Nianqiao Ju
Mathematics · Purdue University West Lafayette
Publications
22
Citations
92
Est. group size
~2
Recurring co-author estimate
Active years
11
Publishing since 2016
Nianqiao Ju works on computational statistics, with a focus on Markov chain Monte Carlo (MCMC) methods - algorithms that generate random samples to approximate complex probability calculations - and their application to Bayesian inference (a framework for updating beliefs using data and probability). Recent work applies these tools to problems involving data privacy, epidemic modeling, and redistricting (drawing legislative district boundaries), often using simulation-based methods to work with data that has been anonymized or protected for privacy reasons.
Publication output has been relatively steady but modest over the last decade, averaging about 2 papers per year in the last five years with no strong upward or downward trend.
Generated by claude-sonnet-5 from public bibliographic data · Jul 20, 2026
- High IFN-γ Responses Following PD-1 Inhibition Identify Progressive Mycobacterium avium Complex Lung Disease: A Pilot Study
2026
- Redistricting from the Bottom Up: Sampling Communities of Interest with Differential Privacy
arXiv (Cornell University) · 2026
- Redistricting from the Bottom Up: Sampling Communities of Interest with Differential Privacy
arXiv (Cornell University) · 2026
- SOMA: A Novel Sampler for Bayesian Inference from Privatized Data
arXiv (Cornell University) · 2025
- Simulation-based Bayesian Inference from Privacy Protected Data
arXiv (Cornell University) · 2023
- Spectral gap bounds for reversible hybrid Gibbs chains
arXiv (Cornell University) · 2023
- BETS: The dangers of selection bias in early analyses of the coronavirus disease (COVID-19) pandemic
The Annals of Applied Statistics · 2021
- Sequential Monte Carlo algorithms for agent-based models of disease transmission
arXiv (Cornell University) · 2021
- Letter to the editor: Generation interval for COVID-19 based on symptom onset data
Eurosurveillance · 2020
- BETS: The dangers of selection bias in early analyses of the coronavirus disease (COVID-19) pandemic
arXiv (Cornell University) · 2020
- A simple Markov chain for independent Bernoulli variables conditioned on their sum
arXiv (Cornell University) · 2020
- The BETS Model for Early Epidemic Data [R package bets.covid19 version 1.0.0]
2020
- Analysis of Simulated Crowd Flow Exit Data: Visualization, Panic Detection and Exit Time Convergence, Attribution, and Estimation
Association for Women in Mathematics series · 2019
- A Sequential Test for Selecting the Better Variant
2019
- Detecting Functional States of the Rat Brain with Topological Data Analysis
Lecture notes in networks and systems · 2018
- arXiv (Cornell University)×10
- The Annals of Applied Statistics×1
- Bioinformatics×1
- Association for Women in Mathematics series×1
- Eurosurveillance×1
- Faming Liang
Mathematics · Purdue University West Lafayette
- Ruqi Zhang
Mathematics · Purdue University West Lafayette
- Vinayak Rao
Computer Science · Purdue University West Lafayette
- Qifan Song
Mathematics · Purdue University West Lafayette
- Antik Chakraborty
Computer Science · Purdue University West Lafayette
This profile was generated automatically from public scholarly data (OpenAlex). Group size and activity levels are estimates derived from co-authorship patterns.
Last updated Jul 20, 2026.
Claim or correct this profile