Lianna Zhao

• Developed an offline Python pipeline for sensitive case files on a locally deployed Qwen-235B model,
using prompt engineering and retrieval-augmented generation (RAG) to select context.

As an intern in the Global Technology Applied Research team, I developed a private, auditable distributed ledger with zero-knowledge proofs to enhance blockchain privacy and security. I led the development of a novel payment framework and refactored code for open-source release, while also conducting research on post-quantum blockchain transaction schemes.

Conducted research on Direct Acyclic Graph Based Distributed Ledger Technologies, focusing on various projects including security enhancements and smart contract applications. Published multiple papers in reputable journals.

Led a project to enhance the diagnosis of Acute Aortic Syndrome using AI, developing a logistic regression model on an AWS-backed platform. Managed datasets of over 6,000 cases and applied statistical techniques to identify key diagnostic patterns.

Conducted time series data analysis on historical natural gas prices and developed a pricing model in Python. Implemented various machine learning models to assess customer default probabilities, achieving high accuracy with XGBoost.

Conducted research on Direct Acyclic Graph Based Distributed Ledger Technologies with applications, supervised by Professor Robert Shorten.

Grade: 3.62/4

Studied methods of optimization and matrix analysis, achieving a GPA of 3.62/4.

Grade: 3.81/4

Focused on automation and process control, with relevant modules including Linear Algebra and Advanced Mathematics, achieving a GPA of 3.81/4.