My primary (academic) research interests lie in the applications of probabilistic and statistical methods in biology and physical science, particularly in Uncertainty Quantification, Bayesian Methods, and Sampling Methods.
During my undergraduate, I tried to use stochastic models to give insight into biochemical networks and corresponding phenomena, for example, HIV latency.
I have been focusing on developing efficient statistical algorithms for inferences, emulations, and model calibration during my Ph.D year.
Publication:
[1] Xiaolu Guo, Tao Tang, Minxuan Duan, Lei Zhang, Hao Ge. The nonequilibrium mechanism of noise-enhanced drug synergy in HIV latency reactivation. Iscience, 2022 – Elsevier arXiv link [link]
[2] Tao Tang and David Dunson. Bayesian spectrum inference and low-rank approximation of continuous-time Markov chain (CTMC). in preparation
[3] Tao Tang, Nan Wu, Xiuyuan Cheng, and David Dunson. Adaptive Bayesian Regression on Data with Low Intrinsic Dimension. arXiv:2407.09286 [arxiv link]
[4] Tao Tang, Simon Mak, and David Dunson. Hierarchical Shrinkage Gaussian Processes for Emulation and Dynamical Recovery. accepted to SIAM/ASA Journal of Uncertainty Quantification (SIAM JUQ) [arxiv link]
[5] Chih-li Song , Irene Ji Yi, Simon Mak, Wenjia Wang, Tao Tang. Stacking designs: designing multi-fidelity computer experiments with confidence. Accepted to SIAM/ASA Journal of Uncertainty Quantification (SIAM JUQ) arXiv:2211.00268 [arxiv link]
[6] Omar Melikechi, Alex Young, Tao Tang, Trevor Bowman, James Johndrow, and David Dunson. Limits of epidemic prediction using SIR models. Journal of Mathematical Biology 85 (4), 36; 2022; [arxiv link]
