Yuanling (Judy) Gan
About Yuanling (Judy) Gan
Yuanling (Judy) Gan is a Quantitative Researcher at Two Sigma, with a strong background in statistics and operations research. She has held various research and teaching positions at prominent institutions, including Amazon and Columbia University.
Work at Two Sigma
Yuanling Gan currently serves as a Quantitative Researcher at Two Sigma, a position she has held since 2023. In this role, she applies her expertise in statistical modeling and quantitative finance to contribute to the firm's research initiatives. Her work involves collaborating with interdisciplinary teams to develop innovative solutions in the financial sector.
Education and Expertise
Yuanling Gan has an extensive educational background. She earned a Doctor of Philosophy (PhD) in Business with a focus on Operations Research from Columbia University, completing her studies in 2023. Prior to that, she obtained a Master of Science (MS) in Industrial Engineering from the University of Illinois at Urbana-Champaign in 2018. She also holds a Bachelor's Degree in Mathematics from Nanjing University, which she completed in 2016. Her studies in Statistics at the University of California, Berkeley further enhanced her analytical skills.
Professional Experience
Yuanling Gan has held various positions in research and engineering. She worked as a Graduate Teaching Assistant at Columbia Business School for two months in 2020. In 2017, she completed a Software Engineer Internship at Boxed in the San Francisco Bay Area. She also interned as a Research Scientist at Amazon in 2021 and again in 2022, gaining valuable experience in research methodologies. Additionally, she served as a Graduate Research Assistant and Graduate Teaching Assistant at the University of Illinois at Urbana-Champaign from 2016 to 2018.
Research Contributions
During her PhD studies, Yuanling Gan published research on quantitative finance topics and presented her findings at academic conferences focused on operations research. She has collaborated with interdisciplinary teams on projects involving statistical modeling and has contributed to open-source projects related to machine learning. Her participation in data science competitions has also led to top rankings, showcasing her skills in the field.