.D., Civil and Environmental Engineering

Engineered a suite of machine learning models—including Artificial Neural Networks (ANN), Long Short-Term Memory (LSTM) networks, Support Vector Machines (SVM), and Random Forests—to forecast drought occurrences, duration, and intensity from complex climate data.

Conducted Rigorous Data Engineering and Preprocessing and managed the data pipeline for a large-scale climate variation analysis, performing data cleaning, feature engineering, and normalization on weather data to ensure model readiness and accuracy.

Presented the research poster at the 2024 Annual Mid-American Environmental Engineering Conference.

Finding variance and correlation in climate data (temperature, precipitation, wind speed, humidity) from different parts of the US by building the following machine learning models:

• Kendall Tau, P value, and slope trend analysis

• Principal Component Analysis (PCA)

• Canonical correlation analysis

• Cluster analysis

• Correlation and regression analysis