Hub Map: A new approach for visualizing traffic data sets with multi-attribute link data. - Andrew J. Simmons; Iman Avazpour; Hai Le Vu; Rajesh Vasa (VL/HCC, 2015)

Agree to disagree: on labelling helpful app reviews. - Andrew J. Simmons; Leonard Hoon (OZCHI, 2016)

Spatio-Temporal Reference Frames as Geographic Objects. - Andrew J. Simmons; Rajesh Vasa (SIGSPATIAL/GIS, 2017)

An interaction model for de-identification of human data held by external custodians. - Andrew J. Simmons; Maheswaree Kissoon Curumsing; Rajesh Vasa (OZCHI, 2018)

Data Provenance for Sport. - Andrew J. Simmons; Scott Barnett; Simon Vajda; Rajesh Vasa (CoRR, 2018)

Visual Languages for Supporting Big Data Analytics Development. - Hourieh Khalajzadeh; Andrew J. Simmons; Mohamed Abdelrazek 001; John Grundy 001; John G. Hosking; Qiang He 001 (ENASE, 2020)

BiDaML in Practice: Collaborative Modeling of Big Data Analytics Application Requirements. - Hourieh Khalajzadeh; Andrew J. Simmons; Tarun Verma; Mohamed Abdelrazek 001; John C. Grundy; John G. Hosking; Qiang He 001; Prasanna Ratnakanthan; Adil Zia; Meng Law (ENASE (Selected Papers), 2020)

A large-scale comparative analysis of Coding Standard conformance in Open-Source Data Science projects. - Andrew J. Simmons; Scott Barnett; Jessica Rivera-Villicana; Akshat Bajaj; Rajesh Vasa (ESEM, 2020)

A practical, collaborative approach for modeling big data analytics application requirements. - Hourieh Khalajzadeh; Andrew J. Simmons; Mohamed Abdelrazek 001; John C. Grundy; John G. Hosking; Qiang He 001; Prasanna Ratnakanthan; Adil Zia; Meng Law (ICSE (Companion Volume), 2020)

User-centred tooling for modelling of big data applications. - Hourieh Khalajzadeh; Tarun Verma; Andrew J. Simmons; John C. Grundy; Mohamed Abdelrazek 001; John G. Hosking (MoDELS (Companion), 2020)

End-User-Oriented Tool Support for Modeling Data Analytics Requirements. - Hourieh Khalajzadeh; Andrew J. Simmons; Mohamed Abdelrazek 001; John Grundy 001; John G. Hosking; Qiang He 001 (VL/HCC, 2020)

An end-to-end model-based approach to support big data analytics development. - Hourieh Khalajzadeh; Andrew J. Simmons; Mohamed Abdelrazek 001; John C. Grundy; John G. Hosking; Qiang He 001 (J. Comput. Lang., 2020)

Towards a taxonomy for annotation of data science experiment repositories. - Shangeetha Sivasothy; Scott Barnett; Niroshinie Fernando; Rajesh Vasa; Roopak Sinha; Andrew J. Simmons (SCAM, 2021)

MLSmellHound: A Context-Aware Code Analysis Tool. - Jai Kannan; Scott Barnett; Luis Cruz 002; Anj Simmons; Akash Agarwal (ICSE (NIER), 2022)

SignalKG: Towards Reasoning about the Underlying Causes of Sensor Observations. - Anj Simmons; Rajesh Vasa; Antonio Giardina (ISWC (Posters/Demos/Industry), 2022)

A reliability measure for smart surveillance systems. - Anj Simmons (CoRR, 2022)

Signal Knowledge Graph. - Anj Simmons; Rajesh Vasa (CoRR, 2022)

Comparative analysis of real bugs in open-source Machine Learning projects - A Registered Report. - Tuan Dung Lai; Anj Simmons; Scott Barnett; Jean-Guy Schneider; Rajesh Vasa (CoRR, 2022)

MLGuard: Defend Your Machine Learning Model! - Sheng Wong; Scott Barnett; Jessica Rivera-Villicana; Anj Simmons; Hala Abdelkader; Jean-Guy Schneider; Rajesh Vasa (SE4SafeML@SIGSOFT FSE, 2023)

Green Runner: A tool for efficient model selection from model repositories. - Jai Kannan; Scott Barnett; Anj Simmons; Taylan Selvi; Luis Cruz 002 (CoRR, 2023)

Garbage in, garbage out: Zero-shot detection of crime using Large Language Models. - Anj Simmons; Rajesh Vasa (CoRR, 2023)

Estimating Patient-Level Uncertainty in Seizure Detection Using Group-Specific Out-of-Distribution Detection Technique. - Sheng Wong; Anj Simmons; Jessica Rivera-Villicana; Scott Barnett (Sensors, 2023)

Detecting out-of-distribution text using topological features of transformer-based language models. - Andres Pollano; Anupam Chaudhuri; Anj Simmons (AISafety@IJCAI, 2024)

Quantifying Manifolds: Do the Manifolds Learned by Generative Adversarial Networks Converge to the Real Data Manifold? - Anupam Chaudhuri; Anj Simmons; Mohamed Abdelrazek 001 (AI (2), 2024)

Green Runner: A Tool for Efficient Deep Learning Component Selection. - Jai Kannan; Scott Barnett; Anj Simmons; Taylan Selvi; Luis Cruz 002 (CAIN, 2024)

Minimising changes to audit when updating decision trees. - Anj Simmons; Scott Barnett; Anupam Chaudhuri; Sankhya Singh; Shangeetha Sivasothy (CoRR, 2024)

Comparative analysis of real issues in open-source machine learning projects. - Tuan Dung Lai; Anj Simmons; Scott Barnett; Jean-Guy Schneider; Rajesh Vasa (Empir. Softw. Eng., 2024)

Channel-annotated deep learning for enhanced interpretability in EEG-based seizure detection. - Sheng Wong; Anj Simmons; Jessica Rivera-Villicana; Scott Barnett; Shobi Sivathamboo; Piero Perucca; Zongyuan Ge; Patrick Kwan; Levin Kuhlmann; Terence J. O'Brien (Biomed. Signal Process. Control., 2025)