I am a builder, I create software, engage in entrepreneurial ventures, lead communities, and challenge myself through fitness.
Conducted research on an exoskeleton project. Co-developed a machine learning algorithm using EEG brainwave data to detect user intent and improve motion control. Also built real-time signal classification models using Python, scikit-learn, TensorFlow, and MNE-Python, supporting neurotech applications for mobility and accessibility.
Engineered and deployed scalable RESTful APIs using Python and Flask, reducing response time by 30% and supporting a 20% increase in concurrent users and implemented full-stack development. Partnered with cross-functional teams to build and integrate a real-time analytics dashboard using React and Chart.js, enhancing data visibility and enabling faster decision-making.
Founded and led a student organization focused on empowering Muslim students in tech. Secured funding for events and managed logistics across workshops, speaker sessions, and networking socials. Organized hands-on workshops covering web development and machine learning to help students build practical skills and portfolios. Created inclusive social events to foster community and connect members with industry professionals.
NeuroFocus is a machine learning project that classifies cognitive attention states—Focused, Distracted, and Overstimulated—by analyzing EEG data from the DEAP dataset. I built a complete ML pipeline that extracts key features from EEG frequency bands (Theta, Alpha, Beta, Gamma) and applies models like SVM, LSTM, and CNN to achieve up to 90% classification accuracy. The system is designed with real-world applications in mind, such as mental focus tracking, neurofeedback, and brain-computer interface (BCI) systems. This project highlights the intersection of neuroscience and AI, showing how neural signal processing can be used for accurate, real-time cognitive state monitoring.
A real-time financial trading system that combines deep learning with reinforcement learning to predict market movements and execute trades. I designed and implemented a hybrid architecture using a Bidirectional LSTM for price forecasting and a Deep Q-Network for action optimization. The system achieved 94.2% prediction accuracy, a 2.52 Sharpe ratio, and a 79.4% win rate across 26+ experiments. Built with PyTorch and deployed on Vercel, the project features a live dashboard for visualizing trading decisions, performance, and risk metrics. Full implementation is open-source and integrates custom risk management logic, real-time data pipelines, and an interactive web demo.
This project explores how large language models (LLMs) respond to politically sensitive prompts, leveraging techniques in Natural Language Processing (NLP) and Automatic Prompt Evaluation (APE). The pipeline systematically probes LLMs with ideologically varied inputs and quantifies their response consistency and sensitivity. It offers a practical framework for analyzing model alignment, political bias, and linguistic stability—critical challenges in modern NLP. The insights contribute to building more robust, transparent, and socially-aware AI systems.
I'm always interested in hearing about new projects and opportunities. Whether you have a question or just want to say hi, I'll try my best to get back to you!
I grew up in the San Francisco Bay Area. I completed my undergrad at UC San Diego in Cognitive Science (Machine Learning) and Computer Science. I love building and scaling AI projects.
I am a builder. I create software, engage in entrepreneurial ventures, lead communities, and challenge myself through fitness. Whether it's coding or CrossFit, I appreciate the struggle and tend to fall in love with the process.
Apart from work, I enjoy staying active through soccer, tennis, biking, and running. I'm also passionate about traveling to new places and winding down with a good book whenever I can. (check out my Strava if you're curious)!