Educational Background

PhD
Systems Design Engineering, University of Waterloo

MASc
Electrical and Computer Engineering, Toronto Metropolitan University

BASc
Electrical and Computer Engineering

Professional Experience

Associate Professor

2018 – Present
Florida Institute of Technology, Department of Mathematics and Systems Engineering

Assistant Professor 

Aug 2012 – Apr 2018
Florida Institute of Technology, Department of Mathematics and Systems Engineering

Research Fellowship 

2010 - 2012
Harvard School of Public Health, Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute

Postdoctoral Fellowship 

2008 - 2010
Harvard-MIT Division of Health Sciences and Technology, Department of Medicine

Harvard Medical School, Brigham and Women's Hospital 

Selected Publications

1. Kachouie, Nezamoddin N., Alain Despeignes, and Daniel Breininger, Survival Times of Transplanted Kidneys Among Different Donor–Recipient Cohorts: The United States Registry Analysis from 1987 to 2018, Part 1: Gender and Ethnicity. Stats (2024): 1-16.
2. Robbins E, Breininger RD, Jiang M, Madera M, White RT, Kachouie NN., Segmentation of Glacier Area Using U-Net through Landsat Satellite Imagery for Quantification of Glacier Recession and Its Impact on Marine Systems. Journal of Marine Science and Engineering 2024 12(10):1788.
3. Jaber, M., Farag, H., Breininger, R., Kachouie N.N., Spatiotemporal Bayesian Machine Learning for Estimation of an Empirical Lower Bound for Probability of Detection with Applications to Stationary Wildlife Photography. Computers10 (2024): 255.
4. Kachouie N.N., Deebani W., Shutaywi M., Christiani D.C., Lung cancer clustering by identification of similarities and discrepancies of DNA copy numbers using maximal information coefficient. Plos one. 2024 May 13;19(5): e0301131.
5. Jaber, M., Hamad, F., Breininger, R.D., Kachouie, N.N. An Enhanced Spatial Capture Model for Population Analysis Using Unidentified Counts through Camera Encounters. Axioms 2023, 12, 1094. http://doi.org/10.3390/axioms12121094
6. Vaidya, H.N., Breininger, R.D., Madrid, M., Lazarus, S., Kachouie, N.N. Generalized Additive Models for Predicting Sea Level Rise in Coastal Florida. Geosciences 2023, 13, 310. http://doi.org/10.3390/geosciences13100310
7. Robbins, E., Hlaing, T.T., Webb, J., Kachouie, N.N. Supervised Methods for Modeling Spatiotemporal Glacier Variations by Quantification of the Area and Terminus of Mountain Glaciers Using Remote Sensing. Algorithms 2023, 16, 486. http://doi.org/10.3390/a16100486
8. Despeignes A, Sharma A, Beltran R, Rech S, Hunsucker K, White RT, Weaver RJ, Kachouie NN. The Impact of Benthic Organisms to Improve Water Quality in the Indian River Lagoon, Florida. Springer Water, Air, & Soil Pollution 234(8):546, 2023.
9. Deebani, W. and Nezamoddini-Kachouie, N., Monte Carlo Ensemble Correlation Coefficient for Association Detection, Communications in Statistics-Simulation and Computation 51(12):7095109, 2022. http://doi.org/10.1080/03610918.
10. Ahmad Fahmi bin Anwar Fadzil, Yunong Yuan, Lingxin Wang, Jaspreet S. Kochhar, Nezamoddin Kachouie, and Lifeng Kang, Recent Progress in Three-Dimensional-Printed Dosage Forms from a Pharmacist Perspective, Wiley: Journal of Pharmacy and Pharmacology, 2022. http://doi.org/10.1093/jpp/rgab168
11. M Shutaywi, Nezamoddin N. Kachouie, Silhouette Analysis for Performance Evaluation in Machine Learning with Applications to Clustering, Entropy 23(6):759, 2021.
12. LA Provost, R Weaver, Nezamoddin N. Kachouie, Statistical Modeling of Fine Sediments Dredged Using a Variable Area Dredging Suction Head to Improve Water Quality, Hydrology, 8(3), 2021.
13. Nezamoddini-Kachouie, N., and Deebani, W., Association Factor for Identifying Linear and Nonlinear Correlations in Noisy Conditions, Entropy, 22(4): 440,
14. Nezamoddini-Kachouie, N. and Hamad, F., Potential Impact of Standing OPTN Committee Plan on Survival Times of Transplanted Kidneys Based on Donors’ Factors, Transplantation Reports, 5 (2), 100042, 2020.
15. Nezamoddini-Kachouie, N., and Shutaywi, M., Weighted Mutual Information for Aggregated Kernel Clustering, Entropy, 22(3):351, 2020.
16. Nezamoddin-Kachouie, N., Shutaywi, DC Christiani, Discriminant Analysis of Lung Cancer Using Nonlinear Clustering of Copy Numbers, Cancer Investigation, 38 (2), 102-112, 2020.
17. Nezamoddini-Kachouie, N., Osita E. Onyejekwe, Climate Change Study via the Centennial Trend of Climate Factors, Hydrology, 7 (2), 2020.
18. Lim, S. H., Tiew, W. J., Zhang, J., Ho, P., Nezamoddini-Kachouie, N., Kang, L., Geometrical Optimization of a Personalized Microneedle Eye Patch for Transdermal Delivery of Anti-Wrinkle Small Peptide, to appear in: Biofabrication, 12(3), 035003, 2020.
19. Folcik, A.M., Haire, T.C., Cutshaw, K., Riddle, M., Shola, C., Nassani, S., Rice, P., Richardson, B., Nazamoddini-Kachouie, N. and Palmer, A.G., Computer assisted tracking of Chlamydomonas species. Frontiers in Plant Science, 2019, 10, p.1616.
20. Hamad, F. and Nezamoddini-Kachouie, N., Potential Impact of Donors’ Factors on Survival Times of Transplanted Hearts and Lungs. Elsevier: Transplantation Reports. 2019, 4:(4):100035. DOI: http://doi.org/10.1016/j.tpr.2019.100035
21. Nezamoddini-Kachouie, N., Deebani W, Christiani D.C. Identifying Similarities and Disparities Between DNA Copy Number Changes in Cancer and Matched Blood Samples. Taylor and Francis: Cancer Investigation. 2019; 37(10):535-545. DOI: http://doi.org/10.1080/07357907.2019.1667368
22. Hamad, F. and Nezamoddini-Kachouie, N., A hybrid method to estimate the full parametric hazard model, Taylor and Francis: Communications in Statistics-Theory and Methods, 2019, 48(22): 54775491. DOI: 1080/03610926.2018.1513149
23. Onyejekwe, O., Holman, B., and Nezamoddini-Kachouie, N., Multivariate Models for Predicting Glacier Termini, Springer: Environmental Earth Sciences, 2017, 76(23): 807. http://doi.org/10.1007/s12665-017-7135-2  
24. Beaubrun, A. and Nezamoddini-Kachouie, N., Analysis of Wage-Gender Discrimination in Connection with Higher Education in The Bahamas, Omics Journal: Industrial Engineering & Management, 2017, 6(3): 222. DOI: 10.4172/2169-0316.1000222.
25. Qin, L., Schwartzman, A., McCall, K., Nezamoddini-Kachouie, N., and Yap, J. “Method for detecting voxelwise changes in fluorodeoxyglucose-positron emission tomography brain images via background adjustment in cancer clinical trials”, Journal of Medical Imaging, 2017, 4(2), 024006.
26. Nezamoddini-Kachouie, N., Transforming Region-Detection, a One-Dimensional (1D) Problem to Point Detection, a Zero-Dimensional (0D) Problem, Journal of Electrical and Electronic Systems, 2017, 6: 217.
27. Nezamoddini-Kachouie, N., Lin, X., Schwartzman, A., “FDR Control of Detected Regions by MultiScale Matched Filtering”, Communications in Statistics - Simulation and Computation, 2017, 46(1), pp.127-144.
28. Nezamoddini-Kachouie, N., Christiani, D., “DNA Copy Number Gain in Lung Cancer and NonInvolved Tissue”, Journal of Bioanalysis and Biostatistics, 2016: 1(1).
29. Nezamoddini-Kachouie, N., Lin, X., Christiani, D., Schwartzman, A., “Detection of Local DNA Copy Number Changes in Lung Cancer Population Analyses Using A Multi-Scale Approach”, Communications in Statistics: Case Studies, Data Analysis and Applications, 2015, 1(4):206-16.   
30. Nezamoddini-Kachouie, N., Gerke, T., Huybers, P., Schwartzman, A., “Nonparametric Regression for Estimation of Spatiotemporal Mountain Glacier Retreat from Satellite Images”, IEEE Transactions on Geoscience and Remote Sensing, 2014, 53(3), pp.1135-1149.
31. Nezamoddini-Kachouie, N., Schwartzman, “Non-Parametric Estimation of a Single Inflection Point in Noisy Observed Signal”, Journal of Electrical and Electronic Systems, 2013, 2(2).
32. Nezamoddini-Kachouie, N., Huybers, P., Schwartzman, A., “Localization of Mountain Glacier Termini in Landsat Multi-Spectral Images, Pattern Recognition Letters, 2013, 34(1), pp.94-106.
33. Mochizuki, N., Kakegawa, T., Osaki, T., Sadr, N., Nezamoddini-Kachouie, N., Suzuki, H., Fukuda, J., “Tissue Engineering Based on Electrochemical Desorption of an RGD-Containing Oligopeptide”, Published: Wiley: Tissue Engineering and Regenerative Medicine, 2013, 7(3), pp.236-243.
34. CoutinhoϮ, D.F., AhariϮ, A.H., Nezamoddini-KachouieϮ, Gomes, M.E., Neves, N.M., Reis, R.L., and Khademhosseini, A., “An automated two-phase system for biodegradable gel micro-bead production”, Biofabrication, 2012, 4(3), pp.035003 (Ϯ Equal Contribution).
35. Hancock, M.J., Yanagawa, F., Jang, Y., He, J., Nezamoddini-Kachouie, N., Kaji, H., Khademhosseini, A., “Designer hydrophilic regions regulate droplet shape for controlled surface patterning and 3D microgel synthesis”, Wiley: Small, 2012, 8(3), pp.393-403.
36. Kwon, C.H., Wheeldon, I., Nezamoddini-Kachouie, N., Lee, S., Bae, H., Sant, S., Fukuda, J., Kang, J.W., Khademhosseini, A., “Drug-eluting microarrays for cell-based screening of chemical-induced apoptosis”, Analytical Chemistry, 2011, 83(11), pp.4118-4125. 
37. Nezamoddini-Kachouie, N., Fieguth, P., and Eric Jervis, “A Probabilistic Cell Model in Background Corrected Image Sequences for Single Cell Analysis”, Biomedical Engineering Online, 2010, 9(1), p.57.
38. Nezamoddini-Kachouie, N., Du, , Bae, H., Khabiry, M., Ahari, A., Zamanian, B., Fukuda, J., Khademhosseini, A., “Directed Assembly of Cell-Laden Hydrogels for Engineering Functional Tissues”, Organogenesis: special issue in Engineering towards functional tissues and organs, 2010, 6(4), pp.234-244.
39. Nezamoddini-Kachouie, N., Fieguth, P., Gamble, D., Jervis, E., Ezziane, Z., and Khademhosseini, A., “Constrained Watershed to Infer Morphology of Mammalian Cells in Microscopic Images”, Wiley: Cytometry-Part A, 2010, 77(12), pp.1148-1159.
40. Hwang, C., Sant, S., Masaeli, M., Nezamoddini-Kachouie, N., Zamanian, B., Lee, S., Khademhosseini, A., “Fabrication of three-dimensional porous cell-laden hydrogel for tissue engineering”, Biofabrication, 2, Issue 3, 2010.
41. Nezamoddini-Kachouie, N., “Image Denoising Using Earth Mover's Distance and Local Histograms”, International Journal of Image Processing, 2010, 4(1), pp. 66-76.
42. Nezamoddini-Kachouie, N., Kang, L., Khademhosseini, A., “Arraycount, an Algorithm for Automatic Cell Counting in Microwell Arrays”, BioTechniques (Preclinical Development), 2009, 47(3), pp.x-xvi.
43. Nezamoddini-Kachouie, N., “Anisotropic Diffusion for Medical Image Enhancement”, International Journal of Image Processing, 2008, 4(4):436.
44. Nezamoddini-Kachouie, N., and Fieguth, P., “Extended-Hungarian-JPDA: Exact Single-Frame Stem Cell Tracking”, IEEE Transactions on Biomedical Engineering (IEEE-TBME), 2007, 54(11), pp. 2011-2019.
45. Nezamoddini-Kachouie, N., Fieguth, P., Ramunas, J., and Jervis E., Sep. 2006, “Probabilistic ModelBased Cell Tracking”, International Journal of Biomedical Imaging, Special Edition on Recent Advances in Mathematical Methods for the Analysis of Biomedical Images, 2006, 2006, pp.1-10.
46. Nezamoddini-Kachouie, N., Fieguth, P., Ramunas, J., and Jervis, E., “A Model-Based Hematopoietic Stem Cell Tracker,” LNCS-Springer Verlag (Lecture Notes in Computer Science- Image Analysis and Recognition), 2005, 3656, pp. 861-868.
47. Nezamoddini-Kachouie, N. and Alirezaie, J., “Optimized Multi-channel Filter Bank with Flat Frequency Response for Texture Segmentation”, EURASIP (European Association for Signal,Speech and Image Processing) - Applied Signal Processing. 2005, 2005(12), pp.1834-1844.
48. Nezamoddini-Kachouie, N., and Fieguth, P., “A Narrow Band Level Set Method with Dynamic Velocity for Neural Stem Cell Cluster Segmentation”, LNCS-Springer Verlag (Lecture Notes in Computer Science- Image Analysis and Recognition), 2005, 3656, pp.1006-1013.
49. Nezamoddini-Kachouie, N., Alirezaie, J. and Li. J., “A Hybrid Texture Segmentation Method for Mapping Urban Land Use,” Geomatica, Special Issue on Remote Sensing of Urban Areas, 2004, 58(1), pp.399-409.
50. Nezamoddini-Kachouie, N., Fieguth, P. and Jernigan, E., “BayesShrink Ridgelets for Image Denoising”, LNCS-Springer Verlag (Lecture Notes in Computer Science- Image Analysis and Recognition), 2004, 3211, pp.163-170.

Recognition & Awards

• Award: Excellence in Research 2024: Department of Mathematics and Systems Engineering, College of Engineering and Science

• Nominated: Excellence in Research 2024: College of Engineering and Science

• NSF Sponsored Research Fellowship Boot Camp in University of California San Diego, 2022
Worked with nationwide scientists on applications of AI and high-performance computing for Climate Change Induced Problems.

• Featured interview in “Infectious disease models aren't crystal balls but are useful tools in Florida's fight against COVID-19, modelers say” by Ryan Mills and Frank Gluck, USA Today, Naples
Daily News, 2020.

Research

AI-Driven Data Assimilation and Risk Assessment in Health and Environment

Dr. Kachouie has actively worked on AI-based data assimilation for various applications, ranging from lung cancer risk assessment to disaster resilience modeling. His recent work includes:

• A Deep Learning Approach to Identify Donor-Recipient Match for Optimized Organ Transplantation 

• A Multimodal Lung Cancer Risk Assessment Model Integrating Clinical Data and Biomarkers

• Multi-Source Data Fusion for Hazard Model Validation and Damage Estimation

Advancements in Machine Learning for Remote Sensing and Climate Science

Dr. Kachouie has made significant contributions to the field of remote sensing, particularly in the application of deep learning and statistical modeling for environmental monitoring. His research has advanced methods for glacier segmentation, wildfire prediction, and climate-related hazard modeling. Key publications include:

• Quantification of Glacier Variations using Deep Learning through Satellite Imagery

• Spatiotemporal Bayesian Machine Learning for Probability of Detection in Wildlife Photography

• Generalized Additive Models for Predicting Sea Level Rise in Coastal Florida

His work integrates geospatial data fusion and deep learning techniques to improve the accuracy and efficiency of climate change monitoring. 

Statistical Modeling for Cancer Research and Organ Transplantation

Dr. Kachouie has conducted extensive research in public health, focusing on predictive modeling in cancer diagnosis and survival analysis in organ transplantation. His research leverages deep learning and Bayesian survival models to improve patient outcomes. Notable contributions include:

• Development of an AI-driven Personalized Lung Cancer Risk Assessment

• Survival Analysis of Transplanted Kidneys using Donor-Recipient Matching Algorithms

• Predictive Models for Tumor Progression Based on Multi-Omics Data Integration