About DISC

About DISC

Mission

Empower transdisciplinary research and collaboration to drive convergent solutions to societal challenges in Oklahoma, the nation, and the world through data science research, tools and capabilities. 

Vision

The University of Oklahoma is a nationally recognized leader for data science research and data-driven solutions to societal challenges. 

Values

Collaboration – Together, we are more effective in solving challenges.  

Innovation – Create new techniques, tools and data-enabled solutions to positively impact our global community. 

Opportunity – Provide opportunities for teams from different backgrounds and experiences to become integrated into our educational initiatives, research activities, and collaborations.

Trust –  Through our actions, we build trust – core to transdisciplinary team success, transition to societal impact, and sustainability. 

Empowerment – Empower convergent research teams to solve societal challenges.

Research

The Data Institute for Societal Challenges (DISC) at OU is creating innovations in data science, artificial intelligence, machine learning, and data-enabled research. DISC develops and grows convergent research teams dedicated to solving local to global-scale challenges. This is achieved through the implementation of foundational data science and data-enabled research related to aerospace, defense, and global security; community and societal transformation; the future of health; and environment, energy, and sustainability. DISC is leading advances in the following strategic focus areas.

Data Science Applications: Focusing on the development of new techniques and approaches to analytics that integrates computer science, AI/ML, cognitive psychology, decision science, and human-computer teaming to create robust, explainable, fair, efficient, and reliable data-enabled solutions that can be readily transitioned into real-world use.
Human-Guided Artificial Intelligence and Machine Learning: Developing and using innovative AI and ML convergent research to advance the next generation in science, engineering, and other disciplines to create impactful and reliable solutions.
Human-Computer Teaming: Efficiently and effectively integrating the capabilities of humans and complex machines to address complex problems.
Predictive Analytics: Creating tools and systems capable of extracting, assimilating, and analyzing data for accurate, timely, and reliable predictions with quantifiable certainty and uncertainty to transform drug discovery, vaccine production, and more accurate forecasts of the impact of various events.
Decision-making Environments and Visual Analytics: Leveraging the skills and expertise of leading interdisciplinary researchers in various research centers to provide innovative solutions for data-driven decision-making and visual analytics.
Scalable Software and Hardware Architectures: Increasing our potential to create real-time, trustable AI and ML solutions by using neuromorphic, probabilistic, and quantum computing.

DISC Strategic Plan & Year-End Reports

Year-End Reports

FY24 Year End Report Download (PDF)

FY23 End of Year Report Download (PDF)

Data Institute for Societal Challenges DISC, FY 2022 Year-End Report

FY22 Year End Report Download (PDF)

FY21 Year End Report Download (PDF)

Strategic Plans

2024-2029 Strategic Plan Download (PDF)

DISC Strategic Plan Download (PDF)

Data Science and AI Initiatives

Data Science and AI Initiatives in 46 U.S. Universities - November 2024 (PDF)

Data Science and AI Initiatives in 46 U.S. Universities May-2024 (PDF)

Journal Publications

Fiscal Year 2025

Faiz, R., Danala, G., Lucero, P., Ray, B., Hegde, S., Ebert, D., “Development Of Composite Clinical-Radiological Tool To Predict Functional Outcomes After Ischemic Stroke Treatment,” SPIE Medical Imaging 2025, 2025.
Basheer, A., Jentner, W., Ebert, D., “Leveraging Visual Analytics and Diverse Datasets for Proactive Pandemic Surveillance: A One Health Approach,” In Proceedings of the Hawaii International Conference on System Science, HICSS 56, 2024.
Khiabani, P., Danala, G., Jentner, W., Ebert, D., “Challenges in Data Integration, Monitoring, and Exploration of Methane Emissions: The Role of Data Analysis and Visualization,” IEEE EnergyVis 2024: 4^th Workshop on Energy Data Visualization, 2024.

Fiscal Year 2024

Aseel Basheer et al (2024), "A comprehensive approach to integrated one health surveillance and response", Open Access Government July 2024, pp.50-51. https://doi.org/10.56367/OAG-043-10923 .(Accessed: 15 Jul 2024)
Wimberly, M. C. 2024. Meteorological data for public health surveillance. Open Access Government. March 6, 2024. https://doi.org/10.56367/OAG-042-10923
Thirumalai Venkatesan, Velmurugan Thavasi, Jordan P. Metcalf, Donna L Tyungu, Aaron M. Wendelboe, David Ebert, Ong Weiqiang, Zhang Fuchang, Jia Zhunan (2024), "Revolutionising disease detection: The emergence of non-invasive VOC breathomics", Open Access Government January 2024, pp.74-75. doi:10.56367/OAG-041-10923
Zhunan Jia, Ong Weiqiang, Zhang Fuchang, Fang Du, Velmurugan Thavasi, Thirumalai Venkatesan (2024), "A Study of 9 Common Breath VOCs in 504 Healthy Subjects Using PTR-TOF-MS", Metabolomics 2024. DOI: 10.1007/s11306-024-02139-6
Kwon, T., Trujillo, J. D., Carossino, M., Lyoo, E. L., McDowell, C. D., Cool, K., … Richt, J. A. (2024). Pigs are highly susceptible to but do not transmit mink-derived highly pathogenic avian influenza virus H5N1 clade 2.3.4.4b. Emerging Microbes & Infections. https://doi.org/10.1080/22221751.2024.2353292
Maryada, Sai Kiran, Devegowda, Deepak, Curtis, Mark, and Chandra Rai. "Improved Data-Driven Method for the Prediction of Elastic Properties in Unconventional Shales from SEM Images." Paper presented at the SPWLA 29th Formation Evaluation Symposium of Japan, Chiba, Japan, September 2024.
M. Y. Araghi, M. H. Parsa, M. Ghane Ezabadi, R. Roumina, H. Mirzadeh, and S. Xu, “Characterizing pearlite transformation in an API X60 pipeline steel through phase-field modeling and experimental validation,” Front. Mater., vol. 11, p. 1390159, Jun. 2024, doi: 10.3389/fmats.2024.1390159.
P. Arcidiacono, J. Kinsler, and T. Ransom, “What the Students for Fair Admissions Cases Reveal about Racial Preferences,” Journal of Political Economy Microeconomics, vol. 1, no. 4, pp. 615–668, Nov. 2023, doi: 10.1086/725336.
P. Arcidiacono, J. Kinsler, and T. Ransom, “Divergent: The Time Path of Legacy and Athlete Admissions at Harvard,” J. Human Resources, vol. 59, no. 3, pp. 653–683, May 2024, doi: 10.3368/jhr.0421-11641R1.
L. Azzopardi and J. Liu, “Search under Uncertainty: Cognitive Biases and Heuristics - Tutorial on Modeling Search Interaction using Behavioral Economics,” in Proceedings of the 2024 ACM SIGIR Conference on Human Information Interaction and Retrieval, Sheffield United Kingdom: ACM, Mar. 2024, pp. 427–430. doi: 10.1145/3627508.3638297.
M. Baby, A. Guptan, J. Broussard, J. K. Allen, F. Mistree, and A. B. Nellippallil, “A Framework to Support Multilevel Robust Co-Design of Manufacturing Supply Networks,” in Volume 3A: 49th Design Automation Conference (DAC), Boston, Massachusetts, USA: American Society of Mechanical Engineers, Aug. 2023, p. V03AT03A031. doi: 10.1115/DETC2023-117145.
M. Baby, A. Guptan, J. Broussard, J. K. Allen, F. Mistree, and A. B. Nellippallil, “A Decision Support Framework for Robust Multilevel Co-Design Exploration of Manufacturing Supply Networks,” Journal of Mechanical Design, vol. 146, no. 11, p. 111704, Nov. 2024, doi: 10.1115/1.4065369.
M. Baby, R. Rama Sushil, P. Ramu, J. K. Allen, F. Mistree, and A. B. Nellippallil, “Robust, Co-design Exploration of Multilevel Product, Material, and Manufacturing Process Systems,” Integr Mater Manuf Innov, vol. 13, no. 1, pp. 14–35, Mar. 2024, doi: 10.1007/s40192-023-00324-4.
Y. M. Banad, S. M. A. Hasan, S. S. Sharif, G. Veronis, and M. R. Gartia, “Optical properties and behavior of whispering gallery mode resonators in complex microsphere configurations: Insights for sensing and information processing applications,” Nano Select, vol. 5, no. 4, p. 2300184, Apr. 2024, doi: 10.1002/nano.202300184.
M. Beattie and T. Tran, “Poster Submission: A clustering and machine learning approach to identification of structural drivers of unsheltered homelessness,” Nov. 2023.
E. Berry-Kravis et al., “Effects of AFQ056 on language learning in fragile X syndrome,” Journal of Clinical Investigation, vol. 134, no. 5, p. e171723, Mar. 2024, doi: 10.1172/JCI171723.
M. J. Bhalerao, W. T. Honeycutt, A. K. Das, J. K. Allen, and F. Mistree, “Framing Wicked Problems Through Evidentiary and Interpretative Analysis,” in Volume 3B: 49th Design Automation Conference (DAC), Boston, Massachusetts, USA: American Society of Mechanical Engineers, Aug. 2023, p. V03BT03A005. doi: 10.1115/DETC2023-117285.
S. Bhattacharjee and C. N. Corbett, “Housing condition and preferences of refugee immigrants in Dallas, TX,” Wellbeing, Space and Society, vol. 4, p. 100150, 2023, doi: 10.1016/j.wss.2023.100150.
K. Bozorgi and R. K. Gaddie, The Philadelphia house: organic architecture and placemaking in Chestnut Hill. Lanham Boulder New York London: Rowman & Littlefield, 2023.
J. Cao, Z. Ming, J. K. Allen, and F. Mistree, “On How a Self-Organizing System Produces Collective Behavior,” in Volume 3B: 49th Design Automation Conference (DAC), Boston, Massachusetts, USA: American Society of Mechanical Engineers, Aug. 2023, p. V03BT03A062. doi: 10.1115/DETC2023116875.
G. B. Carrasco Galvan, J. M. Vadjunec, and T. D. Fagin, “Lessons from the Archives: Understanding Historical Agricultural Change in the Southern Great Plains,” Land, vol. 13, no. 2, p. 196, Feb. 2024, doi: 10.3390/land13020196.
N. Castellanos-Ryan et al., “Modelling executive function across early childhood: Longitudinal invariance, development from 3.5 to 7 years and later academic performance,” Cognitive Development, vol. 68, p. 101365, Oct. 2023, doi: 10.1016/j.cogdev.2023.101365
H. S. Chapman and R. A. Schwartz, “Leveraging Country Expertise: How Scholars in International Studies Can Support the Asylum Process,” International Studies Perspectives, p. ekae002, Feb. 2024, doi: 10.1093/isp/ekae002.
H. S. Chapman, Dialogue with the Dictator: Authoritarian Legitimation and Information Management in Putin’s Russia, 1st ed. Cambridge University Press, 2024. doi: 10.1017/9781009427548.
H. S. Chapman, M. C. Hanson, V. Dzutsati, and P. DeBell, “Under the Veil of Democracy: What Do People Mean When They Say They Support Democracy?,” Perspect. polit., vol. 22, no. 1, pp. 97–115, Mar. 2024, doi: 10.1017/S1537592722004157.
H. Chapman and R. Zhandayeva, “Attitudes toward Russia’s War on Ukraine in Kazakhstan and Kyrgyzstan,” in The Impact of the Russo-Ukrainian War on the Broader Region, PONARS Eurasia Policy Perspectives, 2023.
C. L. Chapple and E. J. Maher, “Trauma-Informed Theory in Criminal Justice,” in Handbook of Forensic Social Work, 1st ed., D. A. McLeod, A. P. Natale, and K. W. Mapson, Eds., Oxford University PressNew York, 2024, pp. 172–187. doi: 10.1093/oso/9780197694732.003.0012.
S. Chaput-Langlois, Z. L. Stickley, T. D. Little, and C. Rioux, “Multiple Imputation When Variables Exceed Observations: An Overview of Challenges and Solutions,” Collabra: Psychology, vol. 10, no. 1, p. 92993, Feb. 2024, doi: 10.1525/collabra.92993.
B. Cilali, K. Barker, A. D. González, and A. Salo, “Two-stage stochastic program for environmental resettlement decision-making,” Socio-Economic Planning Sciences, vol. 93, p. 101875, Jun. 2024, doi: 10.1016/j.seps.2024.101875.
B. Cilali, C. M. Rocco, and K. Barker, “Multi‐objective decision trees with fuzzy TOPSIS: Application to refugee resettlement planning,” Multi Criteria Decision Anal, vol. 31, no. 1–2, p. e1822, Jan. 2024, doi: 10.1002/mcda.1822.
R. R. Cordero et al., “Extreme fire weather in Chile driven by climate change and El Niño–Southern Oscillation (ENSO),” Sci Rep, vol. 14, no. 1, p. 1974, Jan. 2024, doi: 10.1038/s41598-024-52481-x.
C. M. Curry, “OU Libraries’ Statistics Helper Website: An Interactive and Curated Resource List.” STEM Librarian South, p. 7630600 Bytes, 2023. doi: 10.6084/M9.FIGSHARE.23739657.V1.
J. Cutcher-Gershenfeld et al., “Professionalization of Research Computing and Data: An Expanded Agenda,” in Practice and Experience in Advanced Research Computing, Portland OR USA: ACM, Jul. 2023, pp. 129–136. doi: 10.1145/3569951.3593610.
M. Derakhshi and T. Razzaghi, “An imbalance-aware BiLSTM for control chart patterns early detection,” Expert Systems with Applications, vol. 249, p. 123682, Sep. 2024, doi: 10.1016/j.eswa.2024.123682.
D. I. Diochnos, M. C. Golumbic, and F. Hoffman, “ISAIM-2022: international symposium on artificial intelligence and mathematics,” Ann Math Artif Intell, vol. 92, no. 1, pp. 1–4, Jan. 2024, doi: 10.1007/s10472-024-09922-0.
Y. Dong et al., “Wastewater-influenced estuaries are characterized by disproportionately high nitrous oxide emissions but overestimated IPCC emission factor,” Commun Earth Environ, vol. 4, no. 1, p. 395, Oct. 2023, doi: 10.1038/s43247-023-01051-6.
L.-E. Dubois, S. Renard, and D. Guttentag, “Towards monopolistic music promotion: an analysis of North American concert tours,” Cultural Trends, pp. 1–18, May 2024, doi: 10.1080/09548963.2024.2352413.
L.-E. Dubois, S. Renard, and S. Rana, “Left off the circuit: the impact of shrinking live music tours on cities,” International Journal of Cultural Policy, pp. 1–14, Jan. 2024, doi: 10.1080/10286632.2023.2296071.
D. S. Ebert, “Applying data science advances in disease surveillance and control,” Open Access Government, vol. 39, no. 1, pp. 152–153, Jul. 2023, doi: 10.56367/OAG-039-10899.
D. Ebrahimzadeh, S. Sharif, and Y. Banad, “Improving Image Classification using Triple-GAN: A Fusion of Generative Adversarial Networks and Transfer Learning,” in 2023 IEEE Sixth International Conference on Artificial Intelligence and Knowledge Engineering (AIKE), Laguna Hills, CA, USA: IEEE, Sep. 2023, pp. 91–94. doi: 10.1109/AIKE59827.2023.00022.
M. El-Hourani et al., “Longitudinal Study of Early Adversity and Disturbing Dream Frequency: Moderating Role of Early Negative Emotionality,” Res Child Adolesc Psychopathol, vol. 52, no. 2, pp. 277–291, Feb. 2024, doi: 10.1007/s10802-023-01109-1.
J. E. A. Escamilla and D. I. Diochnos, “Perceptrons Under Verifiable Random Data Corruption,” in Machine Learning, Optimization, and Data Science, vol. 14505, G. Nicosia, V. Ojha, E. La Malfa, G. La Malfa, P. M. Pardalos, and R. Umeton, Eds., in Lecture Notes in Computer Science, vol. 14505. , Cham: Springer Nature Switzerland, 2024, pp. 93–103. doi: 10.1007/978-3-031-53969-5_8.
14505. , Cham: Springer Nature Switzerland, 2024, pp. 93–103. doi: 10.1007/978-3-031-53969-5_8. [37] L. E. Ethridge, B. D. Auerbach, A. Contractor, I. M. Ethell, E. A. McCullagh, and E. V. Pedapati, “Editorial: Neural markers of sensory processing in development,” Front. Integr. Neurosci., vol. 17, p. 1256437, Jul. 2023, doi: 10.3389/fnint.2023.1256437.
T. D. Fagin, J. M. Vadjunec, A. L. Boardman, and L. M. Hinsdale, “Use of Participatory sUAS in Resilient Socioecological Systems (SES) Research: A Review and Case Study from the Southern Great Plains, USA,” Drones, vol. 8, no. 6, p. 223, May 2024, doi: 10.3390/drones8060223.
R. Faiz, G. Danala, B. Ray, W. Islam, and D. Ebert, “Evaluation of deep learning frameworks coupled with an interactive user interface to predict clinical complications after aneurysmal subarachnoid hemorrhage,” in Medical Imaging 2024: Clinical and Biomedical Imaging, B. S. Gimi and A. Krol, Eds., San Diego, United States: SPIE, Apr. 2024, p. 28. doi: 10.1117/12.3006983.
L. Fang, F. Liu, H. Ding, and C. Duan, “High-Performance Reversible Solid Oxide Cells for Powering Electric Vehicles, Long-Term Energy Storage, and CO 2 Conversion,” ACS Appl. Mater. Interfaces, p. acsami.4c00780, Apr. 2024, doi: .
J. C. Fisher, H. H. Nelson, J. K. Allen, P. Kazempoor, and F. Mistree, “Analyzing hydrogen hub locations: Resources, energy, and social impact,” International Journal of Hydrogen Energy, vol. 70, pp. 641–653, Jun. 2024, doi: 10.1016/j.ijhydene.2024.03.125.
K. Gaardbo Kuhn, “Wastewater surveillance for infectious disease preparedness,” Open Access Government, vol. 40, no. 1, pp. 22–23, Oct. 2023, doi: 10.56367/OAG-040-10923.
A. Garg, A. Chhikara, N. Kumar, and L. Qiu, “Decoding decision-making: Investigation of bias and discrimination in human vs. algorithmic choices,” in Workshop on Information Technologies and Systems (WITS), Hyderabad, India, Dec. 2023.
S. Gesing et al., “Community of Communities: A Working Group Enhancing Interactions Between Organizations and Projects Supporting RC Professionals,” Oct. 2023, doi: 10.5281/ZENODO.10034867.
L. Guo, J. K. Allen, and F. Mistree, “Optimize or satisfice in engineering design?,” Res Eng Design, Feb. 2024, doi: 10.1007/s00163-023-00431-5.
L. Guo, A. B. Nellippallil, W. F. Smith, J. K. Allen, and F. Mistree, “An Adaptive Linear Programming Algorithm with Parameter Learning,” Algorithms, vol. 17, no. 2, p. 88, Feb. 2024, doi: 10.3390/a17020088.
H. S. Gupta, T. Adluri, D. Sanderson, A. D. González, C. D. Nicholson, and D. Cox, “Multi-objective optimization of mitigation strategies for buildings subject to multiple hazards,” International Journal of Disaster Risk Reduction, vol. 100, p. 104125, Jan. 2024, doi: 10.1016/j.ijdrr.2023.104125.
J. Habashi, “Criminalization of the Right to Home for Palestinian Children,” in Home in Early Childhood Care and Education, A. Gibbons, S. Gaches, S. Arndt, M. Sapon-Shevin, C. Murray, M. Urban, and M. Tesar, Eds., in Critical Cultural Studies of Childhood. , Cham: Springer International Publishing, 2023, pp. 75–93. doi: 10.1007/978-3-031-43695-6_5.
D. Han, R. Babaei, S. Zhao, and S. Cheng, “Exploring the Efficacy of Learning Techniques in Model Extraction Attacks on Image Classifiers: A Comparative Study,” Applied Sciences, vol. 14, no. 9, p. 3785, Apr. 2024, doi: 10.3390/app14093785.
D. Han, P. Huong, and S. Cheng, “Enhancing Semantic Segmentation through Reinforced Active Learning: Combating Dataset Imbalances and Bolstering Annotation Efficiency,” Journal of Electronic & Information Systems, vol. 5, no. 2, pp. 45–60, 2023.
D. Han, B. Mulyana, V. Stankovic, and S. Cheng, “A Survey on Deep Reinforcement Learning Algorithms for Robotic Manipulation,” Sensors, vol. 23, no. 7, p. 3762, Apr. 2023, doi: 10.3390/s23073762.
M. Herzig and S. Renard, “The Impact of Improvisation Training in Arts Entrepreneurship Education on Creative Capacities,” Journal of Entrepreneurship Education, vol. 27, no. 4, pp. 1–33, Apr. 2024.
A. J. Hill, R. S. Schumacher, and M. L. Jr. Green, “Observation Definitions and their Implications in Machine Learning-based Predictions of Excessive Rainfall,” Weather and Forecasting, 2024.
L.-Y. Huang et al., “Double dissociation between P300 components and task switch error type in healthy but not psychosis participants,” Schizophrenia Research, vol. 261, pp. 161–169, Nov. 2023, doi: 10.1016/j.schres.2023.09.025.
S. Jamalzadeh et al., “Weaponized disinformation spread and its impact on multi-commodity critical infrastructure networks,” Reliability Engineering & System Safety, vol. 243, p. 109819, Mar. 2024, doi: 10.1016/j.ress.2023.109819.
Z. Jia, V. Thavasi, T. Venkatesan, and P. Lee, “Breath Analysis for Lung Cancer Early Detection—A Clinical Study,” Metabolites, vol. 13, no. 12, p. 1197, Dec. 2023, doi: 10.3390/metabo13121197.
M. Jiang, Z. Ming, C. Li, J. K. Allen, and F. Mistree, “Design of Self-Organizing Systems Using Multi-Agent Reinforcement Learning and the Compromise Decision Support Problem Construct,” Journal of Mechanical Design, vol. 146, no. 5, p. 051711, May 2024, doi: 10.1115/1.4064672.
T. Jiang and J. Liu, “Reflection on future directions: a systematic review of reported limitations and solutions in interactive information retrieval user studies,” AJIM, vol. 76, no. 1, pp. 104–131, Jan. 2024, doi: 10.1108/AJIM-05-2022-0253.
Y. J. Jung and J. Liu, “Toward a conceptual framework characterizing the interplay of interest development, information search, and knowledge construction (ISK) in Children’s learning,” AJIM, Jun. 2024, doi: 10.1108/AJIM-01-2024-0041.
J.-N. Kim and J. Jung, “AI, Media, and People: The Changing Landscape of User Experiences and Behaviors,” Media & Communication, 2024.
J.-N. Kim et al., “Redressing disruptive over-voicing in e-Rulemaking: Theory-informed AI moderation for regulatory public engagement,” International Journal of Strategic Communication, 2024.
J.-N. Kim and H. G. de Zúñiga, “Commissioned for the 100th Year Special Issue for Journalism & Mass Communication Quarterly,” Journalism & Mass Communication Quarterly, 2024
N. Kim and J.-N. Kim, “A COVID-19 paradox of communication, ignorance, and vaccination intention,” Sage Open, 2024.
N. Kim, S. H. Lee, L. Andreu-Perez, A. Pitluk, and J.-N. Kim, “Coping with Non-COVID-19 Health Problems Through Communicative Action in Cyberspace,” Journal of Health Communication, vol. 29, no. 7, pp. 450–466, Jul. 2024, doi: 10.1080/10810730.2024.2365777.
Y.-M. Kim and C. Noyori-Corbett, “Ethnic Density as a Key Factor to Narrow Health Disparities: A Case of American Indians and Alaska Natives,” Health & Social Work, p. hlae014, Jun. 2024, doi: 10.1093/hsw/hlae014.
Y.-M. Kim and S. Thomas, “Discrepancy between objective and subjective diabetes knowledge: Based on Asian Indian and Korean immigrants,” in International Conference on Knowledge Management, Florianópolis, Brazil, Nov. 2023.
N. Kumar, “Transforming software testing practices through digital transformation and artificial intelligence,” in SOFTECAsia 2003 Conference, Kuala Lumpur, Malaysia, Sep. 2023.
N. Kumar, “Unveiling the impact of large language models on the software testing industry,” in SOFTECAsia 2023 Conference, Kuala Lumpur, Malaysia, Sep. 2023.
N. Kumar, “Will Generative AI become an enduring research tool and topic in Operations Management, or will it be a passing fad that will be forgotten in a few years?,” in Production and Operations Management Society (POMS) Annual Conference, Minneapolis, USA, May 2024.
E. Kuttler, N. Ghorbani-Renani, K. Barker, A. D. González, and J. Johansson, “Protection-interdiction-restoration for resilient multi-commodity networks,” Reliability Engineering & System Safety, vol. 242, p. 109745, Feb. 2024, doi: 10.1016/j.ress.2023.109745.
P. Li, Z.-H. Wang, and C. Wang, “The potential of urban irrigation for counteracting carbon-climate feedback,” Nat Commun, vol. 15, no. 1, p. 2437, Mar. 2024, doi: 10.1038/s41467-024-46826-3.
M. J. Lipson et al., “Evaluation of 30 urban land surface models in the Urban‐PLUMBER project: Phase 1 results,” Quart J Royal Meteoro Soc, vol. 150, no. 758, pp. 126–169, Jan. 2024, doi: 10.1002/qj.4589.
F. Liu et al., “Synergistic Effects of In-Situ Exsolved Ni–Ru Bimetallic Catalyst on High-Performance and Durable Direct-Methane Solid Oxide Fuel Cells,” J. Am. Chem. Soc., p. jacs.3c12121, Jan. 2024, doi: 10.1021/jacs.3c12121.
M. Lu, C. Zhou, C. Wang, R. B. Jackson, and C. P. Kempes, “Worldwide scaling of waste generation in urban systems,” Nat Cities, vol. 1, no. 2, pp. 126–135, Jan. 2024, doi: 10.1038/s44284-023-00021-5.
E. J. Maher, S. A. Stoner, J. Gerlinger, A. C. Ferraro, and H. Lepper-Pappan, “Study protocol for a randomized controlled trial of the Parent–Child Assistance Program: a case management and home visiting program for people using substances during pregnancy,” Trials, vol. 25, no. 1, p. 264, Apr. 2024, doi: 10.1186/s13063-024-08098-6.
B. Maneckshaw, K. Barker, and G. S. Mahapatra, “System reliability optimization with two-sided power distributed component failure times,” Quality Engineering, vol. 36, no. 1, pp. 54–65, Jan. 2024, doi: 10.1080/08982112.2023.2222324.
M. Markwald, J. Liu, and R. Yu, “Constructing and meta-evaluating state-aware evaluation metrics for interactive search systems,” Inf Retrieval J, vol. 26, no. 1–2, p. 10, Dec. 2023, doi: 10.1007/s10791-023-09426-1.
A. McGovern et al., “AI2ES: The NSF AI Institute for Research on Trustworthy AI for Weather, Climate, and Coastal Oceanography,” AI Magazine, vol. 45, no. 1, pp. 105–110, Mar. 2024, doi: 10.1002/aaai.12160.
Z. Ming, Y. Luo, G. Wang, Y. Yan, J. K. Allen, and F. Mistree, “Designing self-organizing systems using surrogate models and the compromise decision support problem construct,” Advanced Engineering Informatics, vol. 59, p. 102350, Jan. 2024, doi: 10.1016/j.aei.2023.102350.
H. Morrison, Ed., Lived Resistance against the War on Palestinian Children. Athens: The University of Georgia Press, 2024.
C. Noyori-Corbett, Y. Sharma, and C. Miller, “Steps towards Internationalization in Social Work Education: A case of study abroad course development,” Indian Journal of Social Work, 2023.
C. Noyori-Corbett and D. P. Moxley, “Teaching Note—The United States Department of State Diplomacy Lab for Supporting MSW Students’ Engagement in Community-Based Refugee Resettlement Research,” Journal of Social Work Education, vol. 59, no. 1, pp. 278–282, Jan. 2023, doi: 10.1080/10437797.2021.1997682.
C. Noyori-Corbett and D. P. Moxley, “Advancing the Human Rights Content of the Social Work Curriculum,” Journal of Social Work Education, pp. 1–12, May 2024, doi: 10.1080/10437797.2024.2338234.
C. Noyori-Corbett, Y. Sharma, S. Bhattacharjee, M. Harden, E. Ratcliffe, and A. W. Cahill, “Social Constructivist and Rights-Based Analysis of Global Governance of Statelessness: The Case of the Rohingya Crisis,” J. Hum. Rights Soc. Work, vol. 9, no. 2, pp. 171–184, Jan. 2024, doi: 10.1007/s41134023-00274-z.
C. C. Nsude, R. Loraamm, J. J. Wimhurst, G. N. Chukwuonye, and R. Debnath, “Renewables but unjust? Critical restoration geography as a framework for addressing global renewable energy injustice,” Energy Research & Social Science, vol. 114, p. 103609, Aug. 2024, doi: 10.1016/j. erss.2024.103609.
H. Pham and S. Cheng, “Non-Iterative Cluster Routing: Analysis and Implementation Strategies,” Applied Sciences, vol. 14, no. 5, p. 1706, Feb. 2024, doi: 10.3390/app14051706.
L. Qiu, S. Yeo, X. Li, and J.-N. Kim, “Enhancing brand equity in popular culture tourism: testing the role of fandom in a serial mediation model,” Asia Pacific Journal of Tourism Research, pp. 1–20, May 2024, doi: 10.1080/10941665.2024.2351123.
A. Rangrazjeddi, A. D. González, and K. Barker, “Game‐theoretic algorithm for interdependent infrastructure network restoration in a decentralized environment,” Risk Analysis, p. risa.14269, Jan. 2024, doi: 10.1111/risa.14269.
S. Renard, “The Role of Arts Incubators in Addressing Digital Divide Inequities,” in Innovating Institutions and Inequities in the Arts, J. Woronkowicz and D. Noonan, Eds., Springer, 2024. [Online]. Available: https://link.springer.com/book/9783031592300
S. Renard, “The Role of Arts Incubators in Addressing Digital Divide Inequities,” in Innovating Institutions and Inequities in the Arts, J. Woronkowicz and D. Noonan, Eds., Springer, 2024. [Online]. Available: https://link.springer.com/book/9783031592300
C. Rioux et al., “Phenotypic Environmental Sensitivity and Mental Health During Pregnancy and Post Partum: Protocol for the Experiences of Pregnancy Longitudinal Cohort Study,” JMIR Res Protoc, vol. 12, p. e49243, Dec. 2023, doi: 10.2196/49243.
C. Rioux, K. London-Nadeau, and R.-P. Juster, “Sex and gender measurement for scientific rigor and data harmonization across studies,” Comprehensive Psychoneuroendocrinology, vol. 16, p. 100199, Nov. 2023, doi: 10.1016/j.cpnec.2023.100199.
C. Rioux, K. London-Nadeau, L. Tomfohr-Madsen, and R.-P. Juster, “Gender-inclusive research instructions in author submission guidelines: insufficient for gender-inclusive obstetrics and gynecology research,” American Journal of Obstetrics & Gynecology MFM, vol. 5, no. 12, p. 101179, Dec. 2023, doi: 10.1016/j.ajogmf.2023.101179.
C. M. Rocco and K. Barker, “A bi-objective model for network restoration considering fairness and graph signal-based functions,” Life Cycle Reliab Saf Eng, vol. 12, no. 4, pp. 299–307, Dec. 2023, doi: 10.1007/s41872-023-00233-7.
M. Satterthwaite-Freiman et al., “The Challenges of Engaging in Conversations and Activities Focused on Race, Ethnicity, and Identity in the Classroom: Learning from U.S. Based Teachers,” Identity, pp. 1–26, May 2024, doi: 10.1080/15283488.2024.2340489.
G. A. Semenov, C. M. Curry, M. A. Patten, J. T. Weir, and S. A. Taylor, “Geographically consistent hybridization dynamics between the Black-crested and Tufted titmouse with evidence of hybrid zone expansion,” Ornithology, vol. 140, no. 3, p. ukad014, Jul. 2023, doi: 10.1093/ornithology/ukad014.
H. Sen Gupta, A. D. Gonzalez, R. Jnad, and S. Kameshwar, “Fairness-Driven Multi-Objective Optimization for Evacuation Planning in Natural Disasters,” in International Conference on Transportation and Development 2024, Atlanta, Georgia: American Society of Civil Engineers, Jun. 2024, pp. 170–180. doi: 10.1061/9780784485521.016.
M. R. Sladek et al., “Professional Development for Providing Time and Opportunities for Change in U.S. Teachers’ Ethnic-Racial Identity,” Identity, pp. 1–22, Jun. 2024, doi: 10.1080/15283488.2024.2366892.
L. Tam, H. Lee, and J.-N. Kim, “Conspiratorial thinking in the workplace: How it happens and why it matters,” in International Communication Association (ICA) Annual Conference, 2021.
A. J. Umaña-Taylor, M. R. Sladek, and M. D. Safa, “Teachers’ Implementation of the Identity Project Is Associated With Increases in U.S. High School Students’ Ethnic-Racial Identity Exploration,” J. Youth Adolescence, Feb. 2024, doi: 10.1007/s10964-024-01955-2.
T. V. Venkatesan, “Revolutionising disease detection: The emergence of non-invasive VOC breathomics,” Open Access Government, vol. 41, no. 1, pp. 74–75, Jan. 2024, doi: 10.56367/OAG-041-10923.
B. Wang and J. Liu, “Characterizing and Early Predicting User Performance for Adaptive Search Path Recommendation,” Proceedings of the Association for Information Science and Technology, vol. 60, no. 1, pp. 408–420, Oct. 2023, doi: 10.1002/pra2.799.
B. Wang and J. Liu, “Cognitively Biased Users Interacting with Algorithmically Biased Results in Whole-Session Search on Debated Topics,” 2024, doi: 10.48550/ARXIV.2403.17286.
B. Wang and J. Liu, “Understanding users’ dynamic perceptions of search gain and cost in sessions: An expectation confirmation model,” Asso for Info Science & Tech, p. asi.24935, Jun. 2024, doi: 10.1002/asi.24935.
B. Wang, J. Liu, J. Karimnazarov, and N. Thompson, “Task Supportive and Personalized Human-Large Language Model Interaction: A User Study,” in Proceedings of the 2024 ACM SIGIR Conference on Human Information Interaction and Retrieval, Sheffield United Kingdom: ACM, Mar. 2024, pp. 370–375. doi: 10.1145/3627508.3638344.
C. Wang et al., “Impacts of climate change, population growth, and power sector decarbonization on urban building energy use,” Nat Commun, vol. 14, no. 1, p. 6434, Oct. 2023, doi: 10.1038/s41467-023-41458-5.
D. Wang, J. Choi, and Q. Jiang, “Cooperative coparenting and the associations with adolescent behavioral problems and delinquency in unmarried families,” Journal of Adolescence, p. jad.12310, Mar. 2024, doi: 10.1002/jad.12310.
D. Wang, X. Tu, M. Rosario De Guzman, and Y. Xia, “Parenting Beliefs and Practices of Immigrant Chinese in the Midwestern United States: A Qualitative Study,” Journal of Family Issues, p. 0192513X231209045, Oct. 2023, doi: 10.1177/0192513X231209045.
X. Wang, H. Rahmani, J. Liu, and E. Yilmaz, “Improving Conversational Recommendation Systems via Bias Analysis and Language-ModelEnhanced Data Augmentation,” in Findings of the Association for Computational Linguistics: EMNLP 2023, Singapore: Association for Computational Linguistics, 2023, pp. 3609–3622. doi: 10.18653/v1/2023.findings-emnlp.233.
Z. H. Wang, Z. J. Ming, G. X. Wang, F. Mistree, and J. K. Allen, “Sentiment Analysis of Semester Learning Essays in Design Education,” in 2023 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), Singapore, Singapore: IEEE, Dec. 2023, pp. 0567–0571. doi: 10.1109/IEEM58616.2023.10406525.
B. Weng, “Monitoring Methane to Curb Climate Change,” 2024. Accessed: Jul. 08, 2024. [Online]. Available: http://www.optica-opn.org/home/ articles/volume_35/july_august_2024/features/monitoring_methane_to_curb_climate_change/
B. Weng, “The road to climate change mitigation via methane emissions monitoring,” Nat Rev Electr Eng, vol. 1, no. 2, pp. 69–70, Feb. 2024, doi: 10.1038/s44287-023-00014-5.
M. Wimberly, “Meteorological data for public health surveillance,” Open Access Government, vol. 42, no. 1, pp. 38–39, Apr. 2024, doi: 10.56367/ OAG-042-10923.
J. Woronkowicz and D. Noonan, “Innovating Institutions and Inequities in the Arts,” American Behavioral Scientist. SAGE Publications Sage CA: Los Angeles, CA, p. 00027642231178517, 2023.
P. Xiao and S. Cheng, “Bayesian Federated Neural Matching That Completes Full Information,” AAAI, vol. 37, no. 9, pp. 10473–10480, Jun. 2023, doi: 10.1609/aaai.v37i9.26245.
S. Yeo, Y. Cha, and J.-N. Kim, “‘Actions speak louder than words’: The experiments of positive megaphoning intentions by message strategy and consumer’s prior evaluation of CEO’s ethical behaviors,” Korean Journal of Journalism and Communication Studies, 2024
Z. Yu et al., “Enhanced observations from an optimized soil-canopy-photosynthesis and energy flux model revealed evapotranspiration-shading cooling dynamics of urban vegetation during extreme heat,” Remote Sensing of Environment, vol. 305, p. 114098, May 2024, doi: 10.1016/j. rse.2024.114098.
Y. Zhang and J. Liu, “Deconstructing proxy health information-seeking behavior: A systematic review,” Library & Information Science Research, vol. 45, no. 3, p. 101250, Jul. 2023, doi: 10.1016/j.lisr.2023.101250.
S. Zheng, W. Bian, and H. Ding, “A robust protonic ceramic fuel cell with a triple conducting oxygen electrode under accelerated stress tests,” Mater. Adv., vol. 5, no. 6, pp. 2296–2305, 2024, doi: 10.1039/D3MA01167D.
P. Zhu et al., “Direct conversion of methane to aromatics and hydrogen via a heterogeneous trimetallic synergistic catalyst,” Nat Commun, vol. 15, no. 1, p. 3280, Apr. 2024, doi: 10.1038/s41467-024-47595-9.

Fiscal Year 2023

David S. Ebert and Aaron M. Wendelboe (2023), "Applying data science advances in disease surveillance and control", Open Access Government July 2023, pp.152-153. doi: 10.56367/OAG-039-10899
Katrin G. Kuhn, Jason R. Vogel and Bradley S. Stevenson (2023), "Wastewater surveillance for infectious disease preparedness", Open Access Government October 2023, pp.22-23. doi: 10.56367/OAG-040-10923
Nekorchuk, D. M., A. Bharadwaja, S. Simonson, E. Ortega, C. M. B. Franca, E. Dinh, R. Reik, R. Burkholder, and M. C. Wimberly. 2024. The Arbovirus Mapping and Prediction (ArboMAP) system for West Nile virus forecasting. JAMIA Open 7: ooad110. doi: 10.1093/jamiaopen/ooad110
Zhunan Jia, Velmurugan Thavasi, Thirumalai Venkatesan, Pyng LeeBreath (2023), "Analysis for Lung Cancer Early Detection - A Clinical Study", Metabolites 2023, 13, 1197. doi: 10.3390/metabo13121197
Katrin G. Kuhn, Rishabh Shukla, Mike Mannell, Grant M. Graves, A. Caitlin Miller,Jason R. Vogel, Kimberly Malloy, Gargi Deshpande, Gabriel Florea, Kristen Shelton, Erin Jeffries, Kara B. De León, Bradley Stevenson (2023), "Using Wastewater Surveillance to Monitor Gastrointestinal Pathogen Infections in the State of Oklahoma", Microorganisms 2023, 11(9), 2193. doi: 10.3390/microorganisms11092193
Ahmed, S. E., San, O., Lakshmivarahan, S., & Lewis, J. M. (2023). On the dual advantage of placing observations through forward sensitivity analysis. arXiv preprint arXiv:2305.00300. https://arxiv.org/pdf/2305.00300
Almoghathawi, Y., Selim, S., & Barker, K. (2023). Community structure recovery optimization for partial disruption, functionality, and restoration in interdependent networks. Reliability Engineering & System Safety, 229, 108853. https://www.sciencedirect.com/science/article/pii/S0951832022004707
Badré, A., & Pan, C. (2023). Explainable multi-task learning improves the parallel estimation of polygenic risk scores for many diseases through shared genetic basis. PLOS Computational Biology, 19(7), e1011211. https://pubmed.ncbi.nlm.nih. gov/37418352/
Bedle, H., Garneau, C. R., & Vera-Arroyo, A. (2023). Clustering energy support beliefs to reveal unique sub-populations using self-organizing maps. Heliyon. https://www.cell.com/heliyon/pdf/S2405-8440(23)05559-7.pdf
Bedle, H., Salazar-Florez, D., & Garneau, C. R. (2022). Recognizing societal influences in earthquake geohazard risk perception with explainable AI while mitigating risks through improved seismic interpretation. The Leading Edge, 41(11), 756-767. https://mcee.ou.edu/aaspi/publications/2022/Bedle_et_al_2022-Recognizing_societal_influences_in_ earthquake_risk_perception_through_improved_seismic_interpretation.pdf
Bourgeois, C.M., L. Soltanisehat, K. Barker, & A.D. González. (2023). Inventory Scheduling Framework to Fulfill Multi-product Orders within a Production Network. Computers and Industrial Engineering, 182: 109343. https://www.sciencedirect.com/ science/article/pii/S0360835223003674
Chapman, H. S., Hanson, M. C., Dzutsati, V., & DeBell, P. (2023). Under the Veil of Democracy: What Do People Mean When They Say They Support Democracy?. Perspectives on Politics, 1-19.
Chen, L., Cheng, S., Stankovic, V., Stankovic, L., & Shi, Q. (2022). How likely is a random network graph shift-enabled?. IEEE Transactions on Signal and Information Processing over Networks, 8, 973-982. https://ieeexplore.ieee.org/ iel7/6884276/9666472/09975143.pdf
Chiu, M. M., Morakhovski, A., Ebert, D., Reinert, A., & Snyder, L. S. (2023). Detecting COVID-19 fake news on Twitter: Followers, emotions, relationships, and uncertainty. American Behavioral Scientist, 00027642231174329. https://journalssagepub.com/doi/pdf/10.1177/00027642231174329
Connelly, S. E., Maher, E. J., & Pharris, A. B. (2022). Playing to Succeed: The Impact of Extracurricular Activity Participation on Academic Achievement for Youth Involved with the Child Welfare System. Child and Adolescent Social Work Journal, 1-15. https://link.springer.com/article/10.1007/s10560-022-00897-7
Cui, Y., Li, Y., Pan, C., Brown, S. R., Gallant, R. E., & Zhu, R. (2023). Bayesian inference for survival prediction of childhood Leukemia. Computers in Biology and Medicine, 156, 106713. https://pubmed.ncbi.nlm.nih.gov/36863191/
Dean, D. A., Roach, J., vonBargen, R. U., Xiong, Y., Kane, S. S., Klechka, L., Wheeler, K., Sandoval, M. J., Lesani, M., Hossain, E., Katemauswa, M., Schaefer, M., Harris, M., Barron, S., Liu, Z., Pan, C., & McCall, L. I. (2023). Persistent biofluid small molecule alterations induced by Trypanosoma cruzi infection are not restored by antiparasitic treatment. bioRxiv : the preprint server for biology, 2023.06.03.543565. https://www.biorxiv.org/content/10.1101/2023.06.03.543565.full.pdf
Demir, F. (2022). IMF conditionality, export structure and economic complexity: The ineffectiveness of structural adjustment programs. Journal of Comparative Economics, 50(3), 750-767. https://firatdemir.oucreate.com/Demir_JCE2022_Structural_Adjustment_Trade_Structure_Appendix.pdf
Demir, F., & Duan, Y. (2020). Target at the right level: Aid, spillovers and growth in sub-saharan Africa. Target at the Right Level: Aid, Spillovers and Growth in Sub-saharan Africa. http://firatdemir.oucreate.com/Demir_Duan_Aid_growth_ spillovers.pdf
Demir, F., & Hu, C. (2022). Institutional similarity, firm heterogeneity and export sophistication. The World Economy, 45(4), 1213-1241. https://firatdemir.oucreate.com/Demir_Hu_Institutions_and_Export%20Sophistication.pdf
Demir, F., Hu, C., Liu, J., & Shen, H. (2022). Local corruption, total factor productivity and firm heterogeneity: Empirical evidence from Chinese manufacturing firms. World Development, 151, 105770. https://www.sciencedirect.com/science/ article/pii/S0305750X21003855
Demir, F., & Lee, S. (2022). Foreign direct investment, capital accumulation, and growth: The rise of the Emerging South. International Review of Economics & Finance, 80, 779-794. https://doi.org/10.1016/j.iref.2022.02.044
Demir, F., & Razmi, A. (2022). The real exchange rate and development theory, evidence, issues and challenges. Journal of Economic Surveys, 36(2), 386-428. https://doi.org/10.1111/joes.12418
Demir, F., & Tabrizy, S. S. (2022). Gendered effects of sanctions on manufacturing employment: Evidence from Iran. Review of Development Economics, 26(4), 2040-2069. https://onlinelibrary.wiley.com/doi/abs/10.1111/rode.12917
Feng, S., Ji, H. L., Wang, H., Zhang, B., Sterzenbach, R., Pan, C., & Guo, X. (2022). MetaLP: An integrative linear programming method for protein inference in metaproteomics. PLOS Computational Biology, 18(10), e1010603. https://journals.plos.org/ ploscompbiol/article?id=10.1371/journal.pcbi.1010603
Ferraro, A. C., Maher, E. J., & Grinnell-Davis, C. (2022). Family ties: A quasi-experimental approach to estimate the impact of kinship care on child well-being. Children and Youth Services Review, 137, 106472. https://www.sciencedirect.com/ science/article/pii/S0190740922001086
Han, D., Mulyana, B., Stankovic, V., & Cheng, S. (2023). A Survey on Deep Reinforcement Learning Algorithms for Robotic Manipulation. Sensors, 23(7), 3762. https://www.mdpi.com/1424-8220/23/7/3762/pdf
Jamalzadeh, S., Barker, K., González, A. D., & Radhakrishnan, S. (2022). Protecting infrastructure performance from disinformation attacks. Scientific Reports, 12(1), 12707. https://www.nature.com/articles/s41598-022-16832-w
Karakoc, D. B., Barker, K., & González, A. D. (2023). Analyzing the tradeoff between vulnerability and recoverability investments for interdependent infrastructure networks. Socio-Economic Planning Sciences, 87, 101508. https://www.sciencedirect.com/science/article/abs/pii/S0038012123000010
Lewis, J. M., & Lakshmivarahan, S. (2022). Role of the Observability Gramian in Parameter Estimation: Application to Nonchaotic and Chaotic Systems via the Forward Sensitivity Method. Atmosphere, 13(10), 1647 https://www.mdpi. com/2073-4433/13/10/1647/htm
Li J, Pan C, Guo X. (2022). IDIA: An Integrative Signal Extractor for Data-Independent Acquisition Proteomics. Proceedings (IEEE Int Conf Bioinformatics Biomed). 2022:266-269. doi:10.1109/bibm55620.2022.9994873. https:// pubmed.ncbi.nlm.nih.gov/37034305/
Pharris, A. B., Lepper-Pappan, H., Maher, E., & Natale, A. P. (2023). The caseworker as an external locus of hope for LGB youth in foster care. Journal of Public Child Welfare, 1-20. https://doi.org/10.1080/15548732.2023.2193141
Rocco, C. M., Nock, D., & Barker, K. (2023). A Fairness-Based Approach to Network Restoration. IEEE Transactions on Systems, Man, and Cybernetics: Systems. https://ieeexplore.ieee.org/document/10014676
Seshadri, A. K., & Lakshmivarahan, S. (2023). Invariants and chaos in the Volterra gyrostat without energy conservation. Chaos, Solitons & Fractals, 173, 113638. https://www.sciencedirect.com/science/article/abs/pii/S0960077923005398
Shotande, M. O., Veirs, K. P., Day, J. D., Ertl, W. J., Fagg, A. H., & Dionne, C. P. (2022). Comparing Temporospatial Performance During Brisk and Self-Paced Walking by Men With Osteomyoplastic Transfemoral Amputation and Controls Using Pressure and Muscle Activation Peak Times. Frontiers in Rehabilitation Sciences, 3, 848657. https://www.frontiersin. org/articles/10.3389/fresc.2022.848657/full
Shotande, M. (2022). Modeling Relationships Between Brain/Muscle Activity and Locomotive Behavior. https://shareok. org/handle/11244/336923
Soltanisehat, L., A.D. González, and K. Barker. 2023. Modeling Social, Economic, and Health Perspectives for Optimal Pandemic Policy Decision-making. Socio-economic Planning Sciences, 86: 101472. https://www.sciencedirect.com/ science/article/abs/pii/S0038012122002737
Soltanisehat, L., Barker, K., & González, A. D. (2023). Multiregional, multi-industry impacts of fairness on pandemic policies. Risk Analysis. https://onlinelibrary.wiley.com/doi/10.1111/risa.14143?af=R
Wang, S., Feng, S., Pan, C., & Guo, X. (2022). FineFDR: Fine-grained Taxonomy-specific False Discovery Rates Control in Metaproteomics. In 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (pp. 287-292). IEEE. https://pubmed.ncbi.nlm.nih.gov/36910011/
Wang, D., Hunt, K. A., Candry, P., Tao, X., Wofford, N. Q., Zhou, J., Mclenerney, M. J., Stahl, D. A., Tanner, R. S., Zhou, A., Winkler, M., & Pan, C. (2023). Cross-Feedings, Competition, and Positive and Negative Synergies in a Four-Species Synthetic Community for Anaerobic Degradation of Cellulose to Methane. Mbio, 14(2), e03189-22. https://pubmed.ncbi. nlm.nih.gov/36847519/
Wanyama, D., Wimberly, M. C., & Mensah, F. (2023). Patterns and drivers of disturbance in tropical forest reserves of southern Ghana. Environmental Research Letters, 18(6), 064022. https://doi.org/10.1088/1748-9326/acd399
Wimberly, M. C., Davis, J. K., Hildreth, M. B., & Clayton, J. L. (2022). Integrated forecasts based on public health surveillance and meteorological data predict West Nile virus in a high-risk region of North America. Environmental Health Perspectives, 130(8), 087006. https://doi.org/10.1289/EHP10287
Xiao, P., & Cheng, S. (2022). Bayesian Federated Neural Matching that Completes Full Information. arXiv preprint arXiv:2211.08010 https://arxiv.org/pdf/2211.08010
Xu, T., Tao, X., He, H., Kempher, M. L., Zhang, S., Liu, X., Wang, J. Wand, D., Ning, D. Pan, C., Ge, H., Zhang, N., He, Y., & Zhou, J. (2023). Functional and structural diversification of incomplete phosphotransferase system in cellulose-degrading clostridia. The ISME Journal, 17(6), 823-835. https://pubmed.ncbi.nlm.nih.gov/36899058/
Zhang, H., Cheng, S., El Amm, C., & Kim, J. (2023). Efficient Pooling Operator for 3D Morphable Models. IEEE Transactions on Visualization and Computer Graphics. https://ieeexplore.ieee.org/iel7/2945/4359476/10068322.pdf
Zhao, J., Demir, F., Ghosh, P. K., Earley, A., & Kim, M. (2022). Reforming the countermeasures injury compensation program for COVID-19 and beyond: An economic perspective. Journal of Law and the Biosciences, 9(1), lsac008. https://academic.oup.com/jlb/article/9/1/lsac008/6555422
Zhao, Z., Zhao, S., & Cheng, S. (2022). Group ensemble block: subspace diversity improves coarse-to-fine image retrieval. IEEE Transactions on Artificial Intelligence, 4(1), 60-70. https://ieeexplore.ieee.org/iel7/9078688/9184921/09889202.pdf

Fiscal Year 2022

Abshirini, M., Saha, M. C., Altan, M. C., & Liu, Y. (2022). 3D Printed Flexible Microscaled Porous Conductive Polymer Nanocomposites for Piezoresistive Sensing Applications. Advanced Materials Technologies. https://doi.org/10.1002/admt.202101555
Badre, A., & Pan, C. (2022). LINA: A Linearizing Neural Network Architecture for Accurate FirstOrder and Second-Order Interpretations. IEEE Access, 10, 36166–36176. https://doi.org/10.1109/ACCESS.2022.3163257
Beauxis-Aussalet, E., Behrisch, M., Borgo, R., Chau, D. H., Collins, C., Ebert, D., El-Assady, M., Endert, A., Keim, D. A., Kohlhammer, J., Oelke, D., Peltonen, J., Riveiro, M., Schreck, T., Strobelt, H., van Wijk, J. J., & Rhyne, T. M. (2021). The Role of Interactive Visualization in Fostering Trust in AI. IEEE Computer Graphics and Applications, 41(6). https://doi.org/10.1109/MCG.2021.3107875
Cai, C., Kim, P., Connor, T. H., Liu, Y., & Floyd, E. L. (2022). Reducing the particles generated by flushing institutional toilets. Journal of Occupational and Environmental Hygiene, 19(5). https://doi.org/10.1080/15459624.2022.2053693
Cao, Y., & Lan, C. (2022a). A model-agnostic randomized learning framework based on random hypothesis subspace sampling. 39th International Conference on Machine Learning (ICML). Cao, Y., & Lan, C. (2022b). Active approximately metric-fair learning. 38th Conference on Uncertainty in Artificial Intelligence (UAI).
Chandrashekhar, R., Wang, H., Rippetoe, J., James, S. A., Fagg, A. H., & Kolobe, T. H. A. (2022). The Impact of Cognition on Motor Learning and Skill Acquisition Using a Robot Intervention in Infants With Cerebral Palsy. Frontiers in Robotics and AI, 9. https://doi.org/10.3389/frobt.2022.805258
Chen, J., Liu, Q., & Kim, M. (2022). Gender gap in tenure and promotion: Evidence from the economics Ph.D. class of 2008. Southern Economic Journal, 88(4). https://doi.org/10.1002/soej.12567
Chen, X., Wang, X., Zhang, K., Fung, K.-M., Thai, T. C., Moore, K., Mannel, R. S., Liu, H., Zheng, B., & Qiu, Y. (2022). Recent advances and clinical applications of deep learning in medical image analysis. Medical Image Analysis, 79, 102444. https://doi.org/10.1016/j.media.2022.102444
Chen, X., Zhang, K., Abdoli, N., Gilley, P. W., Wang, X., Liu, H., Zheng, B., & Qiu, Y. (2022). Transformers Improve Breast Cancer Diagnosis from Unregistered Multi-View Mammograms. Diagnostics, 12(7). https://doi.org/10.3390/diagnostics12071549
Chen, Y., & Liu, Q. (2022). Signaling Through Advertising When an Ad Can Be Blocked. Marketing Science, 41(1). https://doi.org/10.1287/mksc.2021.1288
Chenin, J., & Bedle, H. (2022). Unsupervised Machine Learning, Multi-Attribute Analysis for Identifying Low Saturation Gas Reservoirs within the Deepwater Gulf of Mexico, and Offshore Australia. Geosciences, 12(3), 132. https://doi.org/10.3390/geosciences12030132
Danala, G., Desai, M., Ray, B., Heidari, M., Maryada, S. K. R., Prodan, C. I., & Zheng, B. (2022). Applying Quantitative Radiographic Image Markers to Predict Clinical Complications After Aneurysmal Subarachnoid Hemorrhage: A Pilot Study. Annals of Biomedical Engineering, 50(4). https://doi.org/10.1007/s10439-022-02926-z
Danala, G., Maryada, S. K., Islam, W., Faiz, R., Jones, M., Qiu, Y., & Zheng, B. (2022). A Comparison of Computer-Aided Diagnosis Schemes Optimized Using Radiomics and Deep Transfer Learning Methods. Bioengineering, 9(6), 256. https://doi.org/10.3390/bioengineering9060256
Danala, G., Ray, B., Desai, M., Heidari, M., Mirniaharikandehei, S., Maryada, S. K. R., & Zheng, B. (2022). Developing new quantitative CT image markers to predict prognosis of acute ischemic stroke patients. Journal of X-Ray Science and Technology, 30(3). https://doi.org/10.3233/XST221138
Demir, F. (2022). IMF conditionality, export structure and economic complexity: The ineffectiveness of structural adjustment programs. Journal of Comparative Economics. https://doi.org/10.1016/j.jce.2022.04.003 Demir, F., & Hu, C. (2022). Institutional similarity, firm heterogeneity and export sophistication. The World Economy, 45(4), 1213–1241. https://doi.org/10.1111/twec.13201
Demir, F., Hu, C., Liu, J., & Shen, H. (2022). Local corruption, total factor productivity and firm heterogeneity: Empirical evidence from Chinese manufacturing firms. World Development, 151, 105770. https://doi.org/10.1016/j.worlddev.2021.105770
Demir, F., & Lee, S. (2022). Foreign direct investment, capital accumulation, and growth: The rise of the Emerging South. International Review of Economics & Finance, 80, 779–794. https://doi.org/10.1016/j.iref.2022.02.044-
Demir, F., & Razmi, A. (2022). The Real Exchange Rate and Development Theory, Evidence, Issues and Challenges. Journal of Economic Surveys, 36(2), 386–428. https://doi.org/10.1111/joes.12418
Demir, F., & Tabrizy, S. S. (2022). Gendered Effects of Sanctions on Manufacturing Employment: Evidence from Iran. Review of Development Economics. Dukes, A., Liu, Q., & Shuai, J. (2022). Skippable Ads: Interactive Advertising on Digital Media Platforms. Marketing Science, 41(3). https://doi.org/10.1287/mksc.2021.1324
Ebert, D. S., Fisher, B., & Gaither, K. (2018). Introduction to the minitrack on interactive visual analytics and visualization for decision making. In Proceedings of the Annual Hawaii International Conference on System Sciences (Vols. 2018-January). https://doi.org/10.24251/hicss.2022.209
Esteves, R. B., Liu, Q., & Shuai, J. (2022). Behavior-based price discrimination with nonuniform distribution of consumer preferences. Journal of Economics and Management Strategy, 31(2). https://doi.org/10.1111/jems.12466
Feng, X., Barcelos, G., Gaboardi, J. D., Knaap, E., Wei, R., Wolf, L. J., Zhao, Q., & Rey, S. J. (2022). spopt: a python package for solving spatial optimization problems in PySAL. Journal of Open Source Software, 7(74), 3330. https://doi.org/10.21105/joss.03330
Ferraro, A. C., Maher, E. J., & Grinnell-Davis, C. (2022). Family ties: A quasi-experimental approach to estimate the impact of kinship care on child well-being. Children and Youth Services Review, 137, 106472. https://doi.org/10.1016/J.CHILDYOUTH.2022.106472
Gai, T., Thai, T., Jones, M., Jo, J., & Zheng, B. (2022). Applying a radiomics-based CAD scheme to classify between malignant and benign pancreatic tumors using CT images. Journal of X-Ray Science and Technology, 30(2). https://doi.org/10.3233/XST-211116
Ghani, M. U., Fajardo, L. L., Omoumi, F., Yan, A., Jenkins, P., Wong, M., Li, Y., Peterson, M. E., Callahan, E. J., Hillis, S. L., Zheng, B., Wu, X., & Liu, H. (2021). A phase sensitive x-ray breast tomosynthesis system: Preliminary patient images with cancer lesions. Physics in Medicine and Biology, 66(21). https://doi.org/10.1088/1361-6560/ac2ea6
Ghani, M. U., Omoumi, F. H., Wu, X., Fajardo, L. L., Zheng, B., & Liu, H. (2022). Evaluation and comparison of a CdTe based photon counting detector with an energy integrating detector for X-ray phase sensitive imaging of breast cancer. Journal of X-Ray Science and Technology, 30(2). https://doi.org/10.3233/XST-211028
He, L., Kim, M., & Liu, Q. (2022). Competitive response to unbundled services: An empirical look at Spirit Airlines. Journal of Economics and Management Strategy, 31(1). https://doi.org/10.1111/jems.12448
Heidari, M., Lakshmivarahan, S., Mirniaharikandehei, S., Danala, G., Maryada, S. K. R., Liu, H., & Zheng, B. (2021). Applying a Random Projection Algorithm to Optimize Machine Learning Model for Breast Lesion Classification. IEEE Transactions on Biomedical Engineering, 68(9). https://doi.org/10.1109/TBME.2021.3054248
Herren, B., Saha, M. C., Altan, M. C., & Liu, Y. (2022). Funnel-Shaped Floating Vessel Oil Skimmer with Joule Heating Sorption Functionality. Polymers, 14(11), 2269. https://doi.org/10.3390/polym14112269
Jones, M. A., Faiz, R., Qiu, Y., & Zheng, B. (2022). Improving mammography lesion classification by optimal fusion of handcrafted and deep transfer learning features. Physics in Medicine and Biology, 67(5). https://doi.org/10.1088/1361-6560/ac5297
Kieft, B., Li, Z., Bryson, S., Hettich, R. L., Pan, C., Mayali, X., & Mueller, R. S. (2021). Phytoplankton exudates and lysates support distinct microbial consortia with specialized metabolic and ecophysiological traits. Proceedings of the National Academy of Sciences, 118(41). https://doi.org/10.1073/pnas.2101178118
La Marca, K., & Bedle, H. (2022). User vs. machine-based seismic attribute selection for unsupervised machine learning techniques: Does human insight provide better results than statistically chosen attributes? In Advances in Subsurface Data Analytics (pp. 3–30). Elsevier. https://doi.org/10.1016/B978-0-12-822295-9.00002-9 Liu, Q., Nedelescu, D., & Gu, J. (2021). The impact of strategic agents in two-sided markets. Journal of Economics/ Zeitschrift Fur Nationalokonomie, 134(3). https://doi.org/10.1007/s00712-021-00753-9
Loginova, O., Wang, X. H., & Liu, Q. (2022). The impact of multi-homing in a ride-sharing market. Annals of Regional Science. https://doi.org/10.1007/s00168-022-01120-2
Lubo-Robles, D., Devegowda, D., Jayaram, V., Bedle, H., Marfurt, K. J., & Pranter, M. J. (2022). Quantifying the sensitivity of seismic facies classification to seismic attribute selection: An explainable machine-learning study. Interpretation,10(3), SE41–SE69. https://doi.org/10.1190/INT-2021-0173.1
Maher, E. J., Gerlinger, J., Wood, A. D., & Ho, K. (2021). Won’t You be My Neighbor? Neighborhood Characteristics Associated with Mass Shootings in the USA. Race and Social Problems. https://doi.org/10.1007/s12552-021-09350-3
Marashizadeh, P., Abshirini, M., Saha, M., Huang, L., & Liu, Y. (2022). Functionalization Enhancement on Interfacial Properties Between Graphene and ZnO NW/Ep A Molecular Dynamics Simulation Study. Advanced Theory and Simulations, 5(6). https://doi.org/10.1002/adts.202200010
Merchan-Breuer, D. A., Murphy, E., Berka, B., Nova, L. C. M., Liu, Y., & Merchan-Merchan, W. (2022). Synthesis of Carbonaceous Hydrophobic Layers through a Flame Deposition Process. Applied Sciences, 12(5), 2427. https://doi.org/10.3390/app12052427
Nagle, S., Tzoc, E., Wyatt, K., & Garrett, Z. (2022). Out of the Archives: Making Collections Accessible Through the Implementation of a 3D Scanning Lab . In Innovation and experiential learning in academic libraries: Meeting the needs of today’s students.
Rowman & Littlefield. Nouh, C. D., Ray, B., Xu, C., Zheng, B., Danala, G., Koriesh, A., Hollabaugh, K., Gordon, D., & Sidorov, E. v. (2022). Quantitative Analysis of Stress-Induced Hyperglycemia and Intracranial Blood Volumes for Predicting Mortality After Intracerebral Hemorrhage. Translational Stroke Research, 13(4). https://doi.org/10.1007/s12975-022-00985-x
Noyori-Corbett, C., & Moxley, D. P. (2021). Teaching Note—The United States Department of State Diplomacy Lab for Supporting MSW Students’ Engagement in Community-Based Refugee Resettlement Research. Journal of Social Work Education. https://doi.org/10.1080/10437797.2021.1997682
Pineda-Castillo, S. A., Stiles, A. M., Bohnstedt, B. N., Lee, H., Liu, Y., & Lee, C.-H. (2022). Shape Memory Polymer-Based Endovascular Devices: Design Criteria and Future Perspective. Polymers, 14(13), 2526. https://doi.org/10.3390/polym14132526
Rangrazjeddi, A., Gonzalez, A., & Barker, K. (2022). Adaptive Algorithm for Dependent Infrastructure Network Restoration in an Imperfect Information Sharing Environment. PLoS One. Salazar Florez, D., & Bedle, H. (2022). Study on the parameterization response of probabilistic neural networks for seismic facies classification in the Gulf of Mexico. Interpretation, 10(1), T1T23. https://doi.org/10.1190/INT-2020-0218.1
Sharma, Y., & Noyori-Corbett, C. (2022). Transnational Human Trafficking and HIV/AIDS: Women in Asia. Social Development Issues, 44(1). https://doi.org/10.3998/sdi.2816
Shi, T., Jiang, H., & Zheng, B. (2022). C2MA-Net: Cross-Modal Cross-Attention Network for Acute Ischemic Stroke Lesion Segmentation Based on CT Perfusion Scans. IEEE Transactions on Biomedical Engineering, 69(1). https://doi.org/10.1109/TBME.2021.3087612
Shotande, M. O., Veirs, K. P., Day, J. D., Ertl, W. J. J., Fagg, A. H., & Dionne, C. P. (2022). Comparing Temporospatial Performance During Brisk and Self-Paced Walking by Men With Osteomyoplastic Transfemoral Amputation and Controls Using Pressure and Muscle Activation Peak Times. Frontiers in Rehabilitation Sciences, 3.https://doi.org/10.3389/fresc.2022.848657
Soltanisehat, L., Ghorbani-Renani, N., Gonzalez, A. D., & Barker, K. (2022). Assessing Production Fulfillment Time Risk: Application to Pandemic-Related Health Equipment. International Journal of Production Research. https://doi.org/10.1080/00207543.2022.2036381
Tabbutt, K., Maher, E. J., & Horm, D. (2022). Foundations for Success: A Mixed-Methods Evaluation of a Statewide, Cross-Sector Early Childhood Collaborative. Child and Youth Care Forum, 51(1). https://doi.org/10.1007/s10566-021-09622-4
Veirs, K. P., Fagg, A. H., Haleem, A. M., Jeffries, L. M., Randall, K., Sisson, S. B., & Dionne, C. P. (2022). Applications of Biomechanical Foot Models to Evaluate Dance Movements Using Three-Dimensional Motion Capture: A Review of the Literature. Journal of Dance Medicine & Science, 26(2), 69–86. https://doi.org/10.12678/1089-313X.061522a
Wang, C., Reynolds, J. C., Calle, P., Ladymon, A. D., Yan, F., Yan, Y., Ton, S., Fung, K., Patel, S. G., Yu, Z., Pan, C., & Tang, Q. (2022). Computer‐aided Veress needle guidance using endoscopic optical coherence tomography and convolutional neural networks. Journal of Biophotonics, 15(5). https://doi.org/10.1002/jbio.202100347
Wang, J., Marashizadeh, P., Weng, B., Larson, P., Altan, M. C., & Liu, Y. (2022). Synthesis, Characterization, and Modeling of Aligned ZnO Nanowire-Enhanced Carbon-Fiber-Reinforced Composites. Materials, 15(7), 2618. https://doi.org/10.3390/ma15072618
Zhang, K., Lu, X., Chen, X., Zhang, R., Fung, K.-M., Liu, H., Zheng, B., Li, S., & Qiu, Y. (2022). Using Fourier ptychography microscopy to achieve high-resolution chromosome imaging: an initial evaluation. Journal of Biomedical Optics, 27(01). https://doi.org/10.1117/1.jbo.27.1.016504
Zhang, L., Jonscher, K. R., Zhang, Z., Xiong, Y., Mueller, R. S., Friedman, J. E., & Pan, C. (2022). Islet autoantibody seroconversion in type-1 diabetes is associated with metagenome-assembled genomes in infant gut microbiomes. Nature Communications, 13(1), 3551. https://doi.org/10.1038/s41467-022-31227-1
Zhao, Z., & Cheng, S. (2021). Capsule networks with non-iterative cluster routing. Neural Networks, 143, 690–697. https://doi.org/10.1016/j.neunet.2021.07.032
Zhao, Z., Cheng, S., & Li, L. (2022). Robust depth estimation on real-world light field images using Gaussian belief propagation. Image and Vision Computing, 122, 104447. https://doi.org/10.1016/j.imavis.2022.104447
Zhao, J., Demir, F., Ghosh, P. K., Earley, A., & Kim, M. (2022). Reforming the countermeasures injury compensation program for COVID-19 and beyond: An economic perspective. Journal of Law and the Biosciences, 9(1).https://doi.org/10.1093/jlb/lsac008
Zuber, J., Schroeder, S. J., Sun, H., Turner, D. H., & Mathews, D. H. (2022). Nearest neighbor rules for RNA helix folding thermodynamics: improved end effects. Nucleic Acids Research, 50(9), 5251–5262. https://doi.org/10.1093/nar/gkac261
Zuo, T., Zheng, Y., He, L., Chen, T., Zheng, B., Zheng, S., You, J., Li, X., Liu, R., Bai, J., Si, S., Wang, Y., Zhang, S., Wang, L., & Chen, J. (2021). Automated Classification of Papillary Renal Cell Carcinoma and Chromophobe Renal Cell Carcinoma Based on a Small Computed Tomography Imaging Dataset Using Deep Learning. Frontiers in Oncology, 11. https://doi.org/10.3389/fonc.2021.746750

Fiscal Year 2021

Abshirini, M., Saha, M.C., Altan, M.C., Liu, Y., Cummings, L., Robison, T. (2021). Investigation of Porous Polydimethylsiloxane (PDMS) Structures with Tunable Properties induced by the Phase Separation Technique. Journal of Applied Polymer Science, 138 (29): 50688.
Azfal, S., Ghani, S., Jenkins-Smith, H.C., Ebert, D.S., Hadwiger, M., Hoteit, I. (2020). A Visual Analytics Based Decision Making Environment for COVID-19 Modeling and Visualization. In 2020 IEEE Visualization Conference (VIS) (pp. 86-90). IEEE.
Billings, C., Cai, C., Liu, Y. (2021). Utilization of Antibacterial Nanoparticles in Photocurable Additive Manufacturing of Advanced Composites for Improved Public Health. Polymers, 13(16): 2616.
Bray, M. T., Cavallo, S. M., & Bluestein, H. B. (2021). Examining the Relationship between Tropopause Polar Vortices and Tornado Outbreaks. Weather and Forecasting.
Chenin, J., & Bedle, H. (2020). Multi-attribute machine learning analysis for weak BSR detection in the Pegasus Basin, Offshore New Zealand. Marine Geophysical Research, 41(4), 1-20.
Ebert, D., Fisher, B., Gaither, K. (2021). Interactive Visual Analytics and Visualization for Decision Making Minitrack. In Proceedings of the 54th Hawaii International Conference on System Sciences (p. 1445).
Ebert D.S., Reinert A., & Fisher, B. (2021). Visual Analytics Review: An Early and Continuing Success of Convergent Research with Impact. Computing in Science & Engineering, 23(3), 99-108.
Guo, Y., Lee, S., & Kramer, M. (in press). We work in international companies: Affordances of communication media in Chinese employees’ organizational socialization. Accepted for publication in Communication Studies.
Heidari, M., Lakshmivarahan, S., Mirniaharikandehei, S., Danala, G., Maryada, S. K. R., Liu, H., & Zheng, B. (2021). Applying a random projection algorithm to optimize machine learning model for breast lesion classification. IEEE Transactions on Biomedical Engineering.
Herren, B., Webster, V., Davidson, E., Saha, M.C., Altan, M.C, Liu, Y. (2021). PDMS Sponges with Embedded Carbon Nanotubes as Piezoresistive Sensors for Human Motion Detection. Nanomaterials, 11(7): 1740.
Hott, B. L., Brigham, F. J., & Peltier, C. (2021). Research Methods in Special Education. Slack Inc.
Lan, Q., Li, Y., Robertson, J., & Jin, R. (2021). Modeling of pre-transplantation liver viability with spatial-temporal smooth variable selection. Computer Methods and Programs in Biomedicine, 208, 106264.
Lee, C.H., Liu, Y., Moore, M., Xu, G., Siddique, Z. (2021). Enhancement of Stay-at-Home Learning for Laboratory Courses in Biomedical Engineering During COVID-19 Pandemic. Biomedical Engineering Education, 1, 149-156.
Lee, S. K., Kavya, P., & Lasser, S. C. (2021). Social interactions and relationships with an intelligent virtual agent. International Journal of Human-Computer Studies, 150, 102608.
Li, Y., Deng, X., Ba, S., Myers, W. R., Brenneman, W. A., Lange, S. J., ... & Jin, R. (2021). Cluster-based data filtering for manufacturing big data systems. Journal of Quality Technology, 1-13.
Lillo, S. P., Cavallo, S. M., Parsons, D. B., & Riedel, C. (2021). The role of a tropopause polar vortex in the generation of the January 2019 extreme Arctic outbreak. Journal of the Atmospheric Sciences.
Marashizadeh, P., Abshirini, M., Saha, M.C., Huang, L., Liu, Y. (2021). Interfacial Properties of ZnO Nanowire Enhanced Carbon Fiber Composite: A Molecular Dynamics Simulation Study. Langmuir, 37 (23):7138-7146.
Mirniaharikandehei, S., Heidari, M., Danala, G., Lakshmivarahan, S., & Zheng, B. (2021). Applying a random projection algorithm to optimize machine learning model for predicting peritoneal metastasis in gastric cancer patients using CT images. Computer Methods and Programs in Biomedicine, 200, 105937.
Noyori-Corbett, C., Moxley, D. P. (2021). Research Informed Competencies for Human Rights Field Education in Social Work. Journal of Human Rights and Social Work, 6(1), 59-66. Noyori-Corbett, C., Moxley, D.P. (accepted March 2021). The United States Department of State Diplomacy Lab for Supporting MSW Students’ Engagement in Community-Based Refugee Resettlement Research. Journal of Social Work Education.
Pineda-Castillo, S.A., Luo, J., Lee, H., Bohnstedt, B.N., Liu, Y., Lee, C.H. (2021). Effects of Carbon Nanotube Infiltration on a Shape Memory Polymer-based Device for Brain Aneurysm Therapeutics: Design and Characterization of a Joule-Heating Triggering Mechanism. Advanced Engineering Materials, 23(6): 2100322.
Reinert, A., Ebert, D.S. (2021). Humane Design for Inclusion. In Proceedings of the 21st Congress of the International Ergonomics Assocation (IEA) (pp. 307-316)
Reinert, A., Snyder, L.S., Zhao, J., Fox, A.S., Hougen, D.F., Nicholson, C., Ebert, D.S. (2020). Visual Analytics for Decision-Making During Pandemics. Computing in Science & Engineering, 22(6), 48-59.
Riedel, C.P., Cavallo, S.M. and Parsons, D.B., (2021). Mesoscale prediction in the Antarctic using cycled ensemble data assimilation. Monthly Weather Review, 149(2), pp.443-462.
Sharma, Y., Noyori-Corbett, C. (2021). Transnational Human Trafficking and HIV/AIDS among Women in Asia. Social Development Issues.
Shi, T., Jiang, H., & Zheng, B. (2021). C2 MA-Net: Cross-modal Cross-Attention Network for Acute Ischemic Stroke Lesion Segmentation based on CT Perfusion Scans. IEEE Transactions on Biomedical Engineering.
Snyder, L., Reinert, A., Ebert, D. (2021). Panviz 2.0: Integrating AI into visual analytics to adapt to the novel challenges of COVID-19. In Proceedings of the 54th Hawaii International Conference on System Sciences (p. 1457).
Tabbutt, K., Maher, E. J., & Horm, D. (2021). Foundations for Success: A Mixed-Methods Evaluation of a Statewide, Cross-Sector Early Childhood Collaborative. In Child & Youth Care Forum (pp. 1-25).
Springer US. Wang, G., Guo, J., Tang, M., de Queiroz Neto, J.F., Yao, C., Daghistani, A., Karimzadeh, M., Aref, W., Ebert, D.S. (2020). STULL: Unbiased Online Sampling for Visual Exploration of Large Spatiotemporal Data. In 2020 IEEE Conference on Visual Analytics Science and Technology (VAST) (pp. 72-83). IEEE.
Wang, Y. F., Lee, S. K., & Ye, Q. (2021). Opinion leaders in eco-innovation diffusion: Analysis of information networks for waste separation in Shanghai. Resources, Conservation and Recycling, 174, 105822.
Yang, K.W., Chapman, S., Carpenter, N., Hammer, G., McLean, G., Doherty, A., Zheng, B., Chen, Y., Delp, E., Masjedi, A., Crawford, M., Ebert, C., Habib, A., Thompson, A., Weil, C., & Tuinstra, M.R. (2021). Integrating crop growth models with remote sensing for predicting biomass yield of sorghum. in Silico Plants 3(1), diab001.
Zhang, R., Lukasczyk, J., Wang, F., Ebert, D., Shakarian, P. Mack, E., Maciejewski, R. (to appear, 2022). Exploring Geographic Hotspots Using Topological Data Analysis. Transactions on Geographic Information Systems.
Zhao, Z., & Cheng, S. (2021). Capsule networks with non-iterative cluster routing. Neural Networks.

Commitment to an Empowering Environment

The Data Institute for Societal Challenges (DISC) is dedicated to fostering a community that values the contributions, backgrounds, and perspectives of every individual. Recognizing the historical impact of data science and data-enabled research on social, political, and economic systems, we are committed to ensuring that data science is used to improve societal conditions, does not introduce inequities seeks to rectify them, and is trustworthy and reduces bias. We prioritize building collaborative teams that reflect various backgrounds and experiences and encourage broad participation. Our efforts to develop innovative solutions to societal challenges are rooted in creating an environment where everyone feels supported and empowered.