About Me
I am a full stack web developer with a passion for creating interactive and responsive web applications. I have experience working with JavaScript, React, Node.js, Express, PostgreSQL, HTML, CSS, and Git. I am a quick learner and I am always looking to expand my knowledge and skill set. I am a team player and I am excited to work with others to create amazing applications.
- Node.js
- Express
- PostgreSQL
- JavaScript
- React
- HTML
- CSS
- C
- Python
6+
Years of Experience
20+
Projects Completed
JavaScript
HTML5
CSS3
TypeScript
React
Tailwind CSS
Python
Node.js
Express
Flask
Django
MongoDB
MySQL
PostgreSQL
SQLite
Git
Slack
Docker
C
C++
My Projects
Dynamic Personal Website: Next.js, React, Tailwind CSS
This dynamic personal portfolio was built with Next.js, React, and Tailwind CSS, featuring a sleek, responsive design and smooth animations via Framer Motion. The project showcases modern front-end development practices, state management, and a focus on UI/UX excellence.
Stock Price Trend Prediction (VAE + CV)
Leveraged Variational Encoder-Decoder (VED) and candlestick chart images to predict 5-day stock price trends based on the past 20-day SSE50 index. Combined deep learning with financial visualization to explore an image-based approach to forecasting—an innovation over traditional raw data models.
Lightweight ResNet for CIFAR-10 with 2.7M Params
Designed a compact ResNet variant with only 2.7M parameters, achieving 94.06% accuracy on CIFAR-10. Outperformed standard ResNet-18 (11.2M) by balancing depth and efficiency—an effective solution for small dataset generalization.
Parameter-Efficient Text Classification with LoRA
Fine-tuned a RoBERTa model using Low-Rank Adaptation (LoRA) on the AGNEWS dataset with fewer than 1M trainable parameters. Achieved 87.4% accuracy, demonstrating that LoRA can deliver competitive performance with minimal computational cost in real-world NLP tasks.
Adversarial Robustness of ResNet-34 on ImageNet
Studied ResNet-34’s vulnerability to FGSM, PGD, and patch attacks. Demonstrated drastic accuracy drops and evaluated the transferability of adversarial examples to DenseNet-121 and ViT-B/16, highlighting security weaknesses in deep image classifiers.
Tremor & Dyskinesia Detection on STM32
Built an embedded system on STM32 to detect Parkinsonian tremors (3–5Hz) and dyskinesia (5–7Hz) using accelerometer/gyroscope FFT analysis. Implemented LED indicators to signal movement type and intensity, enabling medication-level tuning without terminal output.