Faculty Profile

Yanyan He

Title
Assistant Professor
Department
Mathematics
College
College of Science
Assistant Professor
Computer Science and Engineering
College of Engineering

    

Education

PhD, Florida State University, 2013.
Major: Applied and Computational Mathematics
Dissertation Title: Uncertainty quantification and data fusion based on Dempster-Shafer theory
MS, Florida State University, 2010.
Major: Applied and Computational Mathematics
MS, Huazhong University of Science and Technology, 2007.
Major: Computational Mathematics
BS, Huazhong University of Science and Technology, 2004.
Major: Computational Mathematics

Current Scheduled Teaching*

MATH 3410.001, Differential Equations I, Fall 2021 Syllabus
MATH 5900.712, Special Problems, Fall 2021

* Texas Education Code 51.974 (HB 2504) requires each institution of higher education to make available to the public, a syllabus for undergraduate lecture courses offered for credit by the institution.

Previous Scheduled Teaching*

CSCE 5230.001, Methods of Numerical Computations, Spring 2021 Syllabus SPOT
MATH 5290.001, Numerical Methods, Spring 2021 Syllabus SPOT
CSCE 6900.759, Special Problems, Spring 2021
MATH 5900.707, Special Problems, Spring 2021
CSCE 4930.001, Topics in Computer Science and Engineering, Spring 2021 Syllabus SPOT
MATH 3350.002, Introduction to Numerical Analysis, Fall 2020 Syllabus SPOT
MATH 3350.201, Introduction to Numerical Analysis, Fall 2020 Syllabus SPOT
MATH 3350.001, Introduction to Numerical Analysis, Spring 2020 Syllabus
CSCE 5230.001, Methods of Numerical Computations, Spring 2020 Syllabus
MATH 5290.001, Numerical Methods, Spring 2020 Syllabus

* Texas Education Code 51.974 (HB 2504) requires each institution of higher education to make available to the public, a syllabus for undergraduate lecture courses offered for credit by the institution.

Published Publications

Published Intellectual Contributions

Conference Proceeding
He, Y., Hussaini, M. Y. (2020). Constructing Belief Functions Using the Principle of Minimum Uncertainty. 6. Virtual: IEEE FUZZY.
Mirzargar, M., He, Y., Kirby, R. M. (2015). Application of uncertainty modeling frameworks to uncertain isosurface extraction. 336-349. Cham: Springer.
He, Y., Hussaini, M. Y. (2014). An optimal unified combination rule. 39-48. Cham: Springer.
Journal Article
Chilleri, J., He, Y., Bedrov, D., Kirby, R. M. (2021). Optimal allocation of computational resources based on Gaussian process: Application to molecular dynamics simulations. Computational Materials Science. 188, 110178. Elsevier.
He, Y., Chilleri, J., O'Leary, S. K., Shur, M. S., Kirby, R. M. (2020). Sensitivity analysis for an electron transport system: application to the case of wurtzite gallium nitride. Journal of Computational Electronics. 19, 103-110. https://doi.org/10.1007/s10825-019-01424-1
Waldrop, L. D., He, Y., Hedrick, T. L., Rader, J. A. (2020). Functional Morphology of Gliding Flight I: Modeling Reveals Distinct Performance Landscapes Based on Soaring Strategies. Integrative and Comparative Biology. 60(5), 1283-1296. https://api.elsevier.com/content/abstract/scopus_id/85096814648
Rader, J. A., Hedrick, T. L., He, Y., Waldrop, L. D. (2020). Functional Morphology of Gliding Flight II. Morphology Follows Predictions of Gliding Performance. Integrative and Comparative Biology. 60(5), 1297-1308. https://api.elsevier.com/content/abstract/scopus_id/85096814520
Waldrop, L. D., He, Y., Battista, N. A., Neary Peterman, T., Miller, L. A. (2020). Uncertainty quantification reveals the physical constraints on pumping by peristaltic hearts.. Other. 17(170), 20200232.
Razi, M., Wang, R., He, Y., Kirby, R. M., Dal Negro, Luca, (2019). Optimization of Large-Scale Vogel Spiral Arrays of Plasmonic Nanoparticles. Plasmonics. 14(1), 253-261.
Waldrop, L. D., He, Y., Khatri, S. (2018). What Can Computational Modeling Tell Us about the Diversity of Odor-Capture Structures in the Pancrustacea?. Other. 44(12), 1084-1100.
Bhaduri, A., He, Y., Shields, M. D., Graham-Brady, L., Kirby, R. M. (2018). Stochastic collocation approach with adaptive mesh refinement for parametric uncertainty analysis. Journal of Computational Physics. 371, 732-750. https://api.elsevier.com/content/abstract/scopus_id/85044111499
He, Y., Razi, M., Forestiere, C., Dal Negro, L., Kirby, R. M. (2018). Uncertainty quantification guided robust design for nanoparticles’ morphology. Computer Methods in Applied Mechanics and Engineering. 336, 578-593. https://api.elsevier.com/content/abstract/scopus_id/85056362031
Forestiere, C., He, Y., Wang, R., Kirby, R. M., Dal Negro, Luca, (2016). Inverse Design of Metal Nanoparticles' Morphology. Other. 3(1), 68-78.
He, Y., Xiu, D. (2016). Numerical strategy for model correction using physical constraints. Journal of Computational Physics. 313, 617-634.
He, Y., Hussaini, M. Y., Gong, Y. U., Xiao, Y. (2016). Optimal unified combination rule in application of Dempster-Shafer theory to lung cancer radiotherapy dose response outcome analysis.. Other. 17(1), 4-11.
Chen, X., He, Y., Xiu, D. (2015). AN EFFICIENT METHOD FOR UNCERTAINTY PROPAGATION USING FUZZY SETS. SIAM Journal on Scientific Computing. 37(6), A2488-A2507.
He, Y., Mirzargar, M., Hudson, S., Kirby, R. M., Whitaker, R. T. (2015). AN UNCERTAINTY VISUALIZATION TECHNIQUE USING POSSIBILITY THEORY: POSSIBILISTIC MARCHING CUBES. Other. 5(5), 433-451.
Wang, C., Qiu, Z., He, Y. (2015). Fuzzy interval perturbation method for uncertain heat conduction problem with interval and fuzzy parameters. International Journal for Numerical Methods in Engineering. 104(5), 330-346.
Wang, C., Qiu, Z., He, Y. (2015). Fuzzy stochastic finite element method for the hybrid uncertain temperature field prediction. International Journal of Heat and Mass Transfer. 91, 512-519.
He, Y., Mirzargar, M., Kirby, R. M. (2015). Mixed aleatory and epistemic uncertainty quantification using fuzzy set theory. Other. 66, 1-15.
He, Y., Hussaini, M. Y., Ma, J., Shafei, B., Steidl, G. (2012). A new fuzzy c-means method with total variation regularization for segmentation of images with noisy and incomplete data. Pattern Recognition. 45(9), 3463-3471.
Chen, W., Cui, Y., He, Y., Yu, Y., Galvin, J., Hussaini, M. Y., Xiao, Y. (2012). Application of Dempster-Shafer theory in dose response outcome analysis. 57(17), 5575.

Awarded Grants

Contracts, Grants and Sponsored Research

Grant - Research
He, Y. (Principal), "Material Design under Uncertainty (Funded to UNT)," Sponsored by Army Research Laboratory, Federal, $59727 Funded. (June 30, 2020November 8, 2020).
He, Y. (Principal), "Material Design Under Uncertainty (Funded to New Mexico Tech)," Sponsored by Army Research Laboratory, Federal, $276111 Funded. (November 2016May 2020).
,
Overall
Summative Rating
Challenge and
Engagement Index
Response Rate

out of 5

out of 7
%
of
students responded
  • Overall Summative Rating (median):
    This rating represents the combined responses of students to the four global summative items and is presented to provide an overall index of the class’s quality. Overall summative statements include the following (response options include a Likert scale ranging from 5 = Excellent, 3 = Good, and 1= Very poor):
    • The course as a whole was
    • The course content was
    • The instructor’s contribution to the course was
    • The instructor’s effectiveness in teaching the subject matter was
  • Challenge and Engagement Index:
    This rating combines student responses to several SPOT items relating to how academically challenging students found the course to be and how engaged they were. Challenge and Engagement Index items include the following (response options include a Likert scale ranging from 7 = Much higher, 4 = Average, and 1 = Much lower):
    • Do you expect your grade in this course to be
    • The intellectual challenge presented was
    • The amount of effort you put into this course was
    • The amount of effort to succeed in this course was
    • Your involvement in course (doing assignments, attending classes, etc.) was
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