Hello, I'm

I'm a graduate student passionate about leveraging AI/ML to solve real-world problems.

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About Me

I'm a graduate student at NYU with a strong foundation in full-stack development and machine learning. I have hands-on experience building web applications using React, Node.js, and modern frameworks, as well as developing ML/AI projects involving computer vision, NLP, and big data analytics. I'm passionate about leveraging technology to solve real-world problems and creating innovative solutions that make a meaningful impact. 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.

Scroll down to see my whole skill set. =)

6+

Years of Experience

20+

Projects Completed

JavaScript

JavaScript

HTML5

HTML5

CSS3

CSS3

TypeScript

TypeScript

React

React

Tailwind CSS

Tailwind CSS

Next.js

Next.js

Python

Python

Node.js

Node.js

Express

Express

Flask

Flask

Django

Django

MongoDB

MongoDB

MySQL

MySQL

PostgreSQL

PostgreSQL

SQLite

SQLite

Hadoop

Hadoop

Hive

Hive

PyTorch

PyTorch

OpenCV

OpenCV

Git

Git

Slack

Slack

Docker

Docker

C

C

C++

C++

FRONTEND
BACKEND
BIGDATA & AI
LANGUAGES & TOOLS

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.

  • JFK Airport Lane Traffic Detection and Analysis

    A computer vision project focused on image recognition and object detection using yolo11 computer vision models.

  • Evaluating the Impact of Tariff-Sentiment on Excess Returns

    Built a fully automated Python event-study pipeline integrating CRSP, Fama-French, and FinBERT data to quantify the market relevance of tariff sentiment in corporate earnings disclosures.

  • Big Data Analytics Project

    25 Fall BigData course project. To be updated soon.