MOCU | Mean Objective Cost of Uncertainty
- [NEW] Byung-Jun Yoon, Xiaoning Qian, Edward R. Dougherty, “Quantifying the multi-objective cost of uncertainty”, arXiv:2010.04653 [math.OC]
- Shahin Boluki, Xiaoning Qian, and Edward R. Dougherty, “Experimental design via generalized mean objective cost of uncertainty,” IEEE Access, vol. 7, no. 1, pp. 2223-2230, 2019.
- Byung-Jun Yoon, Xiaoning Qian, and Edward R. Dougherty, “Quantifying the objective cost of uncertainty in complex dynamical systems,” IEEE Transactions on Signal Processing, vol. 61, no. 9, pp. 2256-2266, May 2013.
OED | Optimal Experimental Design
- [NEW] Youngjoon Hong, Bongsuk Kwon, Byung-Jun Yoon, “Optimal experimental design for uncertain systems based on coupled differential equations,” arXiv:2007.06117 [math.OC]
- [NEW] Guang Zhao, Xiaoning Qian, Byung-Jun Yoon, Francis J. Alexander, and Edward R. Dougherty, “Model-based robust filtering and experimental design for stochastic differential equation systems“, IEEE Transactions on Signal Processing, vol. 68, 3849-3859, 2020.
- Roozbeh Dehghannasiri, Mohammad Shahrokh Esfahani, and Edward R. Dougherty. “An experimental design framework for Markovian gene regulatory networks under stationary control policy“, BMC Systems Biology 12, no. 8 (2018): 5-20.
- Mahdi Imani,, Roozbeh Dehghannasiri, Ulisses M. Braga-Neto, and Edward R. Dougherty. “Sequential experimental design for optimal structural intervention in gene regulatory networks based on the mean objective cost of uncertainty“, Cancer informatics 17 (2018): 1176935118790247.
- Daniel N. Mohsenizadeh, Roozbeh Dehghannasiri, and Edward R. Dougherty. “Optimal objective-based experimental design for uncertain dynamical gene networks with experimental error“, IEEE/ACM transactions on computational biology and bioinformatics 15, no. 1 (2016): 218-230.
- Roozbeh Dehghannasiri, Xiaoning Qian, and Edward R Dougherty, “Optimal experimental design in the context of canonical expansions”, IET Signal Processing 11(8), 942-951, 2017.
- Roozbeh Dehghannasiri, Byung-Jun Yoon, and Edward R. Dougherty, “Efficient experimental design for uncertainty reduction in gene regulatory networks,” BMC Bioinformatics, 16(Suppl 13):S2, 2015.
- Ariana Broumand, Mohammad Shahrokh Esfahani, Byung-Jun Yoon, Edward R. Dougherty, “Discrete optimal Bayesian classification with error-conditioned sequential sampling,” Pattern Recognition, vol. 48, no. 11, pp 3766–3782, 2015.
- Roozbeh Dehghannasiri, Byung-Jun Yoon, and Edward R. Dougherty, “Optimal experimental design for gene regulatory networks in the presence of uncertainty,” IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol.12, no.4, pp.938-950, 2015.
Robust Operators | Classification, Filtering, Compression, Learning
- Shahin Boluki, Siamak Zamani Dadaneh, Xiaoning Qian, and Edward R Dougherty, “Optimal clustering with missing values”, BMC Bioinformatics 20(12), 321, 2019.
- Siamak Zamani Dadaneh, Edward R. Dougherty, Xiaoning Qian, “Optimal Bayesian classification with missing values”, IEEE Transactions on Signal Processing 26(16), 4182-4192, 2018.
- Roozbeh Dehghannasiri, Mohammad Shahrokh Esfahani, Xiaoning Qian, and Edward R Dougherty, “Optimal Bayesian Kalman filtering with prior update”, IEEE Transactions on Signal Processing 26(8), 1982-1996, 2018.
- Roozbeh Dehghannasiri, Xiaoning Qian, and Edward R Dougherty, “Intrinsically Bayesian robust Karhunen-Loeve compression”, Signal Processing 144, 311-322, 2018.
OBTL | Optimal Bayesian Transfer Learning
- Alireza Karbalayghareh, Xiaoning Qian, and Edward R. Dougherty, “Optimal Bayesian transfer learning for count data“, IEEE/ACM Transactions on Computational Biology and Bioinformatics, early access, doi: 10.1109/TCBB.2019.2920981.
- Alireza Karbalayghareh, Xiaoning Qian, and Edward R. Dougherty, “Optimal Bayesian transfer regression“, IEEE Signal Processing Letters 25 (11), 1655-1659, 2018.
- Alireza Karbalayghareh, Xiaoning Qian, and Edward R. Dougherty, “Optimal Bayesian transfer learning“, IEEE Transactions on Signal Processing 66 (14), 3724-3739, 2018.
MKDIP | Maximal Knowledge-Driven Information Priors
- Shahin Boluki, Mohammad Shahrokh Esfahani, Xiaoning Qian, and Edward R Dougherty, “Constructing pathway-based priors within a Gaussian Mixture Model for Bayesian regression and classification”, IEEE/ACM Transactions on Computational Biology and Bioinformatics 16(2), 524-537, 2019.
- Shahin Boluki, Mohammad Shahrokh Esfahani, Xiaoning Qian, and Edward R Dougherty, “Incorporating biological prior knowledge for Bayesian learning via maximal knowledge-driven information priors”, BMC Bioinformatics 18, 552, 2017.
Applications | Materials Discovery
- Anjana Anu Talapatra, Shahin Boluki, Pejman Honarmandi, Alexandros Solomou, Guang Zhao, Seyede Fatemeh Ghoreishi, Abhilash Molkeri, Douglas Allaire, Ankit Srivastava, Xiaoning Qian, Edward R. Dougherty, Dimitris C Lagoudas, Raymundo Arroyave, “Experiment design frameworks for accelerated discovery of targeted materials across scales”, Frontiers in Materials 6(82), doi: 10.3389/fmats.2019.00082, 2019.
- Anjana Anu Talapatra, Shahin Boluki, Thien Duong, Xiaoning Qian, Edward R Dougherty, Raymundo Arroyave, “Autonomous efficient experiment design for materials discovery with Bayesian model averaging”, Physical Review Materials 2(11), 113803-(1-18), 2018.
- Alex Solomou, Guang Zhao, Shahin Boluki, Jobin K Joy, Xiaoning Qian, Ibrahim Karaman, Raymundo Arroyave, Dimitris C Lagoudas, “Multi-objective Bayesian materials discovery: Application on the discovery of precipitation strengthened NiTi shape memory alloys through micromechanical modeling”, Materials & Design 160, 810-827, 2018.
- Roozbeh Dehghannasiri, Dezhen Xue, Prasanna V. Balachandran, Mohammadmahdi R. Yousefi, Lori A. Dalton, Turab Lookman, and Edward R. Dougherty. “Optimal experimental design for materials discovery“, Computational Materials Science 129 (2017): 311-322.
- Dezhen Xue, Prasanna V. Balachandran, Ruihao Yuan, Tao Hu, Xiaoning Qian, Edward Dougherty, Turab Lookman, “Accelerated search for BaTiO3-based piezoelectrics with vertical morphotropic phase boundary using Bayesian learning”, Proceedings of the National Academy of Sciences of the United States of America (PNAS) 113(47), 13301-13306, 2016.