I'm an AI Developer & Research Assistant at the University at Buffalo (Class of 2026), passionate about leveraging artificial intelligence to solve real-world, human-centric problems. My work bridges theoretical deep learning with practical applications, focusing on Healthcare AI and Material Science Discovery.

• 🔬 Researcher at Embedded Sensing and Computing (ESC) Group and Peng Research Lab
• 🌱 Deep interests in Computer Vision, Graph Neural Networks, and AI for Science
• 🎓 B.S. Computer Science, Minor in Statistics | GPA: 3.8+
• 💡 Driven to create original, high-impact research beyond applying known techniques

AI-Powered Geriatric Oral Healthcare Assistant

Mobile application utilizing YOLOv8 for real-time dental disease classification and tooth numbering from intraoral images. Designed to improve accessibility of oral healthcare for elderly populations.

React Native Python YOLOv8 ResNet50 Computer Vision

Satellite-Based Ship Detection System

Computer vision pipeline achieving 98.72% accuracy in detecting ships from satellite imagery using custom roundness-based classification combined with MobileNetV2.

TensorFlow OpenCV MobileNetV2 EfficientNet Geospatial Analysis

Dense Crowd Counting with Deep Learning

Robust people counting system for high-density scenarios, handling severe occlusions using dilated convolutional neural networks.

PyTorch CSRNet CUDA Deep Learning

Real-Time Emotion Classification

Leverages Vision Transformers (ViT) and ResNet-34 for accurate human emotion recognition from facial expressions.

PyTorch Vision Transformers CNN Transfer Learning

• 📐 Developing novel Symmetry-Aware Graph Neural Networks for material science applications
• 🏥 Advancing AI-driven healthcare solutions for underserved populations
• 📚 Preparing for Ph.D. applications in Computer Science with focus on AI/ML
• 🔬 Publishing research in top-tier conferences and journals

I'm always open to collaborating on innovative AI projects, discussing research opportunities, or just connecting with fellow AI enthusiasts!