PERSISTENCE & INNOVATION
Mohsin Khawaja

Hey, I'm Mohsin

I am a builder, I create software, engage in entrepreneurial ventures, lead communities, and challenge myself through fitness.

PROFESSIONAL EXPERIENCE

Where I've Worked

UC Berkeley College of Engineering preview
Machine Learning Engineer & Nanotech Researcher / Intern
June 2024 - Present

UC Berkeley College of Engineering

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.

classification accuracy
95%
response time
<100ms
PythonTensorFlowscikit-learnML/AI
InterestingSoup preview
Software Engineering Intern
June 2021 - January 2022

InterestingSoup

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.

Browser AgentsFull-Stack DevelopmentReactPythonFlask
Muslim Tech Collaborative @ UC San Diego preview
Founder & VP
May 2023 - June 2024

Muslim Tech Collaborative @ UC San Diego

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.

members reached
100+
events organized
15+
LeadershipNetworkingFinanceComputer ScienceEntrepreneurship
FEATURED PROJECTS

What I've Built

NeuroFocus preview
Using EEG and AI to Classify Human Attention States

NeuroFocus

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.

classification accuracy
90%
EEGML/AIPythonTensorFlowscikit-learn
RL-LSTM Trading Agent preview
Real-time Financial Trading with Deep Learning & Reinforcement Learning

RL-LSTM Trading Agent

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.

prediction accuracy
94.2%
Sharpe ratio
2.52
win rate
79.4%
PyTorchLSTMDeep Q-NetworkRLTradingBlockchainCryptoML/AI
LLM Sensitivity Evaluation in Political Contexts preview
Analyzing Large Language Model Political Bias & Response Consistency

LLM Sensitivity Evaluation in Political Contexts

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.

models analyzed
5+
prompt variations
100+
NLPLLMPolitical AnalysisBias DetectionPythonML/AIAPE
GET IN TOUCH

Let's Connect

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!

Location
San Francisco Bay Area
A BIT ABOUT ME

WHO I AM

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)!

LIFE IN PICTURES

PHOTO GALLERY

Mountain Adventure
Friends & Sunset
Mosque Visit
Paris Adventures
Friends & Culture
Japan Adventures
Tech & Projects
Formal Events
Group Celebrations