XNet: a convolutional neural network (CNN) implementation for medical x-ray image segmentation suitable for small datasets. - Joseph Bullock; Carolina Cuesta-Lázaro; Arnau Quera-Bofarull (Biomedical Applications in Molecular, Structural, and Functional Imaging, 2019)

Operational response simulation tool for epidemics within refugee and IDP settlements: A scenario-based case study of the Cox's Bazar settlement. - Joseph Aylett-Bullock; Carolina Cuesta-Lázaro; Arnau Quera-Bofarull; Anjali Katta; Katherine Hoffmann Pham; Benjamin Hoover; Hendrik Strobelt; Rebeca Moreno Jimenez; Aidan Sedgewick; Egmond Samir Evers; David Kennedy; Sandra Harlass; et al. (PLoS Comput. Biol., 2021)

What does the sea say to the shore? A BERT based DST style approach for speaker to dialogue attribution in novels. - Carolina Cuesta-Lázaro; Animesh Prasad; Trevor Wood (ACL (1), 2022)

Don't Simulate Twice: One-Shot Sensitivity Analyses via Automatic Differentiation. - Arnau Quera-Bofarull; Ayush Chopra; Joseph Aylett-Bullock; Carolina Cuesta-Lázaro; Anisoara Calinescu; Ramesh Raskar; Michael J. Wooldridge (AAMAS, 2023)

Simulation-Based Inference for Exoplanet Atmospheric Retrieval: Insights from Winning the Ariel Data Challenge 2023 Using Normalizing Flows. - Mayeul Aubin; Carolina Cuesta-Lázaro; Ethan Tregidga; Javier Viaña; Cecilia Garraffo; Iouli E. Gordon; Mercedes López-Morales; Robert J. Hargreaves; Vladimir Yu. Makhnev; Jeremy J. Drake; Douglas P. Finkbeiner; Phillip Cargile (PKDD/ECML Workshops (5), 2023)

Probabilistic reconstruction of Dark Matter fields from biased tracers using diffusion models. - Core Francisco Park; Victoria Ono; Nayantara Mudur; Yueying Ni; Carolina Cuesta-Lázaro (CoRR, 2023)

A point cloud approach to generative modeling for galaxy surveys at the field level. - Carolina Cuesta-Lázaro; Siddharth Mishra-Sharma (CoRR, 2023)

Cosmological Field Emulation and Parameter Inference with Diffusion Models. - Nayantara Mudur; Carolina Cuesta-Lázaro; Douglas P. Finkbeiner (CoRR, 2023)

LtU-ILI: An All-in-One Framework for Implicit Inference in Astrophysics and Cosmology. - Matthew Ho; Deaglan J. Bartlett; Nicolas Chartier; Carolina Cuesta-Lázaro; Simon Ding; Axel Lapel; Pablo Lemos; Christopher C. Lovell; T. Lucas Makinen; Chirag Modi 002; Viraj Pandya; Shivam Pandey; et al. (CoRR, 2024)

Diffusion-HMC: Parameter Inference with Diffusion Model driven Hamiltonian Monte Carlo. - Nayantara Mudur; Carolina Cuesta-Lázaro; Douglas P. Finkbeiner (CoRR, 2024)

How DREAMS are made: Emulating Satellite Galaxy and Subhalo Populations with Diffusion Models and Point Clouds. - Tri Nguyen; Francisco Villaescusa-Navarro; Siddharth Mishra-Sharma; Carolina Cuesta-Lázaro; Paul Torrey; Arya Farahi; Alex M. Garcia; Jonah C. Rose; Stephanie O'Neil; Mark Vogelsberger; Xuejian Shen; Cian Roche; et al. (CoRR, 2024)

A Cosmic-Scale Benchmark for Symmetry-Preserving Data Processing. - Julia Balla; Siddharth Mishra-Sharma; Carolina Cuesta-Lázaro; Tommi S. Jaakkola; Tess E. Smidt (CoRR, 2024)

CosmoFlow: Scale-Aware Representation Learning for Cosmology with Flow Matching. - Sidharth Kannan; Tian Qiu; Carolina Cuesta-Lázaro; Haewon Jeong (CoRR, 2025)