Professional Experience

Education

Additional Faculty Affiliations

Honors & Awards

Research Grants

RESEARCH

• Statistical and machine learning methodology
• Causal explanation vs. predictive analytics
• Statistical strategy: Explain vs. Predict, Information Quality, Behavioral Big Data
• Business analytics

Fields of Applications: Information Systems, Healthcare

Books

1. Shmueli, G. and Polak, J. (2024) Practical Time Series Forecasting with R: A Hands-On Guide, 3rd edition, Axelrod Schnall Publishers, ISBN 978-0997847949.
2. Shmueli, G., Bruce, P.C., Stephens, M., Anandamurthy, M., and Patel, N.R. (2023) Machine Learning for Business Analytics: Concepts, Techniques, and Applications in JMP Pro, 2nd edition, John Wiley & Sons Inc., ISBN 978-1-119-90383-3
3. Shmueli, G., Bruce, P.C., Deokar, K.R., and Patel, N.R. (2023) Machine Learning for Business Analytics: Concepts, Techniques, and Applications in Analytic Solver Data Mining, 4th edition, John Wiley & Sons Inc., ISBN 978-1-119-82983-6
4. Shmueli, G., Bruce, P.C., Gedeck, P., Yahav I., and Patel N.R. (2023) Machine Learning for Business Analytics: Concepts, Techniques, and Applications in R, 2nd edition, John Wiley & Sons Inc., ISBN 978-1-118-83517-2
5. Shmueli, G., Bruce, P.C., Deokar, A.V., and Patel, N.R. (2023) Machine Learning for Business Analytics: Concepts, Techniques, and Applications in RapidMiner, John Wiley & Sons Inc., ISBN 9781119828792.
6. Shmueli, G. Lichtendahl Jr., K. C. and Shin, T. (translator) (2020) Practical Time Series Forecasting with R: A Hands-On Guide, 2nd edition, Korean edition, ISBN 978-89-5972-783-4, Chungram Publishing Co.
7. Shmueli, G., Bruce, P.C., Gedeck, P., and Patel, N.R. (2019) Data Mining for Business Analytics: Concepts, Techniques, and Applications in Python, John Wiley & Sons Inc., ISBN 9781119549840.
8. Shmueli, G., Bruce, P.C., Yahav, I., Patel, N.R., and Lichtendahl, K.C. (2017) Data Mining for Business Analytics: Concepts, Techniques, and Applications in R, John Wiley & Sons Inc., ISBN 978-1118879368.
9. Kenett R.S. and Shmueli, G. (2017), Information Quality: The Potential of Data and Analytics to Generate Knowledge, John Wiley & Sons, Inc., ISBN 9781118874448.
10. Shmueli, G. and Lichtendahl Jr., K. C. (2016) Practical Time Series Forecasting with R: A Hands-On Guide, 2nd edition, Axelrod Schnall Publishers, ISBN 978-0997847918.
11. Shmueli, G. (2016) Practical Time Series Forecasting: A Hands-On Guide, 3rd edition, Axelrod Schnall Publishers, ISBN 978-0991576654 (also Indian edition ISBN 978-0991576661).
12. Shmueli, G., Bruce, P.C., and Patel, N.R. (2016) Data Mining for Business Analytics: Concepts, Techniques, and Applications with XLMiner, 3rd edition, John Wiley & Sons Inc., ISBN 978-1118-729274.
13. Shmueli, G., Bruce, P.C., Stephens, M., and Patel, N.R. (2016) Data Mining for Business Analytics: Concepts, Techniques, and Applications with JMP Pro, John Wiley & Sons Inc., ISBN 978-1-118-87743-2.
14. Shmueli, G. and Lichtendahl Jr., K.C. (2015) Practical Time Series Forecasting with R: A Hands-On Guide, Axelrod Schnall Publishers, ISBN 978-0-9-915-7663-0.
15. Shmueli, G. (2014) Practical Time Series Forecasting: A Hands-On Guide (2nd edition), Indian edition, Axelrod Schnall Publishers, ISBN 978-0-9-915-7660-9.
16. Shmueli, G. (2013), To Publish or To Self-Publish My Textbook?, ASIN B00CJ94OSO [Kindle Edition], Axelrod Schnall Publishers.
17. Hardoon, D. R. and Shmueli, G. (2013), Getting Started with Business Analytics: Insightful Decision-Making, Chapman and Hall/CRC, ISBN 978-1439896532, eBook 978-1439896549.
18. Shmueli, G. and Li H. (translator) (2012) Practical Time Series Forecasting: A Hands-On Guide, Chinese edition, Tsinghua University Press, ISBN 978-7302291121.
19. Shmueli, G. (2011) Practical Time Series Forecasting: A Hands-On Guide, 2nd edition, CreateSpace, ISBN 978-1-4-680-5345-6.
20. Shmueli, G. (2011) Practical Risk Analysis for Project Planning: A Hands-On Guide Using Excel, CreateSpace, ISBN 978-1-466-43064-8.
21. Shmueli, G. (2011) Practical Acceptance Sampling: A Hands-On Guide, 2nd edition, CreateSpace, ISBN 978-1-4-637-8904-6.
22. Shmueli, G. (2011) Practical Time Series Forecasting: A Hands-On Guide, CreateSpace, ISBN 978-1-4-609-7763-7.
23. Shmueli, G. (2011) Practical Acceptance Sampling: A Hands-On Guide, CreateSpace, ISBN 978-1-4-610-4130-6.
24. Shmueli, G., Patel, N. R., and Bruce, P.C., (2010) Data Mining for Business Intelligence: Concepts, Techniques, and Applications in Microsoft Office Excel with XLMiner, 2nd edition,  John Wiley and Sons Inc., ISBN 978-0-470-52682-8.
25. Jank, W. and Shmueli, G. (2010) Modeling Online Auctions. John Wiley and Sons Inc., ISBN 978-0-470-47565-2.
26. Jank, W. and Shmueli, G. (2008) Statistical Methods in eCommerce Research, John Wiley & Sons, ISBN 978-0-470-12012-5.
27. Shmueli, G., Patel, N. R., and Bruce, P., (2008), Data Mining for Business Intelligence: Concepts, Techniques, and Applications in Microsoft Office Excel with XLMiner, Wiley India, ISBN 978-8-126-51758-9.
28. Shmueli, G., Patel, N. R., and Bruce, P., (2006), Data Mining for Business Intelligence: Concepts, Techniques, and Applications in Microsoft Office Excel with XLMiner, John Wiley and Sons Inc., ISBN 0-470-08485-5.

Publications (Peer Reviewed Journals)

1. Shmueli, G. (2025), To Explain, To Predict, or To Describe: Figuring Out the Study Goal [Commentary on “On the uses and abuses of regression models” by Carlin and Moreno-Betancur], Statistics in Medicine. Forthcoming.
2. Tafti, A. and Shmueli, G. (2025), Causal Inference Grounded in Causal Diagrams: Benefits, Limitations, and Opportunities for the IS Field. MIS Quarterly. Forthcoming.
3. Park, S., Tafti, A., and Shmueli, G. (2024), Transporting Causal Effects Across Populations Using Structural Causal Modeling: An Illustration to Work-From-Home Productivity, Information Systems Research, vol 35 issue 2, pp. 686-705.
4. Shmueli, G. and Ray, S. (2024), Reimagining the Journal Editorial Process: An AI-Augmented Versus an AI-Driven Future, Special issue on The  Future  Impact of  AI  on Academic  Journals  and  the Editorial Process, Journal of the Association for Information Systems (JAIS), vol 25 issue 1, article 10, pp. 35-181.
5. Danks, N., Ray, S., and Shmueli, G. (2023), The Composite Overfit Analysis Framework: Assessing the Out-of-sample Generalizability of Construct-based Models Using Predictive Deviance, Deviance Trees, and Unstable Paths, Management Science, vol 70 issue 1, pp. 647-‌669.
6. Greene, T., Shmueli, G., and Ray, S. (2023), Taking the Person Seriously: Ethically-aware IS Research in the Era of Reinforcement Learning-based Personalization, Journal of the Association for Information Systems (JAIS), vol 24 issue 6, 1527-1561.
7. Shmueli, G., and Tafti, A. (2023), How to “Improve” Prediction Using Behavior Modification, International Journal on Forecasting, vol 39 issue 2, pp. 541-555.
8. Shmueli, G., and Tafti, A. (2023), Rejoinder: How to “Improve” Prediction Using Behavior Modification, International Journal on Forecasting, vol 39 issue 2, pp. 566-569.
9. Greene, T., Dhurandhar, A., and Shmueli, G. (2023), Atomist or Holist? A Diagnosis and Vision for More Productive Interdisciplinary AI Ethics Dialogue, Patterns, vol 4 issue 1, 100652.
10. Ashouri, M., Phoa, F. K-H, Chen, C-H, and Shmueli, G. (2023), An Interactive Clustering-based Visualization Tool for Air Quality Data Analysis, Aerosol and Air Quality Research, vol 23 issue 12, pp. 1-18.
11. Greene, T., Shmueli, G., Fell, J., Lin, C.-F., and Liu, H.-W. (2022), Forks Over Knives: Predictive Inconsistency in Criminal Justice Algorithmic Risk Assessment Tools, Journal of the Royal Statistical Society, Series A, vol 182 issue S2, pp. S692-S723.
12. Greene, T., Martens, D., and Shmueli, G. (2022), Barriers to Academic Data Science Research in the New Realm of Algorithmic Behaviour Modification by Digital Platforms, Nature Machine Intelligence, issue 4, pp. 323–330.
13. Ashouri, M., Hyndman, R. J., and Shmueli, G. (2022). Fast forecast reconciliation using linear models. Journal of Computational and Graphical Statistics, vol 31 issue 1, pp. 263-282.
14. Sharma, P., Shmueli, G., Sarstedt, M., Danks, N., and Ray, S. (2021), Prediction-oriented model selection in partial least squares path modeling, Decision Sciences Journal, vol 52 issue 3, pp. 567-607. Awarded DSJ’s “Highest Citation Rate for 2020-2021”.
15. Shmueli, G. (2021). Comment on Breiman's "Two Cultures"(2002): From Two Cultures to Multicultural. Observational Studies, 7(1), 197-201.
16. Tafti, A. R. and Shmueli, G. (2020), Beyond overall treatment effects: Leveraging covariates in randomized experiments guided by causal structure, Information Systems Research, vol 31 issue 4, pp. 1183-1199.
17. Chatla, S. B. and Shmueli, G. (2020), A Tree-based Semi-Varying Coefficient Model for the COM-Poisson Distribution, Journal of Computational and Graphical Statistics, vol 29 issue 4, pp. 827-846.
18. Shmueli, G. (2020), Discussion on "Assessing the goodness of fit of logistic regression models in large samples: A modification of the Hosmer-Lemeshow test" by Giovanni Nattino, Michael L. Pennell, and Stanley Lemeshow, Biometrics, vol. 76 no. 2, pp. 561-563.
19. Ashouri, M., Shmueli, G., and Sin, C. Y. (2019), Tree-based Methods for Clustering Time Series Using Domain-Relevant Attributes, Journal of Business Analytics, vol 2 no 1, pp. 1-23.
20. Shmueli G., Sarstedt, M., Hair, J. F., Cheah, J.H., Ting, H., Vaithilingam, S., and Ringle C.M. (2019), Predictive Model Assessment in PLS-SEM: Guidelines for Using PLSpredict, European Journal of Marketing, vol. 53 no. 11, pp. 2322-2347.
21. Greene, T., Shmueli, G., Ray, S., and Fell, J. (2019), Adjusting to the GDPR: The Impact on Data Scientists and Behavioral Researchers, Big Data, vol 7 no 3, pp. 140-162.
22. Sharma, P., Sarstedt, M., Shmueli, G., and Thiele, K. O. (2019), PLS-Based Model Selection: The Role of Alternative Explanations in Information Systems Research, Journal of the Association for Information Systems (JAIS), vol 20 no 4, Article 4.
23. Ashouri, M., Cai, J. W., Lin, F., and Shmueli, G. (2018), Assessing the Value of an Information System for Developing Predictive Analytics: The Case of Forecasting School-Level Demand in Taiwan, Service Science, vol 10 issue 1, pp. 58-75.
24. Shmueli, G. and Yahav, I. (2018), The Forest or the Trees? Tackling Simpson’s Paradox with Classification Trees, Production and Operations Management, vol 27 no 4, pp. 696-716.
25. Chatla, S., and Shmueli, G. (2018), Efficient Estimation of COM-Poisson Regression and a Generalized Additive Model, Computational Statistics and Data Analysis, vol 121, pp. 71–88.
26. Shmueli, G. (2017), Research Dilemmas With Behavioral Big Data, Big Data, vol 5 issue 2, pp. 98-119.
27. Chatla, S. and Shmueli, G. (2017), An Extensive Examination of Regression Models with a Binary Outcome Variable, Journal of the Association for Information Systems (JAIS), vol 18 no 4, article 1.
28. Zhu, L., Sellers, K. F., Morris, D. S., and Shmueli, G. (2017), Bridging the Gap: A Generalized Stochastic Process For Count Data, The American Statistician, vol 71 no 1, pp. 71-80.
29. Shmueli, G. (2017), Analyzing Behavioral Big Data: Methodological, Practical, Ethical, and Moral Issues, with discussion and rejoinder, Quality Engineering, vol 29 no 1, pp. 57-74 and 88-90.
30. Bapna, R., Ramprasad, J., Shmueli, G. and Umyarov, A. (2016), One-Way Mirrors in Online Dating: A Randomized Field Experiment, Management Science, vol 62 no 11, pp. 3100-3122.
31. Yahav, I., Shmueli, G. and Mani, D. (2016), A Tree-Based Approach for Addressing Self-Selection in Impact Studies with Big Data, MIS Quarterly, vol 40 no 4, pp. 819-848.
32. Kenett R. S. and Shmueli, G. (2016), From Quality to Information Quality in Official Statistics, Journal of Official Statistics, vol 32 no 4, pp. 867-885.
33. Shmueli, G., Ray, S., Velasquez Estrada, J. M. and Chatla, S. B. (2016), The Elephant in the Room: Predictive Performance of PLS Models, Journal of Business Research, vol 69, pp. 4552–4564.
34. Kenett R. S. and Shmueli, G. (2016), Helping Authors and Reviewers Ask the Right Questions: The InfoQ Framework for Reviewing Applied Research, with discussion and rejoinder, Statistical Journal of the International Association for Official Statistics (IAOS), vol 32 issue 1, pp. 11-19 and 33-35.
35. Kenett, R. S. and Shmueli, G. (2015), Clarifying the Terminology that Describes Scientific Reproducibility, Nature Methods, Correspondence, vol 12 no 8, p. 699. 
36. Sur, P., Shmueli, G., Bose, S., and Dubey, P. (2015), Modeling Bimodal Discrete Data Using Conway-Maxwell-Poisson Mixture Models, Journal of Business & Economic Statistics, vol 33 issue 3, pp. 352-365.
37. Yahav, I. and Shmueli, G.  (2014), Outcomes Matter: Estimating Pre-Transplant Survival Rates of Kidney-Transplant Patients Using Simulator-Based Propensity Scores, Annals of Operations Research, vol 216 issue 1, pp. 101-128.
38. Yahav, I. and Shmueli, G. (2014), Directionally-Sensitive Multivariate Control Charts in Practice: Application to Biosurveillance, Quality & Reliability Engineering International, vol 30 issue 2, pp. 159-179.
39. Kenett, R. S. and Shmueli, G. (2014), On Information Quality, with discussion, Journal of the Royal Statistical Society, Series A, vol 177 issue 1, pp. 3-38.
40. Lin, M., Lucas, H., and Shmueli, G. (2013), Too Big To Fail: Larger Samples and the P-Value Problem, Information Systems Research, vol 24 issue 4, pp. 906-917.
41. Sellers, K. F. and Shmueli, G. (2013), Data Dispersion: Now You See It… Now You Don’t, Communications in Statistics: Theory & Methods, vol 32 issue 17, pp. 3134-3147.
42. Shmueli, G. (2013), Wavelet-based Monitoring for Biosurveillance, Axioms, vol 2, pp. 345-370.
43. Sellers, K. F., Borle, S., and Shmueli, G. (2012), The COM-Poisson Model for Count Data: A Survey of Methods and Applications, with discussion, Applied Stochastic Models in Business and Industry, vol 28 issue 2, pp. 104-116.
44. Sellers, K. F., Borle, S., and Shmueli, G. (2012), Rejoinder: The COM-Poisson Model for Count Data: A Survey of Methods and Applications, Applied Stochastic Models in Business and Industry, vol 28 issue 2, pp. 128-129.
45. Yahav, I. and Shmueli, G. (2012), On Generating Multivariate Poisson Data in Management Science Applications, Applied Stochastic Models in Business and Industry, vol 28 issue 1, pp. 91-102.
46. Dass, M., Jank W., and Shmueli, G. (2011), Maximizing Bidder Surplus in Simultaneous Online Art Auctions Via Dynamic Forecasting, International Journal of Forecasting, vol 27 no 4, pp. 1259-1270.
47. Shmueli, G. and Koppius, O. (2011), Predictive Analytics in Information Systems Research, MIS Quarterly, vol 35 no 3, pp. 553-572.
48. Shmueli, G. (2010), To Explain or To Predict?, Statistical Science, vol 25 no 3, pp. 289-310.
49. Jank, W., Shmueli, G., and Zhang, S. (2010),  A Flexible Model for Estimating Price Dynamics in Online Auctions, Journal of the Royal Statistical Society, Series C, vol 59 no 5, pp. 781-804.
50. Zhang, S., Jank, W., and Shmueli, G. (2010), Real-Time Forecasting of Online Auctions via Functional K-Nearest Neighbors, International Journal of Forecasting, vol 26, pp. 666-683.
51. Sellers, K. F. and Shmueli, G. (2010), A Flexible Regression Model for Count Data, Annals of Applied Statistics, vol 4 no 2, pp. 943-961.
52. Shmueli, G., and Burkom, H. S. (2010), Statistical Challenges Facing Early Outbreak Detection in Biosurveillance, Technometrics, vol 52 no 1, pp. 39-51. Featured Article. One of five most-cited articles in 2010-2012.
53. Dillard, B. L. and Shmueli G. (2010), Wavelet-Based Monitoring for Disease Outbreaks and Bioterrorism: Methods and Challenges, InterStat, March #3.
54. Lotze, T. and Shmueli, G. (2009), How does improved forecasting benefit detection? An application to biosurveillance, International Journal of Forecasting, vol 25, pp. 467-483.
55. Lotze, T., Murphy, S. P., and Shmueli, G. (2008), Preparing Biosurveillance Data for Classic Monitoring, Advances in Disease Surveillance, vol. 6.
56. Lotze, T., Murphy, S., & Shmueli, G. (2008). Implementation and comparison of preprocessing methods for biosurveillance data. Advances in Disease Surveillance, 6(1), 1-20.
57. Wang, S., Jank, W., Shmueli, G., and Smith, P. (2008), Modeling Price Dynamics in eBay Auctions Using Principal Differential Analysis, Journal of the American Statistical Association (JASA), vol 103 (483), pp. 1100-1118.
58. Wang, S., Jank W., and Shmueli, G. (2008), Explaining and Forecasting Online Auction Prices and their Dynamics using Functional Data Analysis, Journal of Business and Economic Statistics, vol. 26 no 3, pp. 144-160.
59. Bapna, R.., Jank, W. and Shmueli, G. (2008), Consumer Surplus in Online Auctions, Information Systems Research, vol. 19 no 4, pp. 400 - 416. Best 2008 AIS Publication award.
60. Shmueli, G., Jank, W., and Hyde, V. (2008), Transformations for Semi-Continuous Data, Computational Statistics & Data Analysis, vol 52 no 8, pp. 4000-4020.
61. Rettinger, F., Jank, W., Tutz, G., and Shmueli, G. (2008), Modelling Price Paths in On-Line Auctions: Smoothing Sparse and Unevenly-Sampled Curves using Semiparametric Mixed Models, Journal of The Royal Statistical Society, Series C (Applied Statistics), vol 57 no 2, pp. 127-148.
62. Bapna, R., Jank, W. and Shmueli, G. (2008), Price Formation and its Dynamics in Online Auctions, Decision Support Systems, vol 44, pp. 641-656.
63. Shmueli, G., Russo, R. P., and Jank, W. (2007), The BARISTA: A Model for Bid Arrivals in Online Auctions, Annals of Applied Statistics, vol 1 no 2, pp. 412-441.
64. Burkom, H. S., Murphy, S. P., and Shmueli, G. (2007), Automated Time Series Forecasting for Biosurveillance, Statistics in Medicine, vol 26 no 22, pp. 4202-4218.
65. Jank, W. and Shmueli, G. (2007), Modeling Concurrency of Events in Online Auctions via Spatio-Temporal Semiparametric Models, Journal of Royal Statistical Society, Series C (Applied Statistics), vol 60 no 1, pp. 1-27.
66. Shmueli, G., Jank, W , Aris, A., Plaisant C., and Shneiderman, B. (2006), Exploring Auction Databases through Interactive Visualization, Decision Support Systems, vol. 42 no 3, pp. 1521-1538.
67. Kadane, J. B., Krishnan, R., and Shmueli, G. (2006), A Data Disclosure Policy for Count Data Based on the COM-Poisson Distribution, Management Science, vol 52 no 10, pp. 1610-1617.
68. Hyde, V., Jank W., and Shmueli, G. (2006), Investigating Concurrency in Online Auctions Through Visualization, The American Statistician, vol. 60 no 3, pp. 241-250.
69. Jank W., and Shmueli, G. (2006), Functional Data Analysis in Electronic Commerce Research, Statistical Science, vol 21 no 2, pp. 155-166.
70. Bilker W, Gogolak V, Goldsmith D, Hauben M, Herrera G, Hochberg A, Jolley S, Kulldorff M, Madigan D, Nelson R, Shapiro A, Shmueli G. (2006), Accelerating Statistical Research in Drug Safety, Pharmacoepidemiology and Drug Safety, Letter to Editor, vol 15 no 9, pp. 687-688.
71. Fienberg, S. E., and Shmueli, G. (2006), Comment on A Bayesian Dynamic Model for Influenza Surveillance by Sebastiani, Mandl, Szolovits, Kohane, and Ramoni, Statistics in Medicine, vol 25 no 11, pp. 1821-1822.
72. Boatwright, P. , Borle, S., Kadane, J. B., Minka, T. P., and Shmueli, G. (2006), Conjugate Analysis of the Conway-Maxwell-Poisson Distribution, Bayesian Analysis, vol 1 no 2, pp. 363-374.
73. Borle, S., Boatwright, P., Kadane, J. B., Nunes, J. C., and Shmueli, G. (2005), The Effect of Product Assortment Changes on Customer Retention, Marketing Science,  vol 24 no 4, pp. 616-622.
74. Shmueli, G. and Jank W. (2005), Visualizing Online Auctions, Journal of Computational and Graphical Statistics, vol 14 no 2, pp. 299-319.
75. Fienberg, S. E. and Shmueli, G. (2005), Statistical Issues and Challenges Associated with Rapid Detection of Bio-terrorist Attacks, Statistics in Medicine, vol 24 no 4, pp. 513-529.
76. Shmueli, G., Minka, T. P., Kadane, J. B., Borle, S. and Boatwright, P. (2005), A Useful Distribution for Fitting Discrete Data: Revival of the COM-Poisson, Journal of the Royal Statistical Society, Series C (Applied Statistics), vol 54 no 1, pp. 127-142.
77. Fu, J. C., Shmueli, G., and Chang, Y. M. (2003), A Unified Markov Chain Approach for Computing the Run Length Distribution in Control Charts with Simple or Compound Rules, Statistics & Probability Letters, vol 65 no 4, pp. 457-466.
78. Shmueli, G. (2003), Computing Consecutive-Type Reliabilities Non-Recursively, IEEE Transactions on Reliability, vol 52 no 3, pp. 367-372.
79. Shmueli, G. and Cohen, A. (2003), Run Length Distribution for Control Charts with Runs Rules, Communications in Statistics- Theory & Methods, vol 32 no 2, pp. 475-495.
80. Shmueli, G. (2003), System-Wide Probabilities for Systems with Runs and Scans Rules, Methodology and Computing in Applied Probability, vol 4, pp. 401-419.
81. Goldenberg, A., Shmueli, G., Caruana, R. A., and Fienberg, S. E. (2002), Early Statistical Detection of Anthrax Outbreaks by Tracking Over-the-Counter Medication Sales, Proceedings of the National Academy of Sciences, vol 99 issue 8, pp. 5237-5240.
82. Shmueli, G. and Cohen A. (2000), Run Related Probability Functions Applied to Sampling Inspection, Technometrics, vol 42 no 2, pp. 188-202.
83. Shmueli, G. and Cohen, A. (1999), Analysis and Display of Hierarchical Life-Time Data, The American Statistician, vol 53 no 2, pp. 140-146.                                                                                             

Papers in Conference Proceedings (Peer Reviewed)

1. Schwartz, D. G., Avital, M., Jarvenpaa, S. L., Lyytinen, K., Shmueli, G., and Te’Eni, D (2023), The Future Impact of AI on Academic Journals and the Editorial Process, International Conference on Information Systems (ICIS), Hyderabad, Indian, Dec 2023.
2. Park, S., Tafti, A., and Shmueli, G. (2021), Transporting causal effects across populations: The Example of Telecommuting Productivity, Workshop on Information Systems Economics (WISE) Austin, TX, Dec 2021.
3. Greene, T. and Shmueli, G., (2021), Hidden Sources of Predictive Inconsistency in Criminal Justice Algorithmic Risk Assessment Tools, INFORMS Conference on Information Systems and Technology (CIST), Newport Beach, CA, Oct 2021.
4. Fell, J, Shmueli, G., Greene, T., Wang, J.-C., Ray, S., and Wu, S.-Y. (2021), Seeing Humans in the Data: Ethical Blind Spots of Academic Researchers in the Era of Behavioral Big Data, HICSS-54, online, Jan 2021.
5. Kuan, T.-C., Wu, S.-W., Liao, C.-C., Ashouri, M., Shmueli, G. and Lin, C. (2019), Forecasting Daily Accommodation Occupancy for Supply Preparation by a Sharing Economy Platform, Proceedings of  IEEE 5th International Conference on Big Data Intelligence and Computing (DATACOM), Kaohsiung, Taiwan, Nov 2019. DOI 10.1109/DataCom.2019.00030
6. Lee, H.-H., Mach, P., Shmueli, G., and Yahav, I. (2019), A Data Mining Approach to Surveying Literature on Behavioral Big Data in Operations Management Academic Research, IEEE DataCom 2019, Kaohsiung, Taiwan, Nov 2019.
7. Greene, T., Shmueli, G., and Ray, S. (2018), Adjusting to the GDPR: Impact on Data Scientists and Behavioral Researchers, 2nd INFORMS Workshop on Data Science, Phoenix, AZ, Nov 2018.
8. Ashouri, M., Shmueli, G. and Sin, C.-Y. (2018), Clustering Time Series By Domain-Relevant Features Using Model-Based Trees, Proceedings of the 2018 Data Science, Statistics & Visualization (DSSV), Vienna, Austria, July 2018.
9. Hsu, T.-C., Chen, J.-S., Chen, W.-Z., Chien, Y.-H., Ashouri, M., Wu, S.-W., Lin, C., and Shmueli, G. (2018), A Deep Learning Approach To Bank Direct Marketing. Proceedings of the 2018 INFORMS International Conference, Taipei, Taiwan.
10. Shmueli, G. (2018), Statistical Modeling in 3D: Describing, Explaining and Predicting,  Proceedings of the 11th International Conference of the Thailand Econometric Society (TES2018), Chiang-Mai, Thailand, Jan 2018.
11. Chatla, S. B. and Shmueli, G., (2016), Modeling Big Count Data: An IRLS Framework for CMP Regression and GAM, Proceedings of the 11th INFORMS Workshop on Data Mining and Decision Analytics (DM-DA 2016) C. Iyigun, R. Moghaddess, A. Oztekin, eds.
12. Chatla, S. B. and Shmueli, G., (2016), Selection Bias with Linear Probability Models, INFORMS Conference on Information Systems and Technology (CIST), Nashville, TN.
13. Chatla, S. B. and Shmueli, G., (2016), Selection Bias with Linear Probability Models, Proceedings of the 11th INFORMS Workshop on Data Mining and Decision Analytics (DM-DA 2016) C. Iyigun, R. Moghaddess, A. Oztekin, eds.
14. Agarwal, R., Bapna, R., Ghose, A., Shmueli, G., Slaughter, S., Tambe, P., Goh, K.Y. (2014), Does Growing Demand for Data Science Create New Opportunities for Information Systems?, International Conference on Information Systems (ICIS), Auckland, New Zealand.
15. Shmueli, G. and Chatla, S. (2014), Linear Probability Models in Information Systems Research, European Conference on Information Systems (ECIS), Tel Aviv, Israel. Best Research-In-Progress Nominee.
16. Yahav, I. and Shmueli, G. (2014), Tackling Simpson’s Paradox in Big Data with Classification & Regression Trees, European Conference on Information Systems (ECIS), Tel Aviv, Israel.
17. Umyarov, A., Bapna, R., Ramprasad, J. and Shmueli, G. (2013), One-Way Mirrors and Weak-Signaling in Online Dating: A Randomized Field Experiment, International Conference on Information Systems (ICIS), Milan, Italy. Best Paper Nominee.
18. Bose, S., Shmueli, G., Sur, P., and Dubey, P. (2013), Fitting COM-Poisson Mixtures to Bimodal Count Data, 1st International Conference on Information, Operations Management and Statistics (ICIOMS), Kuala Lumpur, Malaysia. Best Paper Award.
19. Shmueli, G. and Kenett, R. S. (2013), An Information Quality (InfoQ) Framework for Ex-Ante and Ex-Post Evaluation of Empirical Studies, Proceeding of the 3rd International Workshop on Intelligent Data Analysis and Management, Kaohsiung, Taiwan, Springer Proceedings in Complexity, Eds. L Uden, L SL Wang, T-P Hong, H-C Yang and I-H Ting, pp. 1-13.
20. Bapna, R., Ramprasad, J., Shmueli, G. and Umyarov A. (2012), One-Way Mirrors in Online Dating: A Randomized Field Experiment, Workshop on Information Systems & Economics (WISE), Orlando, FL.
21. Shmueli, G. and Soares, C. (2012), Teaching Data Mining in the Business School: Experience from Three Continents, Proceedings of the 29th International Conference on Machine Learning (ICML), Workshop on Teaching Machine Learning, Edinburgh, Scotland UK.
22. Yahav, I. and Shmueli, G. (2010), Predicting Potential Survival Rates of Kidney Transplant Candidates from Databases with Existing Allocation Policies, Proceedings of the 5th INFORMS Workshop on Data Mining and Health Informatics (DM-HI 2010), Ed. D. Sundaramoorthi, M. Lavieri, and H. Zhao, Austin, TX.
23. Sellers K. F., and Shmueli, G. (2009), A Regression Model for Count Data with Observation-Level Dispersion, Proceedings of the 24th International Workshop on Statistical Modelling (IWSM), Ithaca, NY.
24. Lotze, T. and Shmueli, G. (2008), On the relationship between forecast accuracy and detection performance: An application to biosurveillance, Proceedings of the 2008 IEEE Conference on Technologies for Homeland Security, Boston, MA.
25. Lotze, T. and Shmueli, G. (2008), Ensemble Forecasting for Disease Outbreak Detection, Proceedings of the 23rd AAAI Conference on Artificial Intelligence (AAAI-08), Chicago, IL.
26. Shmueli, G. and Koppius, O. (2008), Contrasting Predictive and Explanatory Modeling in IS Research, Conference on Information Systems & Technology (CIST), Best Paper Award, Seattle, WA.
27. Hyde, V., Jank, W, and Shmueli, G. (2007), A Family of Growth Models for Representing the Price Evolution in Online Auctions, 9th Intl Conference on Electronic Commerce, Minneapolis, MN.
28. Buono, P., Plaisant, C., Simeone, A., Aris, A., Shneiderman, B., Shmueli, G., and Jank, W. (2007), Similarity-Based Forecasting with Simultaneous Previews: A River Plot Interface for Time Series Forecasting, 11th Intl Conference on Information Visualization (InfoViz), Zurich, Switzerland.
29. Lotze, T., Shmueli, G., Murphy, S., and Burkom, H. (2006), A Wavelet-based Anomaly Detector for Early Detection of Disease Outbreaks, Proceedings of the 23rd International Conference on Machine Learning (ICML), Workshop on Machine Learning Algorithms for Surveillance and Event Detection, Pittsburgh, PA.
30.  Jank, W., Shmueli, G., and Wang, S. (2006), Dynamic, Real-time Forecasting of Online Auctions via Functional Models, Proceedings of 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.
31. Murphy, S. P., Burkom, H. B., and Shmueli, G. (2006), Data Adaptive Multivariate Control Charts for Routine Health Monitoring, Syndromic Surveillance Conference, in Advances in Disease Surveillance, 1:53.
32. Shmueli, G., Jank W, and Bapna, R. (2005), Sampling eCommerce Data from the Web: Methodological and Practical Issues, 2005 Proceedings of the American Statistical Association, Statistical Computing Section [CD-ROM], Alexandria, VA: American Statistical Association.

Book Chapters (Peer Reviewed)

1. Chatla, S. B. and Shmueli, G. (2025), COM-Poisson Regression Model: Application to Bike sharing in the United States. Chapter in  Data Science for Modeling Managerial and Socioeconomic Problems: Concepts, Techniques, and Applications (ISBN 978-981-97-9059-3) F. Hamid and D. Mukherjee (Eds.), Springer, forthcoming.
2. Greene, T., and Shmueli, G. (2023), Persons and Personalization on Digital Platforms: A Philosophical Perspective. Chapter in Philosophy of Artificial Intelligence and Its Place in Society (pp. 214-270). L. Moutinho, L. Cavique, & E. Bigné (Eds.). IGI Global. https://doi.org/10.4018/978-1-6684-9591-9.ch011
3. Chatla, S. B., Chen, C.-H., and Shmueli, G. (2017), Selected Topics in Statistical Computing. In Encyclopedia with Semantic Computing and Robotic Intelligence, World Scientific, vol 1, no. 1 (doi: 10.1142/S2425038416300135).
4. Yahav, I., Lotze, T. and Shmueli, G. (2011), Algorithm Combination for Improved Detection in Biosurveillance, chapter in Infectious Disease Informatics and Biosurveillance: Research, Systems, and Case Studies (ISBN 978-1-4419-6891-3), Eds. Castillo-Chavez, Chen, Lober, Thurmond & Zeng , Springer.
5. Jank, W. and Shmueli, G. (2010), Forecasting Online Auctions using Dynamic Models, in Frontiers in Artificial Intelligence and Applications, Vol 218 (Data Mining for Business Applications), Editors: Carlos Soares & Rayid Ghani, IOS Press, pp. 137-148.
6. Russo, R. P., Shmueli, G.,  Jank, W., and Shyamalkumar, N. D. (2010), Models For Bid Arrivals and Bidder Arrivals in Online Auctions, Chapter 23 in Methods and Applications of Statistics in Business, Finance and Management Sciences, (ISBN 978-0-470-40510-9 ) Ed. N Balakrishnan. John Wiley & Sons, Newark, NJ, pp. 283-309.
7. Dass, M., Jank, W. and Shmueli, G. (2010), Dynamic Price Forecasting In Simultaneous Online Art Auctions, chapter in Marketing Intelligent Systems using Soft Computing (ISBN 978-3-642-15605-2), Eds. Jorge Casillas and Francisco J. Martínez-López, Springer, pp. 417-445.
8. Lotze, T. Shmueli, G, and Yahav, I. (2010), Simulating and Evaluating Biosurveillance Datasets , chapter in Biosurveillance: Methods & Case Studies, (ISBN 9781439800461), Eds. Kass-Hout T. and Zhang, X., CRC Press.
9. Jank W., and Shmueli, G. (2009), Studying Heterogeneity of Price Evolution in eBay Auctions Via Functional Clustering, in Handbook in Information Systems Series: Business Computing, Vol 3, Eds: Adomavicius and Gupta, Emerald.
10. Jank, W., Shmueli, G., Dass, M., Yahav, I., and Zhang, S. (2008), Statistical Challenges in eCommerce: Modeling Dynamic and Networked Data, Tutorials in Operations Research, INFORMS 2008.
11. Hyde, V., Jank, W., and Shmueli, G. (2008), A Family of Growth Models for Representing the Price Process in Online Auctions, in Statistical Methods in eCommerce Research, Editors: Jank & Shmueli, Wiley, pp. 291-324.
12. Jank, W., Shmueli, G., and Wang, S. (2008), Modeling Price Dynamics in Online Auctions via Regression Trees, in Statistical Methods in eCommerce Research, Editors: Jank & Shmueli, Wiley, pp. 363-381.
13. Russo, R. P., Shmueli, G., and Shyamalkumar, N. D. (2008), Models of Bidder Activity Consistent with Self-Similar Bid Arrivals, in Statistical Methods in eCommerce Research, Editors: Jank & Shmueli, Wiley, pp. 325-339.
14. Jank W., Shmueli G., Plaisant C., and Shneiderman B. (2008), Visualizing Functional Data with an Application to eBay’s Online Auctions. in Handbook on Computational Statistics on Data Visualization, Eds: Chen, Haerdle, and Unwin, Springer Verlag, Heidelberg, ISBN: 3-540-33036-4, pp. 873-898.
15. Shmueli, G. and Jank W. (2008), Modeling Dynamics in Online Auctions: A Modern Statistical Approach, chapter in Economics, Information Systems and Electronic Commerce: Empirical Research, (Eds. Kauffman R and Tallon P), M.E. Sharpe, Armonk, NY, pp. 157-180, ISBN: 978-0-7656-1532-9.
16. Yahav, I. and Shmueli, G. (2007), Algorithm Combination for Improved Performance in Biosurveillance Systems, in Lecture Notes in Computer Science, vol 4506 (“Intelligence and Security Informatics: Biosurveillance”), Proceeding of the Second NSF Workshop, Biosurveillance 2007, pp. 91-102.
17. Shmueli, G., and Fienberg, S. E. (2006), Current and Potential Statistical Methods for Monitoring Multiple Data Streams for Bio-Surveillance, in Statistical Methods in Counter-Terrorism: Game Theory, Modeling, Syndromic Surveillance, and Biometric Authentication, Eds: A Wilson, G Wilson, and D H Olwell, ISBN 0-387-32904-8, Springer.
18. Aris, A., Shneiderman, B., Plaisant, C., Shmueli, G., Jank, W. (2005), Representing Unevenly-Spaced Time Series Data for Visualization and Interactive Exploration, in Lecture Notes in Computer Science, vol 3585, Human-Computer Interaction - INTERACT 2005: IFIP TC13 International Conference, Rome, Italy, September 12-16, 2005.
19. Shmueli, G. and Cohen A. (2000), Algorithms Based on Runs in Statistical Quality Control: Applying Theoretical Results in the New Millennium, in Productivity & Quality Management Frontiers - IX. pp. 354-355.

Editorials

1. Shmueli, G. (2024). Chronicles of a New Journal: Reflections by the Inaugural Editor-in-Chief. INFORMS Journal on Data Science, 3(1), pp. 1-5.
2. Shmueli, G., Colosimo, B. M., Martens, D., Padman, R., Saar-Tsechansky, M., Sheng, O. R. L., Street, W. N., and Tsui K-L (2023), “How Can IJDS Authors, Reviewers, and Editors Use (and Misuse) Generative AI?”, INFORMS Journal on Data Science, 2(1), pp. 1-9.
3. Shmueli, G. (2022). Congratulations, It’s Our Inaugural Issue!. INFORMS Journal on Data Science, 1(1), 1-3.
4. Kenett R. S. and Shmueli, G. (2015), A special issue on: Actual impact and future perspectives on stochastic modelling in business and industry, Applied Stochastic Models in Business and Industry, vol 31 issue 1, pp. 1-2.
5. Jank W., and Shmueli, G. (2006), A Special Issue on Statistical Challenges and Opportunities in Electronic Commerce Research, Statistical Science, vol 21 no 2, pp. 113-115.

Other Publications (Management Magazines, Book Reviews)

1. Micheli, P. and Shmueli, G. (2025). Improving Performance in the World of AI: The Rise of Prediction Products. California Management Review Insights.
2. Shmueli, G., Saha, R., and Gupta, R. (2013), Industry-Academia Partnerships, OR/MS Today (INFORMS Magazine), August 2013 issue, pp. 32-34.
3. Book review: Fundamentals of Clinical Trials (2010) by Friedman et al., 4th edition, in Journal of the American Statistical Association (JASA), Mar 2012, Vol. 107(497).
4. Book review: Business Statistics (2011) by Sharpe, DeVeaux & Velleman, 2nd edition, in The American Statistician, Nov 2011, Vol. 65(4), p. 297.

Technical Reports and Working Papers

1. Feuerriegel, S., Provost, F., and Shmueli, G. (2025) Leveraging AI for Management Decision-Making (Dagstuhl Seminar 24342). In Dagstuhl Reports, Volume 14, Issue 8, pp. 24-35, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025) https://doi.org/10.4230/DagRep.14.8.24
2. Rostami-Tabar, B., Greene, T., Shmueli, G., Hyndman, R. J. (2025) Good intentions, unintended consequences: exploring forecasting harms Working Paper (https://doi.org/10.48550/arXiv.2411.16531)
3. Bobea, M., Shmueli, G., and Kuan, T.-J. (2024). TreeAlert: Detecting Patterns of Forecasting Failures in AI-Enabled Devices. Working Paper (https://ssrn.com/abstract=4896147)
4. Greene, T., Goethals, S., Martens, D., and Shmueli, G. (2023). Monetizing Explainable AI: A Double-edged Sword. Working Paper (https://arxiv.org/abs/2304.06483)
5. Tafti, A. and Shmueli, G. (2022). How SCM Causal Diagrams Differ from PLS-PM and CB-SEM Path Diagrams. Working Paper (https://ssrn.com/abstract=4215924)
6. Park, S., Tafti, A. R., & Shmueli, G. (2021). Transporting Causal Effects Across Populations Using Structural Causal Modeling: The Example of Work-From-Home Productivity. Working Paper (http://ssrn.com/abstract=3968187)
7. Greene, T., Shmueli, G., Fell, J., Lin C.-F., Shope, M. L. & Liu H.-W. (2020). The Hidden Inconsistencies Introduced by Predictive Algorithms in Judicial Decision Making, Working Paper (https://arxiv.org/abs/2012.00289)
8. Greene, T., & Shmueli, G. (2020). Beyond Our Behavior: The GDPR and Humanistic Personalization. Working Paper, (http://arxiv.org/abs/2008.13404)
9. Shmueli, G. (2020). "Improving" prediction of human behavior using behavior modification. Working Paper (http://arxiv.org/abs/2008.12138)
10. Greene, T., and Shmueli, G. (2019). How Personal is Machine Learning Personalization?. Working Paper (https://arxiv.org/abs/1912.07938)
11. Ashouri, M., Hyndman, R.J., and Shmueli, G. (2019) Fast forecast reconciliation using linear models, Working Paper 29/19, Department of Econometrics & Business Statistics, Monash University.
12. Shmueli, G. and Donaldson, M. (2019) Bridging Medical Research and Clinical Work by Evaluating Predictive Ability, Working Paper (http://ssrn.com/abstract=3479569)
13. Shmueli, G. (2019) Lift Up and Act! Classifier Performance in Resource-Constrained Applications,  Working Paper (http://arxiv.org/abs/1906.03374)
14. Tafti, A. R. and Shmueli, G. (2018) Beyond overall treatment effects: Leveraging covariates in randomized experiments guided by causal structure Working Paper (http://ssrn.com/abstract=3331772)
15. Ashouri, M., Shmueli, G., and Sin, C. Y. (2018), Tree-based Methods for Clustering Time Series Using Domain-Relevant Attributes, Working Paper (https://ssrn.com/abstract=3282849)
16. Shmueli, G. and Greene, T. (2018), Analyzing the Impact of GDPR on Data Scientists Using the InfoQ Framework, Working Paper (https://ssrn.com/abstract=3183625)
17. Danks, N., Ray, S., and Shmueli, G. (2017), Evaluating the Predictive Performance of Composites in PLS Path Modeling, Working Paper (https://ssrn.com/abstract=3055222)
18. Shmueli, G. (2017), Beauty is in the Eye of the (Data) Beholder: Behavioral Researchers’ Dilemmas Using Behavioral Big Data, Working Paper (http://ssrn.com/abstract=2840563)
19. Chatla, S. and Shmueli, G. (2016), An Efficient Estimation of Conway-Maxwell Poisson Regression and Additive Model with an Application to Bike Sharing, Working Paper (http://arxiv.org/abs/1610.08244)
20. Shmueli, G. (2016), Analyzing Behavioral Big Data: Methodological, Practical, Ethical, and Moral Issues, Working Paper (http://ssrn.com/abstract=2736189)
21. Shmueli, G., Ray, S., Velasquez Estrada, J. M. and Chatla, S. (2015), The Elephant in the Room: Evaluating the Predictive Performance of Partial Least Squares (PLS) Path Models, Working Paper (http://ssrn.com/abstract=2659233)
22. Kenett, R. S. and Shmueli, G., (2014), From Quality to Information Quality in Official Statistics, Working Paper, Indian School of Business (http://ssrn.com/abstract=2425216)
23. Shmueli, G. and Yahav, I. (2014), The Forest or the Trees? Tackling Simpson's Paradox with Classification and Regression Tree, Working Paper, Indian School of Business (http://ssrn.com/abstract=2392953).
24. Chatla, S. and Shmueli, G. (2013), Linear Probability Models (LPM) and Big Data: The Good, The Bad, and The Ugly, Working Paper, Indian School of Business (http://ssrn.com/abstract=2353841).
25. Sur, P., Shmueli, G., Bose, S., and Dubey, P. (2013), Modeling Bimodal Discrete Data Using Conway-Maxwell-Poisson Mixture Models, Working Paper (http://arxiv.org/abs/1309.0579).
26. Mani, D., Shmueli, G., and Yahav, I. (2013), Impact Assessment in Observational Studies: A Classification and Regression Tree Approach, Working Paper, Indian School of Business (http://ssrn.com/abstract=2276770)
27. Yahav, I. and Shmueli, G. (2011), Outcomes Matter: Estimating Pre-Transplant Survival Rates of Kidney-Transplant Patients Using Simulation-Based Propensity Scores, Working Paper, Robert H. Smith School, University of Maryland  (http://ssrn.com/abstract=1900918)
28. Lin, M., Lucas, H., and Shmueli, G. (2011), Too Big To Fail: Larger Samples and False Discoveries, Working Paper RHS-06-068, Robert H. Smith School, University of Maryland  (http://ssrn.com/abstract=1336700)
29. Dass, M., Jank W., and Shmueli, G. (2010), SOABER: An Innovative Approach to Maximize Bidder Surplus in Simultaneous Online Art Auctions, Working Paper, Robert H. Smith School, University of Maryland (http://ssrn.com/abstract= 1715166).  Published in Economics of Networks eJournal, vol. 2(153), Dec 09, 2010
30. Sellers, K. F. and Shmueli, G. (2010), Predicting Censored Count Data with COM-Poisson Regression, Working Paper RHS-06-129, Robert H. Smith School, University of Maryland (http://ssrn.com/abstract= 1702845).
31. Sellers, K. F. and Shmueli, G. (2010), Data Dispersion: Now You See It… Now You Don’t, Working Paper RHS-06-122, Robert H. Smith School, University of Maryland (http://ssrn.com/abstract= 1612755).
32. Shmueli, G. and Koppius, O. (2010), Predictive Analytics in Information Systems Research, Working Paper RHS-06-127, Robert H. Smith School, University of Maryland (http://ssrn.com/abstract=1606674)
33. Kenett, R. S. and Shmueli, G. (2009), Information Quality, Working Paper RHS-06-100, Robert H. Smith School, University of Maryland  (http://ssrn.com/abstract=1464444)
34. Yahav, I. and Shmueli, G. (2009), On Generating Multivariate Poisson Data in Management Science Applications, Working Paper RHS-06-085, Robert H. Smith School, University of Maryland  (http://ssrn.com/abstract=1457347)
35. Shmueli, G., (2009) To Explain or To Predict?, Working Paper RHS-06-099, Robert H. Smith School, University of Maryland  (http://ssrn.com/abstract=1351252)
36. Shmueli, G. and Koppius, O. (2009), The Challenge of Prediction in IS Research, Working Paper RHS-06-064, Robert H. Smith School, University of Maryland (http://ssrn.com/abstract=1112893)
37. Sellers, K. F., and Shmueli, G. (2008), A Flexible Regression Model for Count Data,  Working Paper RHS-06-061, Robert H. Smith School, University of Maryland (http://ssrn.com/abstract=1127359)
38. Yahav, I. and Shmueli, G. (2007), Evaluating Directionally-Sensitive Multivariate Control Charts with an Application to Biosurveillance, Working Paper RHS-06-059, Robert H. Smith School, University of Maryland (http://ssrn.com/abstract=1119279)
39. Shmueli, G., Lotze, T. and Yahav, I. (2007), Simulating Multivariate Syndromic Time Series and Outbreak Signatures, Working Paper RHS-06-054, Robert H. Smith School, University of Maryland (http://ssrn.com/abstract=990020)
40. Shmueli, G., Jank, W. and Bapna, R. (2007), Measuring Consumer Surplus on eBay: An Empirical Study, Working Paper RHS-06-053, Robert H. Smith School, University of Maryland (http://ssrn.com/abstract=990014).
41. Jank, W., Shmueli, G. , Wang, S., and Smith, P. (2006), Modeling Price Dynamics in Ebay Auctions Using Principal Differential Analysis, Working Paper RHS-06-052, Robert H Smith School, University of Maryland (http://ssrn.com/abstract=990009).
42. Shmueli, G., (2005), Wavelet-Based Monitoring in Modern Biosurveillance, Working Paper RHS-06-002, Robert H Smith School, University of Maryland (http://ssrn.com/abstract=902878).
43. Aris, A., Shneiderman, B., Plaisant, C., Shmueli, G., Jank, W. (2005), Representing Unevenly-Spaced Time Series Data for Visualization and Interactive Exploration, Technical Report HCIL-2005-01, HCIL, University of Maryland.
44. Jank, W. and Shmueli, G. (2005), Modeling Concurrency of Events in Online Auctions via Spatio-Temporal Semiparametric Models Working Paper RHS-06-030, Robert H Smith School, University of Maryland (http://ssrn.com/abstract=918809).
45. Jank, W. and Shmueli, G. (2005), Profiling Price Dynamics in Online Auctions Using Curve Clustering, Working Paper RHS-06-004, Robert H Smith School, University of Maryland (http://ssrn.com/abstract=902893).
46. Bapna, R., Jank, W. and Shmueli, G. (2004), Price Formation and its Dynamics in Online Auctions, Working Paper RHS-06-003, Robert H Smith School, University of Maryland (http://ssrn.com/abstract=902887).
47. Shmueli, G., Russo, R. P., Jank, W. (2004), Modeling Bid Arrivals in Online Auctions, Working Paper RHS-06-001, Robert H Smith School, University of Maryland (http://ssrn.com/abstract=902868).
48. Minka, T. P., Shmueli, G., Kadane, J. B., Borle, S. and Boatwright, P. (2003), Computing with the COM-Poisson Distribution, Technical Report #776, Dept. of Statistics, Carnegie Mellon University.
49. Shmueli, G., Minka, T. P., Kadane, J. B., Borle, S. and Boatwright, P. (2001), Using Computational and Mathematical Methods to Explore a New Distribution: The nu-Poisson, Technical Report #740, Dept. of Statistics, Carnegie Mellon Univ.

Invited Talks, Keynote Addresses, and Plenary Talks

1. How to “Improve” Prediction Using Behavior Modification, LMU Munich School of Management, Aug 2024.
2. How to “Improve” Prediction Using Behavior Modification, Monash Business School, Dept. of Econometrics and Business Statistics Distinguished Speaker series, May 2024.
3. How to “Improve” Prediction Using Behavior Modification, Nanyang Business School, NTU, Singapore, Mar 2024.
4. How to “Improve” Prediction Using Behavior Modification, Indian School of Business, India, Jan 2024.
5. Introducing the INFORMS Journal on Data Science (IJDS): Data + Models + Decisions + Implications, Indian School of Business, Jan 2024.
6. Panel: The Future Impact of AI on Academic Journals and the Editorial Process, 44th International Conference on Information Systems, Hyderabad, December 2023
7. Panel on Governing AI – Managing the Human-machine Dynamic, AI Governance Research Symposium, Singapore, June 2023.
8. The Role of Academia In Digital Economy Research, Tsinghua-HKUST Digital Economy Expert Perspectives (DEEP) seminar series, HKUST, Hong Kong, May 2023.
9. How to “Improve” Prediction Using Behavior Modification, Graduate Institute of Management, Chang Gung University, Taiwan, May 2023.
10. Forks Over Knives: Predictive Inconsistency in Criminal Justice Algorithmic Risk Assessment Tools, Trinity College Dublin, Mar 2023.
11. How to “Improve” Prediction Using Behavior Modification, Analytics Insight/Distinguished Seminar Series, Warwick Business School, University of Warwick, UK, Mar 2023.
12. Forks Over Knives: Predictive Inconsistency in Criminal Justice Algorithmic Risk Assessment Tools, Data Science and Statistics Webinar (DaSSWeb), University of Porto, Dept. of Economics, Feb 2023.
13. How to “Improve” Prediction Using Behavior Modification, Tel Aviv University Coller School of Management, Tel Aviv, Israel, Feb 2023.
14. Leveraging Covariates in Randomized Experiments Guided by Causal Diagrams, Tippie College of Business, University of Iowa, Oct 2022. 
15. How to “Improve” Prediction Using Behavior Modification, University of Antwerp, Belgium, June 2022.
16. How to “Improve” Prediction Using Behavior Modification, Dept of Information and Decision Sciences, University of Chicago at Illinois Business School, May 2022.
17. How to “Improve” Prediction Using Behavior Modification, University of Porto Faculty of Engineering, Portugal, May 2022.
18. How to “Improve” Prediction Using Behavior Modification, Fraunhofer Center for Assistive Information and Communication Solutions, Porto, Portugal, May 2022.
19. How to “Improve” Prediction Using Behavior Modification, Nova School of Business & Economics, Lisbon, Mar 2022.
20. Introducing the INFORMS Journal on Data Science (IJDS): Data + Models + Decisions + Implications, ENBIS “Meet the Editors” Webinar, online, Mar 2022.
21. Panelist, Responsible AI Conference: Privacy, Fairness & Regulation, Israel Data Science Society, Association of Engineers, online, Dec 2021.
22. How to “Improve” Prediction Using Behavior Modification, Trinity College Dublin, Center of Digital Business and Analytics, Dublin, Ireland, Nov 2021.
23. To Explain, To Predict, or To Describe, Trinity Business School, Dublin, Ireland, Nov 2021.
24. Re-purposing Classification & Regression Trees for Causal Research with High-Dimensional Data, Department of Statistics, Purdue University, Nov 2021.
25. How to “Improve” Prediction Using Behavior Modification, Krannert Business School, Purdue University, Nov 2021.
26. Re-purposing Classification & Regression Trees for Causal Research with High-Dimensional Data, Dept of Information and Decision Sciences, University of Chicago at Illinois Business School, Oct 2021.
27. How to “Improve” Prediction Using Behavior Modification, INFORMS Decision Analysis Society Webinar Series, online, Oct 2021.
28. The Language of Statistics (and What’s Lost in Translation), 63rd ISI World Statistics Congress, ISBIS Gosset Lecture, online, July 2021.
29. "Improving" Prediction of Human Behavior Using Behavior Modification, International Symposium on Forecasting (ISF), keynote address, June 2021.
30. Teaching AI for Business Without Programming, 2021 IT Teaching Workshop, online, May 2021.
31. "Improving" Prediction of Human Behavior Using Behavior Modification, Interdisciplinary Research Seminar, College of Technology Management, National Tsing Hua University, Hsinchu, Taiwan, May 2021.
32. "Improving" Prediction of Human Behavior Using Behavior Modification, School of Business, University at Albany – State University of New York, online, April 2021.
33. "Improving" Prediction of Human Behavior Using Behavior Modification, Israel Data Science Society, Association of Engineers, Architects and Graduates in Technological Sciences in Israel (AEAI), online, March 2021.
34. "Improving" Prediction of Human Behavior Using Behavior Modification, Business Analytics Thought Leaders Symposium, Department of Business Analytics, Tippie College of Business, University of Iowa (online), March 2021.
35. "Improving" Prediction of Human Behavior Using Behavior Modification, in Online Seminar Series "Machine Learning NeEDS Mathematical Optimization”, NeEDS – Network of European Data Scientists, March 2021.
36. SITES panel on Empirical Research in Light of Recent Developments with Big Data, Machine Learning and Computational Social Science, panelist, HICSS, online, Jan 2021.
37. The new INFORMS Journal on Data Science (IJDS): An Introduction for Authors, Reviewers, and Readers. Academic Panel: Journals’ Editors, 30th Workshop on Information Technologies and Systems (WITS), online, Dec 2020.
38. The new INFORMS Journal on Data Science (IJDS): An Introduction for Authors, Reviewers, and Readers. Panel Discussion on Editor's Perspective in Publishing Data Science-Focused Papers, INFORMS 2020 Annual Conference, online, Nov 2020.
39. The new INFORMS Journal on Data Science (IJDS): An Introduction for Authors, Reviewers, and Readers. Joint DMDA/DS Editorial Panel Session, INFORMS 2020 Annual Conference, online, Nov 2020.
40. "Improving" Prediction of Human Behavior Using Behavior Modification, ENBIS 2020, plenary talk, Online, Sept 2020.
41. "Improving" Prediction of Human Behavior Using Behavior Modification, ACEMS Public Lecture, Online, Aug 2020.
42. Reinventing the Data Analytics Classroom, Seminar, Taipei University of Science & Technology, Taipei, July 2020.
43. What Do You Know About Data Ethics? Dept of Econometrics & Business Statistics, Monash University, Melbourne, Australia, Dec 2019.
44. Re-purposing Classification & Regression Trees for Causal Research with High-Dimensional Data, Workshop Organised by the Monash Business Analytics Team (WOMBAT), keynote address, Melbourne, Australia, Nov 2019.
45. To Explain, To Predict, or To Describe? The ISBIS 2019 Satellite Conference, keynote address, Kuala Lumpur, Malaysia, Aug 2019.
46. Reinventing the Data Analytics Classroom, International Symposium on University Teaching Innovation, keynote address, National Yunlin University of Science & Technology, Yunlin, Taiwan, May 2019.
47. Behavioral Big Data & Healthcare Research, Women in Data Science Taipei, National Taiwan University, Taipei, Mar 2019
48. Behavioral Big Data Research in Healthcare: Challenges and Opportunities, SRITNE Distinguished Speaker Series, Indian School of Business, India, Feb 2019.
49. Industry-academia Big Data Collaborations in the New Era of Data Regulation, INFORMS 2018 International Meeting, Taipei, June 2018, presented by Travis Greene.
50. A New Tree-Based Method for Clustering Many Time Series, INFORMS 2018 International Meeting, Taipei, June 2018,  presented by Mahsa Ashouri.
51. Evaluating the Predictive Performance of Constructs in PLS Path Modeling, INFORMS 2018 International Meeting, June 2018, presented by Nicholas Danks.
52. Repurposing Trees for Causal Research, Questrom School of Business, Boston University, Oct 2018.
53. Statistical Modeling in 3D: Describing, Explaining, and Predicting, University of Padua, Statistics Department, June 2018.
54. Behavioral Big Data: Why Quality Engineers Should Care, 10th Galilee Quality Conference, keynote address, Karmiel, Israel, May 2018.
55. Information Quality: What have you learned from your data?, 10th Galilee Quality Conference, Karmiel, Israel, May 2018.
56. Behavioral Big Data Research in Healthcare: Challenges and Opportunities, Samuel Neaman Institute for National Policy Research, Technion, Israel, May 2018.
57. Collaborating with Taiwan Startups to Fire Up Big Data & Business Analytics, AppWorks, Taipei, Taiwan, April 2018.
58. Statistical Modeling in 3D: Describing, Explaining, and Predicting, invited talk, 11th International Conference of the Thailand Econometric Society (TES2018), Chiang-Mai University, Thailand, Jan 2018.
59. Repurposing Predictive Tools for Causal Research, Humans + Machines: making decisions and working together, Workshop at University of Antwerp, Belgium, Dec 2017.
60. Research Dilemmas with Behavioral Big Data, UT Dallas, Naveen Jindal School of Management, Dallas, TX, Oct 2017.
61. Researcher Dilemmas using Behavioral Big Data in Healthcare, 12th INFORMS Workshop on Data Mining and Decision Analytics, keynote address, Houston, TX, Oct 2017.
62. A Tree-based Approach for Addressing Self-Selection in Impact Studies with Big Data, 1st INFORMS Workshop on Data Science, invited talk, Houston, TX, Oct 2017.
63. When Prediction Met PLS: What We Learned in the 3 Years of Marriage, keynote address, 9th International Conference on PLS and Related Methods (PLS’17), Macau, June 2017.
64. Prediction-oriented model selection in PLS path modeling, 9th International Conference on PLS and Related Methods (PLS’17), Macau, June 2017.
65. The Piggy in the Middle: The Role of Mediators in PLS Prediction (presented by Nicholas Danks), 9th International Conference on PLS and Related Methods (PLS’17), Macau, June 2017.
66. Behavioral Big Data in Healthcare Research, keynote address, 12th International Conference on Healthcare Information Management, National Chiao Tung University, Taiwan, June 2017.
67. A Tree-based Approach for Addressing Self-Selection in Impact Studies with Big Data, Hong Kong University of Science and Technology (HKUST) Business School, May 2017.
68. Research Using Behavioral Big Data, National Chengchi University, Department of MIS, Taiwan, May 2017.
69. To Explain or To Predict?, keynote address, Discovery Summit Europe, Prague, Czech Republic, March 2017.
70. Research Using Behavioral Big Data, National University of Kaohsiung, Dept. Information Management, Taiwan, Mar 2017.
71. A Tree-Based Approach for Addressing Self-Selection in Impact Studies with Big Data, City University of Hong Kong, College of Business, Jan 2017.
72. To Explain or To Predict?, City University of Hong Kong, College of Business, Jan 2017.
73. Research Using Behavioral Big Data: A Tour and Why Mechanical Engineers Should Care, keynote address, 34th Israeli Conference on Mechanical Engineering, Haifa, Israel, Nov 2016.
74. Linear Probability Models and Big Data: Prediction, Inference, and Selection Bias, INFORMS, Nashville, TN, Nov 2016.
75. To Explain or To Predict, SAS JMP, Cary, North Carolina, Nov 2016.
76. Analytically Speaking Featuring Galit Shmueli, SAS JMP series of Conversations with Thought Leaders, Nov 2016. (www.jmp.com/en_us/events/live-webcasts/analytically-speaking/analytically-speaking-16nov2016/watch.html)
77. Research Using Behavioral Big Data, National Sun Yat-sen University, College of Management, Taiwan, Oct 2016.
78. Research Using Behavioral Big Data, keynote address, 4th Taiwan Summer Workshop on Information Management (TSWIM), Chiayi, Taiwan, July 2016.
79. Research with Behavioral Big Data, keynote address, IEEE BigData Congress, Taipei Satellite Session, Taiwan, May 2016.
80. To Explain or To Predict, The University of Hong Kong, Faculty of Business & Economics, Hong Kong, May 2016.
81. To Explain or To Predict, City University of Hong Kong, Department of Systems Engineering and Engineering Management, Hong Kong, May 2016.
82. To Explain or To Predict, Institute of Software Applications, National Tsing Hua University, Taiwan, Mar 2016.
83. Analyzing Behavioral Big Data: Methodological, Practical, Ethical, and Moral Issues, plenary talk, Stu Hunter Research Conference, Waterloo, Canada, Mar 2016.
84. Big Data: To Explain or To Predict?, University of Toronto Rotman School of Management, Big Data Experts Speakers Series, Toronto, Canada, Mar 2016.
85. Information Quality: A Framework for Evaluating Empirical Studies, Interdisciplinary Statistical Research Unit, Indian Statistical Institute, Kolkata, India, Feb 2016.
86. Information Quality: A Framework for Evaluating Empirical Studies, SRITNE Knowledge Seminar, Indian School of Business, Hyderabad, India, Feb 2016.
87. To Explain or To Predict? [and How Prediction Can Advance Research], Microsoft Research, NYC, Nov 2015.
88. The Forest or the Trees? Tackling Simpson’s Paradox with Classification and Regression Trees, INFORMS Annual Conference, Philadelphia, PA, Nov 2015.
89. A Tree-Based Approach for Addressing Self-Selection in Impact Studies with Big Data, MISQ Special Issue Workshop on Transformational Issues of Big Data and Analytics in Networked Business, Leuven, Belgium, August 2015.
90. To Explain or To Predict? Predictive Analytics in Information Systems Research, keynote address, 3rd Taiwan Summer Workshop on Information Management (TSWIM), Taipei, Taiwan, July 2015.
91. Information Quality: Can Your Data Do the Job?, keynote address, 11th Statistical Challenges in eCommerce Research (SCECR) Symposium, Addis Ababa, Ethiopia, June 2015.
92. Distinguishing Between Prediction and Explanation in PLS, Invited panel “The Future of Partial Least Squares Path Modeling:  Prediction or Explanation?”, 2015 PLS Conference, Seville, Spain, June 2015.
93. To Explain or To Predict?, Institute of Statistics, National Tsing Hua University, Hsinchu, Taiwan, April 2015.
94. To Explain or To Predict?, Institute of Population Health Sciences, National Health Research Institute (NHRI), Miaoli, Taiwan, March 2015.
95. Predicting, Explaining and the Business Analytics Toolkit, keynote address, NASSCOM Big Data & Analytics Summit, Hyderabad, India, June 2014.
96. Too Big To Fail: Large Samples and False Discoveries, Annual Conference of the Israel Statistical Association, Open University, Raanana, Israel, June 2014.
97. Harnessing CART for Causal Modeling,  The Institute of Statistical Science, Academia Sinica, Jan 2014.
98. To Explain or To Predict?, Institute of Service Science, National Tsing Hua University, Hsinchu, Taiwan, Jan 2014.
99. To Explain or To Predict?, Indian Institute of Management, Ahmedabad (IIM-A), India, Dec 2013.
100. To Explain or To Predict?, University of Virginia, Darden School of Business, Charlottesville, VA, Nov 2013.
101. Disrupting with a Vision: The semi-MOOC, Rotterdam School of Management, Erasmus University, Rotterdam, Netherlands, Nov 2013.
102. To Explain or To Predict?, Rotterdam School of Management, Erasmus University, Rotterdam, Netherlands, Nov 2013.
103. To Explain or To Predict?, USC Marshall School of Business, Los Angeles, CA, Oct 2013.
104. Panel on Teaching Data Mining, INFORMS Annual Conference, Minneapolis, MN, Oct 2013.
105. Insights from Teaching Business Analytics to MBAs in a Flipped Style, INFORMS Annual Conference, Minneapolis, MN, Oct 2013.
106. To Explain or To Predict?, The Institute of Statistical Science, Academia Sinica, Taipei, Taiwan, Sept 2013.
107. Flipping and sMOOCing a Business Analytics Course, Annual Convention of the Association of Indian Management Schools (AIMS), Panel: Academic Leadership: The Technology Factor, Mumbai, India, Aug 2013.
108. One-Way Mirrors in Online Dating: A Randomized Field Experiment, Summer Institute 2013 Economics of IT and Digitization Workshop, National Bureau of Economics Research (NBER), Cambridge MA, July 2013. Presented by Akhmed Umyarov.
109. To Explain or To Predict?, Faculty of Engineering, University of Porto, Portugal, June 2013.
110. Data Liberation Through Visualization: Visualizing Cancer in India, 3rd Open Data Camp, Indian School of Business, Hyderabad, India, June 2013.
111. Harnessing CART for Causal Modeling, Indian Statistical Institute, Kolkata, India, June 2013.
112. On Information Quality, Royal Statistical Society Journal Club, online, June 2013.
113. Social Media Data: The Promise and The Challenge, Digital Summit, Indian School of Business, Nov 2012.
114. A Flexible Model for Count Data: The COM-Poisson, Artificial Intelligence Lab, Institut Jozef Stefan, Ljubljana, Slovenia, Sept 2012.
115. The COM-Poisson Model for Count Data: Methods and Applications, Invited ISBIS Session at ENBIS 2012, Ljubljana, Slovenia, Sept 2012.
116. To Explain or To Predict?, talk with discussion, Israel Statistical Association, Tel Aviv, Israel, July 2012.
117. Introduction to Business Analytics, Indian School of Business, June 2012.
118. De-Mystifying Predictive Analytics, keynote address, ReTechCon, conference of the Retailers Association of India, Mumbai, India, May 2012.
119. To Explain or To Predict? The Challenge of Prediction in IS Research, Indian School of Business, Hyderabad, India, Dec 2010.
120. To Explain or To Predict?, University of Texas at Austin, McCombs School of Business, Austin, TX, June 2010.
121. To Explain or To Predict?, The 24th New England Statistics Symposium, Harvard, Cambridge MA, Apr 2010.
122. To Explain or To Predict?, University of Maryland, Statistics Seminar (Math Dept), College Park MD, Apr 2010.
123. To Explain or To Predict?, University of Maryland, Dept of Measurement, Statistics and Evaluation, College Park MD, Apr 2010.
124. To Explain or To Predict?, Georgetown University, Math & Statistics Dept colloquium, Washington DC, Nov 2009.
125. To Explain or To Predict?, Columbia University, Statistics Dept PhD Seminar, NY, Oct 2009.
126. The Challenge of Prediction in IS Research, Tel Aviv University Recanati School of Business Administration, Israel, Aug 2009.
127. To Explain or To Predict? Explanatory Modeling vs. Predictive Modeling in Scientific Research, School of Mathematical and Geospatial Sciences, RMIT, Melbourne, Australia, Feb 2009.
128. To Explain or To Predict? Explanatory Modeling vs. Predictive Modeling in Scientific Research, Melbourne Business School, Melbourne, Australia, Feb 2009.
129. Explanatory Modeling vs. Predictive Modeling in Scientific Research, Econometrics & Business Statistics Group, Monash University, Melbourne, Australia, Jan 2009.
130. Explanatory vs. Predictive Modeling in Scientific Research, Dept of Statistics, University of Canterbury, Christchurch, New Zealand, Dec 2008.
131. Explanatory vs. Predictive Modeling in Scientific Research, Dept of Statistics, Auckland University, Auckland, New Zealand, Nov 2008.
132. A Flexible Regression Model for Count Data, Bayesian Interdisciplinary Research Unit, Indian Statistical Institute (ISI), Kolkata, India, Nov 2008.
133. Predicting Delays in The Operating Room, INFORMS, Washington DC, Oct 2008.
134. Measuring the Effect of Improved Forecasting on Detection: An Application to Biosurveillance, INFORMS, Washington DC, Oct 2008.
135. Directionally-Sensitive Multivariate Control Charts with an Application to Biosurveillance, JSM, Denver, CO, Aug 2008.
136. Statistical Challenges in Biosurveillance, Spring Research Conference, Special Technometrics Session, Atlanta, GA, May 2008.
137. Explanatory vs. Predictive Modeling in Scientific Research, Statistics Workshop, Dept. of Mathematical Sciences, United States Military Academy at West Point, NY, April 2008.
138. Explanatory vs. Predictive Modeling in Scientific Research, Statistics Week Speaker, ASU, Phoenix, AZ, Mar 2008.
139. Statistical Challenges in Biosurveillance, Statistics Week Speaker, ASU, Phoenix, AZ, Mar 2008.
140. Explanatory vs. Predictive Modeling in Scientific Research, Technion- Israel Institute of Technology, Haifa, Israel, Dec 2007.
141. Explanatory vs. Predictive Modeling in Scientific Research, Statistics Department, Tel Aviv University, Israel, Dec 2007
142. A Family of Growth Models for Representing the Price Evolution in Online Auctions, INFORMS, Seattle Nov 2007.
143. Automated Time Series Forecasting for Biosurveillance, Intl Symposium on Forecasting, NY, June 2007.
144. Automated Time Series Forecasting for Biosurveillance, Biosurveillance and Anomaly Detection session, Intl Biometic Society ENAR meeting, Atlanta, GA, Mar 2007.
145. Plenary speaker, Attacking Biosurveillance Challenges with Statistical Weapons, U.S. Army Conference on Applied Statistics, Durham, NC, Oct 2006.
146. Quantifying Bid Shading in Online Auctions via A Functional Approach, INFORMS 2006, Pittsburgh, PA, Nov 2006.
147. Time Series Forecasting for Biosurveillance, INFORMS 2006, Pittsburgh, PA, Nov 2006.
148. A Wavelet-based Anomaly Detector for Early Detection of Disease Outbreaks, ICML workshop on Machine Learning Algorithms for Surveillance and Event Detection, Pittsburgh, PA, June 2006.
149. The BARISTA: A Model for Bid Arrivals in Online Auctions, International Workshop on Applied Probability, Storrs, CT, May 2006.
150. Fundamentals of Statistical Monitoring in Biosurveillance, Johns Hopkins School of Medicine, Health Sciences Informatics, Baltimore, MD, May 2006.
151. Wavelet-Based Monitoring for Bio-Surveillance, Columbia Business School, Dept. of Decision Risk & Operations, NY, Mar 2006.
152. Wavelet-Based Monitoring for Bio-Surveillance, Carnegie Mellon University, Heinz School of Public Policy, Pittsburgh, PA, Mar 2006.
153. Wavelet-Based Monitoring for Bio-Surveillance, Technion – Israel Institute of Technology, Faculty of Industrial Engineering & Management, Haifa, Israel, Jan 2006.
154. A Functional Data Analytic Approach to Empirical eCommerce Research, NIST, Statistical Engineering Division, Gaithersburg, MD, June 2006.
155. A Functional Data Analytic Approach to Empirical eCommerce Research, Keynote address, First National Seminar of Israel Statistical Association, Tel Aviv, Israel, January, 2006.
156. A Functional Data Analytic Approach to Empirical eCommerce Research, NYU, Stern School of Business, NY, Oct 2005.
157. Panel on Inference from Internet Auction Data, FTC Roundtable on The Economics of Internet Auctions, Washington, DC, Oct 2005.  
158. Panel on Syndromic Surveillance, SAMSI Program on National Defense and Homeland Security Workshop, Durham, NC, Sept 2005.
159. Current & Potential Methods for Anomaly Detection in Modern Time Series: The Case of Biosurveillance, Data Mining Methods for Anomaly Detection, KDD Workshop, Chicago, IL, Aug 2005.
160. Sampling eCommerce Data from the Web: Methodological and Practical Issues, JSM, Minneapolis, MN, Aug 2005.
161. Invited panel on National Systems for Biosurveillance, JSM, Minneapolis, Aug 2005.
162. Wavelet-based Monitoring for Biosurveillance, 12th Annual Spring Research Conference on Statistics in Industry & Technology, Utah, June 2005.
163. Profiling Price Dynamics in Online Auctions Using Curve Clustering, Statistical Challenges in eCommerce: 1st Interdisciplinary Symposium between Statistics, IS, and other Fields,  Maryland, May 2005.
164. Modeling the Dynamics of Online Auctions Using a Functional Data Analytic (FDA) Approach, Intl Sri Lankan Statistical Conference, Sri Lanka, Dec 2004.
165. Modeling Consumer Surplus in Online Auctions, INFORMS, Denver, Oct 2004.
166. Detecting Bio-Terrorist Attacks By Monitoring Multiple Streams of Data, Symposium on Machine Learning for Anomaly Detection, Stanford, May 2004.
167. A Useful Model for Count Data: The COM-Poisson, The International Workshop of Applied Probability (IWAP 2004), Piraeus, Greece, Mar 2004.
168. Modeling Count Data with the COM-Poisson, 5th International Conference on E-Commerce, Workshop on Revolutionary Methods in eCommerce, Pittsburgh, PA, Sept 2003.
169. Modeling Discrete Data with the (nu)COM-Poisson, EURO/INFORMS, Istanbul, Turkey, July 2003.
170. Statistical Issues and Challenges Associated with Rapid Detection of Bio-Terrorist Attacks, DIMACS Tutorial On Statistical And Other Health Surveillance Methods, Rutgers University, NJ, June 2003.
171. Real-time Monitoring of Daily Sales Using Wavelets, The 2003 Quality & Productivity Research Conference, IBM Research Center, NY, May 2003.
172. Rapid Detection of Bio-Terrorist Attacks, The 9th Biennial CDC/ATSDR Symposium on Statistical Methods, Atlanta, GA, Jan 2003.
173. Early Statistical Detection of Bio-Terrorism Attacks by Tracking OTC Medication, The 2002 Quality & Productivity Research Conference, Tempe, AZ, June 2002.
174. A Method for Computing Runs- and Scans-Related Probabilities, University of British Columbia, Dept of Statistics, Vancouver, CA, Jan 2002.
175. A Method for Computing Run-Related Probabilities: Theory & Applications, The Second Statistics Workshop, Winnipeg, Canada, July 2001.
176. Run-Related Probability Functions Applied to Quality Control, Wharton School of Business, Dept. of Statistics, Philadelphia, PA, Feb 2000.
177. Run-Related Probability Functions Applied to Quality Control, Carnegie Mellon University, Dept. of Statistics, Pittsburgh, PA, Feb 2000.
178. Run-Related Probability Functions Applied to Quality Control, Rutgers University, Dept of Statistics, NJ, Feb 2000.
179. Run-Related Probability Functions Applied to Quality Control, Bell Labs, Statistics & Data Mining Research, NJ, Feb 2000.
180. Run-Related Probability Functions Applied to Quality Control, IBM Research Center, NY, Jan 2000.
181. Run-Related Probability Functions Applied to Quality Control, Tel-Aviv University, Dept of Statistics, Tel Aviv, Israel, May 1999.

Contributed Conference Presentations

1. When Will My Model Fail? A Method for Detecting Patterns of Forecasting Failures, the 44th International Symposium on Forecasting, Dijon, France, July 2024 presented by Matthew Bobea.
2. The Platformization of Explainable AI: Exploring Ethical Risks via Simulation, 19th Statistical Challenges in eCommerce Research (SCECR) Symposium, Lisbon, Portugal, June 2024, presented by Travis Greene
3. TreeAlert: Detecting Patterns of Forecasting Failures in AI-Enabled Devices, 19th Statistical Challenges in eCommerce Research (SCECR) Symposium, Lisbon, Portugal, June 2024, presented by Matthew Bobea
4. TreeAlert: Detecting Patterns of Forecasting Failures in AI-Enabled Devices, 25th International Conference on Electronic Commerce, Seoul, South Korea, May 2024, presented by Matthew Bobea
5. Taking the Person Seriously: A Proposal for Ethically-aware Research in the Era of Reinforcement Learning-based Personalization, 18th Statistical Challenges in eCommerce Research (SCECR) Symposium, Madrid, Spain, June 2022, presented by Travis Greene
6. Hidden Sources of Predictive Inconsistency in Criminal Justice Algorithmic Risk Assessment Tools, INFORMS Conference on Information Systems and Technology (CIST), Newport Beach, CA, Oct 2021, presented by Travis Greene
7. Seeing Humans in the Data: Ethical Blind Spots of Academic Researchers in the Era of Behavioral Big Data,  HICSS-54, online, Jan 2021, presented by Jan Fell
8. Serving the Digital Person: The GDPR and Humanistic Personalization, International Conference on Service Science & Innovation, Hsinchu, Taiwan, Oct 2020, presented by Travis Greene
9. Beyond Our Behavior: The GDPR and Humanistic Personalized Recommendation, 3rd FAccTRec Workshop: Responsible Recommendation, Sept 2020, online, presented by Travis Greene
10. Wrestling Prediction Error: Better Predictions or Better "Actuals"?, 16th Statistical Challenges in eCommerce Research (SCECR) Symposium, Online, June 2020
11. DAGifying a Structural Equations Model: Advantages and Challenges, 16th Statistical Challenges in eCommerce Research (SCECR) Symposium, Online, June 2020 (with Ali Tafti and Soumya Ray)
12. Data Ethics Perceptions and Knowledge of Academic Researchers in the Era of Behavioral Big Data, 7th Taiwan Summer Workshop on Information Management (TSWIM), Taoyuan, Taiwan, July 2019, presented by Jan Fell
13. A Fast and Elegant Method for Forecast Reconciliation using Linear Forecasting Models, 15th Statistical Challenges in eCommerce Research (SCECR) Symposium, Hong Kong, June 2019, presented by Mahsa Ashouri
14. Beyond Overall Treatment Effects: Leveraging covariates in randomized experiments guided by causal structure, 2019 Winter Conference on Business Analytics, Snowbird, Utah, Mar 2019, presented by Ali Tafti
15. Adjusting to the GDPR: Impact on Data Scientists and Behavioral Researchers, 2nd INFORMS Workshop on Data Science, Phoenix, AZ, Nov 2018, presented by Travis Greene
16. Controlling or Losing Control? Conditioning on Covariates in Randomized Experiments Guided by Causal Structure, 2018 Conference on Digital Experimentation (CODE@MIT), Cambridge, MA, Oct 2018, poster, with Ali Tafti
17. Clustering Time Series By Domain-Relevant Features Using Model-Based Trees, Data Science, Statistics and Visualisation (DSSV 2018), Vienna, Austria, July 2018, presented by Mahsa Ashouri
18. Interpretable Clustering of Large Collection of Time Series Using Model-based Trees, 6th Taiwan Summer Workshop on Information Management (TSWIM), Hsinchu, Taiwan, June 2018, presented by Mahsa Ashouri
19. Controlling or losing control? The dangers of subgroup analysis in randomized experiments, 14th Statistical Challenges in eCommerce Research (SCECR) Symposium, Rotterdam, Netherlands, June 2018, presented by Ali Tafti
20. Using predictive modeling for identifying heterogeneity in causal research, 14th Statistical Challenges in eCommerce Research (SCECR) Symposium, Rotterdam, Netherlands, June 2018, presented by Otto Koppius
21. Confidence Sets for Split Points in Model-based Regression Trees, 2017 INFORMS (talk) and 1st INFORMS Workshop on Data Science (poster), Houston, TX, Oct 2017, presented by Suneel Chatla
22. A New Tree-based Method for Clustering Time Series, 13th Statistical Challenges in eCommerce Research (SCECR) Symposium, Ho Chi Minh City, Vietnam, June 2017, presented by Mahsa Ashouri.
23. Do your friends make you learn better? The effect of social signals on online learning: A randomized field experiment on MOOCs 13th Statistical Challenges in eCommerce Research (SCECR) Symposium, Ho Chi Minh City, Vietnam, June 2017, presented by Tonny (Meng-Lun) Kuo.
24. Selection Bias with Linear Probability Models (LPM), Conference on Information Systems and Technology (CIST), Nashville, TN, Nov 2016. (Poster)
25. Modeling Big Count Data: An IRLS Framework for CMP Regression and GAM, INFORMS Workshop on Data Mining and Decision Analytics, Nashville, TN, Nov 2016. Presented by Suneel Chatla.
26. Flexible Estimation of Conway-Maxwell Poisson Distribution, INFORMS, Nashville, TN, Nov 2016. Presented by Suneel Chatla.
27. Selection Bias with Linear Probability Models, INFORMS Workshop on Data Mining and Decision Analytics, Nashville, TN, Nov 2016. Presented by Suneel Chatla.
28. Statistical and Conceptual Challenges in Evaluating the Predictive Performance of PLS Models, 12th Statistical Challenges in eCommerce Research (SCECR) Symposium, Naxos, Greece, June 2016.
29. Predictive Model Selection in Partial Least Squares Path Modeling, 11th Statistical Challenges in eCommerce Research (SCECR) Symposium, Addis Ababa, Ethiopia, June 2015.
30. Predictive Model Selection in Partial Least Squares Path Modeling, PLS Conference, Seville, Spain, June 2015.
31. Linear Probability Models and Big Data: Kosher or Not?  10th Statistical Challenges in eCommerce Research (SCECR) Symposium, Tel Aviv, Israel, June 2014.
32. Linear Probability Models in Information Systems Research, 22nd European Conference on Information Systems (ECIS), Tel Aviv, Israel, June 2014.
33. The Forest or the Trees? Tackling Simpson's Paradox in Big Data Using Trees, 10th Statistical Challenges in eCommerce Research (SCECR) Symposium, Tel Aviv, Israel, June 2014.
34. Tackling Simpson’s Paradox in Big Data Using Classification and Regression Trees, 22nd European Conference on Information Systems (ECIS), Tel Aviv, Israel, June 2014.
35. Linear Probability Models (LPM) and Big Data: An Extensive Evaluation, International Symposium on Information Systems (ISIS), Manvar, India, Jan 2014.
36. Linear Probability Models and Big data, Statistics 2013 International Conference, C. R. Rao Advanced Institute of Mathematics, Statistics and  Computer Science, Hyderabad, India, Dec 2013. Presented by Suneel Chatla.
37. Impact Assessment in Observational Studies: A Classification and Regression Tree Approach, Conference on Information Systems and Technology (CIST), Minneapolis, MN, October 2013.
38. One-Way Mirrors in Online Dating: A Randomized Field Experiment, Conference on Information Systems and Technology (CIST), Minneapolis, MN, October 2013, Presented by Akhmed Umyarov.
39. An Information Quality (InfoQ) Framework for Ex-Ante and Ex-Post Evaluation of Empirical Studies, 3rd International Workshop on Intelligent Data Analysis and Management, (IDAM) Kaohsiung, Taiwan, Sept 2013.
40. Tree-Based Matching for Addressing Self-Selection in Impact Studies: Application to eGov in India, 9th Statistical Challenges in eCommerce Research (SCECR) Symposium, Lisbon, Portugal, June 2013.
41. One-Way Mirrors in Online Dating: A Randomized Field Experiment, 9th Statistical Challenges in eCommerce Research (SCECR) Symposium, Lisbon, Portugal, June 2013. Presented by Akhmed Umyarov.
42. One-Way Mirrors in Online Dating: A Randomized Field Experiment, Winter Conference on Business Intelligence, Utah, Feb-Mar 2013. Presented by Jui Ramprasad.
43. Tree-Based Matching for Addressing Self-Selection in Impact Studies: Application to eGov in India, International Symposium on Information Systems (ISIS), Goa, India, Jan 2013.
44. One-Way Mirrors in Online Dating: A Randomized Field Experiment, Workshop on Information Systems & Economics (WISE), Orlando, FL, Dec 2012. Presented by Ravi Bapna.
45. To Explain or To Predict?, 8th World Conference in Probability & Statistics, Istanbul, Turkey, July 2012.
46. Inference with Big Data, 8th Statistical Challenges in eCommerce Research (SCECR) Symposium, Montreal, Canada, June 2012.
47. Tree Matching Solution for Self-Selection in Impact Surveys, 8th Statistical Challenges in eCommerce Research (SCECR) Symposium, Montreal, Canada, June 2012.
48. Data Liberation Via Visualization: Migration in India, 2nd Open Data Camp, Hyderabad, India, June 2012.
49. Teaching Data Mining in the Business School: Experience from Three Continents, 29th International Conference on Machine Learning (ICML), Workshop on Teaching Machine Learning, Edinburgh, Scotland, June 2012 - presented by Carlos Soares.
50. Testing Theories with Large Samples: A Superpower Approach, International Symposium on Information Systems (ISIS), Hyderabad India, Dec 2010.
51. What is Predictive About Partial Least Squares? 6th Statistical Challenges in eCommerce Research  (SCECR) Symposium, Austin TX, June 2010.
52. Ensemble Forecasting for Disease Outbreak Detection, 23rd AAAI Conference on Artificial Intelligence, Chicago IL, July 2008 (short paper, acceptance rate: 25%) – presented by Thomas Lotze.
53. Contrasting Explanatory and Predictive Modeling in IS Research, INFORMS Conference on Information Systems and Technology (CIST), Seattle, Nov 2007.
54. Explanatory vs. Predictive Models in eCommerce Research, 1st Symposium on Information Systems, Hyderabad (ISB), India, Dec 2006.
55. An Evaluation of Wavelet-Based Techniques for Prediction and Anomaly Detection in Syndromic Data, Syndromic Surveillance Conference, Baltimore, MD,  Oct 2006.
56. Preparing Biosurveillance Data for Classic Monitoring, Syndromic Surveillance Conference, Baltimore, MD,  Oct 2006.
57. Dynamic, Real-Time Forecasting of Online Auctions using Functional Models, KDD, Philadelphia, PA, Aug 2006.
58. Quantifying Bid Shading in Online Auctions via a Functional Approach, 2nd Statistical Challenges in eCommerce Research Symposium, May 2006.
59. Explanatory vs. Predictive Modeling in Electronic Commerce (panel), 2nd Statistical Challenges in eCommerce Research (SCECR) Symposium, May 2006.
60. Wavelet-based Monitoring for Biosurveillance (poster), A Conference on Nonparametric Inference and Probability with Applications to Science, University of Michigan, Ann Arbor, Sept 2005.
61. Wavelet-based Monitoring Methods for the Rapid Detection of Bioterrorist Attacks, The 10th Biennial CDC/ATSDR Symposium on Statistical Methods, Bethesda, MD, Mar 2005.
62. Current and Potential Statistical Methods for Bio-Surveillance, Workshop on Statistics and Counterterrorism, NY, Nov 2004.
63. Computing Reliabilities of Large Consecutive-Type Systems, Conference On Mathematical Methods In Reliability (MMR2004), Santa Fe, NM, June 2004.
64. Dynamic Profiling of Online Auctions Using Curve Clustering (poster), University of Florida 6th Annual Winter Workshop on Data Mining, Statistical Learning, and Bioinformatics, Gainesville, FL, Jan 2004.
65. System-Wide Probabilities for Systems with Runs and Scans Rules, The International Workshop of Applied Probability (IWAP 2002), Caracas, Venezuela, Jan 2002.
66. Early Statistical Detection of Bio-Terrorism Attacks by Tracking OTC Medication Sales, The Haifa Winter Workshop on Computer Science and Statistics (CsStat 2001), Haifa, Israel, Dec 2001.
67. Observations from Two Collaborations, The Second Young Statisticians’ Meeting, sponsored by Indigo - a Hewlett Packard company, Rehovot, Israel, Aug 2001.
68. Statistics in Industry in the New Millennium: Using Web Applications to Bridge the Academia-Industry Gap, The 2001 Quality & Productivity Research Conference, Austin, Texas, May 2001.
69. Systems with Multi-State Components Compared to Control Charts with Supplemental Runs-Rules, The Second International Conference On Mathematical Methods In Reliability (MMR2000), Bordeaux, France, July 2000.
70. Teaching Industrial Statistics with Excel, presented at The First Young Statisticians’ Meeting, sponsored by Indigo - a Hewlett Packard company, Rehovot, Israel, Dec 1999.
71. Run Related Probability Functions Applied to Quality Control, The First International Symposium on Industrial Statistics, Linkoping, Sweden, Aug 1999.
72. Deriving k-Order Probability Functions from their Generating Functions, Prague Stochastics '98, Prague, Czech Republic, Aug 1998.
73. Graphical Methods for the Presentation of Promotion Data, The Annual Conference of the Israeli Statistical Association, Jerusalem, Israel, May 1996.

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TEACHING

Courses at NTHU

FutureLearn MOOC “Business Analytics Using Forecasting” (6-week course) runs:

Courses at ISB

Courses at UMD

Seminar-type courses at UMD (3-credits)

Workshops (half-day to multi-day)

1. Predictive Inconsistency in Criminal Justice Algorithmic Risk Assessment Tools, Monash Business School, PhD Workshop, Dept. of Econometrics & Business Statistics, Melbourne, Australia, May 7, 2024.
2. Information Quality, 10th Galilee Quality Conference, Carmiel, Israel, May 24, 2018
3. Data Exploration and Visualization, INFORMS Continuing Education program, Redwood City, CA, Sept 30-Oct 1 2013
4. Data Exploration and Visualization, INFORMS Continuing Education program, Minneapolis, MN, Oct 3-4, 2013
5. Visual Analytics pre-conference workshop, Open Data Camp, Indian School of Business, India, June 21, 2013
6. Effective Data Presentation, Rigsum Institute of IT & Management, Bhutan, Mar 28-29, 2013
7. Intelligent Business Through Big Data, Equinox Event, Indian School of Business, India, Oct 20, 2012
8. To Explain or To Predict?, European Network of Business & Industrial Statistics (ENBIS) Annual Conference, Ljubljana, Slovenia, Sept 16, 2012
9. Data Visualization Workshop, Statistical Challenges in E-Commerce Research (SCECR) Annual Conference, Montreal, Canada, June 29, 2012
10. Effective Data Presentation, Rigsum Institute of IT & Management, Bhutan, July 26-27, 2012
11. Effective Data Presentation, Rigsum Institute of IT & Management, Bhutan, May 10-11, 2012
12. Effective Data Presentation, Royal Institute of Health Sciences, Bhutan, April 3, 2012
13. Visual Analytics, Solstice Event, Indian School of Business, India, Dec 2011
14. Risk Analysis for Project Planning, Rigsum Institute of IT & Management, Bhutan, Nov 21-22, 2011
15. Decision Making Using Microsoft Excel, Rigsum Institute of IT & Management, Bhutan, June 1-3, 2011
16. Decision Making Using Microsoft Excel, Rigsum Institute of IT & Management, Bhutan, June 20-22, 2010
17. Decision Making Using Microsoft Excel, Rigsum Institute of IT & Management, Bhutan, , Apr 29-May 1, 2009
18. Decision Making Using Microsoft Excel, Rigsum Institute of IT & Management, Bhutan, Mar 12-14, 2009
19. Decision Making Using Microsoft Excel, Rigsum Institute of IT & Management, Bhutan, Nov 20-21, 2008

SERVICE

Data Science, Business Analytics, Statistics, and Information Systems Professions

Journal Editor (EIC, DE, SE, Guest Editor, Board Member)

Associate Editor                

Reviewer (alphabetical)                             

Books: John Wiley & Sons, Inc., McGraw-Hill, SAGE publications

Grant reviews: Israel Science Foundation (ISF), NSA Mathematical Sciences Grant Program (American Mathematical Association), The Research Foundation – Flanders (FWO) Belgium, Interdisciplinary Cyber Research Center at Tel Aviv University

Conference and Session Organization and Committees

Professional Roles and Membership

Committee Member, AIS Taskforce on Conference Guidelines (2025)

Committee Member, MISQ Impact Award (2023)

Chair, INFORMS Information Systems Society (ISS) Teaching Innovation Award (2023)

Committee Member, EIC Search Committee, Applied Stochastic Models in Business & Industry (2023)

Committee Member, INFORMS Information Systems Society (ISS) Teaching Innovation Award (2022)

Committee Member, MISQ Impact Award (2021)

Committee Member, INFORMS Information Systems Society (ISS) President’s Service Award (2021)

Elected Fellow, Institute of Mathematical Statistics (IMS) (since 2020)

Elected Member, The International Statistical Institute (ISI) (since 2006)

Member, American Statistical Association (ASA)

Member, Institute of Mathematical Statistics (IMS)

Member, European Network for Business and Industrial Statistics (ENBIS)

Member, Institute For Operations Research and the Management Sciences (INFORMS)

Member, Statistical Research Committee, International Society for Disease Surveillance (ISDS)

Committee member, The International Statistical Institute (ISI) Mahalanobis Award, 2018

Council member, International Society for Business and Industrial Statistics (ISBIS), 2013-2017

Service at NTHU

Service at ISB (2012-2014)

Service at UMD (2002-2010)

Mentoring, Advising, and Research Supervision

Dissertation Advisor: PhD

Travis Greene, “Persons and AI-driven Personalization: Tensions and Opportunities in an Algorithmic Society”, PhD, Institute of Service Science, College of Technology Management, NTHU, graduated 2023.

Nicholas Danks (co-advisor), “Extending Predictive Methods for Partial Least Squares Path Models (PLS-PM)”, PhD, Institute of Service Science, College of Technology Management, NTHU, graduated 2020.

Mahsa Ashouri, “Forecasting and Clustering Large Collections of Time Series”, PhD, Institute of Service Science, College of Technology Management, NTHU, graduated 2019.

Suneel Babu Chatla, “Unconventional Regression Models for Count and Binary Data”, PhD, Institute of Service Science, College of Technology Management, NTHU, graduated 2018.

Inbal Yahav, “A Data Analytical Framework for Improving Real-time Decision Support Systems in Healthcare”, PhD in OM and Data Mining, Smith School of Business, UMD, graduated 2010.

Thomas Lotze, “Anomaly Detection in Time Series: Theoretical and Practical Improvements for Disease Outbreak Detection”, PhD in Applied Mathematics & Scientific Computation Program, UMD,  graduated 2009.

Valerie Hyde, “Representing, Visualizing, and Modeling Online Auction Data”, PhD in Applied Mathematics and Scientific Computation Program, UMD, graduated 2007.

Thesis Advisor: Masters

Yu-Hsuan Liu, “Leveraging Causal Diagrams for Enhancing Randomized Experiments in Human-Computer Interaction Research”, MBA, Institute of Service Science, College of Technology Management, NTHU, graduated 2024.

Mei (Jamie) Chan, “Generating and Comparing Explanations for Health Lifestyle Recommender Systems: A Unified Study” MBA, Institute of Service Science, College of Technology Management, NTHU, graduated 2024.

Hsuan-Yu Chen, “Literature Review Automation Using Supervised & Semi-supervised Learning”, MBA, Institute of Service Science, College of Technology Management, NTHU, graduated 2021.

Ta-Chun (Will) Kuan, “Evaluating Peak-Capturing Performance of Time Series Forecasting Algorithms”, MBA, Institute of Service Science, College of Technology Management, NTHU, graduated 2020.

Wan-Fen (Silvia) Yang, “Using Causal Structure for Counterfactual Explanations of Predicted Scores”, MBA, Institute of Service Science, College of Technology Management, NTHU, graduated 2020.

Jo-Han (Beverly) Lin, “Using Classification Trees for Detecting (Almost)-Perfectly-Classified Minority Groups”, MBA, Institute of Service Science, College of Technology Management, NTHU, graduated 2020.

Pei-Pei Chen, “Simulating IoT-type Time Series for Privacy-Protecting Data Sharing”, MBA, Institute of Service Science, College of Technology Management, NTHU, graduated 2020.

Travis Greene, “Situating Data Science in the Wake of the GDPR: Practical Implications for Data Scientists, Their Managers, and Academic Researchers”, International MBA, College of Technology Management, NTHU, graduated 2019.

Patrizia Mach, “A Data Mining Approach to Surveying Academic Literature on Behavioral Big Data in Operations Management”, International MBA, College of Technology Management, NTHU, graduated 2019.

William Feng, “Using Machine Learning to Evaluate the Value of FICO Scores in P2P Lending”, International MBA, College of Technology Management, NTHU, graduated 2019.

Edward Song, “Improving Inter-Rater Reliability in InfoQ Assessment Using Contextual Questions”, MBA, Institute of Service Science, College of Technology Management, NTHU, graduated 2019.

Cheng-Che (John) Liao, “Uplift Model Performance With Imbalanced Treatment Groups”, MBA, Institute of Service Science, College of Technology Management, NTHU, graduated 2019.

Sz-Wei (Sandy) Wu, “Clarifying Confusions About Gains And Lift Charts To Improve Their Current Underuse In Practice”, MBA, Institute of Service Science, College of Technology Management, NTHU, graduated 2019.

Tzu-Han (Angela) Hung, “Investigating the effects of unbalanced predictors on identifying discriminatory predictors”, MBA, Institute of Service Science, College of Technology Management, NTHU, graduated 2018.

Hsing-Chun (Celia) Chen, “Performance of tree algorithms on imbalanced data under different sampling strategies”, MBA, Institute of Service Science, College of Technology Management, NTHU, graduated 2018.

Chang-Ming (Dobby) Yang, “Sampling, Weighting and Probability Correction for Classifying Imbalanced Data Using Decision Trees”, MBA, Institute of Service Science, College of Technology Management, NTHU, graduated 2017.

Po-Wei Huang, “Forecasting Online Restaurant Bookings: The Value of Bookings Data to Service Providers and Booking Platforms”, MBA, Institute of Service Science, College of Technology Management, NTHU, graduated 2016.

Dai-sin Li, “Forecasting Weekly Cabbage Price in Kaohsiung, Taiwan: The Value of Private Data”, International MBA, College of Technology Management, NTHU, graduated 2016.

Juan Manuel Velasquez Estrada, “Generating and Evaluating Predictions with PLS Path Modeling”, International MBA, College of Technology Management, NTHU, graduated 2015.

Bernard Dillard, “Wavelet-Based Statistical Monitoring Methods for Biosurveillance”, M.Sc. in Applied Mathematics and Scientific Computation Program, UMD, graduated 2005.

Dissertation Committees

Yi-Chun (Jean) Liu, MBA, Institute of Service Science, College of Technology Management, NTHU, 2023.

Ziv Litmanovitz, MA, University of Haifa, 2022.

Thiyanga Talagala, PhD, Monash University, 2019.

Sofie De Cnudde, PhD, Universiteit Antwerpen, 2017.

Yu-Chu (Eva) Shih, MBA, Institute of Service Science, College of Technology Management, NTHU, 2017.

Nicholas Danks, IMBA, College of Technology Management, NTHU, 2016.

Cesar Ordonez, IMBA, College of Technology Management, NTHU, 2016.

Li-Chan Chen, MBA, Institute of Service Science, College of Technology Management, NTHU, 2016.

Taowei David Wang, PhD, Computer Science, UMD (Dean’s representative), 2010.

Carolina Franco, PhD, Applied Mathematics and Scientific Computation program, UMD, 2009.

Ampun Janpengpen, PhD, Dept of Civil & Environmental Engineering, UMD (Dean’s representative), 2009-2010.

Jagan Sankaranarayanan, PhD, Dept of Computer Science, UMD (Dean’s representative), 2008.

Shamir Mukhi, PhD, Department of Electrical and Computer Engineering, University of Manitoba, Canada, 2007.

Abhishek Pani, PhD, Smith School of Business, Dept of Decision & Information Technologies, UMD, 2007.

Shanshan Wang, PhD, Statistics Program, UMD, 2007.

Shihua Wen, PhD, Statistics Program, UMD, 2007.

Yufeng Tu, PhD, Smith School of Business, Dept of Decision & Information Technologies, UMD, 2006.

Haiming Guo, PhD, Statistics Program, UMD, 2006.

Anna Goldenberg, MSc, Carnegie Mellon University.

Cristian Ghiuvea, PhD, Dept of  Statistics, Carnegie Mellon University.

Other Student Research Advising

Ravdeep Chawla, MBA student, Indian School of Business, 2012-13.

Igor Nakshin, MBA student, Smith School of Business, UMD, 2008.

Adam Wilson, graduate student, Applied Mathematics & Scientific Computation, UMD (advisor), 2007-8.

Abhishek Pani, graduate student, Dept of Decision & Information Technologies, UMD, 2005-6.

Aleks Aris, graduate student, Human-Computer Interaction Lab, Computer Science, UMD, 2004-5.

Shiping Zhang, undergraduate student, Mathematics department, UMD, 2004.

Emily Marker, undergraduate student, Smith School of Business, UMD, 2003.