- Ph.D., Physics| The University of Texas at Dallas (Aug 2024)
- M.S., Physics| The University of Texas at Dallas (May 2021)
Graduate Research Student (Jun 2021 - Present)
- Analyzed Birds and climate data to fit and validate predictive models in Python, achieving a high R2 value of 0.90. These Models predicted the population decline of some bird species across North America.
- Built a Random Forest classification model to estimate the probability of occurrences of certain bird species with a 95% accuracy. The model also predicted bird migration towards northern regions over the past 5 decades.
- Led data collection, processing, and analysis of novel study on the influence of environmental temperature on bird vocal behavior and diversity. The analysis revealed a Pearson correlation coefficient of 0.79 and p-value < 0.001, indicating statistically significant correlation.
- Developed a deep convolutional neural network image classifier utilizing MFCC features extracted from birdsongs to accurately classify 27 bird species, achieving an 87% accuracy.
- Build Machine Learning Regression Models in Python that can estimate the sun’s position in the sky (Solar Zenith Angle) and ambient temperature from the vocal activity of the top 12 most common birds in the sensor’s surroundings.
- Developed empirical machine learning models that can classify 4 cognitive states based on data from Electroencephalogram (EEG) with 94 input variables using MATLAB.
- Graduate Studies Scholarship, The University of Texas at Dallas, 2019
- Best Teaching Assitant Award, The University of Texas at Dallas, 2022
- EEG Cognitive States Classification - Scientific Computing (PHYS 5315), Fall 2019
- Predicting Sun Position (Solar Zenith Angle) from Birds Vocal Behavior - Big Data and Machine Learning for Scientific Discovery (PHYS 5336), Spring 2023.