PORTFOLIO
I build solutions that bridge technical innovation with human needs. The projects below reflect just this: my passion for creating intelligent systems that make a meaningful difference in people's lives.
Featured Current Research:
AI Prediction of Enhancer-Promoter Interactions in Neurodegenerative Diseases
Description:
My ongoing research focuses on developing advanced machine learning models to predict enhancer-promoter interactions in the context of neurodegenerative diseases. By leveraging graph neural networks and attention mechanisms, this work aims to decode the complex regulatory relationships within the genome that contribute to conditions like Alzheimer's and Parkinson's disease.
Research Abstract:
Dysregulation of gene expression plays a critical role in neurodegenerative disease progression. This project applies computational methods to identify and characterize enhancer-promoter interactions that may be altered in neurodegenerative contexts. Using multiomics data integration and supervised learning approaches, we're building predictive models that can identify disease-specific regulatory patterns. Our preliminary results have identified several previously unknown enhancer-promoter interactions that show altered activity in disease models, potentially revealing new therapeutic targets and advancing our understanding of the genomic architecture underlying neurodegeneration.
AI Healthcare Projects
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Personalized Contraceptive Recommendation Bot
National Conference of Undergraduate Research awarded LLM-powered GenAI system that provides personalized contraceptive recommendations based on a women’s health data.
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Diabetes Diagnose using Machine Learning
Predicting diabetes risk based on inputted user health metrics, demonstrating how AI can make preventative healthcare more accessible.
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DNA Classification Algorithm
Explored the fascinating field of bioinformatics by developing a classification system for DNA sequences, transforms genetic data into numerical formats suitable for analysis.
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Pneumonia Detection using Chest X-ray Images
Built an automated diagnostic tool that identifies pneumonia in chest X-ray images through deep learning addressing a critical healthcare need
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Neural Interfaces for Healthcare Applications
Implemented a neural signal processing system using EMOTIV BCI technology that translates brain activity into game controls, exploring its applications to healthcare.
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LSTM Networks for Automated Cardiac Arrhythmia Detection
Technical analysis of the use of recurrent neural networks (RNNs), specifically long short-term memory (LSTM), to enable temporal modeling for improved detection
AI Integration Projects
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Automated Livestock Quantification System
Implemented a YOLOv5-based object detection system for automated livestock quantification from aerial imagery. Engineered custom data augmentation pipeline to enhance model robustness against variable lighting and occlusion scenarios.
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Ticketmaster Event Management System
Real-time queue optimization system leveraging predictive analytics and dynamic resource allocation algorithms to streamline event entry processes. Implements crowd density monitoring via computer vision for adaptive capacity.
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Political Bias Detector
KNN-powered algorithmic system that can automatically classify political bias in news articles. This tool helps readers understand potential slants in their news consumption and promotes media literacy.
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Spotify Personalized Music Assistant
AI system utilizing natural language processing and collaborative filtering algorithms to generate contextual music recommendations based on user preferences and acoustic feature analysis.
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Smart Meal Prepping Platform
App that helps make the transition to independent living smoother and more enjoyable through organized, efficient meal management. Using ingredient recognition and preference learning, it suggests personalized recipes that match your pantry and tastes.
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Google Smart Home Mirror
Ambient computing interface integrating Google Gemini multimodal AI with computer vision and voice recognition. Features contextual awareness through environmental sensors and personalized interaction through user recognition algorithms.