Professor
Undergraduate Coordinator

Contact Info

Office:
310 Herty Dr. (Room 412)
Phone Number:
Curriculum Vitae:
Personal Website:

Work Experience

Research Areas:
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 aliasingThe 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 Practice11, 269-295.

5. Yang, J.; Tong, L. & Mandal, A. (2017), D-optimal designs with ordered categorical dataStatistica Sinica, 27, 1879-1902.

6. Yang, J.; Mandal, A. & Majumdar, D. (2016), Optimal Designs for 2k Factorial Experiments with Binary ResponseStatistica Sinica, 26, 385-411.

7. Yang, J. & Mandal, A. (2015), D-Optimal Factorial Designs under Generalized Linear ModelsCommunications in Statistics44, 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 FactorialsStatistica 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 modelInternational 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 modelJoint 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 ModelAnnals of Applied Statistics7, 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 StudiesNeuroImage, 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 ContrastsSpecial 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 ProcessesAnnals 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 codesR 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 TechniquesJournal 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 AlgorithmsTechnometrics, 48, 273-283. (slides)

Applications

1. Banik, P.; Mandal, A. & Rahaman, S. (2002), Markov Chain Analysis of Weekly Rainfall Data in Determining Drought-pronenessDiscrete 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 potentialJournal 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 Environmenthttps://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 Research106, 1535-1542.

6. Bhattacharjeea, N.; Ranjan, P.; Mandal, A. & Tollner, E. W. (2019), A history matching approach for calibrating hydrological modelsEnvironmental 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.

Undergrads

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

Articles Featuring Abhyuday Mandal

Friday, March 11, 2011 - 5:00pm

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 will continue to excel!