Neuro magnetic resonance spectroscopy using wavelet decomposition and statistical testing identifies biochemical changes in people with spinal cord injury and pain. - Peter Stanwell; Philip Siddall; Nirmal Keshava; Daniel Cocuzzo; Saadallah Ramadan; Alexander Lin; David Herbert 002; Ashley Craig; Yvonne Tran; James W. Middleton; Shiva Gautam; Michael Cousins; et al. (NeuroImage, 2010)

Algorithms for characterizing brain metabolites in two-dimensional in vivo MR correlation spectroscopy. - Daniel Cocuzzo; Alexander Lin; Saadallah Ramadan; Carolyn Mountford; Nirmal Keshava (EMBC, 2011)

Clustering Time Series with Nonlinear Dynamics: A Bayesian Non-Parametric and Particle-Based Approach. - Alexander Lin; Yingzhuo Zhang; Jeremy Heng; Stephen A. Allsop; Kay M. Tye; Pierre E. Jacob; Demba E. Ba (AISTATS, 2019)

Universal Causal Evaluation Engine: An API for empirically evaluating causal inference models. - Alexander Lin; Amil Merchant; Suproteem K. Sarkar; Alexander D'Amour (CD@KDD, 2019)

Autoregressive Knowledge Distillation through Imitation Learning. - Alexander Lin; Jeremy Wohlwend; Howard Chen 003; Tao Lei 001 (EMNLP (1), 2020)

Action-Based Conversations Dataset: A Corpus for Building More In-Depth Task-Oriented Dialogue Systems. - Derek Chen; Howard Chen 003; Yi Yang 001; Alexander Lin; Zhou Yu 001 (NAACL-HLT, 2021)

Educational impact of an Augmented Reality (AR) application for teaching structural systems to non-engineering students. - Xinping Hu; Yang Miang Goh; Alexander Lin (Adv. Eng. Informatics, 2021)

Mixture Model Auto-Encoders: Deep Clustering Through Dictionary Learning. - Alexander Lin; Andrew H. Song; Demba E. Ba (ICASSP, 2022)

High-Dimensional Sparse Bayesian Learning without Covariance Matrices. - Alexander Lin; Andrew H. Song; Berkin Bilgic; Demba E. Ba (ICASSP, 2022)

Incorporating knowledge of plates in batch normalization improves generalization of deep learning for microscopy images. - Alexander Lin; Alex Lu (MLCB, 2022)

Covariance-Free Sparse Bayesian Learning. - Alexander Lin; Andrew H. Song; Berkin Bilgic; Demba E. Ba (IEEE Trans. Signal Process., 2022)

Investigating Scientific Misinformation Using Different Modes of Learning. - Autumn Toney-Wails; Kornraphop Kawintiranon; Lisa Singh; Paul-Emmanuel Courtines; Alexander Lin; Haofei Zhang (SDU@AAAI, 2023)

Probabilistic Unrolling: Scalable, Inverse-Free Maximum Likelihood Estimation for Latent Gaussian Models. - Alexander Lin; Bahareh Tolooshams; Yves F. Atchadé; Demba E. Ba (ICML, 2023)

Change Management in Physical Network Lifecycle Automation. - Mohammad Al-Fares; Virginia Beauregard; Kevin Grant; Angus Griffith; Jahangir Hasan; Chen Huang; Quan Leng; Jiayao Li; Alexander Lin; Zhuotao Liu; Ahmed Mansy; Bill Martinusen; et al. (USENIX ATC, 2023)

Word-Level Explanations for Analyzing Bias in Text-to-Image Models. - Alexander Lin; Lucas Monteiro Paes; Sree Harsha Tanneru; Suraj Srinivas; Himabindu Lakkaraju (CoRR, 2023)

An Efficient Algorithm For Clustered Multi-Task Compressive Sensing. - Alexander Lin; Demba E. Ba (ICASSP, 2024)

A Deep Learning Approach for Placing Magnetic Resonance Spectroscopy Voxels in Brain Tumors. - Sangyoon Lee 008; Francesca Branzoli; Thanh Nguyen; Ovidiu C. Andronesi; Alexander Lin; Roberto Liserre; Gerd Melkus; Clark Chen; Malgorzata Marjanska; Patrick J. Bolan (MICCAI (3), 2024)