Yinqiu He
University of Wisconsin-Madison, WI·Mathematics & Statistics
Networks capture how things are connected, from social ties to biological interactions. Real networks have become so large and tangled that many analyses are slow or impossible to run.
- Fields
Mathematics & Statistics
Computer Science
- Topics
network embedding
embedding learning
network analysis
- Grants
- 1 known grant, through Jul 2028
What they work on
- Develop methods with theoretical guarantees for estimating low-dimensional network embeddings and quantifying their uncertainty under general network models with relaxed assumptions.
- Design statistically principled procedures to compare network embeddings across conditions, accounting for natural variability and detecting structural anomalies.
- Build a framework that jointly analyzes and integrates embeddings from multiple heterogeneous networks, using their shared structure to improve statistical efficiency.
More about the research
One common fix is embedding. This means compressing each node's position in the network into a short list of numbers that keeps the essential structure. Scientists also need to know how much to trust these compressed summaries. They often have several related networks collected under different conditions, at different times or from different data types, and need to compare or combine them.
Why it matters
This is mainly methods work. It adds rigorous statistical understanding of network embeddings and how far to trust them. The resulting toolkit could help scientists in many fields analyze large networks reliably, compare networks across conditions, and spot structural anomalies, supporting more dependable conclusions and decisions from network data.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Joshua Cape
University of Wisconsin-Madison, WI·Mathematics & Statistics·Early-career
Develop nonparametric spectral clustering and dimension-reduction methods that tolerate messy, mixed data, and apply them to brain network studies of schizophrenia with psychiatry collaborators.
Weijing Tang
Carnegie Mellon University, PA·Mathematics & Statistics
Develop priority-aware rules for valuing training data based on the Shapley value, a game-theory method for fairly splitting credit, that account for originality, provenance and individual risk within a unified axiomatic framework.
Kaizheng Wang
Columbia University, NY·Mathematics & Statistics·Early-career
Develop methods to measure the overall fidelity of black-box generative AI models using effective sample size, an interpretable estimate of how much reliable information synthetic data contain.
The school
University of Wisconsin-Madison: 91 labs on PI Pages, $1.9B a year on research, 854 research doctorates a year.
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