Professor Work Experience Professor, University of Georgia, Athens, August 2018 - present. Undergraduate Coordinator, University of Georgia, Athens, August 2016 - 2024. Associate Professor, University of Georgia, Athens, August 2011 - August 2018. Assistant Professor, University of Georgia, Athens, August 2005 - August 2011. Graduate Research Assistant, Georgia Institute of Technology, Atlanta, August 2003 - July 2005. Graduate Student Instructor/Research Assistant, University of Michigan, Ann Arbor, Sept 2001 - July 2003. Fellowship Student, Pfizer Global Research and Development, Ann Arbor, May 2002 - April 2003. Student Analyst, J. N. Center for Advanced Scientific Research, Bangalore, India, June 1999 - August 1999. Visiting Student Research Scientist, Tata Institute of Fundamental Research, Mumbai, India, Nov 1998 - Dec 1998. Education Ph. D. in Applied Statistics, Georgia Institute of Technology, Atlanta, 2005. M. A. in Statistics, University of Michigan, Ann Arbor, 2002. M. Stat. in Mathematical Statistics and Probability, Indian Statistical Institute, Calcutta, India, 2001. B. Stat. in Statistics, Indian Statistical Institute, Calcutta, India, 1999. Research Research Areas: Design of Experiments Big Data Analytics Research Interests: 1. Computer Experiments Xiao, Q.; Wang, Y.; Mandal, A. & Deng, X. (2024), ``Modelling and active learning for experiments with quantitative-sequence factors'', Journal of American Statistical Association -- Theory and Methods. DOI: 10.1080/01621459.2022.2123335 Ranjan, P.; Resch, J. & Mandal, A. (2023), ``Solving an inverse problem for time series valued computer simulators via multiple contour estimation'', Journal of Statistical Theory and Practice. 17, 23. DOI: 10.1007/s42519-022-00312-5 Jankar, J.; Wang, H.; Wilkes, L. R.; Xiao, Q. & Mandal, A. (2022), ``Design and Analysis of Complex Computer Models'', in Advances in Computational Modeling and Simulation, Eds Srinivas, R.; Kumar, R. and Dutta, M., Springer Nature Singapore, Series: Lecture Notes in Mechanical Engineering. Lukemire, J.; Xiao, Q.; Mandal, A. & Wong, W. K. (2021), ``Statistical analysis of complex computer models in astronomy'', The European Physical Journal Special Topics, 230, 2253 -- 2263. Xiao, Q.; Mandal, A.; Lin, C. D. & Deng, X. (2021) ``EzGP: Easy-to-interpret Gaussian Process models for computer experiments with both quantitative and qualitative factors'', SIAM / ASA Journal on Uncertainty Quantification, 9(2), 333 -- 353. Bhattacharjeea, N.; Ranjan, P.; Mandal, A. & Tollner, E. W. (2019) ``A history matching approach for calibrating hydrological models'', Environmental and Ecological Statistics, 26, 87 -- 105. Mandal, A.; Ranjan, P; & Wu, C. F. J. (2009), ``D-SELC: Optimization by Sequential Elimination of Level Combinations using Genetic Algorithms and Gaussian Processes'', Annals of Applied Statistics, 3, 398-421. 2. Design of Experiments, Optimization and Statistical Process Control Crossover Designs Jankar, J.; Yang, J. & Mandal, A. (2023), ``A general equivalence theorem for crossover designs under generalized linear models'', Sankhya -- Series B. Jankar, J. & Mandal, A. (2021), ``Optimal crossover designs for generalized linear models: an application to work environment experiment'', Statistics and Applications, 19(1), 319 -- 336. Jankar, J.; Mandal, A. & Yang, J. (2020), ``Optimal cross-over designs for generalized linear models'', Journal of Statistical Theory and Practice, 14:23, DOI: 10.1007/s42519-020-00089-5. Big Data Analytics Meng, C., Xie, R., Mandal, A., Zhang, X., Zhong, W. & Ma, P. (2021), ``LowCon: A design-based subsampling approach in a misspecified linear model'', Journal of Computational and Graphical Statistics, 30(3), 694 -- 708. Meng, C.; Wang, Y.; Zhang, X.; Mandal, A.; Zhong, W.; & Ma, P. (2017) ``Effective Statistical Methods for Big Data Analytics'', in Handbook of Research on Applied Cybernetics and Systems Science, Eds. Saha, S.; Mandal, A.; Narasimhamurthy, A.; Sarasvathi, V. and Sangam, S. , IGI Global, DOI: 10.4018/978-1-5225-2498-4.ch014. Algorithmic Searches for Designs Lukemire, J.; Mandal, A. & Wong, W. K. (2020), ``Optimal Experimental Designs for Ordinal Models with Mixed Factors for Industrial and Healthcare Applications'', Journal of Quality Technology, DOI: 10.1080/00224065.2020.1829215. Stokes, Z.; Mandal, A. & Wong, W. K. (2020), ``Using differential evolution to design optimal experiments'', Chemometrics and Intelligent Laboratory Systems, 199, 103955, DOI: 10.1016/j.chemolab.2020.103955. Lukemire, J.; Mandal, A. & Wong, W. K. (2019), ``D-QPSO: A quantum particle swarm technique for finding D-Optimal designs with mixed factors and a binary response'', Technometrics, 26, 87 -- 105. Mandal, A.; Yu, Y. & Wong, W.-K. (2015), ``Algorithmic Searches for Optimal Designs'', in Handbook of Design and Analysis of Experiments, Eds Dean, A., Morris, M., Stufken, J. and Bingham, D., Chapman and Hall/CRC, Series: Chapman & Hall/CRC Handbooks of Modern Statistical Methods, 755 -- 783. Johnson, K.; Mandal, A. & Ding, T. (2008) ``Software for Implementing the Sequential Elimination of Level Combinations Algorithm'', Journal of Statistical Software, 25, 1-13. Generalized Linear Models and More Yang, J.; Tong, L. & Mandal, A. (2017), ``D-optimal designs with ordered categorical data'', Statistica Sinica, 27, 1879 -- 1902. Yang, J.; Mandal, A. & Majumdar, D. (2016), ``Optimal Designs for 2^k factorial experiments with binary response'', Statistica Sinica, 26, 385 -- 411. Yang, J. & Mandal, A. (2015), ``D-optimal Designs under Generalized Linear Models'', Communications in Statistics -- Simulation and Computation, 44, 2264 -- 2277. Yang, J.; Mandal, A. & Majumdar, D. (2012), ``Optimal Designs for Two-level Factorial Experiments with Binary Response'', Statistica Sinica, 22, 885 -- 907. Functional Magnatic Resonance Imaging (fMRI) Kao, M. H.; Majumdar, D.; Mandal, A. & Stufken, J. (2013), ``Maximin and Maximin-Efficient Event-Related FMRI Designs Under A Nonlinear Model'', Annals of Applied Statistics, 7, 1940 -- 1959. Kao, M. H.; Mandal, A & Stufken, J. (2012), ``Constrained Multiobjective Designs for Functional Magnetic Resonance Imaging Experiments via a Modified Non-Dominated Sorting Genetic Algorithm'', Journal of the Royal Statistical Society: Series C (Applied Statistics), 61, 1-20. Kao, M. H.; Mandal, A. & Stufken, J. (2009), ``Efficient Designs for Event-Related Functional Magnetic Resonance Imaging with Multiple Scanning Sessions'', Communications in Statistics -- Theory and Methods: Celebrating 50 Years in Statistics Honoring Professor Shelley Zacks, 38, 3170-3182. Kao, M. H.; Mandal, A.; Lazar, N.; & Stufken, J. (2009), ``Multi-objective Optimal Experimental Designs for Event-Related fMRI Studies'', NeuroImage, 44, 849-856. Kao, M. H.; Mandal, A. & Stufken, J. (2008), ``Optimal Design for Event-related Functional Magnetic Resonance Imaging Considering Both Individual Stimulus Effects and Pairwise Contrasts'', Special Volume of Statistics and Applications in Honour of Professor Aloke Dey, 6, 225-241. Choice Experiments Zhang, W.; Mandal, A. & Stufken, J. (2017), ``Approximations of the information matrix for a panel mixed logit model'', Journal of Statistical Theory and Practice, 11, $269-295$. Process Control Dasgupta, T. & Mandal, A. (2008), ``Estimation of process parameters to determine the optimum diagnosis interval for control of defective items'', Technometrics, 50, 167-181. Misc. Designs Chowdhury, S.; Lukemire, J. & Mandal, A. (2020), ``A-ComVar: A Flexible Extension of Common Variance Designs'', Journal of Statistical Theory and Practice, 14:16, DOI: 10.1007/s42519-019-0079-y. Kane, A. & Mandal, A. (2020), ``A new analysis strategy for designs with complex aliasing'', The American Statistician, 74 (3), 274 -- 281. Mandal, A. & Mukerjee, R. (2005), ``Design Efficiency under Model Uncertainty for Nonregular Fractions of General Factorials'', Statistica Sinica, 15, 697-707. Mandal, A. (2005), ``An Approach for Studying Aliasing Relations of Mixed Fractional Factorials Based on Product Arrays'', Stat. & Prob. Letters, 75, 203-210. Applications in Textile Engineering and Materials Research Nandy, A., Lee, E., Mandal, A., Saremi, R. & Sharma, S. (2020), ``Microencapsulation of retinyl palmitate by melt dispersion for cosmetic application'', Journal of Microencapsulation, 37 (3), 205 -- 219. Lee, B. J.; Daubenmire, S.; Lee, E.; Saremi, R.; Rai, S.; Sriram, T. N.; Mandal, A. and Sharma, S. (2019) ``The optimization of novel nanocellulose gel-reactive dye coating for textile applications'', Colourage, 66 (6), 32 -- 41. Jones, A.; Pant, J.; Lee, A.; Goudie, M.; Gruzd, A.; Mansfield, J.; Mandal, A.; Sharma, S. & Handa, H. (2018), ``Nitric oxide releasing antibacterial albumin plastic for biomedical applications'', Journal of Biomedical Materials Research: Part A, 106, 1535 -- 1542. Jones, A.; Mandal, A. & Sharma, S. (2018), ``Antibacterial and drug elution performance of thermoplastic blends'', Journal of Polymers and the Environment, 26(1), 132 -- 144. Wang, K.; Mandal, A., Ayton, E., Hunt, R., Zeller, A. & Sharma, S. (2016) ``Modification of protein rich algal-biomass to form bio-plastics and odor removal'', In: Protein Byproducts: Transformation from Environmental Burden Into Value-Added Products, Ed. Dhillon, G.S., Elsevier publishers, 107 -- 117. Jones, A.; Mandal, A. & Sharma, S. (2015), ``Protein based bioplastics and their antibacterial potential'', Journal of Applied Polymer Science, 132, 41931. 3. Survey Sampling and Bayesian Methods Goyal, S.; Datta, G. & Mandal, A. (2021), ``Hierarchical Bayes unit-level small area estimation model for normal mixture populations'', Sankhya -- Series B, 83, 215 -- 241. Chakraborty, A.; Datta, G. & Mandal, A. (2019), ``Robust hierarchical Bayes small area estimation for nested error regression model'', International Statistical Review, 87, 158 -- 176. Chakraborty, A.; Datta, G. & Mandal, A. (2016), ``A two-component normal mixture alternative to the Fay-Herriot model'', Joint issue of Statistics in Transition new series and Survey Methodology Part II, 17, 67 -- 90. Datta, G. & Mandal, A., (2015) ``Small Area Estimation with Uncertain Random Effects'', Journal of the American Statistical Association - Theory and Methods, 110, 1735 -- 1744. Datta, G.; Hall, P. & Mandal, A. (2011), ``Model Selection by Testing for the Presence of Small-area Effects in Area-level Data'', Journal of the American Statistical Association - Theory and Methods, 106, 362-374. 4. Applications Drug Discovery Mandal, A.; Johnson, K.; Wu, C. F. J. & Bornemeier, D. (2007), ``Identifying Promising Compounds in Drug Discovery: Genetic Algorithms and Some New Statistical Techniques'', Journal of Chemical Information and Modeling, 47, 981-988. Mandal, A.; Wu, C. F. J. & Johnson, K. (2006), ``SELC: Sequential Elimination of Level Combinations by means of modified Genetic Algorithms'', Technometrics, 48, 273-283. Other Applications Wang, H.; Baker, E. W.; Mandal, A.; Pidaparti, R. A.; West, F. D. & Kinder, H. A. (2021), ``Identification of predictive MRI and functional biomarkers in a pediatric piglet traumatic brain injury model'', Neural Regeneration Research, 16(2), 338 -- 344. Kaimal, A.; Al Mansi, M.; Bou Dagher, J.; Pope, C.; Varghese, M.; Rudi, T.; Almond, A.; Cagle, L.; Beyene, H.; Bradford, W.; Whisnant, B.; Bougouma, B.; Rifai, K. J., Chuang, Y-J.; Campbell, E.; Mandal, A.; MohanKumar, P. & MohanKumar, S. (2021), ``Prenatal exposure to bisphenols affects pregnancy outcomes and offspring development in rats'', Chemosphere, 276, 130118. Bou Dagher, J.; Hahn-Townsend, C.; Kaimal, A.; Al Mansi, M.; Henriquez, J.; Tran, D.; Laurent, C.; Bacak, C.; Buechter, H.; Cambric, C.; Spivey, J.; Chuang, Y-J.;Campbell, E.; Mandal, A.; MohanKumar, P. & MohanKumar, S. (2021), ``Independent and combined effects of Bisphenol A and Diethylhexyl Phthalate on gestational outcomes and offspring development in Sprague-Dawley rats'', Chemosphere, 263, 128307. Banik, P.; Mandal, A. & Rahaman, S. (2002), ``Markov Chain Analysis of Weekly Rainfall Data in Determining Drought-proneness'', Discrete Dynamics in Nature and Society, 7, 231-239. Mandal, A. & Sengupta, D. (2000), ``Fatal accidents in Indian Coal Mines'', Calcutta Statistical Association Bulletin, 50, 95-120. 5. Book Review Mandal, A. (2008), Matrix Algebra: Theory, Computations, and Applications in Statistics by James E. Gentle, Journal of the American Statistical Association, 103, 1716-1717. 6. Unpublished Research Bargo, A. M.; Mandal, A.; Seymour, L.; McDowell, J. & Lazar, N. A., ``Social Network Models for Identifying Active Brain Regions from fMRI Data''. Chakraborty, A.; Lukemire, J.; Mandal, A. & Johnson, K., ``In Search of Desirable Compounds''. 7. Software Li, J; Xiao, Q.; Mandal, A.; Lin, C. D. & Deng, X. (2023), EzGP: Easy-to-Interpret Gaussian Process Models for Computer Experiments, R Library \urlhttps://cran.r-project.org/web/packages/EzGP/index.html. Wang H.; Xiao, Q. & Mandal, A. (2021), LHD: Latin Hypercube Designs (LHDs), R Library \urlhttps://cran.r-project.org/web/packages/LHD/index.html, 22,776 cumulative downloads as of August 23, 2023. Wang H.; Xiao, Q. & Mandal, A. (2021), LA: Lioness Algorithm (LA), R Library \urlhttps://cran.r-project.org/web/packages/LA/index.html, 3,843 cumulative downloads as of 3/20/2022. 7. Software Li, J; Xiao, Q.; Mandal, A.; Lin, C. D. & Deng, X. (2023), EzGP: Easy-to-Interpret Gaussian Process Models for Computer Experiments, R Library https://cran.r-project.org/web/packages/EzGP/index.html. Wang H.; Xiao, Q. & Mandal, A. (2021), LHD: Latin Hypercube Designs (LHDs), R Library https://cran.r-project.org/web/packages/LHD/index.html, 22,776 cumulative downloads as of August 23, 2023. Wang H.; Xiao, Q. & Mandal, A. (2021), LA: Lioness Algorithm (LA), R Library https://cran.r-project.org/web/packages/LA/index.html, 3,843 cumulative downloads as of March 20, 2022. Selected Publications Some contributions to Design Theory and Applications - A thesis presented to the academic faculty