Sifan Liu

Journal article Nuclear Instruments and Methods in Physics Research Section A Accelerators Spectrometers Detectors and Associated Equipment · October 1, 2026 Accurate neutron spectrum measurement is vital for nuclear energy and nuclear medicine applications. While iterative unfolding methods are favored for their efficiency and accuracy, their precision critically depends on the initial guess spectrum. Without ... Full text Cite

A high-dimensional Bayesian approach for spectrum unfolding and uncertainty quantification in spectrometric measurements

Journal article Nuclear Instruments and Methods in Physics Research Section A Accelerators Spectrometers Detectors and Associated Equipment · February 1, 2026 Neutron spectrum unfolding and uncertainty quantification face inherent challenges due to high dimensionality and ill-posed characteristics. We propose a high-dimensional Bayesian approach leveraging local probability decomposition, which comprises a three ... Full text Cite

Selective Inference with Distributed Data

Journal article Journal of Machine Learning Research · January 1, 2025 When data are distributed across multiple sites or machines rather than centralized in one location, researchers face the challenge of extracting meaningful information without directly sharing individual data points. While there are many distributed metho ... Cite

Experimental design of spherical deep penetration shielding benchmark and the effect verification

Journal article Harbin Gongcheng Daxue Xuebao Journal of Harbin Engineering University · December 1, 2024 To address the lack of benchmark experimental data for verifying autonomous shielding calculation programs and databases, this study designed a series of spherical deep penetration shielding benchmark experiments using a Cf-252 neutron source. A shadow con ... Full text Cite

CONDITIONAL QUASI-MONTE CARLO WITH CONSTRAINED ACTIVE SUBSPACES

Journal article SIAM Journal on Scientific Computing · October 1, 2024 Conditional Monte Carlo or pre-integration is a powerful tool for reducing variance and improving the regularity of integrands when using Monte Carlo and quasi-Monte Carlo (QMC) methods. To select the variable to pre-integrate, one must consider both the v ... Full text Cite

PREINTEGRATION VIA ACTIVE SUBSPACE

Journal article SIAM Journal on Numerical Analysis · January 1, 2023 Preintegration is an extension of conditional Monte Carlo to quasi-Monte Carlo and randomized quasi-Monte Carlo. Conditioning can reduce but not increase the variance in Monte Carlo. For quasi-Monte Carlo it can bring about improved regularity of the integ ... Full text Cite

GLOBAL AND INDIVIDUALIZED COMMUNITY DETECTION IN INHOMOGENEOUS MULTILAYER NETWORKS

Journal article Annals of Statistics · October 1, 2022 In network applications, it has become increasingly common to obtain datasets in the form of multiple networks observed on the same set of subjects, where each network is obtained in a related but different experiment condition or application scenario. Suc ... Full text Cite

Statistical Challenges in Tracking the Evolution of SARS-CoV-2

Journal article Statistical Science · May 1, 2022 Genomic surveillance of SARS-CoV-2 has been instrumental in tracking the spread and evolution of the virus during the pandemic. The availability of SARS-CoV-2 molecular sequences isolated from infected individuals, coupled with phylodynamic methods, have p ... Full text Cite

How to Reduce Dimension with PCA and Random Projections?

Journal article IEEE Transactions on Information Theory · December 1, 2021 In our 'big data' age, the size and complexity of data is steadily increasing. Methods for dimension reduction are ever more popular and useful. Two distinct types of dimension reduction are 'data-oblivious' methods such as random projections and sketching ... Full text Cite

Quasi-Monte Carlo quasi-Newton in variational bayes

Journal article Journal of Machine Learning Research · January 1, 2021 Many machine learning problems optimize an objective that must be measured with noise. The primary method is a first order stochastic gradient descent using one or more Monte Carlo (MC) samples at each step. There are settings where ill-conditioning makes ... Cite