San Diego, California, United States
I’m a University of California, San Diego Computer Engineering & Economics graduate passionate about building practical, scalable software—ranging from backend APIs to AI-driven tools. I’ve developed full-stack applications using Python, React, Django, Docker, AWS, and SQL, and I work comfortably across cloud services (AWS, GCP, Firebase), DevOps tools (Docker, GitHub Actions, Jenkins), and modern development practices. I had participated in several computing and AI competitions to improve my coding abilities and earned strong rankings. My recent projects include an ML-based stock-return prediction model, a Blackjack Max Profit AI agent, and full-stack web and apps for my co-founded startup. I love learning new technologies quickly, solving real problems, and building systems that are reliable, efficient, and enjoyable to use. Always open to collaborating on backend, AI/ML, or product-focused projects.
• Selected for UC Berkeley Haas UC Launch Startup Accelerator Program • Conducted customer discovery (50+ interviews) to validate product-market fit and user requirements • Defined value proposition, pricing strategy, and go-to-market (GTM) strategy • Built MVP using API architecture, focusing on credential verification, fraud detection, and anti-spoofing • Designed reusable identity verification system to reduce verification costs and improve efficiency • Developed early B2B sales pipeline, including outreach, pilot programs, and partnership discussions • Collaborated with mentors and cross-functional teams to iterate on product development and user experience
▪ Founded and scaled an AI-driven B2B SaaS platform for credential, ID, and identity-related document verification, enabling automated background checks and trust infrastructure for businesses. Built and launched full-stack web application with ownership to enable API integrations and AI agentic real-time document processing workflows. ▪ Architected and deployed scalable full-stack web and mobile applications using React, React Native, Node.js, Supabase, Google Cloud Platform (GCP), RESTful APIs for AI-powered document verification workflows. ▪ Integrated OCR pipelines, OpenAI APIs, Firebase Authentication, Firestore, and cloud storage systems to automate identity and employment verification with real-time fraud-detection processing capabilities. ▪ Designed and launched cross-platform mobile applications for iOS and Android using React, Xcode, Expo, and Firebase, implementing authentication, push notifications, real-time data synchronization, and subscription workflows. Patent pending. ▪ Earned an A+ rating during Better Business Bureau evaluation. Partnered up with multiple startup programs like Google Cloud, IBM Startup Partner Plus, etc. Achieved early total traction with 3k users, $5.5k revenue, and ~$600 ARR. Got accepted into UC Berkeley’s UC Launch accelerator program.
▪ Led early-stage development of AI-powered educational SaaS tools for automated grading, assignment recommendation, and personalized learning systems for student users. ▪ Built and trained LLM-integrated workflows using Python, TensorFlow, PyTorch, OpenAI APIs, and machine learning pipelines to generate dynamic AI feedback and adaptive learning recommendations. ▪ Developed scalable backend services, RESTful APIs, and data processing pipelines using Node.js, SQL, PostgreSQL, and cloud-based infrastructure. ▪ Collaborated with cross-functional stakeholders on product architecture, feature prioritization, UI/UX implementation, and technical strategy within startup engineering workflows. ▪ Conducted testing, debugging, and iterative feature optimization using Git, agile development workflows, and deployment tooling to improve system accuracy and application performance. ▪ Supported platform scalability, deployment planning, and future infrastructure expansion for early few-hundred users.
Collaborated with a 5-person team to design and develop a fully playable game. Engineered core gameplay mechanics and systems using C# and the .NET Framework, and implemented custom visual shader effects using ShaderLab to enhance in-game aesthetics. Utilized C++ for performance-critical components and managed version control and collaborative workflows through Git.
Led Conducted machine learning and acoustic signal processing research focused on species identification using large-scale environmental audio datasets collected through field research initiatives. ▪ Processed and analyzed acoustic datasets using Python, Pandas, NumPy, SciPy.signal, Matplotlib, and ETL workflows for scientific data analysis and preprocessing. ▪ Improved processing efficiency by approximately 10% through optimization of preprocessing pipelines, data cleaning, and acoustic feature extraction workflows. ▪ Increased species detection accuracy by refining acoustic classification methodologies, validating experimental outputs, and improving extracted signal quality. ▪ Documented research findings and technical reports using LaTeX, Google Workspace, and collaborative research tools.