Investigating the intersection of statistical theory and real-world applications
Springer
Comprehensive review of mathematical foundations essential for data science and machine learning.
Nutrition Research
This study demonstrates the superior performance of deep learning architectures compared to traditional machine learning methods in predicting childhood malnutrition using comprehensive survey data from Nepal. Our findings suggest significant potential for deep learning applications in public health interventions.
Read MorePublic Health Nutrition
Applying advanced ML and DL techniques to predict anemia among under-five children in Nepal, contributing to early intervention strategies in pediatric healthcare and informing public health policy.
Read MoreEvaluating DNNs, CNNs, RNNs, LSTMs, Transformers, and Generative Models for marine mammal audio classification, advancing conservation technology.
Detailed investigation of CNN and DNN approaches for automatic facial age and gender classification using real-world UTKFace datasets.
Developed SARIMA models in predicting crime patterns across Chicago using 24 years of historical data.
Research at the Interface of Applied Mathematics and Machine Learning CBMS Conference
University of Houston, Houston, TX, USA
Data Science Week 2025 (Virtual)
Purdue University Fort Wayne, Fort Wayne, IN, USA
iLead Student Leadership Conference, Florida Atlantic University
Florida Atlantic University, Boca Raton, FL, USA
Florida Sectional Conference, Mathematical Association of America
Embry-Riddle Aeronautical University, Daytona Beach, FL, USA
Investigating the performance of Bayesian approaches versus traditional ML methods in human activity recognition using wearable sensor data.
Applied Cox proportional hazards models to evaluate the efficacy of laser treatment on 197 patient records.
Investigated the interplay between demographic factors, lifestyle, and metabolic health using ANOVA and statistical modeling.