Select Citations

2023

  • Bruce, L.K., Kasl, P., Soltani, S. et al. Variability of temperature measurements recorded by a wearable device by biological sex. Biol Sex Differ 14, 76 (2023). https://doi.org/10.1186/s13293-023-00558-z
  • Dickman, L.T., Jonko, A.K., Linn, R.R., Altintas, I., Atchley, A.L., Bär, A., Collins, A.D., Dupuy, J.-L., Gallagher, M.R., Hiers, J.K., Hoffman, C.M., Hood, S.M., Hurteau, M.D., Jolly, W.M., Josephson, A., Loudermilk, E.L., Ma, W., Michaletz, S.T., Nolan, R.H., O'Brien, J.J., Parsons, R.A., Partelli-Feltrin, R., Pimont, F., Resco de Dios, V., Restaino, J., Robbins, Z.J., Sartor, K.A., Schultz-Fellenz, E., Serbin, S.P., Sevanto, S., Shuman, J.K., Sieg, C.H., Skowronski, N.S., Weise, D.R., Wright, M., Xu, C., Yebra, M. and Younes, N. (2023), "Integrating plant physiology into simulation of fire behavior and effects," New Phytol, 238: 952-970. https://doi.org/10.1111/nph.18770 
  • Escobar, V. M., Hill, A., Block, J., Pavolonis, M. J., McCaskill, E. C., Murphy, Y., Brewer, M. J., Lana, R. I., Steckel, A. (2023, January 8-12). WIFIRE and NESDIS User Engagement: Leveraging NOAA's Pathfinder Initiative to develop future tools, products and services for wildfire [Conference presentation abstract]. American Meteorological Society (AMS) 103rd Annual Meeting, Denver, CO, United States. https://ams.confex.com/ams/103ANNUAL/meetingapp.cgi/Paper/410121

 

2022

  • Baldota, S., Ramaprasad, S.A., Bhamra, J.K., Luna, S., Ramachandra, R., Zen, E., Kim, H., Crawl, D., Pérez, I., Altintas, I., Cottrell, G., & Nguyen, M.H. (2022). "Multimodal Wildland Fire Smoke Detection," ArXiv, abs/2212.14143.
  • D. Roten, J. Block, D. Crawl, J. Lee and I. Altintas, "Machine Learning for Improved Post-fire Debris Flow Likelihood Prediction," 2022 IEEE International Conference on Big Data (Big Data), Osaka, Japan, 2022, pp. 1681-1690, doi: 10.1109/BigData55660.2022.10020574.
  • Altintas, I., Pérez, I., Mishin, D., Trouillaud, A., Irving, C., Graham, J., Tatineni, M., DeFanti, T.A., Strande, S., Smarr, L., & Norman, M.L. (2022). "Towards a Dynamic Composability Approach for using Heterogeneous Systems in Remote Sensing," 2022 IEEE 18th International Conference on e-Science (e-Science), 336-345.
  • I. Nealey, D. E. Pacheco, I. G. Moreno, M. Floca, D. Crawl and I. Altintas, "A Science-Enabled Virtual Reality Demonstration to Increase Social Acceptance of Prescribed Burns," 2022 IEEE 18th International Conference on e-Science (e-Science), Salt Lake City, UT, USA, 2022, pp. 429-430, doi: 10.1109/eScience55777.2022.00072.
  • Block, J., Werle, T., Crawl, D., Altintas, I., Calvillo, C., Fennessy, B., Lopez, L. (2022, August 8). Responding to Emerging Wildfires through Integration of NOAA Satellites with Real-Time Ground Intelligence [Conference presentation abstract]. American Meteorological Society (AMS) Panel Discussion PD4 - An Overview of the GeoXO Mission Instruments, Value Chains and the NOAA Pathfinder Initiative (Submissions By Invitation Only), Madison, WI, United States. https://ams.confex.com/ams/CMM2022/meetingapp.cgi/Session/61303 
  • Donovan, R. P., Kim, Y. G., Manzo, A., Ren, Y., Bian, S., Wu, T., Purawat, S., Helvajian, H., Wheaton, M., Li, B., Li, G.-P. "Smart connected worker edge platform for smart manufacturing: Part 2—Implementation and on-site deployment case study," J. Adv. Manuf. Process. 2022, 4(4), e10130. https://doi.org/10.1002/amp2.10130
  • Kim, Y. G., Donovan, R. P., Ren, Y., Bian, S., Wu, T., Purawat, S., Manzo, A. J., Altintas, I., Li, B., Li, G.-P. "Smart connected worker edge platform for smart manufacturing: Part 1—Architecture and platform design," J. Adv. Manuf. Process. 2022, 4(4), e10129. https://doi.org/10.1002/amp2.10129
  • Baru, C., Pozmantier, M., Altintas, I., Baek, S., Cohen, J., Condon, L., Fanti, G., Fernandez, R. C., Jackson, E., Lall, U., Landman, B., Li, H. H., Marin, C., Lopez, B. M., Metaxas, D., Olsen, B., Page, G., Shang, J., Turkan, Y., and Zhang, P. 2022. “Enabling AI innovation via data and model sharing: An overview of the NSF Convergence Accelerator Track D.” AI Magazine 43: 93– 104. https://doi.org/10.1002/aaai.12042 
  • Mason, A.E., Hecht, F.M., Davis, S.K. et al. Detection of COVID-19 using multimodal data from a wearable device: results from the first TemPredict Study. Sci Rep 12, 3463 (2022). https://doi.org/10.1038/s41598-022-07314-0
  • Tan, L., de Callafon, R. A., Block, J., Crawl, D., Çağlar, T., & Altıntaş, I. (2022). Estimation of wildfire wind conditions via perimeter and surface area optimization. Journal of Computational Science, 61, 101633. https://doi.org/10.1016/j.jocs.2022.101633 
  • Dewangan, A.; Pande, Y.; Braun, H.-W.; Vernon, F.; Perez, I.; Altintas, I.; Cottrell, G.W.; Nguyen, M.H. FIgLib & SmokeyNet: Dataset and Deep Learning Model for Real-Time Wildland Fire Smoke Detection. Remote Sens. 2022, 14, 1007. https://doi.org/10.3390/rs14041007 
  • Mason, A.E., Kasl, P., Hartogensis, W., Natale, J.L., Dilchert, S., Dasgupta, S., Purawat, S., Chowdhary, A., Anglo, C., Veasna, D., Pandya, L.S., Fox, L.M., Puldon, K.Y., Prather, J.G., Gupta, A., Altintas, I., Smarr, B.L., Hecht, F.M. "Metrics from Wearable Devices as Candidate Predictors of Antibody Response Following Vaccination against COVID-19: Data from the Second TemPredict Study," Vaccines 2022, 10, 264. https://doi.org/10.3390/vaccines10020264

 

2021

  • S. Purawat et al., "TemPredict: A Big Data Analytical Platform for Scalable Exploration and Monitoring of Personalized Multimodal Data for COVID-19," 2021 IEEE International Conference on Big Data (Big Data), Orlando, FL, USA, 2021, pp. 4411-4420, doi: 10.1109/BigData52589.2021.9671441.
  • Purawat, S., et al., “Autonomous Provenance to Drive Reproducibility in Computational Hydrology,” 2021 AGU Fall Meeting, New Orleans, LA, USA, 2021, https://ui.adsabs.harvard.edu/abs/2021AGUFMIN55D..09P/abstract.
  • Chennault, C., et al., “HydroFrame Infrastructure: Developments in the Software Behind a National Hydrologic Modeling Framework,” 2021 AGU Fall Meeting, New Orleans, LA, USA, 2021, https://ui.adsabs.harvard.edu/abs/2021AGUFMIN55D..09P/abstract.
  • R. F. da Silva et al., "A Community Roadmap for Scientific Workflows Research and Development," 2021 IEEE Workshop on Workflows in Support of Large-Scale Science (WORKS), St. Louis, MO, USA, 2021, pp. 81-90, doi: 10.1109/WORKS54523.2021.00016.
  • Altintas, I. Integrated End-to-end Performance Prediction and Diagnosis for Extreme Scientific Workflows (IPPD) (Final Report). United States. https://doi.org/10.2172/1830050
  • Lamprecht AL, Palmblad M, Ison J et al. Perspectives on automated composition of workflows in the life sciences [version 1; peer review: 2 approved]. F1000Research 2021, 10:897 (https://doi.org/10.12688/f1000research.54159.1)
  • Strande, S. et al. (2021). Expanse: Computing without Boundaries: Architecture, Deployment, and Early Operations Experiences of a Supercomputer Designed for the Rapid Evolution in Science and Engineering. In Practice and Experience in Advanced Research Computing (PEARC '21). Association for Computing Machinery, New York, NY, USA, Article 47, 1–4. https://doi.org/10.1145/3437359.3465588 
  • Ferreira da Silva, R., Casanova, H., Chard, K., Coleman, T., Laney, D.E., Ahn, D.H., Jha, S., Howell, D., Soiland-Reys, S., Altintas, I., Thain, D., Filgueira, R., Babuji, Y., Badia, R.M., Balis, B., Caíno-Lores, S., Callaghan, S., Coppens, F., Crusoe, M.R., De, K., Natale, F.D., Do, T.M., Enders, B., Fahringer, T., Fouilloux, A., Fursin, G., Gaignard, A., Ganose, A.M., Garijo, D., Gesing, S., Goble, C.A., Hasan, A., Huber, S.P., Katz, D.S., Leser, U., Lowe, D., Ludäscher, B., Maheshwari, K., Malawski, M., Mayani, R., Mehta, K., Merzky, A., Munson, T.S., Ozik, J., Pottier, L., Ristov, S., Roozmeh, M., Souza, R., Suter, F., Tovar, B., Turilli, M., Vahi, K., Vidal-Torreira, A., Whitcup, W.R., Wilde, M., Williams, A.R., Wolf, M., & Wozniak, J.M. (2021). Workflows Community Summit: Advancing the State-of-the-art of Scientific Workflows Management Systems Research and Development. ArXiv, abs/2106.05177.
  • Purawat, S. et al. (2021). Quantum Data Hub: A Collaborative Data and Analysis Platform for Quantum Material Science. In: Paszynski, M., Kranzlmüller, D., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M.A. (eds) Computational Science – ICCS 2021. ICCS 2021. Lecture Notes in Computer Science, vol 12742. Springer, Cham. https://doi.org/10.1007/978-3-030-77961-0_52
  • Tan, L., de Callafon, R.A., Block, J., Crawl, D., Altıntaş, I. (2021). Improving Wildfire Simulations by Estimation of Wildfire Wind Conditions from Fire Perimeter Measurements. In: Paszynski, M., Kranzlmüller, D., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M. (eds) Computational Science – ICCS 2021. ICCS 2021. Lecture Notes in Computer Science, vol 12746. Springer, Cham. https://doi.org/10.1007/978-3-030-77977-1_18
  • Altintas, I. (2021). Building Cyberinfrastructure for translational impact: The WIFIRE example. Journal of Computational Science, 52, 101210. https://doi.org/10.1016/j.jocs.2020.101210 
  • Ferreira da Silva, R., Casanova, H., Chard, K., Laney, D.E., Ahn, D.H., Jha, S., Goble, C.A., Ramakrishnan, L., Peterson, L., Enders, B., Thain, D., Altintas, I., Babuji, Y., Badia, R.M., Bonazzi, V.R., Coleman, T., Crusoe, M.R., Deelman, E., Natale, F.D., Tommaso, P.D., Fahringer, T., Filgueira, R., Fursin, G., Ganose, A.M., Gruning, B., Katz, D.S., Kuchar, O.A., Kupresanin, A., Ludäscher, B., Maheshwari, K., Mattoso, M., Mehta, K., Munson, T.S., Ozik, J., Peterka, T., Pottier, L., Randles, T., Soiland-Reyes, S., Tovar, B., Turilli, M., Uram, T.D., Vahi, K., Wilde, M., Wolf, M., & Wozniak, J.M. (2021). Workflows Community Summit: Bringing the Scientific Workflows Community Together. ArXiv, abs/2103.09181.

 

2020

  • M. Madany, K. Marcus, S. Peltier, M. H. Ellisman and I. Altintas, "NeuroKube: An Automated and Autoscaling Neuroimaging Reconstruction Framework using Cloud Native Computing and A.I.," 2020 IEEE International Conference on Big Data (Big Data), Atlanta, GA, USA, 2020, pp. 320-330, doi: 10.1109/BigData50022.2020.9378053.
  • Castronova, A.M., et al., “Cloud software for enabling community-oriented integrated hydrologic modeling," 2020 AGU Fall Meeting, https://ui.adsabs.harvard.edu/abs/2020AGUFMH121...08C/abstract
  • Scourtas, A., Perez, I., Nguyen, M.H., Altintas, I., “Automated Early Detection of Wildfire Smoke Using Deep Learning with Combined Spatial-Temporal Information," 2020 AGU Fall Meeting, https://ui.adsabs.harvard.edu/abs/2020AGUFMNH0070017S/abstract 
  • Altintas, I. (2020). Using Dynamic Data Driven Cyberinfrastructure for Next Generation Disaster Intelligence. In: Darema, F., Blasch, E., Ravela, S., Aved, A. (eds) Dynamic Data Driven Applications Systems. DDDAS 2020. Lecture Notes in Computer Science, vol 12312. Springer, Cham. https://doi.org/10.1007/978-3-030-61725-7_4 
  • Purawat, S., Olschanowsky, C., Condon, L.E., Maxwell, R.M., Altintas, I. (2020). Scalable Workflow-Driven Hydrologic Analysis in HydroFrame. In: , et al. Computational Science – ICCS 2020. ICCS 2020. Lecture Notes in Computer Science, vol 12137. Springer, Cham. https://doi.org/10.1007/978-3-030-50371-0_20
  • Subramanian, A., Tan, L., de Callafon, R.A., Crawl, D., Altintas, I. (2020). Recursive Updates of Wildfire Perimeters Using Barrier Points and Ensemble Kalman Filtering. In: , et al. Computational Science – ICCS 2020. ICCS 2020. Lecture Notes in Computer Science, vol 12142. Springer, Cham. https://doi.org/10.1007/978-3-030-50433-5_18 
  • Singh, A., Purawat, S., Rao, A., & Altintas, I. (2020). Modular performance prediction for scientific workflows using machine learning. Future Generation Computer Systems, 114, 1–14. https://doi.org/10.1016/j.future.2020.04.048 

 

2019

  • M. H. Nguyen, J. Li, D. Crawl, J. Block and I. Altintas, "Scaling Deep Learning-Based Analysis of High-Resolution Satellite Imagery with Distributed Processing," 2019 IEEE International Conference on Big Data (Big Data), Los Angeles, CA, USA, 2019, pp. 5437-5443, doi: 10.1109/BigData47090.2019.9006205.
  • Maxwell, R.M., et al., "HydroFrame: A Software Framework to Enable Continental Scale Hydrologic Simulation," 2019 AGU Fall Meeting, https://agu.confex.com/agu/fm19/meetingapp.cgi/Paper/631272
  • Purawat, S., Olschanowsky, C., Condon, L.E., Maxwell, R.M., Altintas, I. "End-to-End Workflow-Driven Hydrologic Analysis for Different User Groups in HydroFrame," 2019 AGU Fall Meeting, https://ui.adsabs.harvard.edu/abs/2019AGUFM.A13H3033P/abstract 
  • Benz, S., Nguyen, M.H., Park, H., Li, J., Crawl, D., Block, J., Altintas, I., Burney, J. "Assessing the Rohingya Displacement Crisis Using Satellite Data and Convolutional Neural Networks," 2019 AGU Fall Meeting, https://ui.adsabs.harvard.edu/abs/2019AGUFMGC11A..08B/abstract 
  • Tang, Y.; Wang, J.; Nguyen, M.; Altintas, I. PEnBayes: A Multi-Layered Ensemble Approach for Learning Bayesian Network Structure from Big Data. Sensors 2019, 19, 4400. https://doi.org/10.3390/s19204400
  • S. Benz et al., "Understanding a Rapidly Expanding Refugee Camp Using Convolutional Neural Networks and Satellite Imagery," 2019 15th International Conference on eScience (eScience), San Diego, CA, USA, 2019, pp. 243-251, doi: 10.1109/eScience.2019.00034.
  • S. Labou, H. J. Yoo, D. Minor and I. Altintas, "Sharing and Archiving Data Science Course Projects to Support Pedagogy for Future Cohorts," 2019 15th International Conference on eScience (eScience), San Diego, CA, USA, 2019, pp. 644-645, doi: 10.1109/eScience.2019.00099.
  • S. Labou, H. J. Yoo, D. Minor and I. Altintas, "Sharing and Archiving Data Science Course Projects to Support Pedagogy for Future Cohorts," 2019 15th International Conference on eScience (eScience), San Diego, CA, USA, 2019, pp. 644-645, doi: 10.1109/eScience.2019.00099.
  • S. L. Sellars et al., "The Evolution of Bits and Bottlenecks in a Scientific Workflow Trying to Keep Up with Technology: Accelerating 4D Image Segmentation Applied to NASA Data," 2019 15th International Conference on eScience (eScience), San Diego, CA, USA, 2019, pp. 77-85, doi: 10.1109/eScience.2019.00016.
  • Seo, B., Mariano, D., Beckfield, J., Madenur, V., Hu, Y., Reina, T., Bobar, M., Nguyen, M.H., & Altintas, I. (2019). Cardiac MRI Image Segmentation for Left Ventricle and Right Ventricle using Deep Learning. ArXiv, abs/1909.08028.
  • Schram, M., Tallent, N., Friese, R., Singh, A., & Altintas, I. (2019). Application of deep learning on integrating prediction, provenance, and Optimization. EPJ Web of Conferences, 214, 06007. https://doi.org/10.1051/epjconf/201921406007 
  • Rule A, Birmingham A, Zuniga C, Altintas I, Huang SC, et al. (2019) Ten simple rules for writing and sharing computational analyses in Jupyter Notebooks. PLOS Computational Biology 15(7): e1007007. https://doi.org/10.1371/journal.pcbi.1007007 
  • I. Altintas, S. Purawat, D. Crawl, A. Singh and K. Marcus, "Toward a Methodology and Framework for Workflow-Driven Team Science," in Computing in Science & Engineering, vol. 21, no. 4, pp. 37-48, 1 July-Aug. 2019, doi: 10.1109/MCSE.2019.2919688.
  • I. Altintas et al., "Workflow-Driven Distributed Machine Learning in CHASE-CI: A Cognitive Hardware and Software Ecosystem Community Infrastructure," 2019 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), Rio de Janeiro, Brazil, 2019, pp. 865-873, doi: 10.1109/IPDPSW.2019.00142.
  • İ. Altıntaş, "PAISE 2019 Keynote Speaker," 2019 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), Rio de Janeiro, Brazil, 2019, pp. 876-876, doi: 10.1109/IPDPSW.2019.00144.
  • Garcia-Silva, A., Gomez-Perez, J. M., Palma, R., Krystek, M., Mantovani, S., Foglini, F., Grande, V., De Leo, F., Salvi, S., Trasatti, E., Romaniello, V., Albani, M., Silvagni, C., Leone, R., Marelli, F., Albani, S., Lazzarini, M., Napier, H. J., Glaves, H. M., … Altintas, I. (2019). Enabling fair research in earth science through research objects. Future Generation Computer Systems, 98, 550–564. https://doi.org/10.1016/j.future.2019.03.046 
  • Yang PC, Purawat S, Ieong PU, Jeng MT, DeMarco KR, et al. (2019) A demonstration of modularity, reuse, reproducibility, portability and scalability for modeling and simulation of cardiac electrophysiology using Kepler Workflows. PLOS Computational Biology 15(3): e1006856. https://doi.org/10.1371/journal.pcbi.1006856