Curriculum vitae

Graduate Teaching assistant

Elctrical and Computer Engineering Dept., Michigan Technological University

Teaching assistant with experience supporting lab instruction, and student guidance in electrical and computer engineering courses

• EE2174 (Digital Logic)
• EE3010 (Circuits & Instrumentation CPS)
• EE3171 (Microcontroller Applications)

PhD

Michigan Technological University

Computer Engineering

MS

Michigan Technological University

Data Science

Jonathan Bara Award for Outstanding Teaching Assistant

Michigan Technological University

FractionalNet: a symmetric neural network to compute fractional-order derivatives

Nonlinear Dynamics, Springer

Fractional calculus extends classical differentiation to non-integer orders, providing a more flexible mathematical framework for modeling systems with memory effects and nonlocal behavior. However, the use of fractional calculus requires quite a bit of mathematical expertise and familiarity with some mathematical concepts that are not in everyday use across the broad spectrum of engineering disciplines. In this work, we present FractionalNet, a computational tool to approximate fractional derivatives. The tool design uses a symmetric neural network that is trained exclusively on integer-order data but can predict fractional-order derivatives. We demonstrate training a FractionalNet to compute half-order derivatives by using first-order derivative data. A Genetic Algorithm is employed to optimize key hyperparameters in the training process to improve the model performance. These parameters are evaluated across models with varying depths, defined by the number of identical hidden layers symmetrically placed around a central output layer. We further investigate how weight initialization techniques can improve prediction accuracy and training stability. Experimental results show that a FractionalNet with three symmetric hidden layers, particularly when paired with He-Uniform initialization and ReLU activation, consistently achieves high accuracy and consistency when predicting half-order derivatives. The results also demonstrate that combining evolutionary optimization with structured weight initialization enables FractionalNet to serve as an effective and less-complex tool for fractional derivative computation, highlighting the potential of using FractionalNet in broad engineering applications.

RL-Based Trajectory Optimization for Cassie Robot

Enhanced reinforcement learning control for Cassie using MuJoCo, targeting improved trajectory accuracy and smooth locomotion.

• Achieved 16% improvement in joint configuration
• Developed neural network for predicting joint positions
• Aiming for 25% increase in trajectory precision

Evaluation of Detection Models with Enhanced Underwater Imagery

Compared object detection accuracy on underwater images enhanced with the Semi-UiR algorithm using YOLOv8 models.

• 12% improvement in image clarity
• 30% better detection accuracy with regular images

Franchise Data-Driven Benchmarking

Developed a dashboard using SSIS, SSMS, and Power BI for franchise performance benchmarking and cost optimization.

• 15% improvement in decision-making efficiency
• 35% cost optimization using predictive analytics

Time Series Forecasting for Mortality

Analyzed CDC’s 11-year mortality dataset and built predictive models for classifying top causes of death.

• Used XGBoost for data regression
• Achieved 92% classification accuracy

Pneumonia Detection Using Deep Learning

Built a deep learning pipeline to detect pneumonia in X-ray images using Keras and TensorFlow.

• Processed 6,500 X-ray images
• Achieved 91.5% accuracy