Caren Marzban
Journal Articles:
- 57. Marzban, C., U. Yurtsever, M. Richman, 2024: Principal Component Analysis for Equation Discovery . Submitted to Journal of Atmospheric Sciences.
- 56. Marzban, C., J. Liu, P. Tissot, 2022: On Variability due to Local Minima and K-fold Cross-validation. Artificial Intelligence for Earth Systems. vol. 1, no. 4
- 55. Haupt, S. E., D. J. Gagne, W. Hsieh, V. Krasnopolsky, V. Lakshmanan, A. McGovern, C. Marzban, W. Monager, P. Tissot, J. Williams, 2022: The History and Practice of AI in the Environmental Sciences. Bulletin of the American Meteorological Society, 103, 5, 1351-1370.
- 54. Marzban, C., R. Tardif, S. Sandgathe, 2020: A Sensitivity Analysis of Two Mesoscale Models: COAMPS and WRF. Monthly Weather Review. 148(7), 2997-3014.
- 53. Marzban, C., R. Tardif, S. Sandgathe, 2020: Sensitivity Analysis of Spatial Structure of Forecasts in Mesoscale Models; Noncontinuous Model Parameters. Monthly Weather Review, 148 (4), 1717-1735 .
- 52. Marzban, C., R. Tardif, S. Sandgathe, N. Hryniw, 2018: A Methodology for Sensitivity Analysis of Spatial Features in Forecasts: The Stochastic Kinetic Energy Backscatter Scheme (SKEBS). Meteorological Applications, 26, 454-467. https://doi.org/10.1002/met.1775.
- 51. Marzban, C., C. Jones, N. Li, S. Sandgathe 2018: On the effect of model parameters on forecast objects. Geoscientific Model Development (11), 1-14.
- 50. Marzban, C., Xiaochuan Du, Scott Sandgathe, James D. Doyle, Yi Jin, Nicholas C. Lederer 2018: Sensitivity Analysis of the
Spatial Structure of Forecasts in Mesoscale Models:
Continuous Model Parameters. Mon. Wea. Rev. (146), 967-983.
- 49. Marzban, C., Wenxiao Gu, P. D. Mourad 2016: Mixture Models for estimating maximum blood flow velocity. Journal of Ultrasound in Medicine,
35(1), 93-101.
- 48. Marzban, Ethan P, C. Marzban 2014: On the usage of musical keys: A descriptive statistical perspective. The Journal of Experimental Secondary Science. Vol. 3, issue 3. ISSN#2162-8092.
- 47. Marzban, C., Scott Sandgathe, James D. Doyle 2014: Model tuning
with canonical correlation analysis.
Monthly Weather Review, 142(5), 2018-2027.
- 46. Marzban, C., R. Viswanathan, U. Yurtsever 2014:
Earth before Life.
Biology Direct, 9:1. doi:10.1186/1745-6150-9-1 . OR
here.
(2014 Top Cited Article).
- 45. Marzban, C., Scott Sandgathe, James D. Doyle, Nicholas C. Lederer 2014:
Variance-based sensitivity analysis: Preliminary
results in COAMPS. Monthly Weather Review, 142, 2028-2042.
- 44. Marzban, C., R. Illian, D. Morison, P. D. Mourad 2013: A double-gaussian, percentile-based method for estimating maximum blood
flow velocity. Journal of Ultrasound in Medicine, 32(11), 1913-1920.
- 43. Marzban, C. 2013: Variance-based Sensitivity analysis: An illustration on the Lorenz '63 model. Monthly Weather Review,
141(11), 4069-4079.
- 42. Marzban, C., R. Illian, D. Morison, A. Moore, M. Kliot, M. Czosnyka, P. D. Mourad 2013: A method for estimating zero-flow pressure and intracranial pressure. Journal of Neurosurgical Anaesthesiology, 25(1), 25-32.
- 41. Marzban, C. 2012: Displaying economic value. Weather and Forecasting, 27 (6), 1604-1612.
- 40. Yurtsever, U., C. Marzban, M. Meila, 2011: On the gravitational inverse problem. Applied Mathematical Sciences, Vol. 5, no. 57, 2839-2854.
- 39. Marzban, C. and S. Sandgathe 2010:
Optical Flow for verification.
Weather and Forecasting, 25 (5), 1479-1494.
- 38. Marzban, C., R. Wang, F. Kong, S. Leyton 2011:
On the effect of correlations on Rank Histograms :
Reliability of Temperature and wind-speed forecasts from Fine-scale Ensemble Reforecasts.
Monthly Weather Review, 139(1), 295-310.
An intimately related paper is by Dan Wilks .
- 37. Mourad, P. D., C. Marzban, and M. Kliot 2009:
Towards predicting intracranial pressure using transcranial Doppler and
arterial blood pressure data.
J. Acoust. Soc. Am., 125, 2514.
- 36. Marzban, C., S. Sandgathe, H. Lyons, N. Lederer 2009:
Three Spatial Verification Techniques:
Cluster Analysis, Variogram, and Optical Flow. Wea. Forecasting,
24(6), 1457-1471.
- 35. Kim, A. Y., C. Marzban, D. Percival, W. Stuetzle 2009:
Using labeled Data to evaluate change detectors
in a multivariate streaming environment. Signal Processing, 89(12), 2529-2536;
doi:10.1016/j.sigpro.2009.04.011.
- 34. Marzban, C. and S. Sandgathe 2009: Verification with
variograms. Weather and Forecasting, Vol. 24, No. 4,
1102-1120.
- 33. Marzban, C., S. Sandgathe, and H. Lyons 2008: An
Object-oriented Verification of Three NWP Model
Formulations via Cluster Analysis: An objective and a subjective analysis.
Monthly Weather Review, Vol. 136, No. 9., 3392-3407.
- 32. Marzban, C., S. Sandgathe, 2008:
Cluster Analysis for Object-Oriented Verification of Fields: A Variation. Monthly Weather Review, Vol. 136, 1013-1025.
- 31. Marzban, C., S. Leyton, and B. Colman 2007:
Ceiling & Visibility forecasts via Neural Nets.
Wea. Forecasting, Vol. 22, No. 3, 466-479.
- 30. Marzban, C., S. Sandgathe 2006: Cluster analysis for
verification of precipitation fields. Wea. Forecasting, Vol. 21,
No. 5, 824-838.
- 29. Marzban, C., S. Sandgathe, E. Kalnay, 2005:
MOS, Perfect Prog, and Reanalysis Data. Monthly Weather Review, Vol.
134, No. 2, 657-663.
- 28. Trites, A. W. et al, 2004: Bottom-up forcing and the decline of
Steller sea lions in Alaska: Assessing the ocean
climate hypothesis. Fisheries Oceanography, doi:10.1111/j.1365-2419.2006.00408.
- 27. Hennon, C., C. Marzban, J. S. Hobgood, 2004: Improving tropical cyclogenesis statistical model forecasts
through the application of a neural network classifier. Wea. Forecasting.,
Vol. 20, No. 6, 1073-1083.
- 26. Marzban, C. 2004: The ROC Curve and the Area
Under it as a Performance Measure. Weather and Forecasting, Vol. 19,
No. 6, 1106-1114.
- 25. Marzban, C., 2003: A Neural Network for
Post-processing Model Output: ARPS . Monthly Weather Review, Vol. 131,
No. 6., pp. 1103-1111.
- 24. Drton, M., Marzban, C., P. Guttorp, & J. T. Schaefer, 2003:
A Markov Chain Model of Tornadic Activity. Monthly Weather Review,
Vol 131, No. 12, 2941-2953. An earlier version of the paper is by Marzban &
Guttorp .
- 23. Marzban, C., and A. Witt, 2001: A Bayesian
Neural Network for Hail Size Prediction. Wea. Forecasting, Vol. 16,
No. 5, pp. 600-610. A neural network for the detection of hail can be
found here .
- 22. Marzban, C., and J. Schaefer, 2001: The
Correlation Between U.S. Tornados and Pacific Sea Surface Temperature.
Monthly Weather Review, Vol. 129, No. 4, 884-895.
- 21. Marzban, C. 2000: A neural network for tornado
diagnosis. Neural Computing and Applications, Vol. 9 (2), 133-141.
- 20. Marzban, C., E. D. Mitchell, G. Stumpf, 1999: The
notion of "best predictors:" An application to tornado prediction.
Weather and Forecasting, Vol. 14, No. 6, 1007-1016.
- 19. Marzban, C., V. Lakshmanan, 1999: On the uniqueness
of Gandin and Murphy's equitable performance measures. Monthly Weather
Review, Vol. 127, No.6, 1134-1136.
- 18. Marzban, C., G. J. Stumpf, 1998: A Neural Network for Tornado and/or
Damaging Wind Prediction Based on Doppler Radar-derived Attributes.
Microcomputer Applications, Vol. 17, 21-28.
- 17. Marzban, C., G. J. Stumpf, 1998: A neural network
for damaging wind prediction, Weather and Forecasting, Vol. 13, No.1,
151-163.
- 16. Marzban, C. 1998: Scalar measures of performance in
rare-event situations, Weather and Forecasting, Vol. 13, 753-763.
- 15. Marzban, C. 1998: Bayesian probability and scalar
performance measures in gaussian models, Journal of Applied Meteorology,
Vol. 37, 72-82.
- 14. Marzban, C., H. Paik, and G. Stumpf, 1997: Neural
networks vs. gaussian discriminant analysis, AI Applications, Vol. 10,
No.1, 49-58.
- 13. Marzban, C., G. J. Stumpf, 1996: A neural network
for tornado prediction ..., Journal of Applied Meteorology, Vol. 35, 617.
- 12. Kantowski, R., C. Marzban, 1995: A Neural Network for Locating the
Primary Vertex in a Pixel Detector. Nuclear Instruments and Methods in
Physics Research, A 355, 582.
- 11. Paik, H., C. Marzban, 1995: Predicting Television Extreme-viewer vs.
Non-viewer: A Neural Network Analysis. Human Communication Research, Vol. 22,
284.
- 10. Marzban, C., R. Viswanathan, 1994: Stochastic Neural
Networks with the Weighted Hebb Rule, Physics Letters A Vol. 191, 127.
- 9. Kantowski, R., C. Marzban, 1992: One-loop Vilkovisky-DeWitt
Counterterms for Two-dimensional Gravity Plus Scalar Field Theory. Phys.
Rev. D46.
- 8. Marzban, C., R. Viswanathan, 1992: Matrix Models With
\gamma_{string}>0. Phys. Lett. B277, 289.
- 7. Marzban, C., R. Viswanathan, 1991: Matrix Models With Non-even Potentials.
Int. Journ. of Mod. Phys. A6, 2559.
- 6. Marzban, C., 1990: Morse Theory Applied to N=1 and 2 Superconformal
Theories. Phys. Lett. B238, 257.
- 5. Marzban, C., 1990: Remarks on the Landau-Ginzburg Potential and
RG-flow for SU(2)-coset Models. Phys. lett. B236, 298.
- 4. Marzban, C., B. F. Whiting, H. Van Dam, 1989: Hamiltonian Reduction for
Massive Fields Coupled to Sources. Jour. Math. Phys. 30, 1877.
- 3. Kikuchi, Y., C. Marzban, 1987: Two-loop Modular Invariance and Proper
Spin-Statistics Projection for General Boundary Conditions. Phys. Rev. D36,
2583.
- 2. Kikuchi, Y., C. Marzban, 1987: Low-energy Effective Lagrangian of
Heterotic String Theory. Phys. Rev. D35, 1400.
- 1. Kikuchi, Y., C. Marzban, Y. J. Ng, 1986: Heterotic String Modifications
of Einstein's and Yang-Mills' Actions. Phys. Lett. B176, 57.
Technical Reports and Unpublished/In-Progress Work:
- 9. Marzban, C., Hoi Yi Ng, and Corinne Jones, 2017: R Supplement to Applied Statistics For Engineers and Scientists, by
Devore, Farnum, and Doi.
- 8. Marzban, C., Wenxiao Gu, P. D. Mourad 2014: A robust noninvasive estimator of Intracranial pressure. Submitted to Journal of Neurosurgical Anaesthesiology.
- 7. Marzban, C., R. Illian, D. Morison, P. D. Mourad 2013:
Within-group and between-group
correlation: Illustration on noninvasive estimation of intracranial
pressure. Submitted to the IEEE Journal of Biomedical and Health
Informatics. (A related paper with more
detail has not been submitted for publication).
- 6. Marzban, C., U. Yurtsever, 2011: Baby Morse theory for statistical inference from point cloud data.
- 5. Stuetzle, W., D. Percival, and C. Marzban 2010:
Targeted event detection.
- 4. Kim, A., C. Marzban, D. B. Percival, and W. Stuetzle, Using
Labeled Data to Evaluate Change Detectors in a Multivariate Streaming
Environment, 2008.
- 3. Marzban, C., N. Mantua, S. Hare, 2004: Retrospective study of
climate impact on Alaska Stellar Sea Lion
- 2. Marzban, C., D. Lettenmaier, and L. Bowling, 2004:
Trends in Extreme Precipitation and Streamflow.
- 1. Marzban, C. 1997: Local minima in Bootstrapping.
Selected Conference Papers:
- Marzban, C. 2017: A few meteorological applications of Sparse PCA. Paper presented at the 16th Conference on Artificial Intelligence, Baltimore, MD, 28-29 July.
- Marzban, C., R. Tardif, S, Sandgathe, C. Jones, X. Du, N. Li, N. Hryniw,
N. C. Lederer, J. D. Doyle, Y. Jin, 2017: The effect of model parameters on the spatial structure of forecast fields. Paper presented at the 24th Conference on Probability
and Statistics, Baltimore, MD , 28-29 July.
- Marzban, C., and R. Viswanathan, 2017: On the Complexity of
Neural-Network-Learned Functions. Paper presented at the 15th Conference
on Artificial Intelligence, at the 97th American Meteorological Society Annual
Meeting, Seattle, Jan. 22-26.
- Marzban, C., and U. Yurtsever, 2017: On the Shape of Data.
Paper presented at the 15th Conference
on Artificial Intelligence, at the 97th American Meteorological Society Annual
Meeting, Seattle, Jan. 22-26.
- Marzban, C., and U. Yurtsever 2011: Baby Morse theory in data analysis.
Paper at the workshop on Knowledge Discovery, Modeling and Simulation (KDMS), held
in conjunction with the 17th ACM SIGKDD Conference on Knowledge Discovery and Data
Mining, San Diego, CA., August 21-24.
- Marzban, C. 2011: Sensitivity Analysis in linear and nonlinear models: A review. Paper presented at the 9th Conference
on Artificial Intelligence, at the 91st American Meteorological Society Annual
Meeting, Seattle, Jan. 23-27.
- Marzban, C. 2008: Quantile Regression. Invited
paper presented at the joint session between AI and Prob & Stats Conference. 88th
American Meteorological Society Annual Meeting, New Orleans, Jan. 20-24.
- Marzban, C., S. Sandgathe, and H. Lyons 2007: Assessment of an automatic, object-oriented approach to the verification of spatial fields . Paper presented at
7th Euopean Meteorological Society Annual MeetingEl Escorial, Spain, October.
- Marzban, C. 2004: Probabilistic Forecasts in Meteorology. Talk presented at a Neural Information Processing Systems, 2004,
workshop on Calibration and Probabilistic Prediction in Supervised Learning.
Whistler, Canada.
- Marzban, C. 1998: Bayesian inference in neural
networks. 78th meeting of the American Meteorological Society,
Probability and Statistics Session, Phoenix Arizona, January.
- Marzban, C., G. J. Stumpf, 1996: A Neural Network for Tornado and/or
Severe Weather Prediction Based on Doppler Radar-derived Attributes.
10th Annual Mid-American Symposium on Emerging Computer Technologies,
University of Oklahoma, October 28-29. (Top-paper Award.)
- Marzban, C., R. Viswanathan, 1993: Stochastic Neural Networks and the
Weighted Hebb Rule. Proceedings of the IJCNN conference, Nagoya, Japan.
Books:
Artificial Intelligence Methods in the Environmental Sciences, 2008;
Springer-Verlag. Co-editor and contributor to 2 chapters.