Curious by nature.
Hands-on by choice.

This tool simplifies the complex process of estimating car ownership expenses by dynamically calculating costs based on user input. It offers visualizations and customization options, making it a great resource for comparing vehicles and selecting one that fits within the budget.

The platform integrates Firebase Realtime Database for instant order processing and allows for customization of coffee orders. Designed with a responsive interface, the site ensures a smooth user experience, demonstrating my progress toward full-stack development.

It adapts to various devices, ensuring an optimal viewing experience. The site is continually updated to reflect evolving skills and new content. Future features will include more interactive elements and additional pages for deeper exploration of my work and interests. The site is a continuous work in progress, reflecting my evolving skills in web development.

The system utilizes CNN, SVM, and KNN algorithms to differentiate between familiar and unfamiliar faces, providing added security. Trained on the Labeled Faces in the Wild dataset, the model emphasizes real-time adaptability and accuracy. The project also considers the ethical implications of integrating such technology into everyday life.

This project explored how digital screens influence children's growth and behavior. It highlighted both the positive aspects, like learning opportunities, and the potential downsides, such as too much screen time. The project provided simple strategies for parents to manage their children's screen use effectively. An infographic was also created to present the findings in a clear and easy-to-understand way.

The courses covered foundational skills in Unix and Python for managing large datasets, techniques for genome sequencing and data validation, and RNA-Seq analysis, including experimental design and clustering. Practical experience was gained using the MSU High Performance Computing Cluster (HPCC) to process biological data, with a strong focus on applying these skills in real-world research contexts.

Tools: Python, R, HPCC, Unix/Linux

The project combined spatial data analysis with database management to identify the most suitable locations for solar installations. The tool assists in the decision-making process by integrating various environmental and economic factors into the site selection criteria.

Tools: Python, GIS, MySQL, Database management

The project explored various optimization algorithms, such as SGD, Momentum, RMSprop, and Adam, applied to the MNIST dataset. RMSprop provided the best results, but the study also highlighted the importance of tuning hyperparameters for each algorithm. This work demonstrated a deep understanding of machine learning optimization techniques and their practical applications.

The model analyzed past team performance, player statistics, and other factors to generate predictions, backed by Python script and Streamlit. Users could interact with the data and explore different scenarios. This project applied machine learning to real-world problems and showcased the development of user-friendly applications.

The application integrates real-time financial data from `fredapi` and `yfinance` with visulation charts using altair package in Python, helping users evaluate different scenarios under inflationary pressures. The tool allows for input customization, providing tailored advice based on current economic conditions. The project involved both front-end and back-end development, ensuring a robust and user-friendly application.

Using Python, planetary orbits were simulated with algorithms like Euler and Runge-Kutta. The resulting animations provide an engaging way to understand astronomical concepts, designed for educational purposes. The visualizations offer an interactive experience, making learning about the solar system more accessible and engaging.

Experiments explored gamma-ray spectroscopy principles to identify unknown radioactive sources, using NaI and HPGe detectors. Calibration techniques ensured accurate readings, with data visualization tools used to analyze spectra, identifying specific isotopes based on their unique energy signatures. The project provided valuable insights into nuclear physics and spectroscopy applications.

The project examined the relationship between carbon dioxide levels and global sea level changes, creating predictive models to forecast future trends. Time-series analysis and curve fitting techniques were applied, with the results highlighting the impact of climate change on sea levels. The project emphasized the importance of data-driven approaches to studying environmental changes.