Qiujiang Jin is an Engineering Associate at Goldman Sachs, leveraging a deep academic background from his PhD in Electrical and Computer Engineering at the University of Texas at Austin. His expertise spans GPU programming, deep learning, and Natural Language Processing, with specific experience in developing financial models and search engine improvements.
His research at UT Austin focuses on high-level synthesis, approximate computing, and machine learning, particularly in creating specialized hardware accelerators for complex systems.
He has applied CUDA C/C++ programming to model interest rates, demonstrating a strong intersection of finance and high-performance computing.
Qiujiang Jin
Engineering Associate at Goldman Sachs
Austin, Texas, United States
Overview
Topics They Care About
GPU Programming
Utilized CUDA C/C++ programming for interest rate modeling during his internship and employment at Goldman Sachs.
Deep Learning
Has hands-on experience developing deep learning NLP models for enhancing search engine capabilities through question-answer generation.
Financial Modeling
His work at Goldman Sachs involves applying advanced computing techniques to solve complex financial challenges like interest rate modeling.
Media Appearances
Qiujiang has no verified media appearances
Work History
7-2024
Engineering Associate at Goldman Sachs
8-2021 - 5-2024
Research Assistant at The University of Texas at Austin
6-2023 - 8-2023
Engineering Associate Internship at Goldman Sachs
5-2022 - 8-2022
Software Engineer Internship at Meta
Education
8-2021 - 5-2024
Doctor of Philosophy - PhD from The University of Texas at Austin
8-2017 - 8-2021
Doctor of Philosophy - PhD from The University of Texas at Austin
More Information
Social Presence :
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Exp : 2
Location : Austin, Texas, United States
Job Level : N/A
Designation : Engineering Associate at Goldman Sachs
