"The strongest trees grow in the harshest winds."
I'm pursuing a Master's in Computer Science at Georgia Tech, specializing in Machine Learning. I'm passionate about building intelligent systems that push the boundaries of what's possible with AI, from semantic search and agentic systems to neural networks that understand human cognition.
Adobe | Adobe Experience Platform
May 2025 – August 2025 | San Jose, CA
Built end-to-end semantic search solution for Adobe's new Audience Agent, generating intent-driven audiences for marketing campaigns using natural language queries and scaling to 20M+ profiles with sub-second retrieval on Qdrant vector database. Invented attribute-based chunking algorithm via agglomerative clustering to intelligently segment user data, raising semantic search success rate to 90%.
Patent pending for chunking-based semantic search solution.
Amazon
May 2024 – August 2024 | Seattle, WA
Designed and implemented a real-time notification system within AWS division using Slack API, enhancing communication strategies for 1000+ enterprise-level AWS customers. Explored integration of AWS Bedrock Generative AI and Anthropic Claude LLM to automate root cause analyses for impacted AWS services.
Barclays
June 2023 – August 2023 | New York City Metropolitan Area
Developed a highly efficient, scalable, production-ready user interface for capturing and visualizing real-time financial instrument and derivative metadata, resulting in a 25% reduction in customer attrition and 80% faster data retrieval. Emerged 1st in Barclays global intern innovation competition among 100 teams.
Georgia Tech Hack4Impact: Bits of Good
August 2022 – Present | Atlanta, GA
Collaborated with executive board and 20+ nonprofit stakeholders through informational interviews and weekly ideation sessions to develop software solutions and design product proposals that align with nonprofit long-term initiatives.
Research Assistant
January 2024 - Present | Atlanta, GA
Explored robustness of RTNet, a novel artificial neural network designed to predict human reaction times by generating stochastic decisions and human-like RT distributions. Tested RTNet's resilience to adversarial attacks including FGSM and Projected Gradient Descent, demonstrating a 25% improvement over standard feedforward CNNs.
Machine Learning Engineer
December 2022 - December 2024 | Atlanta, GA
Integrated Gaussian Process Model and reinforcement learning techniques to improve safety for autonomous robot racing. Led optimization efforts for MPPI and CSSMPC vehicle controllers, reducing vehicle collision rates by over 75%.