Conferences

1. C. Dai, F. Ke, Y. Pan and Y. Liu, A mixed-methods study exploring learning support use in digital game-based math learning, American Educational Research Association (AERA) Annual Meeting, Chicago, IL, USA, Apr. 13-16, 2023.
2. C. Dai, F. Ke, Y. Pan and Y. Liu, Cluster analysis of learning support use in digital game-based math learning, Association for Educational Communications & Technology international convention (AECT), Las Vegas, NV, USA, Oct. 24-28, 2022.
3. E. Shapiro, E. Austin and Y. Liu, Feature space in biological data science, CU Denver Data Science Symposium 2022, Denver, CO, USA, Sep. 2, 2022.
4. T. Butler, Y. Liu and A. Spiegler (all presenters), Creating a dynamic learning environment with computational notebooks, 2022 Colorado Learning and Teaching with Technology Conference, Boulder, CO, USA, Aug. 3-4, 2022.
5. T. Butler, Y. Liu and A. Spiegler (all presenters), OER for the Creation of Interactive Computational Notebooks and a Computational Pathway in Mathematics and Statistics, 2022 Colorado OER Conference, Denver, CO, USA, June 24, 2022.
6. Y. Liu, Machine Learning in Python, Association for Women in Mathematics (AWM) Programming Workshop, Denver, Colorado, Apr. 2022.
7. Y. Liu, N. Alhubieshi, Global Sensitivity Analysis with Surrogate Modeling using Fourier Amplitude Sensitivity Testing, Tenth International Conference ON Sensitivity Analysis OF Model Output (SAMO), Tallahassee, Florida, Mar. 14-16, 2022.
8. Y. Liu, High Dimensional Model Representation Based on Fourier Amplitude Sensitivity Testing, SIAM Conference on Computational Science and Engineering, Mar. 1-5, 2021, chair of the session titled Recent Advances in Regression and Interpolation Methods for Surrogate and Data-Driven Modeling.
9. Lu Vy and Y. Liu, Variance Reduction Methods Based on Multilevel Monte Carlo, 16th Front Range Applied Mathematics (FRAM) Student Conference, Denver, Colorado, Mar. 7, 2020.
10. Y. Liu, From HDMR to FAST-HDMR: Surrogate Modeling for Uncertainty Quantification, Computational Mathematics Colloquium, CU Denver, Feb. 10, 2020.
11. Lu Vy and Y. Liu, Variance Reduction Methods Based on Multilevel Monte Carlo for Option Pricing, 2020 Joint Mathematics Meetings, Denver, Colorado, Jan. 15--Jan. 18, 2020.
12. Y. Liu, J. Tang and W.J. Riley, Uncertainty Quantification for the Duke Forest Ecosystem Modeling with the Ecosys Model, 2019 American Geophysical Union Fall Meeting, San Francisco, California, Dec. 9--14, 2019
13. A.R. Marklein et al., Modeling carbon storage and water use efficiency in a California agro-ecosystem, 2019 American Geophysical Union Fall Meeting, San Francisco, California, Dec. 9--14, 2019
14. Lu Vy, E. Austin and Y. Liu, Multi-Level Monte Carlo Using Quasi-Random Numbers, 2019 Joint Statistical Meetings, Denver, Colorado, Jul. 27--Aug. 1, 2019.
15. Y. Liu and L. Zheng, Efficient reduced-order models for evaluating the impact of CO2 and brine leakage on groundwater, 2018 TOUGH Symposium, Lawrence Berkeley National Laboratory, Berkeley, CA, Oct. 2--4, 2018 (poster)
16. Y. Liu, Accurate and efficient Bayesian parameter inversion based on low-fidelity model solutions, International Applied Computational Electromagnetics Society Symposium, Denver, Colorado, Mar. 24-29, 2018.
17. Marklein, A. R.; Riley, W. J.; Grant, R. F.; Mezbahuddin, S.; Mekonnen, Z. A.; \textbf{Liu, Y.}; Ying, S., Modeling applications for precision agriculture in the California Central Valley, 2017 American Geophysical Union Fall Meeting, New Orleans, Louisiana, Dec. 11-15, 2017.
18. Y. Liu Mining Amazon product reviews-An example of data science project, University of Colorado Denver Statistics Seminar, Sep. 18, 2017.
19. Y. Liu Bayesian inversion with implicit particle filters and reduced order modeling with application to a hydrological model, University of Colorado Denver Computational Mathematics Seminar, Sep. 11, 2017.
20. Y. Liu Machine learning-based surrogate modeling for uncertainty quantification with applications to Earth system models, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China, May 4, 2017. (invited seminar talk)
21. Efficient Monte Carlo methods and machine learning-based surrogate modeling for uncertainty quantification, Department of Mathematics, University of Arizona, Mar. 23, 2017. (invited seminar talk)
22. Y. Liu Accelerating uncertainty quantification with efficient Monte Carlo methods and machine learning-based surrogate modeling, Department of Mathematical and Statistical Sciences, University of Colorado Denver, Jan. 27, 2017. (invited presentation)
23. Y. Liu Probabilistic uncertainty quantification with Monte Carlo methods and machine learning based surrogate modeling, Department of Mathematics, Baruch College of the CUNY, Jan. 19, 2017. (invited presentation)
24. Y. Liu, L. Zheng, G.S.H. Pau, Reduced-order modeling with sparse polynomial chaos expansion and dimension reduction for evaluating the impact of CO2 and brine leakage on groundwater, 2016 American Geophysical Union Fall Meeting, San Francisco, California, Dec. 12-16, 2016.
25. Y. Liu, L. Zheng, G.S.H. Pau, Reduced-order modeling based on sparse polynomial expansion for models with large-dimensional outputs, Data Day 2016, Lawrence Berkeley National Laboratory, Berkeley, California, Aug. 22-23, 2016 (poster)
26. Y. Liu, L. Zheng, J. Birkholzer, Reduced-order modeling for evaluating the impact of CO2 and brine leakage on groundwater based on sparse polynomial expansion, Carbon Capture, Utilization & Storage Conference, Tysons, VA, USA, June 14-16. (poster)
27. Y. Liu Reduced order modeling and pROME--the Parallel Reduced Order Models for Earth Systems, NERSC, Lawrence Berkeley National Laboratory, May 05, 2016. (invited presentation)
28. Y. Liu Probabilistic uncertainty quantification of nonlinear stochastic phenomena with reduced-order modeling, Department of Mathematics, Tufts University, Feb. 08, 2016. (invited presentation)
29. Y. Liu, G.S.H. Pau, and S. Finsterle, Efficient Bayesian parameter estimation with implicit sampling and surrogate modeling for a vadose zone hydrological problem, 2015 American Geophysical Union Fall Meeting, San Francisco, California, Dec. 14-18, 2015. (poster)
30. Z.M. Subin, G.S.H. Pau, Y. Liu, W.J. Riley and C. Koven, Application of reduced-order modeling to uncertainty in the vulnerability of permafrost carbon to climate change, 2015 American Geophysical Union Fall Meeting, San Francisco, California, Dec. 14-18, 2015. (poster)
31. Y. Liu, G.S.H. Pau, and S. Finsterle, Efficient Bayesian parameter estimation with implicit sampling for a vadose zone hydrological problem, 2015 Bay Area Scientific Computing Day, Lawrence Berkeley National Laboratory, Berkeley, California, Dec. 11, 2015. (poster)
32. Y. Liu, G.S.H. Pau, and S. Finsterle, Bayesian parameter inversion with implicit sampling for a vadose zone hydrological model, TOUGH Symposium 2015, Lawrence Berkeley National Laboratory, Berkeley, CA, Sep. 28--30, 2015. (presentation)
33. Y. Liu, G.S.H. Pau, H. Wainwright, S. Finsterle and X. Tu, Inverse modeling of contaminant transport with implicit sampling, SIAM Conference on Mathematical and Computational Issues in the Geosciences, Stanford, CA, Jun. 29-Jul. 2, 2015. (poster)
34. Y. Liu, G.S.H. Pau, and Z. Subin, PETSc-based parallel reduced-order models for earth systems, PETSc Tutorial and Workshop, Argonne National Laboratory, IL, Jun. 15-18, 2015. (presentation)
35. D.R. Feldman, W.D. Collins, Z.M. Subin, Y. Liu, Y.L. Roberts, J.L. Paige, and G. Pau, Monte Carlo Integration and Principal Component Analysis of Pan-Spectral OSSE Data, CLARREO Spring 2015 Science Team Meeting, LBNL, Berkeley, CA, Apr. 28-30, 2015. (presentation)
36. G.S.H. Pau, Y. Liu, and Z. Subin, Scalable reduced-order models for fine-resolution earth system simulations, CESM Land Model Working Group Winter Meeting, Mar. 2-5, 2015. (poster)
37. Y. Liu, G.S.H. Pau, and Z. Subin, Scalable reduced-order models for fine-resolution earth system simulations, 14th Annual Berkley Atmospheric Sciences Symposium, Berkeley, California, Feb. 6, 2015. (poster)
38. Y. Liu, G.S.H. Pau, and Z. Subin, Scalable reduced-order models for fine-resolution earth system simulations, 2014 American Geophysical Union Fall Meeting, San Francisco, California, Dec. 15-19, 2014. (poster)
39. G.S.H. Pau, G. Bisht, Z. Subin, Y. Liu, and W. Riley, A POD Mapping Approach to Emulate Land Surface Models, 2014 American Geophysical Union Fall Meeting, San Francisco, California, Dec. 15-19, 2014. (poster)
40. Y. Zhang, S. Oladyshkin, Y. Liu, and G.S.H. Pau, Comparison of applying four reduced order models to a global sensitivity analysis, 2014 American Geophysical Union Fall Meeting, San Francisco, California, Dec. 15-19, 2014. (poster)
41. Z. Subin, G.S.H. Pau, Y. Liu, W. Riley, and C. Koven, Efficient Global Prediction of Permafrost Carbon Scenarios Using Sparse Spatially-Dependent Data, NGEE-Arctic All Hands Meeting, San Francisco, CA, Dec. 13-14, 2014. (poster)
42. Y. Liu, G.S.H. Pau, G. Bisht, and W. Riley, Reduced-order modeling of fine-resolution hydrologic simulations at NGEE-Arctic study sites, Complex Soil Systems Conference, Berkeley, California, Sep. 3-5, 2014. (poster)
43. Y. Zhang, S. Oladyshkin, Y. Liu, and G.S.H. Pau, Comparison of four reduced order models for uncertainty quantification in subsurface flow and transport problems, International Conference on Computational Methods in Water Resources (CMWR), Stuttgart, Germany, June 10-13, 2014. (poster)
44. G.S.H. Pau, G. Bisht, W. Riley, and Y. Liu, Application of Proper Orthogonal Decomposition Mapping Method in Land Surface Models-A Multiscale Reduced-Order Method for Integrated Earth System Modeling, 2014 Climate Modeling PI Meeting, Potomac, MD, May 12-15, 2014 (poster)
45. A. GÖnc{\"u}, Y. Liu, G. Ökten and M.Y. Hussaini, Global sensitivity analysis in weather derivatives pricing, Eleventh International Conference on Monte Carlo and Quasi-Monte Carlo Methods in Scientific Computing, KU Leuven, Belgium, April 8 - 11, 2014.
46. Kick-Off Meeting for Mathematical and Statistical Methodologies for DOE Data-Centric Science at Scale, Lawrence Berkeley National Laboratory, Berkeley, California, Mar. 3-4, 2014. (attendance)
47. Y. Liu Multilevel Monte Carlo Methods, Monte Carlo Methods Seminar, Department of Mathematics, Florida State University, Tallahassee, FL, Mar. 2013. (presentation)
48. Y. Liu, E. Jiménez, M.Y. Hussaini, G. Ökten, and S. Goodrick, Quantifying parametric uncertainty in the Rothermel model with efficient sampling methods, 4th Fire Behavior and Fuels Conference, Raleigh, NC, Feb. 18-22, 2013. (presentation)
49. Y. Liu, M.Y. Hussaini, and G. Ökten, Optimization of a Monte Carlo Variance Reduction Method Based on Sensitivity Derivatives, Joint Math Meeting, San Diego, California, Jan. 9-12, 2013. (presentation)
50. Y. Liu Parametric Uncertainty Quantification in the Rothermel Model with Randomized Quasi-Monte Carlo Methods, Monte Carlo Methods Seminar, Department of Mathematics, Florida State University, Tallahassee, FL, Oct. 2012. (presentation)
51. Y. Liu, M.Y. Hussaini, and G. Ökten,Parametric Uncertainty Quantification in the Rothermel Model with Randomized Quasi-Monte Carlo Methods, Workshop on Advances in Computational Mathematics and Engineering, Florida State University, Tallahassee, FL, Sep. 28-29, 2012. (poster)
52. Y. Liu Global Sensitivity Analysis, Monte Carlo Methods Seminar, Department of Mathematics, Florida State University, Tallahassee, FL, Jan. 2012. (presentation)