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Abhyuday Mandal

Blurred image of the arch used as background for stylistic purposes.
Undergraduate Coordinator

Work Experience

Research Interests:
1.    Computer Experiments
  1. 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
  2. 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
  3. 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.
  4. 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.
  5. 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.
  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. 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
  1. Jankar, J.; Yang, J. & Mandal, A. (2023), ``A general equivalence theorem for crossover designs under generalized linear models'',  Sankhya  --  Series B.
  2. 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.
  3. 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
  1. 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.
  2. 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
  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. 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
  1. Yang, J.; Tong, L. & Mandal, A. (2017), ``D-optimal designs with ordered categorical data'', Statistica Sinica,  27, 1879 -- 1902.
  2. Yang, J.; Mandal, A. & Majumdar, D. (2016), ``Optimal Designs for 2^k factorial experiments with binary response'',   Statistica Sinica, 26,  385 -- 411.
  3. Yang, J. & Mandal, A. (2015), ``D-optimal Designs under Generalized Linear Models'',  Communications in Statistics  --  Simulation and Computation, 44, 2264 -- 2277.
  4. 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)
  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.
  2. 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.
  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.
  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.      
  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.      

Choice Experiments
  1. 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
  1. 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
  1. 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.
  2. Kane, A. & Mandal, A. (2020), ``A new analysis strategy for designs with complex aliasing'', The American Statistician, 74 (3), 274 -- 281.
  3. Mandal, A. & Mukerjee, R. (2005), ``Design Efficiency under Model Uncertainty for Nonregular Fractions of General Factorials'',  Statistica Sinica, 15, 697-707.      
  4.  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
  1. 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.
  2. 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.
  3.  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.
  4. 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.
  5. 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.
  6. 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
  1.  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.
  2.  Chakraborty, A.; Datta, G. & Mandal, A. (2019), ``Robust hierarchical Bayes small area estimation for nested error regression model'',  International Statistical Review, 87, 158 -- 176.
  3. 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.
  4. Datta, G. & 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'',  Journal of the American Statistical Association - Theory and Methods, 106, 362-374.      


4. Applications

Drug Discovery
  1. 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.      
  2. 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
  1. 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.
  2. 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.
  3. 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.
  4. 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.      
  5.  Mandal, A. & Sengupta, D. (2000), ``Fatal accidents in Indian Coal Mines'',  Calcutta Statistical Association Bulletin,  50,  95-120.


5. 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.


6. Unpublished Research
  1. Bargo, A. M.; Mandal, A.; Seymour, L.; McDowell, J. & Lazar, N. A., ``Social Network Models for Identifying Active Brain Regions from fMRI Data''.
  2. Chakraborty, A.; Lukemire, J.; Mandal, A. & Johnson, K., ``In Search of Desirable Compounds''. 7. Software
  3. Li, J; Xiao, Q.; Mandal, A.; Lin, C. D. & Deng, X. (2023), EzGP: Easy-to-Interpret Gaussian Process Models for Computer Experiments, R Library \url
  4. Wang H.; Xiao, Q. & Mandal, A. (2021), LHD: Latin Hypercube Designs (LHDs), R Library \url,  22,776 cumulative downloads as of August 23, 2023.
  5. Wang H.; Xiao, Q. & Mandal, A. (2021), LA: Lioness Algorithm (LA), R Library \url,  3,843 cumulative downloads as of 3/20/2022.
7. Software
  1.  Li, J; Xiao, Q.; Mandal, A.; Lin, C. D. & Deng, X. (2023), EzGP: Easy-to-Interpret Gaussian Process Models for Computer Experiments, R Library
  2.  Wang H.; Xiao, Q. & Mandal, A. (2021), LHD: Latin Hypercube Designs (LHDs), R Library,  22,776 cumulative downloads as of August 23, 2023. 
  3.  Wang H.; Xiao, Q. & Mandal, A. (2021), LA: Lioness Algorithm (LA), R Library,  3,843 cumulative downloads as of March 20, 2022. 


Some contributions to Design Theory and Applications - A thesis presented to the academic faculty

Articles Featuring Abhyuday Mandal

The grant proposal titled “Equitable Statistics Education through Active Learning” led by team facilitators, Maduranga Dassanayake and…

Lauren Rose Wilkes, a Foundation Fellow majoring in data science with a minor in Chinese language and literature, has been selected as a Marshall Scholar. Established in 1953, the Marshall Scholarship is a postgraduate scholarship for "intellectually…

This award for excellence in teaching annually honors outstanding faculty in the Franklin College who have shown a sustained commitment to high quality instruction. Abhyuday is a very popular and successful instructor, as well as an outstanding mentor for…

Abhyuday Mandal has been recognized for the second time for his superior teaching at UGA. He was one of the five finalists from the Franklin College for an Outstanding Undergraduate Teaching Award. He was recognized at a Faculty Recognition Banquet organized by…

The Department of Statistics congratulates Dan Hall, Abhyuday Mandal, Cheolwoo Park, and Wenxuan Zhong on their recent promotion to the rank of Full Professor and Pengsheng Ji on his promotion to the rank of Associate Professor with tenure. This is an…

Congratulations to Abhyuday Mandal for receiving the 2015 Faculty division Outstanding Academic Advisor Award in the Franklin College of Arts and Sciences. He was presented with this honor for his advising skills, deep concern for his students, and going…

Abhyuday Mandal's research study with Suraj Sharma of the department of textiles, merchandising, and interiors was featured in an article for UGA Today! The study, "Protein-based bioplastics and their antibacterial potential," is available online at http…

The Department would like to congratulate Dr. Mandal on being a recipient of the 2011-2012 Sarah Moss Fellowship. Dr. Mandal will use his award in further pursuit of collaborative research at the University of Illinois at Chicago.

The department is pleased to announce the promotion of Xiangrong Yin to full professor, and the promotions of Abhyuday Mandal and Cheolwoo Park to associate professor with tenure. They have all been true assets to the department and we are confident that they…

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