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Journal Articles
- 2024
[j2]Priya Deshpande, Alexander Rasin, Roselyne Tchoua, Jacob D. Furst, Daniela Raicu, Michiel Schinkel, Hari Trivedi
, Sameer K. Antani:
Biomedical heterogeneous data categorization and schema mapping toward data integration. Frontiers Big Data 6 (2024)- 2014
- [j1]Qing Liu, Jeremy Logan, Yuan Tian, Hasan Abbasi, Norbert Podhorszki, Jong Youl Choi, Scott Klasky, Roselyne Tchoua, Jay F. Lofstead, Ron A. Oldfield, Manish Parashar, Nagiza F. Samatova, Karsten Schwan, Arie Shoshani, Matthew Wolf, Kesheng Wu, Weikuan Yu:
Hello ADIOS: the challenges and lessons of developing leadership class I/O frameworks. Concurr. Comput. Pract. Exp. 26(7): 1453-1473 (2014)
Conference and Workshop Papers
- 2023
- [c31]Arun Sivakumar, Yiyang Wang, Roselyne Tchoua, Thiruvarangan Ramaraj, Jacob Furst, Daniela Stan Raicu:
Curriculum gDRO: Improving Lung Malignancy Classification through Robust Curriculum Task Learning. CBMS 2023: 622-627 - [c30]Crystal Contreras, Hristina Dokic, Zhen Huang, Daniela Stan Raicu, Jacob Furst, Roselyne Tchoua:
Multiclass Classification of Software Vulnerabilities with Deep Learning. ICMLC 2023: 134-140 - [c29]Katherine Aubert, Catherine Huber, Jacob Furst, Daniela Stan Raicu, Roselyne Tchoua:
Iterative K-means clustering for disease subtype discovery. Medical Imaging: Computer-Aided Diagnosis 2023 - [c28]Thomas Zeng, Elias Furst, Yiyang Wang, Roselyne Tchoua, Jacob D. Furst, Daniela Raicu:
No nodule left behind: evaluating lung nodule malignancy classification with different stratification schemes. Medical Imaging: Computer-Aided Diagnosis 2023 - 2022
- [c27]Abigail Keller, Jacob Furst, Daniela Raicu, Peter M. Hastings, Roselyne Tchoua:
Text Summarization towards Scientific Information Extraction. e-Science 2022: 225-235 - [c26]Hana Ahmed, Roselyne Tchoua, Jay F. Lofstead:
Failure Sources in Machine Learning for Medicine - A Study. e-Science 2022: 501-506 - [c25]Suhev Shakya, Mariana Vasquez, Yiyang Wang, Roselyne Tchoua, Jacob Furst, Daniela Raicu:
Human-in-the-loop deep learning retinal image classification with customized loss function. Medical Imaging: Computer-Aided Diagnosis 2022 - [c24]Claire Weissman, Lilly Roelofs, Jacob Furst, Daniela Stan Raicu, Roselyne Tchoua:
Similarity-based uncertainty scores for computer-aided diagnosis. Medical Imaging: Computer-Aided Diagnosis 2022 - [c23]Mariana Vasquez, Suhev Shakya, Ian Wang, Jacob Furst, Roselyne Tchoua, Daniela Raicu:
Interactive deep learning for explainable retinal disease classification. Medical Imaging: Image Processing 2022 - [c22]Lilly Roelofs, Claire Weissman, Daniela Raicu, Jacob Furst, Roselyne Tchoua:
Similarity-based uncertainty scores for interpretable deep learning in scientific applications. SAC 2022: 1152-1155 - [c21]Hana Ahmed, Roselyne Tchoua, Jay F. Lofstead:
Ensuring AI For Science is Science: Making Randomness Portable. AI4S 2022: 32-37 - [c20]Hana Ahmed, Roselyne Tchoua, Jay F. Lofstead:
Measuring Reproduciblity of Machine Learning Methods for Medical Diagnosis. TransAI 2022: 9-16 - [c19]Edward Xu, Thiruvarangan Ramaraj, Roselyne Tchoua, Jacob Furst, Daniela Raicu:
Contextualizing Lung Nodule Malignancy Predictions with Easy vs. Hard Image Classification. TransAI 2022: 122-127 - 2021
- [c18]Erin Murphy, Alexander Rasin, Jacob Furst, Daniela Raicu, Roselyne Tchoua:
Ensemble Labeling towards Scientific Information Extraction (ELSIE) - Blob Extraction. e-Science 2021: 11-20 - [c17]Erin Murphy, Alexander Rasin, Jacob Furst, Daniela Raicu, Roselyne Tchoua:
Ensemble Labeling Towards Scientific Information Extraction (ELSIE). ICCS (1) 2021: 750-764 - 2020
- [c16]Priya Deshpande, Alexander Rasin, Roselyne Tchoua, Jacob Furst, Daniela Raicu, Sameer K. Antani:
Enhancing Recall Using Data Cleaning for Biomedical Big Data. CBMS 2020: 265-270 - [c15]Bowen Qiu, Jacob Furst, Alexander Rasin, Roselyne Tchoua, Daniela Raicu:
Learning Latent Spiculated Features for Lung Nodule Characterization. EMBC 2020: 1254-1257 - [c14]Zhi Hong, Roselyne Tchoua, Kyle Chard, Ian T. Foster:
SciNER: Extracting Named Entities from Scientific Literature. ICCS (2) 2020: 308-321 - 2019
- [c13]Roselyne Tchoua, Aswathy Ajith, Zhi Hong, Logan T. Ward, Kyle Chard, Debra Audus, Shrayesh Patel, Juan de Pablo, Ian T. Foster:
Active Learning Yields Better Training Data for Scientific Named Entity Recognition. eScience 2019: 126-135 - [c12]Roselyne B. Tchoua, Aswathy Ajith, Zhi Hong, Logan T. Ward, Kyle Chard, Alexander Belikov, Debra J. Audus, Shrayesh Patel, Juan J. de Pablo, Ian T. Foster:
Creating Training Data for Scientific Named Entity Recognition with Minimal Human Effort. ICCS (1) 2019: 398-411 - 2017
- [c11]Roselyne B. Tchoua, Kyle Chard, Debra J. Audus, Logan T. Ward, Joshua Lequieu, Juan J. de Pablo, Ian T. Foster:
Towards a Hybrid Human-Computer Scientific Information Extraction Pipeline. eScience 2017: 109-118 - 2016
- [c10]Roselyne Tchoua, Kyle Chard, Debra Audus, Jian Qin, Juan de Pablo, Ian T. Foster:
A Hybrid Human-computer Approach to the Extraction of Scientific Facts from the Literature. ICCS 2016: 386-397 - 2013
- [c9]Roselyne Tchoua, Jong Youl Choi, Scott Klasky, Qing Liu, Jeremy Logan, Kenneth Moreland, Jingqing Mu, Manish Parashar, Norbert Podhorszki, David Pugmire, Matthew Wolf:
ADIOS Visualization Schema: A First Step Towards Improving Interdisciplinary Collaboration in High Performance Computing. e-Science 2013: 27-34 - 2012
- [c8]Roselyne Tchoua, Hasan Abbasi, Scott Klasky, Qing Liu, Norbert Podhorszki, David Pugmire, Yuan Tian, Matthew Wolf:
Collaborative monitoring and visualization of HPC data. CTS 2012: 397-403 - 2010
- [c7]Roselyne Tchoua, Scott Klasky, Norbert Podhorszki, Brad Grimm, Ayla Khan, Emanuele Santos, Cláudio T. Silva, Pierre Mouallem, Mladen A. Vouk:
Collaborative monitoring and analysis for simulation scientists. CTS 2010: 235-244 - [c6]Julian Cummings, Jay F. Lofstead, Karsten Schwan, Alexander Sim, Arie Shoshani, Ciprian Docan, Manish Parashar, Scott Klasky, Norbert Podhorszki, Roselyne Barreto:
EFFIS: An End-to-end Framework for Fusion Integrated Simulation. PDP 2010: 428-434 - 2009
- [c5]Roselyne Barreto, Scott Klasky, Norbert Podhorszki, Pierre Mouallem, Mladen A. Vouk:
Collaboration portal for petascale simulations. CTS 2009: 384-393 - [c4]Emanuele Santos, Julien Tierny, Ayla Khan, Brad Grimm, Lauro Didier Lins, Juliana Freire, Valerio Pascucci, Cláudio T. Silva, Scott Klasky, Roselyne Barreto, Norbert Podhorszki:
Enabling Advanced Visualization Tools in a Web-Based Simulation Monitoring System. eScience 2009: 358-365 - [c3]Pierre Mouallem, Roselyne Barreto, Scott Klasky, Norbert Podhorszki, Mladen A. Vouk:
Tracking Files in the Kepler Provenance Framework. SSDBM 2009: 273-282 - 2008
- [c2]Scott Klasky, Roselyne Barreto, Ayla Khan, Manish Parashar, Norbert Podhorszki, Steven G. Parker, Deborah Silver, Mladen A. Vouk:
Collaborative visualization spaces for petascale simulations. CTS 2008: 203-211 - 2006
- [c1]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, Norbert Podhorszki, Arie Shoshani, Cláudio T. Silva:
Automation of Network-Based Scientific Workflows. Grid-Based Problem Solving Environments 2006: 35-61
Informal and Other Publications
- 2022
- [i2]Bowen Qiu, Daniela Raicu, Jacob Furst, Roselyne Tchoua:
CompNet: A Designated Model to Handle Combinations of Images and Designed features. CoRR abs/2209.14454 (2022) - 2018
- [i1]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:
Reduced-Order Modeling through Machine Learning Approaches for Brittle Fracture Applications. CoRR abs/1806.01949 (2018)
Coauthor Index
aka: Daniela Stan Raicu
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last updated on 2024-08-05 21:12 CEST by the dblp team
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