Lili Zheng

About

Lili Zheng is an Assistant Professor in the field of Statistics at the University of Illinois. Her research focuses on statistical inference methods, graphical models, and interpretable machine learning. She has contributed to advancing the understanding and development of inference techniques for graphical models, particularly in contexts involving sparsely measured data. Her work addresses key statistical challenges and opportunities in interpretable machine learning, emphasizing model selection and big data applications. Zheng's research also includes applications of nonparanormal graph quilting to calcium imaging data and low-rank tensor completion approaches for imputing functional neuronal data from multiple recordings. Additionally, she has investigated Gaussian process parameter estimation using mini-batch stochastic gradient descent, providing convergence guarantees and empirical benefits. Her scholarly contributions are published in peer-reviewed journals and conference proceedings, reflecting a strong commitment to advancing statistical methodology and its applications in complex data analysis.

Research topics

• Computer Science
• Machine Learning
• Artificial Intelligence
• Data science
• Data Mining
• Finance
• Theoretical computer science
• World Wide Web
• Economics

Selected publications

• A Data-Driven Graph Generative Model for Temporal Interaction Networks

  2020 · 97 citations

  Deep graph generative models have recently received a surge of attention due to its superiority of modeling realistic graphs in a variety of domains, including biology, chemistry, and social science. Despite the initial success, most, if not all, of the existing works are designed for static networks. Nonetheless, many realistic networks are intrinsically dynamic and presented as a collection of system logs (i.e., timestamped interactions/edges between entities), which pose a new research direct…

  DOI

• Domain Adaptive Multi-Modality Neural Attention Network for Financial Forecasting

  2020 · 40 citations

  Financial time series analysis plays a central role in optimizing investment decision and hedging market risks. This is a challenging task as the problems are always accompanied by dual-level (i.e, data-level and task-level) heterogeneity. For instance, in stock price forecasting, a successful portfolio with bounded risks usually consists of a large number of stocks from diverse domains (e.g, utility, information technology, healthcare, etc.), and forecasting stocks in each domain can be treated…

  DOI

• MULAN: Multi-modal Causal Structure Learning and Root Cause Analysis for Microservice Systems

  2024-05-08 · 28 citations

  article1st authorCorresponding

  Effective root cause analysis (RCA) is vital for swiftly restoring services, minimizing losses, and ensuring the smooth operation and management of complex systems. Previous data-driven RCA methods, particularly those employing causal discovery techniques, have primarily focused on constructing dependency or causal graphs for backtracking the root causes. However, these methods often fall short as they rely solely on data from a single modality, thereby resulting in suboptimal solutions. In this…

  PublisherDOI

• Deep Co-Attention Network for Multi-View Subspace Learning

  2021 · 22 citations

  1st authorCorresponding

  Many real-world applications involve data from multiple modalities and thus exhibit the view heterogeneity. For example, user modeling on social media might leverage both the topology of the underlying social network and the content of the users’ posts; in the medical domain, multiple views could be X-ray images taken at different poses. To date, various techniques have been proposed to achieve promising results, such as canonical correlation analysis based methods, etc. In the meanwhile, it is…

  DOI

• Heterogeneous Contrastive Learning for Foundation Models and Beyond

  2024-08-24 · 14 citations

  articleOpen access1st authorCorresponding

  In the era of big data and Artificial Intelligence, an emerging paradigm is to utilize contrastive self-supervised learning to model large-scale heterogeneous data. Many existing foundation models benefit from the generalization capability of contrastive self-supervised learning by learning compact and high-quality representations without relying on any label information. Amidst the explosive advancements in foundation models across multiple domains, including natural language processing and com…

  PublisherOA PDFDOI

Frequent coauthors

• Jingrui He

  23 shared

• Yada Zhu

  8 shared

• Dongqi Fu

  5 shared

• Jiawei Han

  University of Illinois Urbana-Champaign

  4 shared

• Dawei Zhou

  Virginia Tech

  4 shared

• Jiejun Xu

  HRL Laboratories (United States)

  3 shared

• Hanghang Tong

  3 shared

• Jinjun Xiong

  2 shared

Similar researchers at University of Illinois Urbana-Champaign

• Dr. Sabyasachi Chatterjeeh-70
• Ruoqi Yuh-61
• Trevor H Parkh-61