Selective SDS Faculty Research
Articles
2021
A combined finite element and machine learning approach for the prediction of specific cutting forces and maximum tool temperatures in machining Sai Manish Reddy Mekarthy, Maryam Hashemitaheri, and Harish Cherukuri Volume 56, Pages 66-85;
Chen, V. Z., Duran, P., Sauerwald, S., Hitt, M. A., & Van Essen, M. (2021). Multistakeholder Agency: Stakeholder Benefit Alignment and National Institutional Contexts. Journal of Management, In-Press.
Dao, D., and J.-C. Thill. “CrimeScape: Analysis of Socio-spatial Associations of Urban Residential Motor Vehicle Theft,” Social Science Research, 2021, 102618, https://doi.org/10.1016/j.ssresearch.2021.102618
Diaz-Garelli F, Johnson TR, Rahbar MH, Bernstam EV. “Exploring the Hazards of Scaling Up Clinical Data Analyses: A Drug Side Effect Discovery Case Report” AMIA Annu Symp Proc. 2021 May 17;2021:180–9. (Full Paper – 10 pages) (Distinguished paper award at largest health data science conference in US)
Diaz-Garelli F, Strowd R, Ahmed T, Daley S, et al. What Oncologists Want: Identifying Challenges and Preferences on Diagnosis Data Entry to Reduce EHR-Induced Burden and Improve Clinical Data Quality. JCO Clinical Cancer Informatics. 2021;(5):527-540. (Paper linking existing data science work with processes generating them)
Donald J. Jacobs, Victory Tax: A Holistic Income Tax System, Entropy 2021, 23(11), 1492
Fast random algorithms for manifold based optimization in reconstructing 3D chromosomal structures, Duan Chen, Shaoyu Li, Xue Wang, Kelin Xia, Communications in Information and Systems, 21, 1-29
Hong, S., Jiang, J., Jiang, X. and Xiao Z. (2021). Unifying inference for semiparametric regression. The Econometrics Journal 24, 482-501. https://doi.org/10.1093/ectj/utab005. Impact factor: 4.571 (see https://academic.oup.com/ectj)
Hossain MA, Lin Y, Driscoll G, Li J, McMahon A, Matos J, Zhao H, Tsuchimoto D, Nakabeppu Y, Zhao J, Yan S*. 2021. APE2 is a general regulator of the ATR-Chk1 DNA damage response pathway to maintain genome integrity in pancreatic cancer cells. Frontiers in Cell and Developmental Biology. 9: 738502. (PMCID: PMC8593216; PMID: 34796173) DOI: https://doi.org/10.3389/fcell.2021.738502
Hu Y, Yang C, Amorim T, Amorim T, Maqbool M, Lin J, Li C, Fang C, Xue L, Kwart A, Fang H, Yin M, Janocha AJ, Tsuchimoto D, Nakabeppu Y, Jiang X, Mejia-Garcia A, Anwer F, Khouri J, Qi X, Zheng QY, Yu JS, Yan S, LaFramboise T, Anderson KC, Herlitz LC, Munshi NC, Lin J and Zhao J. 2021. Cisplatin-mediated upregulation of APE2 binding to MYH9 provokes mitochondrial fragmentation and acute kidney injury. Cancer Research. 81 (3): 713-723. (PMCID: PMC7869671; PMID: 33288657) DOI: https://doi.org/10.1158/0008-5472.CAN-20-1010
Jun Pang, Angela Xia Liu, Peter N. Golder (2021), “Critics’ Conformity to Consumers in Movie Evaluation,” Journal of the Academy of Marketing Science, forthcoming.
Li X, Wang C, Sheng Y, Zhang J, Wang W, Yin FF, Wu Q , Wu QJ, Ge Y. An Artificial Intelligence-Driven Agent for Real-Time Head-and-Neck IMRT Plan Generation using Conditional Generative Adversarial Network (cGAN). Med Phys. 2021 Feb 12. doi: 10.1002/mp.14770.
Li X, Wu QJ, Wu Q, Wang C, Sheng Y, Wang W, Stephens H, Yin FF, Ge Y. Insights of an AI agent via analysis of prediction errors: a case study of fluence map prediction for radiation therapy planning. Phys Med Biol. 2021 Nov 26;66(23). doi: 10.1088/1361-6560/ac3841.
Moon, S., Kim, M-Y., & Iacobucci, D. (2021). Content analysis of fake consumer reviews by survey-based text categorization. International Journal of Research in Marketing, 38, forthcoming. https://doi.org/10.1016/j.ijresmar.2020.08.001
Nikparvar, B., Rahman, M.M., Hatami, F., and Thill, J.-C. “Spatio-temporal Prediction of the COVID-19 Pandemic in US Counties: Modeling with A Deep LSTM Neural Network,” Scientific Reports
Nikparvar, B., Thill, J.-C. “Machine Learning of Spatial Data.” ISPRS International Journal of Geo-Information, 2021, 10, 600, https://doi.org/10.3390/ijgi10090600
Ryan Wesslen, Karduni, Alireza, Doug Markant, and Wenwen Dou. “Effect of uncertainty visualizations on myopic loss aversion and equity premium puzzle in retirement investment decisions” in IEEE Transactions in Visualization and Computer Graphics, 2021
Sangkil Moon, Moon-Yong Kim, and Dawn Iacobucci (2021), “Content Analysis of Fake Consumer Reviews by Survey-Based Text Categorization,” International Journal of Research in Marketing, 38 (2), 343-364. https://doi.org/10.1016/j.ijresmar.2020.08.001
Sangkil Moon, Nima Jalali, and Sunil Erevelles (2021), “Segmentation of Both Reviewers and Businesses on Social Media,” Journal of Retailing and Consumer Services, 61 (July), 102524. https://doi.org/10.1016/j.jretconser.2021.102524
Scott Tonidandel, Karoline M. Summerville, William A. Gentry, Stephen F. Young, Using structural topic modeling to gain insight into challenges faced by leaders, The Leadership Quarterly, 2021, 101576, ISSN 1048-9843, https://doi.org/10.1016/j.leaqua.2021.101576
Shan, G., Zhou, L., and Zhang, D. (2021). From Conflicts and Confusion to Doubts: Examining Review Inconsistency for Fake Review Detection. Decision Support Systems. 144
Song, J., & Li, B. (2021) Nonlinear and additive principal component analysis for functional data. Journal of Multivariate Analysis, 181. https://doi.org/10.1016/j.jmva.2020.104675
T. Grear, C. Avery, J. Patterson, and D. J. Jacobs, Molecular function recognition by supervised projection pursuit machine learning, Scientific Reports, vol. 11, no. 1, p. 4247, 2021
Yikai Jia, Jiani Li, Chunhao Yuan, Xiang Gao, Weiran Yao, Minwoo Lee, Jun Xu. Data-driven Safety Risk Prediction of Lithium-ion Battery. Advanced Energy Materials, p2003868, 2021
Yu, L., Xiong, J., Zhang, D. (2021) DNCP: An Attention-based Deep Learning Approach Enhanced with Attractiveness and Timeliness of News for Online News Click Prediction. Information & Management. 58(2).
2020
Windett, J. (2020). Amy Coney Barrett is conservative. New data shows us how conservative. The Washington Post. www.washingtonpost.com/politics/2020/10/22/amy-coney-barrett-is-one-most-conservative-appeals-court-justices-40-years-our-new-study-finds/
Chen, V. Z., Montano-Campos, F., & Zadrozny, W. (2020). Causal knowledge extraction from scholarly papers in social sciences. arXiv preprint arXiv:2006.08904. https://arxiv.org/abs/2006.08904
Dash, A., Zhang, D., & Zhou, L. (2020). P2R2: An LCR-based product-feature oriented approach to personalized ranking of online consumer reviews. International Journal of Electronic Commerce
Diaz-Garelli, F., Strowd, R., Lawson, V. L., Mayorga, M. E., Wells, B. J., Lycan, T. W., Topaloglu, U. (2020) Workflow differences affect data accuracy in oncologic EHRs: A first step toward detangling the diagnosis data babel. JCO Clinical Cancer Informatics, 4, 529-538. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7331128
Dorr, B., Bhatia, A., Dalton, A., Mather, B., Hebenstreit, B., Santhanam, S., Cheng, Z., Shaikh, S., Zemel, A., & Strzalkowski, T. (2020). Detecting asks in social engineering attacks: Impact of linguistic and structural knowledge. In Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-20). https://arxiv.org/abs/2002.10931
Gong, Z., Cai, T., Thill, J.-C., Hale, S., Graham, M. (2020). Measuring relative opinion from location-based social media: A case study of the 2016 U.S. presidential election. PLoS ONE, 15(5). https://doi.org/10.1371/journal.pone.0233660
Hashemitaheri, M., Mekarthy, S. M. R., Cherukuri, H. (2020) Prediction of specific cutting forces and maximum tool temperatures in orthogonal machining by Support Vector and Gaussian Process Regression Methods. Procedia Manufacturing, 48, 1000-10008. https://doi.org/10.1016/j.promfg.2020.05.139
Hong, S., Jiang, J., Jiang, X. & Xiao, Z. (2020). Unifying inference for semiparametric regression. The Econometrics Journal, to appear.
Hu, Y., Wang, K., Chen, M., & Hui, S. (2020). Rational herding among retail shoppers: The case of television shopping network. Customer Needs and Solutions, forthcoming. https://www.springerprofessional.de/en/herding-among-retail-shoppers-the-case-of-television-shopping-ne/18328730
Li, Y., Song, L. & Fan, W. (2020) Day-of-week variations and the temporal instability of factors influencing pedestrian injury severity in pedestrian-vehicle crashes: A random parameters logit approach with heterogeneity in means and variances. Analytic Methods in Accident Research, 29. https://doi.org/10.1016/j.amar.2020.100152
Liu, A. X., Li, Y., & Xu, S. X. (2020). Quantifying the unknown: Inferred reviewer personality and review helpfulness. MIS Quarterly, Forthcoming.
Liu, P. & Fan, W. (2020) Exploring injury severity in head-on crashes using latent class clustering analysis and mixed logit model: A case study of north carolina, Accident Analysis and Prevention, 135. https://doi.org/10.1016/j.aap.2019.105388
Radford, B. J. (2020) Seeing the forest and the trees: Detection and cross-document coreference resolution of militarized interstate disputes. Proceedings of the Workshop on Automated Extraction of Socio-political Events. https://arxiv.org/abs/2005.02966
Rahman, M., Thill, J-C., Paul, K. C. (2020) COVID-19 pandemic severity, lockdown regimes, and people’s mobility: Early evidence from 88 countries. Sustainability, 12(21), 9101. https://doi.org/10.3390/su12219101
Safarnejad, L., Xu, Q., Ge, Y., Krishnan, S., Bagarvathi, A., & Chen, S. (2020) Contrasting real and misinformation dissemination network structures on social media during the 2016 Zika epidemic. American Journal of Public Health, 110, S340-347s. https://doi.org/10.2105/AJPH.2020.305854
Santhanam, S., Karduni, A., & Shaikh, S. (2020). Studying the effects of cognitive biases in evaluation of conversational agents. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (CHI ’20). https://arxiv.org/abs/2002.07927
Silver, R. A., Subramaniam, C., Stylianou, A. C. (2020) The impact of portal satisfaction on portal use and health seeking behavior: A structural equation analysis. Journal of Medical Internet Research 22(3). https://www.jmir.org/2020/3/e16260/
Tang, W., & Yang, J. (2020) Agent-based land change modeling of a large watershed: Space-time locations of critical threshold. Journal of Artificial Societies and Social Simulation, 23(1), 15. http://jasss.soc.surrey.ac.uk/23/1/15.html
Xie, W., Ming, C., & Zhu, H. (2020) How to enhance online hotel ad effectiveness based on real-world data: Mobile eye-tracking and machine learning tell. The best paper award in Market Research track of 2020 AMA Winter Conference.
You, H., Guo, J., & Jiang, J., (2020). Inference for the ruin probability in the classical risk model. Computational Statistics and Data Analysis, 144, 106890. https://doi.org/10.1016/j.csda.2019.106890
Zhang, K., McLeod, S., Lee, M., & Xiao, J. (2020). Continuous reinforcement learning to adapt multi-objective optimization online for robot motion. International Journal of Advanced Robotic Systems, 17(2). https://doi.org/10.1177/1729881420911491
2019
Bagavathi, A., Bashiri, P., Reid, S., Phillips, M. & Krishnan, S. (2019). Examining untampered social media: Analyzing cascades of polarized conversations. ASONAM IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, 625–632. https://doi.org/10.1145/3341161.3343695
Cherukuri, H., Perez-Bernabeu, E., Selles, M., & Schmitz, T. (2019). Machining Chatter Prediction Using a Data Learning Model. Journal of Manufacturing and Materials Processing, 3(2), 45. MDPI AG. Retrieved from http://dx.doi.org/10.3390/jmmp3020045
Diaz-Garelli, J.-F., Bernstam, E. V., Lee, M., Hwang, K. O., Rahbar, M. H., & Johnson, T. R. (2019). DataGauge: A practical process for systematically designing and implementing quality assessments of repurposed clinical data. Egems (generating Evidence & Methods to Improve Patient Outcomes), 7(1), 32. http://doi.org/10.5334/egems.286
Fowler, S., Stylianou, A. C., Zhang, D., Reid, S. E., & Mousavi, R. (2019). Predicting violent crime with gang social media postings. 2019 International Conference on Information Systems, Munich, Germany. https://aisel.aisnet.org/icis2019/data_science/data_science/1/
Grandinetti, J. J. (2019). A question of time: HQ trivia and mobile streaming temporality. M/C Journal, 22(6). https://doi.org/10.5204/mcj.1601
Grandinetti, J. J. (2019). Welcome to a new generation of entertainment: Amazon Web Services and the normalization of big data analytics and RFID tracking. Surveillance and Society, 17(1/2), 169-175. https://ojs.library.queensu.ca/index.php/surveillance-and-society/article/view/12919
Guo, X. (2019) Skill-biased technical change again? Estimating the effect of TaskRabbit on local employment in the housekeeping industry. International Conference on Information Systems Proceedings 2019. http://eprints.lse.ac.uk/102955/
Guo, X., Gong, J., Pang, M. S. (2019) Creation or destruction? STEM OPT extension and employment of information technology professionals. International Conference on Information Systems Proceedings 2019. https://aisel.aisnet.org/icis2019/economics_is/economics_is/25/
Hague, D. (2019). Benefits, pitfalls, and potential bias in health care AI. North Carolina Medical Journal, 80(4), 219-223. https://doi.org/10.18043/ncm.80.4.219
Kumar, R. L., & Park, S. (2019). A portfolio approach to supply chain risk management. Decision Sciences, 50(2), 210-244. https://doi.org/10.1111/deci.12332
Lai, J., Zhang, D., Wang, S., Kilic, D., & Zhou, L., (2019), ThumbStroke: A virtual keyboard in support of sight-free and one-handed text entry on touch-screen mobile devices. ACM Transactions on Management Information Systems, 10(3), 1-19. https://dl.acm.org/doi/10.1145/3343858
Moon, S., Kim, M.-Y., & Bergey, P. K. (2019) Estimating deception in consumer reviews based on extreme terms: Comparison analysis of open vs. closed hotel reservation platforms. Journal of Business Research, 102, 83-96. https://doi.org/10.1016/j.jbusres.2019.05.016
Pinyoanuntapong, P., Lee, M., & Wang, P. (2019) Distributed multi-hop traffic engineering via stochastic policy gradient reinforcement learning. IEEE Global Communications Conference (GLOBECOM), 1-6. https://doi.org/10.1109/GLOBECOM38437.2019.9013134
Radford, B. (2019). Automated dictionary generation for political eventcoding. Political Science Research and Methods, 9(1), 157-171. https://doi.org/10.1017/psrm.2019.1
Shalaby, W., Zadrozny, W., & Jin, H. (2019) Beyond word embeddings: Learning entity and concept representations from large scale knowledge bases. Information Retrieval Journal, 22(6), 525-542. https://arxiv.org/abs/1801.00388
Shalaby, W., & Zadrozny, W. (2019) Patent retrieval: A literature review. Knowledge and Information Systems, 61, 631-660. https://doi.org/10.1007/s10115-018-1322-7
Zhang, Y., Zhou, Y., Zhang, D., Song, W. (2019). Improving stroke risk detection using a hybrid feature selection method. Journal of Medical Internet Research, 21(4). https://doi.org/10.2196/12437
2018
Liu, A. X., Steenkamp, J.-B. E. M., & Zhang, J. (2018). Agglomeration as a driver of the volume of electronic word of mouth in the restaurant industry. Journal of Marketing Research, 55(4), 507–523. https://doi.org/10.1509/jmr.16.0182
Park, A., Conway, M., & Chen. A.T. (2018) Examining thematic similarity, difference, and membership in three online mental health communities from Reddit: A text mining and visualization approach. Computers in Human Behavior, 78, 98–112. https://pubmed.ncbi.nlm.nih.gov/29456286/
Tang, W., Zheng, M., Zhao, X., Shi, J., Yang, J., and Trettin, C.C., (2018) Big geospatial data analytics for global mangrove biomass and carbon estimation. Sustainability, 10(2), 472. https://www.mdpi.com/2071-1050/10/2/472
Tonidandel, S., King, E. B., & Cortina, J. M. (2018). Big data methods: Leveraging modern data analytic techniques to build organizational science. Organizational Research Methods, 21, 525-547. https://doi.org/10.1177/1094428116677299
2017
Li, B., & Song, J. (2017), Nonlinear sufficient dimension reduction for functional data. The Annals of Statistics, 45,1059-1095. https://doi.org/10.1214/16-AOS1475
Park, A., & Conway, M. (2017). Longitudinal changes in psychological states in online health community members: Understanding the long-term effects of participating in an online depression community. Journal of medical Internet research, 19(3), e71. https://doi.org/10.2196/jmir.6826
Temizkan, O., Park, S., & Saydam, C. (2017) Software diversity for improved network security: Optimal distribution of software-based shared vulnerabilities. Information Systems Research, 28(4), 828-849. https://pubsonline.informs.org/doi/10.1287/isre.2017.0722
Von Briesen, E., Bacaksizlar, N. G., & Hadzikadic, M. (2017) Modeling genocide at the system and agent levels. Journal of Policy and Complex Systems, 3(2). http://www.ipsonet.org/policy-studies-curriculum-and-courses/modeling-genocide-at-the-system-and-agent-levels/
2016
Tonidandel, S., King, E. B., & Cortina, J. (2016) Big data at work: The data science revolution and organizational psychology. Taylor Francis. https://books.google.com
Zhou, L., Kang, Y., Zhang, D., & Lai, J. (2016) Harmonized authentication based on thumb stroke dynamics on touch-screen mobile phones. Decision Support Systems, 92, 14-24. https://doi.org/10.1016/j.dss.2016.09.007
2014
Su, Z., & Hadzikadic, M. (2014) Applications of complex adaptive systems in portfolio management. Proceedings of the 5th World Congress on Social Simulation, SaoPaolo, Brazil, November 4-7. https://www.researchgate.net