Automation of Network-Based Scientific Workflows. - Mladen A. Vouk; Ilkay Altintas; Roselyne Barreto; J. Blondin; Zhengang Cheng; Terence Critchlow; Ayla Khan; Scott Klasky; J. Ligon; Bertram Ludäscher; Pierre Mouallem; Steven G. Parker; et al. (Grid-Based Problem Solving Environments, 2006)

Collaborative visualization spaces for petascale simulations. - Scott Klasky; Roselyne Barreto; Ayla Khan; Manish Parashar; Norbert Podhorszki; Steven G. Parker; Deborah Silver; Mladen A. Vouk (CTS, 2008)

Collaboration portal for petascale simulations. - Roselyne Barreto; Scott Klasky; Norbert Podhorszki; Pierre Mouallem; Mladen A. Vouk (CTS, 2009)

Enabling Advanced Visualization Tools in a Web-Based Simulation Monitoring System. - Emanuele Santos; Julien Tierny; Ayla Khan; Brad Grimm; Lauro Didier Lins; Juliana Freire; Valerio Pascucci; Cláudio T. Silva; Scott Klasky; Roselyne Barreto; Norbert Podhorszki (eScience, 2009)

Tracking Files in the Kepler Provenance Framework. - Pierre Mouallem; Roselyne Barreto; Scott Klasky; Norbert Podhorszki; Mladen A. Vouk (SSDBM, 2009)

Collaborative monitoring and analysis for simulation scientists. - Roselyne Tchoua; Scott Klasky; Norbert Podhorszki; Brad Grimm; Ayla Khan; Emanuele Santos; Cláudio T. Silva; Pierre Mouallem; Mladen A. Vouk (CTS, 2010)

EFFIS: An End-to-end Framework for Fusion Integrated Simulation. - Julian Cummings; Jay F. Lofstead; Karsten Schwan; Alexander Sim; Arie Shoshani; Ciprian Docan; Manish Parashar; Scott Klasky; Norbert Podhorszki; Roselyne Barreto (PDP, 2010)

Collaborative monitoring and visualization of HPC data. - Roselyne Tchoua; Hasan Abbasi; Scott Klasky; Qing Liu 002; Norbert Podhorszki; David Pugmire; Yuan Tian 004; Matthew Wolf (CTS, 2012)

ADIOS Visualization Schema: A First Step Towards Improving Interdisciplinary Collaboration in High Performance Computing. - Roselyne Tchoua; Jong Youl Choi; Scott Klasky; Qing Liu 002; Jeremy Logan; Kenneth Moreland; Jingqing Mu; Manish Parashar; Norbert Podhorszki; David Pugmire; Matthew Wolf (e-Science, 2013)

Hello ADIOS: the challenges and lessons of developing leadership class I/O frameworks. - Qing Liu 002; Jeremy Logan; Yuan Tian 004; Hasan Abbasi; Norbert Podhorszki; Jong Youl Choi; Scott Klasky; Roselyne Tchoua; Jay F. Lofstead; Ron A. Oldfield; Manish Parashar; Nagiza F. Samatova; et al. (Concurr. Comput. Pract. Exp., 2014)

A Hybrid Human-computer Approach to the Extraction of Scientific Facts from the Literature. - Roselyne Tchoua; Kyle Chard; Debra Audus; Jian Qin; Juan de Pablo; Ian T. Foster (ICCS, 2016)

Towards a Hybrid Human-Computer Scientific Information Extraction Pipeline. - Roselyne B. Tchoua; Kyle Chard; Debra J. Audus; Logan T. Ward; Joshua Lequieu; Juan J. de Pablo; Ian T. Foster (eScience, 2017)

Reduced-Order Modeling through Machine Learning Approaches for Brittle Fracture Applications. - A. Hunter; Bryan A. Moore; Maruti Kumar Mudunuru; Viet T. Chau; Robyn L. Miller; Roselyne B. Tchoua; C. Nyshadham; Satish Karra; Dan O'Malley; Esteban Rougier; Hari S. Viswanathan; Gowri Srinivasan (CoRR, 2018)

Active Learning Yields Better Training Data for Scientific Named Entity Recognition. - Roselyne Tchoua; Aswathy Ajith; Zhi Hong; Logan T. Ward; Kyle Chard; Debra Audus; Shrayesh Patel; Juan de Pablo; Ian T. Foster (eScience, 2019)

Creating Training Data for Scientific Named Entity Recognition with Minimal Human Effort. - Roselyne B. Tchoua; Aswathy Ajith; Zhi Hong; Logan T. Ward; Kyle Chard; Alexander Belikov 003; Debra J. Audus; Shrayesh Patel; Juan J. de Pablo; Ian T. Foster (ICCS (1), 2019)

Enhancing Recall Using Data Cleaning for Biomedical Big Data. - Priya Deshpande; Alexander Rasin; Roselyne Tchoua; Jacob Furst 001; Daniela Raicu; Sameer K. Antani (CBMS, 2020)

Learning Latent Spiculated Features for Lung Nodule Characterization. - Bowen Qiu; Jacob Furst 001; Alexander Rasin; Roselyne Tchoua; Daniela Raicu (EMBC, 2020)

SciNER: Extracting Named Entities from Scientific Literature. - Zhi Hong; Roselyne Tchoua; Kyle Chard; Ian T. Foster (ICCS (2), 2020)

Ensemble Labeling towards Scientific Information Extraction (ELSIE) - Blob Extraction. - Erin Murphy; Alexander Rasin; Jacob Furst 001; Daniela Raicu; Roselyne Tchoua (e-Science, 2021)

Ensemble Labeling Towards Scientific Information Extraction (ELSIE). - Erin Murphy; Alexander Rasin; Jacob Furst 001; Daniela Raicu; Roselyne Tchoua (ICCS (1), 2021)

Failure Sources in Machine Learning for Medicine - A Study. - Hana Ahmed; Roselyne Tchoua; Jay F. Lofstead (e-Science, 2022)

Text Summarization towards Scientific Information Extraction. - Abigail Keller; Jacob Furst 001; Daniela Raicu; Peter M. Hastings; Roselyne Tchoua (e-Science, 2022)

Human-in-the-loop deep learning retinal image classification with customized loss function. - Suhev Shakya; Mariana Vasquez; Yiyang Wang 003; Roselyne Tchoua; Jacob Furst 001; Daniela Raicu (Computer-Aided Diagnosis, 2022)

Similarity-based uncertainty scores for computer-aided diagnosis. - Claire Weissman; Lilly Roelofs; Jacob Furst 001; Daniela Stan Raicu; Roselyne Tchoua (Computer-Aided Diagnosis, 2022)

Interactive deep learning for explainable retinal disease classification. - Mariana Vasquez; Suhev Shakya; Ian Wang; Jacob Furst 001; Roselyne Tchoua; Daniela Raicu (Image Processing, 2022)

Similarity-based uncertainty scores for interpretable deep learning in scientific applications. - Lilly Roelofs; Claire Weissman; Daniela Raicu; Jacob Furst 001; Roselyne Tchoua (SAC, 2022)

Ensuring AI For Science is Science: Making Randomness Portable. - Hana Ahmed; Roselyne Tchoua; Jay F. Lofstead (AI4S, 2022)

Measuring Reproduciblity of Machine Learning Methods for Medical Diagnosis. - Hana Ahmed; Roselyne Tchoua; Jay F. Lofstead (TransAI, 2022)

Contextualizing Lung Nodule Malignancy Predictions with Easy vs. Hard Image Classification. - Edward Xu; Thiruvarangan Ramaraj; Roselyne Tchoua; Jacob Furst 001; Daniela Raicu (TransAI, 2022)

CompNet: A Designated Model to Handle Combinations of Images and Designed features. - Bowen Qiu; Daniela Raicu; Jacob Furst 001; Roselyne Tchoua (CoRR, 2022)

Curriculum gDRO: Improving Lung Malignancy Classification through Robust Curriculum Task Learning. - Arun Sivakumar; Yiyang Wang 003; Roselyne Tchoua; Thiruvarangan Ramaraj; Jacob Furst 001; Daniela Stan Raicu (CBMS, 2023)

Multiclass Classification of Software Vulnerabilities with Deep Learning. - Crystal Contreras; Hristina Dokic; Zhen Huang 002; Daniela Stan Raicu; Jacob Furst 001; Roselyne Tchoua (ICMLC, 2023)

Iterative K-means clustering for disease subtype discovery. - Katherine Aubert; Catherine Huber; Jacob Furst 001; Daniela Stan Raicu; Roselyne Tchoua (Computer-Aided Diagnosis, 2023)

No nodule left behind: evaluating lung nodule malignancy classification with different stratification schemes. - Thomas Zeng 003; Elias Furst; Yiyang Wang 003; Roselyne Tchoua; Jacob D. Furst; Daniela Raicu (Computer-Aided Diagnosis, 2023)

Leveraging Community Health Workers and Social Determinants of Health for Predicting Emergency Department Readmissions. - Kate Karam; Kelly MacCabe; Roselyne Tchoua (TransAI, 2023)

Leveraging Interpretable Features and Clustering Agreements for Assistive Materials Discovery. - Maria Alexandra Theodorescu; Jacob D. Furst; Daniela Stan Raicu; Roselyne Tchoua (TransAI, 2023)

Biomedical heterogeneous data categorization and schema mapping toward data integration. - Priya Deshpande; Alexander Rasin; Roselyne Tchoua; Jacob D. Furst; Daniela Raicu; Michiel Schinkel; Hari Trivedi; Sameer K. Antani (Frontiers Big Data, 2024)

Leveraging Community Health Workers for Predicting Emergency Department Readmissions. - Nuvia Hernandez; Kate Karam; Nicole Baugh; Shilpa Musale; Aditya Patrick Moses; Daniela Raicu; Jacob D. Furst; Kelly McCabe; Roselyne Tchoua (Int. J. Semantic Comput., 2024)

Enhancing Pulmonary Nodule Localization Based on Latent Representations. - Charmi Patel; Yiyang Wang 003; Roselyne Tchoua; Thiruvarangan Ramaraj; Jacob D. Furst; Daniela Raicu (CBMS, 2025)