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@liannazhao
Blockchain researcher with expertise in AI and distributed ledger technologies.
I am a dedicated researcher with a PhD in Design Engineering from Imperial College London, specializing in distributed ledger technologies and their applications. My recent role as an intern at JPMorgan Chase allowed me to lead the development of a private, auditable distributed ledger, enhancing blockchain privacy and security through innovative solutions like zero-knowledge proofs.
In addition to my technical expertise, I have a strong background in AI applications, demonstrated through my work on the NHS Aortic-AI project, where I developed a logistic regression model that significantly improved diagnosis accuracy for Acute Aortic Syndrome. My experience spans various projects, including time series data analysis and the design of smart contract applications, showcasing my ability to tackle complex challenges in the financial and healthcare sectors.
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Work history, roles, and key accomplishments
JPMorgan Chase
Jun 2024 - Jul 2024 (1 month)
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.
Imperial College London
Oct 2020 - Mar 2024 (3 years 5 months)
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.
University of Sheffield
Feb 2024 - Jan 2024 (-1 years 11 months)
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.
JPMorgan Chase
Sep 2023 - Sep 2023 (0 months)
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.
Degrees, certifications, and relevant coursework
PhD, Design Engineering
2020 - 2024
Conducted research on Direct Acyclic Graph Based Distributed Ledger Technologies with applications, supervised by Professor Robert Shorten.
M.Sc., Control Engineering
2017 - 2020
Grade: 3.62/4
Studied methods of optimization and matrix analysis, achieving a GPA of 3.62/4.
B.Eng., Automation
2013 - 2017
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.
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