Neil Lawrence

Selected publications

• Robinson, D., Cabrera, C., Gordon, AD., Lawrence, ND. and Mennen, L., 2025. Requirements Are All You Need: The Final Frontier for End-User Software Engineering ACM Transactions on Software Engineering and Methodology, v. 34 Doi: 10.1145/3708524
• Williams, CR., Thodoroff, P., Arthern, RJ., Byrne, J., Hosking, JS., Kaiser, M., Lawrence, ND. and Kazlauskaite, I., 2025. Calculations of extreme sea level rise scenarios are strongly dependent on ice sheet model resolution Communications Earth and Environment, v. 6 Doi: http://doi.org/10.1038/s43247-025-02010-z
• Amutorine, M., Lawrence, N. and Montgomery, J., 2024. Increasing data sharing and use for social good: Lessons from Africa's data-sharing practices during the COVID-19 response Data and Policy, v. 6 Doi: http://doi.org/10.1017/dap.2024.43
• Lawrence, ND. and Montgomery, J., 2024. Accelerating AI for science: open data science for science. R Soc Open Sci, v. 11 Doi: 10.1098/rsos.231130
• Mishra, C., von Wolff, N., Tripathi, A., Brodie, CN., Lawrence, ND., Ravuri, A., Bremond, É., Preiss, A. and Kumar, A., 2023. Predicting ruthenium catalysed hydrogenation of esters using machine learning Digital Discovery, v. 2 Doi: 10.1039/d3dd00029j
• Vargas, F., Ovsianas, A., Fernandes, D., Girolami, M., Lawrence, ND. and Nüsken, N., 2023. Bayesian learning via neural Schrödinger–Föllmer flows Statistics and Computing, v. 33 Doi: 10.1007/s11222-022-10172-5
• Vargas, F., Thodoroff, P., Lamacraft, A. and Lawrence, N., 2023. Correction: Vargas et al. Solving Schrödinger Bridges via Maximum Likelihood. Entropy 2021, 23, 1134. Entropy (Basel), v. 25 Doi: 10.3390/e25020289
• Ross, J., Fridman, M., Kelepouris, N., Murray, K., Krone, N., Polak, M., Rohrer, TR., Pietropoli, A., Lawrence, N. and Backeljauw, P., 2023. Factors Associated With Response to Growth Hormone in Pediatric Growth Disorders: Results of a 5-year Registry Analysis. J Endocr Soc, v. 7 Doi: 10.1210/jendso/bvad026
• Paleyes, A., Urma, RG. and Lawrence, ND., 2023. Challenges in Deploying Machine Learning: A Survey of Case Studies ACM Computing Surveys, v. 55 Doi: 10.1145/3533378
• Cortes, C. and Lawrence, ND., 2021. Inconsistency in Conference Peer Review: Revisiting the 2014 NeurIPS Experiment