About Me
I'm a Computer Science student at the University of Washington's Paul G. Allen School, graduating in 2026, with a minor in Neural Computation and Engineering. I'm passionate about building innovative software solutions, researching encryption technologies, and leading technical teams.
🧪 Research & Development Focus
My interests span from creating projects utilizing AI/ML technologies, to creating simpler applications for people around me to use.
With hands-on experience as a Software Engineer Intern at Amazon and T-Mobile, I specialize in full-stack development, and building scalable applications. Currently, I am working on a project that enables models to retain information more effectively over the long term as I enter my internship at Amazon.
When I'm not coding: I'm a huge basketball fan and love going to the gym regularly. I also love watching comedies and following many sports such as football, cricket, and basketball.
Contact: 425-598-4686 | rishishar10@gmail.com
💻 Programming Languages
Python, Java, Kotlin, JavaScript, SQL, HTML/CSS, Dart
🧠 ML/AI Technologies
TensorFlow Lite, OpenCV, NumPy, Machine Learning, Computer Vision, Neural Networks
🔧 Frameworks & Tools
React.js, Spring Boot, Node.js, Flutter, Android SDK, Git
☁️ Cloud & Databases
AWS, Microsoft Azure, Firebase, MySQL, SQLite, Amazon DocumentDB
Professional Experience
Software Engineer Intern
• Developed advanced palm authentication feature using Amazon One APIs, implementing computer vision for secure biometric authentication on Alexa devices
• Enhanced security for high-level transactions while simplifying user experience, replacing traditional OTP methods with biometric authentication
• Served as full-stack developer on Alexa team using Kotlin and Java, leveraging OkHttp library for API integration
• Applied computer vision techniques for real-time palm detection and verification systems
Research Assistant
• Researched improvements to homomorphic encryption performance in cloud-based outsourced databases
• Analyzed security improvements between homomorphic and traditional encryption methods
• Designed novel optimizations through innovative algorithms addressing performance limitations
• Applied computational techniques to enhance encryption efficiency in cloud computing environments
Software Engineer Intern
• Collaborated on microchip development for Apple Watches to enable standalone device functionality
• Developed T-Mobile MONEY website improving customer banking experience and navigation
• Worked with SpringBoot and REST APIs for backend development
Projects
🚀 SiteSync - Work Management Platform
Tech Stack: React.js, Node.js, GoJS, AWS, Amazon DocumentDB
• Founded and led technical department of 10-15 person startup developing B2B work order management platform
• Created web application enabling businesses to efficiently manage subcontractor work orders
• Led team of 5-10 developers, delegating tasks based on strengths and ensuring milestone completion
• Implemented advanced data visualization and workflow management features
📱 BizBasics Mobile App
Tech Stack: Flutter, Dart, Java, Firebase, TensorFlow Lite, OpenCV
• Developed mobile application digitalizing tutoring business paperwork and organization systems
• Implemented computer vision features using OpenCV for automated document processing
• Applied machine learning models with TensorFlow Lite for on-device processing and data analysis
• Utilized advanced data structures and algorithms for intelligent search and categorization features
📈 Stock Market Predictor
Tech Stack: Python, PyTorch, NumPy, Pandas
• Built and compared deep learning models (MLP, ResCNN, LSTM+Attention) for next-day stock movement prediction
• Engineered time-series features using RSI, MACD, and Bollinger Bands; improved accuracy through residual and attention-based architectures
• Tuned hyperparameters and applied regularization to improve generalization across SPY, AAPL, and JPM datasets
Education
🎓 University of Washington
Paul G. Allen School of Computer Science
Bachelor of Science in Computer Science • Minor in Neural Computation and Engineering
Seattle, WA • Sep 2022 - Jun 2026
Relevant Coursework:
• Machine Learning & Deep Learning - Advanced algorithms, neural networks, deep learning architectures
• Neuroscience - Brain function, neural systems, computational neuroscience
• Computer Security - Cryptographic systems, security protocols, threat analysis
• Data Structures & Algorithms (Java) - Optimization, algorithmic complexity
• Probability & Statistics in CS (Python) - Statistical foundations, data analysis
• Data Management (MySQL, Azure SQL) - Database systems, data architecture
• Linear Algebra, Discrete Mathematics, Memory & Low-Level Languages (Assembly/C)