My research focuses on the design and analysis of algorithmic systems for large-scale financial data. I study how optimization, probabilistic modeling, and learning-based methods can be combined to build robust, interpretable, and efficient decision-making systems in modern financial markets.

A central theme of my work is understanding how algorithmic performance and risk behave under distribution shift, delayed feedback, and strategic behavior settings that naturally arise in fintech applications such as credit modeling, fraud detection, and algorithmic trading.

Methodologically, I draw from algorithms, statistical learning theory, and large-scale data systems, with the goal of developing principled techniques that remain reliable under real-world constraints including data sparsity, non-stationarity, and regulatory considerations.

Contact Email : zoya.lpf20@gmail.com

Linkedin : Link