Professor Undergraduate Coordinator Work Experience Professor, University of Georgia, Athens, August 2018 - present. Undergraduate Coordinator, University of Georgia, Athens, August 2016 - present. 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: Design of Experiments and Statistical Process Control 1. Chowdhury, S.; Lukemire , J. & Mandal, A. (2019), A-ComVar: A Flexible Extension of Common Variance Designs, submitted. 2. Kane, A. & Mandal, A. (2019), A new analysis strategy for designs with complex aliasing. The American Statistician, accepted. (Codes) 3. Lukemire , J.; Mandal, A. & Wong, W. K. (2019), d-QPSO: A Quantum-Behaved Particle Swarm Technique for Finding D-Optimal Designs for Models with Mixed Factors and a Binary Response Technometrics, 26, 87-105. 4. 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. 5. Yang, J.; Tong, L. & Mandal, A. (2017), D-optimal designs with ordered categorical data. Statistica Sinica, 27, 1879-1902. 6. Yang, J.; Mandal, A. & Majumdar, D. (2016), Optimal Designs for 2k Factorial Experiments with Binary Response. Statistica Sinica, 26, 385-411. 7. Yang, J. & Mandal, A. (2015), D-Optimal Factorial Designs under Generalized Linear Models. Communications in Statistics, 44, 2264-2277. 8. Yang, J.; Mandal, A. & Majumdar, D. (2012), Optimal Designs for Two-level Factorial Experiments with Binary Response. Statistica Sinica, 22, 885-907. 9. Dasgupta, T. & Mandal, A. (2008), Estimation of Process Parameters to Determine the Optimum Diagnosis Interval for Control of Defective Items, Technometrics, 50, 167-181. 10. Mandal, A. & Mukerjee, R. (2005), Design Efficiency under Model Uncertainty for Nonregular Fractions of General Factorials, Statistica Sinica, 15, 697-707. 11. Mandal, A. (2005), A Friendly Approach to Studying Aliasing Relations of Mixed Factorials in the Form of Product Arrays, Stat. Prob. Letters, 75, 203-210. Small Area Estimation 1. Goyal, S.; Datta, G. and Mandal, A. (2019), A Hierarchical Bayes Unit-Level Small Area Estimation Model for Normal Mixture Populations, submitted. 2. Chakraborty, A.; Datta, G. and Mandal, A. (2019), Robust hierarchical Bayes small area estimation for nested error regression model, International Statistical Review, DOI: 10.1111/insr.12283. 3. Chakraborty, A.; Datta, G. and 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. 4. Datta, G. and Mandal, A. (2015), Small Area Estimation with Uncertain Random Effects, Journal of the American Statistical Association - Theory and Methods, 110, 1735-1744. 5. Datta, G.; Hall, P.; & Mandal, A. (2011), Model selection by testing for the presence of small-area effects in area-level data. (Supplementary materials) Journal of the American Statistical Association - Theory and Methods,2011, 362-374. Functional Magnatic Resonance Imaging (fMRI) 1. 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. (supplementary materials) 2. Kao, M. H.; Mandal, A & Stufken, J. (2012) Constrained Multi-objective Designs for Functional MRI Experiments via A Modified NSGA-II. Journal of the Royal Statistical Society: Series C (Applied Statistics), 61, 515-534. Matlab Codes 3. 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. Matlab Codes 4. Kao, M. H.; Mandal, A; Lazar, N; & Stufken, J. (2009), Multi-objective Optimal Experimental Designs for Event-Related fMRI Studies, NeuroImage, 44, 849-856. Technical Report Matlab Codes 5. 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. Drug Discovery 1. Mandal, A.; Ranjan, P; & Wu, C. F. J. (2009), G-SELC: Optimization by Sequential Elimination of Level Combinations using Genetic Algorithms and Gaussian Processes, Annals of Applied Statistics, 3, 398-421. 2. Johnson, K; Mandal, A; & Ding, T. (2008), Software for Implementing the Sequential Elimination of Level Combinations Algorithm, Journal of Statistical Software, 25, 6, 1-13. Matlab codes, SAS codes, R codes, Initial Design, Forbidden Array. 3. 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. DDN News 4. 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. (slides) Applications 1. 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. 2. Mandal, A & Sengupta, D.(2000), Fatal accidents in Indian Coal Mines, Calcutta Statistical Association Bulletin, 50, 95-120. (scanned) 3. Jones, A.; Mandal, A. & Sharma, S. (2015), Protein based bioplastics and their antibacterial potential, Journal of Applied Polymer Science, 132, 41931. 4. Jones, A.; Mandal, A. & Sharma, S. (2017), Antibacterial and drug elution performance of thermo-plastic blends, Journal of Polymers and the Environment, https://doi.org/10.1007/s10924-016-0924-y. 5. Jones, A., Pant, J., Lee, E., 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, 106, 1535-1542. 6. 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. 7. Chakraborty, J.; Mandal, A. & Finkelman, R. B. (2019), Association between geogenic organic contaminants in groundwater from Carrizo-Wilcox aquifer and the incidence of renal diseases: a preliminary study in east Texas, submitted. Book Chapter 1. Meng, C., Wang, Y., Zhang, X., Mandal, A. & Ma, P. (2016) Effective Statistical Methods for Big Data Analytics, in Handbook of Research on Applied Cybernetics and Systems Science, IGI Global. 2. Mandal, A.; Wong, W. K. & Yu, Y. (2014) Algorithmic Searches for Optimal Designs, in Handbooks on Modern Statistical Methods, Chapman & Hall/CRC. 3. Wang, K., Mandal, A., Ayton, E., Hunt, R., Zeller, A. & Sharma, S. (2015) Modification of protein rich algal-biomass to form bio-plastics and odor removal, to appear in "Modification of waste derived proteins products for high value applications", In: Waste-derived proteins: Transformation from environmental burden into value-added products, Ed. Dhillon, G.S., Elsevier publishers. Book Review 1. Mandal, A. (2008), Matrix Algebra: Theory, Computations, and Applications in Statistics by James E. Gentle, Journal of the American Statistical Association, 103, 1716-1717. Unpublished Research 1. Bargo, A.; Mandal, A.; Seymour, L.; McDowell, J.; & Lazar, A. (2011), Social network models for identifying active brain regions from fMRI data. 2. Chakraborty, A.; Mandal, A.; & Johnson, K. (2013), In Search of Desirable Compounds. Selected Publications Some contributions to Design Theory and Applications - A thesis presented to the academic faculty
