MOCU | Mean Objective Cost of Uncertainty

  1. [NEW] Byung-Jun Yoon, Xiaoning Qian, Edward R. Dougherty, “Quantifying the multi-objective cost of uncertainty”, arXiv:2010.04653 [math.OC]
  2. 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.
  3. 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

  1. [NEW] Youngjoon Hong, Bongsuk Kwon, Byung-Jun Yoon, “Optimal experimental design for uncertain systems based on coupled differential equations,” arXiv:2007.06117 [math.OC]
  2. [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.
  3. 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.
  4. 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.
  5. 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.
  6. 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.
  7. 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.
  8. 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.
  9. 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

  1. Shahin Boluki, Siamak Zamani Dadaneh, Xiaoning Qian, and Edward R Dougherty, “Optimal clustering with missing values”, BMC Bioinformatics 20(12), 321, 2019.
  2. Siamak Zamani Dadaneh, Edward R. Dougherty, Xiaoning Qian, “Optimal Bayesian classification with missing values”, IEEE Transactions on Signal Processing 26(16), 4182-4192, 2018.
  3. 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.
  4. 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

  1. 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.
  2. Alireza Karbalayghareh, Xiaoning Qian, and Edward R. Dougherty, “Optimal Bayesian transfer regression“, IEEE Signal Processing Letters 25 (11), 1655-1659, 2018.
  3. 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

  1. 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.
  2. 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

  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. 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.