24 projects
UAV Traffic Management
Built Python-based flight optimization model for Honeywell Hackathon with 3D visualization and live path correction logic.
- Developed real-time UAV path optimization using Python simulation.
- Integrated 3D visualization for UAV airspace situational awareness.
- Enhanced UAV safety via autonomous conflict resolution logic.
NVIDIA Stock & Volume Forecasting
Built time-series models (XGBoost, SVR, ANN) and applied PCA + t-SNE for forecasting optimization.
- Applied supervised learning models for financial forecasting accuracy.
- Performed EDA to visualize trends and trading behavior anomalies.
- Used PCA and t-SNE to enhance input features and reduce dimensionality.
Instacart Basket Modeling
Applied ML and statistical regression to predict reorder behavior across departments and product types.
- Performed regression and EDA on reorder patterns in Instacart datasets.
- Built ML classifiers and conducted confidence interval testing.
- Used probability modeling to optimize reorder prediction accuracy.
Customer Call Clustering (USAA)
Used NLP and K-Means to segment call transcripts, visualize friction, and improve service design.
- Performed clustering and EDA on transcript data to segment call types.
- Used n-gram and topic modeling to identify satisfaction drivers.
- Presented insights in checkpoints to inform client-side UX changes.
Crime Prediction (ML)
Used clustering, logistic regression, and geospatial features to predict crime severity and classify weapon usage.
- Trained classifiers to distinguish violent and armed crime categories.
- Mapped geospatial trends with DBSCAN and choropleth clustering.
- Modeled temporal patterns with Prophet for forecasting.
MATLAB Simulation
Simulated Newton's Cooling Law, baton physics, and Lotka-Volterra equations for dynamics & biology models.
- Built simulations of heat loss and motion in physical systems.
- Modeled predator-prey behavior with Lotka-Volterra in MATLAB.
- Visualized simulations with time plots and parameter sweeps.
Java Dice Game
Developed a simple object-oriented betting simulator using classes, input validation, and game logic.
- Implemented OOP with custom Die and Main classes.
- Validated input and tracked user balance during gameplay.
- Used java.util.Random for dice roll simulation.
Adhesive Heat Testing
Conducted hypothesis testing on calorimetry data, comparing reported and experimental means.
- Performed one-sample hypothesis tests using R.
- Plotted confidence intervals and error bars for clarity.
- Published findings showing statistical significance (p < 0.01).
EPL Goal Analysis
Applied normal distribution modeling to Premier League match data, testing fairness of reported stats.
- Performed z-tests and statistical inference on EPL goal data.
- Interpreted fairness and distribution validity of goal claims.
- Visualized results with normal curves and deviation thresholds.
Energy Usage in AZ
Used regression to correlate energy burden to income tiers and analyze utility cost equity from RECS data.
- Modeled relationships between household energy cost and income.
- Visualized equity gaps using scatterplots and density maps.
- Proposed targeted intervention strategies based on regression insights.
Retail Orders | ML & Statistical Project
Modeled grocery order behavior using regression, classification, and probability distribution.
- Performed regression analysis to examine reorder and purchase trends.
- Implemented ML models with scikit-learn for reorder prediction accuracy.
- Visualized frequency distributions and PMFs using seaborn & matplotlib.
- Applied probability modeling to evaluate checkout likelihood.
- Proposed UX-driven data optimization strategies for cart behavior.
One-Sample Hypothesis Testing App
Developed Shiny R app for real-time hypothesis testing with CI visualization.
- Built hypothesis testing functions with R (shiny, stats) for one-sample analysis.
- Generated confidence intervals and statistical visuals dynamically.
- Used ggplot2 to show hypothesis rejection with input-driven graphics.
- Deployed fully functional app on Shinyapps.io for live demo use.
Abyssinian Style E-Commerce Website
Designed and launched Shopify site to support digital sales and enhance UX.
- Implemented front-end with HTML/CSS and optimized mobile responsiveness.
- Built product pages with integrated checkout and image optimization.
- Customized navigation and branding for an intuitive shopping experience.
- Worked with backend Shopify settings for inventory and SEO features.
UAV Camera Integration | Embedded Systems
Built live-streaming camera pipeline for UAV using Linux, FFmpeg, and OpenCV.
- Developed video pipeline with OpenCV, GStreamer, and FFmpeg for low-latency feed.
- Used Linux scripting for automation of encoding and secure data handling.
- Streamed live video to GCS with reliability under UAV flight conditions.
- Debugged and tuned performance for improved streaming quality.
Product Trends with R
Visualized product sales and inventory patterns using R data visualization libraries.
- Created heatmaps and bar charts for category-level product distributions.
- Used ggplot2 and tidyr for long-format transformations and mapping.
- Highlighted sales performance drivers via aggregation and color maps.
- Built interactive Google Slides for presentation and visual storytelling.
RF Signal Analysis | Statistical Inference
Explored RF detection trends in data center rooms using Python stats methods.
- Cleaned and structured RF signal datasets using pandas for Python analysis.
- Visualized signal behavior using matplotlib, seaborn, and distribution plots.
- Calculated confidence intervals and performed inference on signal variance.
- Applied chi-square and t-tests to estimate signal parameters.
- Delivered reproducible results using statistical scripting workflows.
Machine Learning Hardware Failure Classification System
Engineered advanced dual CNN architecture for automated hardware failure detection using specialized models for continuous and discrete sensor data.
- Implemented custom deep learning pipeline with 48-96-128 progressive feature extraction and strategic dropout regularization (0.1, 0.05 rates).
- Developed comprehensive transfer learning framework utilizing Adam optimization (5e-5 LR), class weight balancing for imbalanced datasets, and L2 regularization (0.0001).
- Built production-ready MLOps pipeline with automated model versioning, timestamped backup systems, and real-time performance monitoring.
- Created interactive GUI framework enabling non-technical users to deploy ML models for industrial hardware diagnostics.
Advanced Signal Demodulation System - DSP/ML Engineering
Engineered comparative BPSK demodulation system using dual methodologies (classical DSP vs. neural networks).
- Processed 10,000-bit transmission with SNR=1 Gaussian noise modeling for realistic communication channel simulation.
- Implemented advanced classical demodulation pipeline using orthogonal basis projection onto cosine/sine vectors with trigonometric phase estimation algorithms.
- Developed machine learning demodulation approach using optimized Multi-Layer Perceptron (10 hidden neurons) for direct signal-to-bit classification.
- Bypassed traditional phase estimation for innovative neural network-based signal recovery.
Semiconductor Diode Modeling System
Engineered advanced semiconductor device simulation platform integrating fundamental physics equations with numerical optimization.
- Achieved 1e-6 convergence tolerance across 30 iterative optimization cycles for precise parameter extraction from experimental I-V data.
- Implemented robust Newton-Raphson circuit solver using SciPy optimization for nonlinear diode-resistor networks at 375K.
- Developed multi-parameter optimization engine using iterative least-squares algorithms to extract barrier height, ideality factor, and series resistance.
- Created comprehensive diode physics model incorporating temperature dependencies for accurate high-temperature semiconductor device characterization.
Submarine Mine Detection System - Machine Learning Approach
Achieved 88.89% accuracy in life-critical submarine mine detection using PCA-optimized Multi-Layer Perceptron neural networks.
- Processed 60-dimensional sonar signals with only 3 missed mines out of 29 test cases for enhanced crew survival.
- Engineered optimal dimensionality reduction pipeline using Principal Component Analysis, reducing feature space from 60 to 8 components (87% reduction).
- Implemented systematic hyperparameter optimization across 60 PCA configurations using scikit-learn MLPClassifier with 100 hidden neurons.
- Delivered mission-critical defense technology solution with 10.3% false negative rate for hostile environment navigation.
VLSI Circuit Optimization System, EDA Automation Approach
Engineered advanced HSPICE-driven circuit optimization platform automating parametric sweeps across fan factors and chain lengths.
- Minimized propagation delay (t_phl) for 30pF high-capacitance loads through intelligent inverter sizing and topology optimization.
- Developed sophisticated Python automation framework integrating industry-standard HSPICE simulation with dynamic netlist generation.
- Enabled systematic exploration of 100+ circuit configurations with automated performance extraction and optimal design identification.
- Implemented robust electronic design automation workflow using subprocess management for seamless HSPICE invocation and CSV parsing.
Heart Disease Prediction System - Machine Learning Approach
Achieved 94.44% diagnostic accuracy in heart disease prediction using Support Vector Machine with RBF kernel.
- Outperformed 5 other ML algorithms on clinical dataset of 270 patient records with 13 cardiovascular features.
- Conducted comprehensive exploratory data analysis using Pearson correlation matrices and covariance analysis.
- Identified 6 critical predictive features with correlations above 0.41 for optimal model performance.
- Implemented and optimized 6 machine learning algorithms achieving consistent 90%+ accuracy across multiple models.
AI Deployment Workflow Optimization
Led enterprise process transformation initiative reducing AI model deployment time by 75% through systematic IDEF0 modeling.
- Reduced deployment time from 3-4 hours to <1 hour through automated workflow design for edge computing platforms.
- Engineered comprehensive automation solution using CLI-based toolchain integration with $5,700 total solution investment.
- Applied advanced enterprise modeling methodologies including Fishbone diagrams and 5 Whys analysis.
- Designed standardized, version-controlled deployment pipeline for Jetson Nano and Coral TPU platforms.
EDA Compliance Validation GUI System
Engineered enterprise-grade desktop application featuring dynamic plugin architecture with importlib-based module loading.
- Implemented sophisticated multi-check execution engine supporting variable schema CSV processing and intelligent data merging.
- Developed advanced tkinter-based GUI framework with Excel-style multi-column filtering and real-time visualization.
- Built production-ready software architecture implementing MVC, Observer, Factory, and Strategy design patterns.
- Delivered scalable validation platform enabling non-technical users to execute complex compliance workflows.