top of page

PUBLICATIONS

In Refereed Journals:​

  1. J. Chilleri, Y. He, D. Bedrov, and M. R. Kirby. Optimal allocation of computational resources based on Gaussian process: Application to molecular dynamics simulations. Computational Materials Science, 2020 (accepted).

  2. L. D. Waldrop, Y. He, N. A. Battista, T. N. Peterman and L. A. Miller. Uncertainty quantifi- cation reveals the physical constraints on pumping by peristaltic hearts. J. R. Soc. Interface, 17(2020): 20200232.

  3. L. D. Waldrop, Y. He, T. L. Hedrick and J. Rader. Functional morphology of gliding flight I. Modeling reveals distinct performance landscapes based on soaring strategies. Integrative and Comparative Biology, icaa114, https://doi.org/10.1093/icb/icaa114. 2020 (accepted)

  4. J. Rader, T. L. Hedrick, Y. He and L. D. Waldrop. Functional morphology of gliding flight II. Morphology follows predictions of gliding performance. Integrative and Comparative Biology, icaa 126, https://doi.org/10.1093/icb/icaa126. 2020 (accepted).

  5. Y. He, J. Chilleri, S. K. O’Leary, M. Shur and R. Kirby. Sensitivity analysis for an electron transport system: application to the case of wurtzite gallium nitride. Journal of computational Electronics, 19(1)(2020): 103-110.

  6. M. Razi, R. Wang, Y. He, R. Kirby and L. Dal Negro. Optimization of large-scale vogel spiral arrays of plasmonic nanoparticles. Plasmonics, 14(1)(2019): 253-261.

  7. L. Waldrop, Y. He and S. Khatri. What can computational modeling tell us about the diversity of odor-capture structures in the pancrustacea? J. Chem. Ecol., 44(12)(2018): 1084-1100.

  8. Y. He, M. Razi, C. Forestiere, L. Dal Negro and R. M.  Kirby. Uncertainty quantification guided robust design for nanoparticles’ morphology. Computer Methods in Applied Mechanics and Engineering. 336(2018): 578-593.

  9. A. Bhaduri, Y. He, M. D. Shields, L. Graham-Brady and R. M. Kirby. Stochastic collocation approach with adaptive mesh refinement for parametric uncertainty analysis. J. Comput. Phys., 371(2018): 732-750.

  10. Y. He and D. Xiu. Numerical strategy for model correction using physical constraints. J. Comput. Phys., 313(2016): 617-634.

  11. C. Forestiere, Y. He, R. Wang, R. Kirby and L. Dal Negro. Inverse design of metal nanoparticles’ morphology. ACS Photonics, 3(1)(2016): 68-78.

  12. Y. He, M. Y. Hussaini, Y. Gong and Y. Xiao. Optimal unified combination rule in application of Dempster-Shafer theory in lung cancer radiotherapy dose response outcome analysis. J. Appl. Clin. Med. Phys., 17(1)(2016): 4-11.

  13. X. Chen, Y. He and D. Xiu. An efficient method for uncertainty propagation using fuzzy sets. SIAM J. Sci. Comput., 37(6)(2015): A248-A2507. 

  14. Y. He, M. Mirzargar, S. Hudson, R. M. Kirby and R. T. Whitaker. An uncertainty visualization technique using possibility theory: possibilistic marching cubes. Int. J. Uncertain. Quantif., 5(5)(2015): 433-451.

  15. Y. He, M. Mirzargar and R. M. Kirby. Mixed aleatory and epistemic uncertainty quantification using fuzzy set theory. Int. J. Approx. Reason., 66(2015): 1-16.

  16. C. Wang, Z. Qiu and Y. He. Fuzzy interval perturbation method for uncertain heat con- duction problem with interval and fuzzy parameters. Int. J. Numer. Meth. Eng., 104(52)(2015): 330-346.

  17. C. Wang, Z. Qiu and Y. He. Fuzzy stochastic finite element method for the hybrid uncertain temperature field prediction. Int. J. Heat Mass Tran., 91(2015): 512-519.

  18. Y. He, M. Y. Hussaini, J. Ma, B. Shafei and G. Steidl. A new fuzzy c-means method with total variation regularization for segmentation of images with noisy and incomplete data. Pattern Recognition, 45(9)(2012): 3463-3471.

  19. W. Chen, Y. Cui, Y. He, Y. Yu, J. Galvin, M. Y. Hussaini and Y. Xiao. Application of Dempster-Shafer theory in dose response outcome analysis. Phys. Med. Biol., 57(17)(2012): 5575-5585.

  20. Y. He, B. Shi and Y. Yang. Complete complementary codes based on shifted multiphase sequences (in Chinese). Signal Processing, 23(6)(2007): 941-945.

  21. Y. Yang, B. Shi and Y. He. A class of unified constructions of sequence sets with zero (low) correlation zone (in Chinese). Telemetry and Telecontrol, 4(2007): 7-11.

In Refereed Conference Proceedings:​

  1. Y. He and M. Y. Hussaini. Constructing belief functions using the principle of minimum uncertainty. 2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Glasgow, United Kingdom, 2020, pp. 1-7, doi: 10.1109/FUZZ48607.2020.9177795.

  2. M. Mirzargar, Y. He and R. M. Kirby. Application of uncertainty modeling frameworks to uncertain isosurface extraction. IUKM 2015: 4th International Symposium on Integrated Uncertainty in Knowledge Modeling and Decision Making, Nha Trang, Vietnam, Oct 2015, Proceedings, V. Huynh, M. Inuiguchi and T. Denoeux (editors), Lecture Notes in Computer Science, 2015, 9376: 336-349.

  3. Y. He and M. Y. Hussaini. An optimal unified combination rule. BELIEF2014: 3rd Interna- tional Conference on Belief Functions, Oxford, UK, Sep 2014, Proceedings, F. Cuzzolin (editor), Lecture Notes in Artificial Intelligence, 2014, 8764: 39-48.

  4. S. V. Poroseva, Y. He, M. Y. Hussaini and R. R. Mankbadi. Uncertainty quantification in the horizontal projection of flight plan trajectories using evidence theory. 13th AIAA Non- Deterministic Approaches Conference, AIAA2011-1759, Denver, CO, Apr 2011.

  5. Y. He, M. Y. Hussaini, S. V. Poroseva and R. R. Mankbadi. Uncertainty quantification in flight plan horizontal path using evidence theory. Florida Center for Advanced Aeropropulsion (FCAAP) Annual Technical Symposium and Exhibition, Tallahassee, FL, Aug 2010.

  6. S. V. Poroseva, Y. He, M. Y. Hussaini, J. J. Pesce and R. R. Mankbadi. Uncertainty quan- tification in flight plans using evidence theory: departure and arrival times. 12th AIAA Non- Deterministic Approaches Conference, AIAA2010-2678, Orlando, FL, Apr 2010.

bottom of page