Ning Hao

Member of the Graduate Faculty | Associate Professor, Mathematics | Associate Professor, Statistics-GIDP

About

I am an associate professor of Mathematics at the University of Arizona UA) I am an editor of the jounral STAT. I received my B.S. in Mathematics from Peking University, and Ph.D. from the Department of Mathematics at Stony Brook University. I spent one year as a research associate at the statistics lab, Princeton University before joining the UA. I am co-organizer of UA TRIPODS research working grou(RWG) 6. I am coaching Sunrise Drive Mathleague Club with my colleagues Selena Niu and Helen Zhang. In 2020-2021, the club has been expanded to all four elementary schools in CFSD16.

Research Area

data analysis, machine learning, statistical analysis, change-point detection, high-dimensional data, statistical modeling, nanopore sequencing, algorithms, screening, dimensionality reduction

Collaborative Research: CDS&E-MSS: Community Detection via Covariance Structures
Active·2023·$40K·
External
Principal Investigator (PI)
community detection, covariance structures, collaborative research, cds&e-mss, data science
Scalable Methods in High Dimensional Statistical Learning
Active·2017·$42K·
External
Principal Investigator (PI)
statistical learning, scalable methods, high dimensional, machine learning, big data analytics
Collaborative Research: Scalable and Flexible Algorithms to Detect Structural Change in Complex Sequencing Data
2017·$66.4K / $166K·
External
Co-Investigator (COI)
bioinformatics, computer science, data analysis, genomics, algorithms
Flexible Modeling for High-Dimensional Complex Data: Theory, Methodology, and Computation
2013·$30K / $150K·
External
Co-Investigator (COI)
statistical modeling, big data, computational methods, machine learning, data analysis

Recent

Variational Estimators of the Degree-corrected Latent Block Model for Bipartite Networks

2024

latent block model, bipartite networks, variational estimators, degree-corrected, networks

Adapting nanopore sequencing basecalling models for modification detection via incremental learning and anomaly detection

2024

nanopore sequencing, anomaly detection

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Calculus I
Calculus II
Theoretical Statistics I
Topics in Modern Analysis
Theory of Statistics

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Ning Hao

About

Research Area

Grants(4)

Collaborative Research: CDS&E-MSS: Community Detection via Covariance Structures

Scalable Methods in High Dimensional Statistical Learning

Collaborative Research: Scalable and Flexible Algorithms to Detect Structural Change in Complex Sequencing Data

Flexible Modeling for High-Dimensional Complex Data: Theory, Methodology, and Computation

Publications(28)

Variational Estimators of the Degree-corrected Latent Block Model for Bipartite Networks

Adapting nanopore sequencing basecalling models for modification detection via incremental learning and anomaly detection

An Iterative Approach to Polish the Nanopore Sequencing Basecalling for Therapeutic RNA Quality Control

Training Data Diversity Enhances the Basecalling of Novel RNA Modification-Induced Nanopore Sequencing Readouts

Equivariant Variance Estimation for Multiple Change-point Model

Simultaneous Change-point Detection and Curve Estimation

Quadratic Discriminant Analysis by Projection

A super scalable algorithm for short segment detection

A Backward Procedure for Change-point Detection with Application to Copy Number Variation Detection

A super scalable algorithm for segment detection

Collaborations(6)

Hao Zhang

Joseph Watkins

Yue Niu

Xiaoxiao Sun

Lingling An

Courses(11)

Calculus I

Calculus II

Theoretical Statistics I

Topics in Modern Analysis

Theory of Statistics