About me

Hi, I’m Jifan! I’m a fourth-year PhD student of Statistics and Data Science at Northwestern University. I’m advised by Prof. Miklos Racz. I received my Bachelor’s degree in Pure and applied mathmatics from Tsinghua University in 2021, where I worked with Prof. Qian Lin.

Research Interest

I am broadly interested in statistics, probabilistic combinatorics, and computer science, with a focus on graph theory and machine learning models for data with graph structure. My theoretical research centers on probabilistic generative graph models, such as the stochastic block model, and studies inference tasks like graph matching, community recovery, and latent structure recovery. Specifically, I explore how multiple (correlated) networks can be leveraged to enhance inference tasks, evaluating how integrating information across graphs can improve outcomes in graph matching and community recovery.

On the empirical side, my work revolves around Graph Neural Networks (GNNs), advanced machine learning models designed for graph-structured data. I am currently investigating how GNNs can be employed for causal representation learning, which is typically depicted by Directed Acyclic Graphs (DAGs). The model achieves superior predictive performance for unseen combinatorial perturbation effects in genomic data. Additionally, I have a strong interest in uncertainty quantification for GNNs, particularly in scenarios where the cost of errors is significant. My research studies multi-step-ahead predictions for time series with spatial dependencies, often modeled as spatio-temporal graphs. I utilize adaptive conformal inference and inefficiency minimization techniques to ensure reliable uncertainty estimates and improve the efficiency of the prediction.

News

I’m actively seeking an internship for summer 2025 for Tech/Quant positions! Feel free to contact me if you have any connections, advice or opportunities you can offer. Thanks!

Other

In my spare time, I like playing badmintons, table tennis and I have two cats, Melon and Sherry.