Lukman Adewale Folaranmi is presently an esteemed Assistant Professor in the Department of Mathematics at the University of North Dakota, USA. In this role, he continues to contribute his expertise and passion for mathematics to both his students and the academic community, further solidifying his reputation as a distinguished scholar and educator. Previously, Lukman Adewale Folaranmi served as a distinguished senior lecturer in the Department of Mathematics at the University of Medical Sciences in Nigeria. In addition to his primary role, he held a concurrent appointment in the Department of Epidemiology and Biostatistics, underscoring his versatile expertise. He also had the honor of serving as the Pioneer Director for the Centre of Health Metrics and Data Science at the University of Medical Sciences. In this pivotal role, he played a pivotal part in advancing the intersection of mathematics, statistics, and healthcare, driving innovative research and shaping the future of healthcare data analysis.
His academic impact extends far and wide, with a remarkable portfolio of over a hundred peer-reviewed articles, published both locally and internationally in high-impact journals. An impressive sixty-six of these articles are indexed in Scopus, attesting to the depth and quality of his research contributions. Lukman's scholarly achievements are underscored by his recognition as one of Nigeria's top 500 authors in terms of Scholarly Output from 2019 to the present, according to SciVal. Lukman's commitment to advancing his field is further exemplified by his role as a respected reviewer for leading journals in mathematics and statistics. He has also had the privilege of being a research laureate at the Henri Poincare Institute in Paris, France, where he delved into the Mathematics of Climate and Environment during a sponsored trimester by IHP-CIMPA. Additionally, Lukman was honoured as a recipient of the 2020 IMU-Simons Africa Fellowship, which enabled him to embark on a research visit to the Department of Mathematics and Statistics at Florida International University. His global research collaborations took him to the Department of Probability and Statistics at Eotvos Lorand University in Hungary, where he served as a research fellow from December 2021 to May 2022, thanks to a prestigious Coimbra Scholarship. Lukman's dedication extends beyond his academic pursuits, as he actively participates in the SPARKLE project, generously sponsored by the Ford Foundation. The project, based at the Centre for Adolescent Health and Development within the School of Public Health at the University of Medical Sciences (UNIMED) in Ondo City, Nigeria, underscores his commitment to impactful research.
Applied Statistical Methods
Lukman's current research interests span a wide spectrum, encompassing applied statistics, data science, computational statistics, biostatistics, and statistical modelling.
1. Lukman, A.F., Norouzirad, M., Marques, F.J., et al. (2023). Combining Kibria-Lukman and principal component estimators for the distributed lag models. Behaviormetrika, 50, 621–652. https://doi.org/10.1007/s41237-023-00198-y
2. Lukman, A.F., Farghali, R.A., Kibria, B.M.G., et al. (2023). Robust-stein estimator for overcoming outliers and multicollinearity. Scientific Reports, 13, 9066. https://doi.org/10.1038/s41598-023-36053-z
3. Lukman A.F., Kibria, B.M.G., Nziku, C.K., Amin, M., Adewuyi, E.T., & Farghali, R. (2023). K-L Estimator: Dealing with Multicollinearity in the Logistic Regression Model. Mathematics, 11, 340. https://doi.org/10.3390/math11020340
4. Lukman A.F., Arashi, M., & Prokaj, V. (2023). Robust biased estimators for Poisson regression model: Simulation and applications. Concurrency Computat Pract Exper. e7594. https://doi.org/10.1002/cpe.7594
5. Arashi, M., Lukman A.F., & Algamal, Z.Y. (2022). Liu regression after random forest for prediction and modeling in high dimension. Journal of Chemometrics. https://doi.org/10.1002/cem.3393
6. Akram, M.N., Amin, M., Kibria, B.M.G., Arashi, M., Lukman, A.F., & Afzal, N. (2022). A new improved Liu estimator for the QSAR model with inverse Gaussian response. Communications in Statistics - Simulation and Computation. https://doi.org/10.1080/03610918.2022.2059088
7. Akram, M.N., Amin, M., Lukman, A.F., & Afzal, S. (2022). Principal component ridge type estimator for the inverse Gaussian regression model. Journal of Statistical Computation and Simulation, 92(10), 2060-2089. https://doi.org/10.1080/00949655.2021.2020274
8. Ibikunle, R.A., Lukman, A.F., Titiladunayo, I.F., & Haadi, A. (2022). Modeling energy content of municipal solid waste based on proximate analysis: R-k class estimator approach. Cogent Engineering, 9(1), 2046243. https://doi.org/10.1080/23311916.2022.2046243
9. Inyinbor, A.A., Adekola, F.A., Bello, O.S., Bankole, D.T., Oreofe, T.A., Lukman, A.F., & Olatunji, G.A. (2022). Surface functionalized plant residue in cu2+ scavenging: Chemometrics of operational parameters for process economy validation. South African Journal of Chemical Engineering, 40, 144-153. https://doi.org/10.1016/j.sajce.2022.03.001
10. Lukman, A.F., Amin, M., & Kibria, B.M.G. (2021). Influence measures in gamma modified ridge type estimator. Communications in Statistics - Simulation and Computation. https://doi.org/10.1080/03610918.2021.2011925
11. Lukman, A.F., Aladeitan, B., Ayinde, K., & Abonazel, M.R. (2021). Modified ridge-type for the Poisson Regression Model: Simulation and Application. Journal of Applied Statistics. https://doi.org/10.1080/02664763.2021.1889998
12. Lukman, A.F., Adewuyi, E., Månsson, K., & Kibria, G.B.M. (2021). A new estimator for the multicollinear Poisson regression model: Simulation and application. Scientific Reports, 11, 3732. https://doi.org/10.1038/s41598-021-82582-w
13. Okunlola, O.A., Alobid, M., Olubusoye, O.E., Ayinde, K., Lukman, A.F., & Sz?cs, I. (2021). Spatial regression and geostatistics discourse with empirical application to precipitation data in Nigeria. Scientific Reports, 11, 16848.
14. Lukman, A.F., Ayinde, K., Kibria, G.B.M., & Adewuyi, E. (2020). Modified ridge-type estimator for the gamma regression model. Communications in Statistics - Simulation and Computation. https://doi.org/10.1080/03610918.2020.1752720.
15. Lukman, A.F., Ayinde, K., Binuomote, S., & Onate, A.C. (2019). Modified ridge?type estimator to combat multicollinearity: Application to chemical data. Journal of Chemometrics. https://doi.org/10.1002/cem.3125