Senlin Liu

Hi, I’m Senlin. I am currently an exchange student at ESCP Business School in Paris, and a second-year Master’s student at Peking University, majoring in Finance.

My academic training and professional experience are primarily rooted in quantitative finance, with research and internships at hedge funds focusing on machine learning, alternative data and behavioral finance. I have developed a strong interest in how advanced AI can be applied to financial markets, from text-based alpha generation leveraging LLMs to the automation of quantitative research workflows using AI agents. I am particularly motivated by the idea of using AI to promote a more objective, efficient, and globally fair market environment.

In parallel, I am actively exploring AI applications in the music industry, with a special focus on music foundation models for composition. Music has always been a central part of my life. I am deeply drawn to artistic expression, especially music and dance, and I am also an enthusiastic follower of football and science fiction. My musical tastes span K-Pop, Chinese ancient-style music, and American pop, and you can listen to my own compositions by clicking the button above.

What fascinates me most is the structure behind melody and harmony: the patterns that make music emotionally resonant and culturally meaningful. I am eager to study how generative models can capture these patterns and learn to compose the kinds of music I love. In the long run, my ambition is to help build a world-class, human-centered entertainment platform that enables anyone to discover and express their musical creativity through AI-assisted composition.

I bring to this exploration a strong mathematical and engineering foundation. I earned my undergraduate degree in Electrical Engineering from Wuhan University, where I ranked 2nd out of more than 140 students in my major. I was awarded the National First Prize in the China Mathematical Modeling Competition (top 0.6%) and the National Second Prize in the Citi Financial Innovation Cup. Across my quantitative finance internships, my work has spanned machine learning models, behavioral finance signals, and alternative data analysis—experience that I now seek to extend beyond finance into creative domains.

At the intersection of mathematics, AI, finance, and music, I am interested in how computational systems can augment human creativity and decision-making, rather than replace them.