## Education

- PhD, Computer Science and Applications, Virginia Tech, 2009
- MS, Computer Science, University of Windsor, 2003
- MS, Applied Mathematics, Michigan Technological University, 2000

## Research Interests

- Scientific computing
- Data Science and machine learning
- Computational sustainability
- Data assimilation techniques: Kalman filter, 4D-Var, Particle filter
- Hybrid numerical methods for data assimilation

- Uncertainty quantification and reduction techniques for large-scale simulations
- Polynomial chaos method
- Uncertainty apportionment

## Courses

- IDS 101 Ethics in Information Technology
- CS 125 Problem Solving with MATLAB
- CS 141 Introduction to Programming (JAVA)
- CS 203X Problem Solving for the ACM Programming Contest
- CS 343 Analysis of Algorithms
- CS 370 Fundamentals of Data Science
- CS 393 Computer Science Junior Seminar
- CS 470 Introduction to Data Science
- CS 451 Topics in Computer Science (World Wide Web Programming) (Issues in Scientific Computing)
- CS 435 Computational Science and Applications
- CS 495W, CS 496W Computer Science Senior Seminar (1)(2)
- GSMDS 5002 Practical Applications of Python for Data Science

I'm using the Willamette WISE system for course management and course material, if you are interested in the course content, please contact me to obtain a guest permission.

All course descriptions are listed in the CLA Course Catalog.

We are offering a new Data Science program, for more information, please click here.

## Publications

- Cheng, H. and VanDeGrift, T. Course Models for Teaching Data Science. The Journal of Computing Sciences in Colleges, 35(1), 44-56, 2019.
- Seeger, F., Little, A., Chen, Y. Woolf, T. Cheng, H. and Mitchell, M. Feature Design for Protein Interface Hotspots using KFC2 and Rosetta, Research in Data Science, Association for Women in Mathematics Series, pp 177-197, Springer, 2019.
- Rowe, P., Cheng, H., Fortmann, L., Wright, A. and Neshyba, S. Teaching Image Processing in an Upper Level CS Undergraduate Class Using Computational Guided Inquiry and Polar Data. The Journal of Computing Sciences in College, 34(1), 171-179, 2018.
- Nino-Ruiz, E, Cheng, H. and Beltran,R. A Robust Non-Gaussian Data Assimilation Method For Highly Non-Linear Models, Atmosphere, 9(4), 126, 2018.
- Sandu, A. and Cheng, H. An error subspace perspective on data assimilation. International Journal for Uncertainty Quantification, 5(6): 491-510, 2015.
- Schmal, K. and Cheng, H. Numerical Study of a Hybrid Particle Filter. Proceedings of The International MultiConference of Engineers and Computer Scientists, pp 419-422, 2015.
- Cheng, H. and Sandu, A. Collocation least-squares polynomial chaos method (PDF). In Proceedings of the 2010 Spring Simulation Multiconference, pages 94-99, April, 2010.
- Cheng, H., Jardak, M., Alexe, M., and Sandu, A. A hybrid approach to estimating error covariances in variational data assimilation. Tellus Series A: Dynamic Meteorology and Oceanography, 62(3):288-297, May 2010.
- Cheng, H. and Sandu, A. Uncertainty quantification and apportionment in air quality models using the polynomial chaos method. Environmental Modelling & Software: 24(8):917-925, August 2009.
- Cheng, H. and Sandu, A. Efficient uncertainty quantification with the polynomial chaos method for stiff systems, Mathematics and Computers in Simulation, 79(11):3278-3295, July 2009.
- Cheng, H and Sandu, A. Uncertainty apportionment for air quality forecast models (PDF). In proceeding of the 24th Annual ACM Symposium on Applied Computing (ACM-SAC), pages 956-960, March, 2009.
- Cheng, H. and Sandu, A. Numerical study of uncertainty quantification techniques for implicit stiff systems. In Proceeding of the 45th Annual ACM Southeast Regional (ACMSE) Conference, pages 367-372, 2007.
- Cheng, H and Bertram, B. On the stopping criteria for conjugate gradient solutions of first-kind integral equations in two variables. Integral Methods in Science and Engineering, Springer, 2002.
- Bertram, B and Cheng, H. On the use of the conjugate gradient method for the numerical solution of first-kind integral equations in two variables. Integral Methods in Science and Engineering, Springer, 2002.

## Invited Talks and Tutorials

- "Computational and Data Science for All." Willamette University Faculty Colloquium talk, Willamette University, February, 6, 2020.
- "The best of both worlds: Computational Science and Data Science." Mathematics and Computer Science Colloquium talk, Lewis and Clark College, Oct 24, 2019.
- "Adaptive Data Assimilation Scheme for Shallow Water Simulation." Data Assimilation Techniques for High-dimensional and Nonlinear Problems Minisymposia, SIAM Conference on Uncertainty Quantification (SIAM-UQ), Lausanne, Switzerland, April 5-8, 2016.
- "Uncertainty Quantification with Polynomial Chaos Method for Practitioners." Institute of Applied Physics and Computational Mathematics, Beijing, China, Aug 11, 2015.
- "Today's Forecast-A Better Forecast." Institute for Continued Learning at the Willamette University, Salem, Oregon, Oct 28, 2014.
- "Data Assimilation with Particle Filter Methods." Applied and Computational Mathematics Seminar, Portland State University, Portland, Oregon, May 12, 2014.
- "Quantify and Reduce Uncertainties to Improve the Model Predictability." CASCADE Computational and Applied Mathematics Seminar, Oregon State University, Corvallis, Oregon, April 5, 2014.
- "Hybrid Data Assimilation Method", Colloquium Talk, Department of Mathematics, Statistics and Computer Science, Marquette University, April 26, 2013.
- "Variational Data Assimilation and Particle Filters." Data Assimilation and PDE-Constrained Optimization, SIAM Conference on Computational Science and Engineering (SIAM-CSE) Feb 25-Mar 1, 2013.
- "Hybrid Methods for Data Assimilation." Data Assimilation and Inverse Problem Minisymposia, SIAM Conference on Uncertainty Quantification (SIAM-UQ), Raleigh, North Carolina, April 2-5, 2012.
- "New Hybrid EnKF and 4D-Var Method." Applied Mathematics and Computational Seminar, Mathematics Department, Oregon State University, Corvallis, Oregon, November 18, 2011.
- "Investigation of Advanced Data Assimilation Schemes for Nonlinear and Non-Gaussian Problems." Invited talk at National Oceanic and Atmospheric Administration, Earth Science Research Lab, Global System Division, Forecast Application Branch (NOAA/ESRL/GSD/FAB), Boulder, Colorado, July 27, 2011.
- "Uncertainty Quantification and Uncertainty Reduction Techniques in Scientific Simulations." Invited talk at NOAA/ESRL/GSD/FAB, Boulder, Colorado, June 23, 2010.
- "Uncertainty Quantification and Uncertainty Reduction Techniques for Scientific Simulations." Half-day tutorial at ACM-SAC conference, Sierre, Switzerland, March 22-26, 2010.
- "Parameter Estimation and Uncertainty Apportionment using a Polynomial Chaos Approach." Invited talk at SIAM Conference on Computational Science and Engineering (SIAM-CSE-09), Miami, Florida, March 2-6, 2009.

## Fundings and Awards

- ICERM "Computational Statistics and Data-Driven Models" workshop, 2020.
- ICERM "Scientific Machine Learning" workshop, 2019.
*i*Human Sciences initiative (*i*HSi) interdisciplinary student/faculty collaborative research grant, 2018.- BD2K Data Science Innovation Lab Workshop, 2018.
- ICERM "Women in Data Science" Workshop, 2017.
- ICERM "Predictive Policing" Workshop, 2016.
- ICERM "Mathematics in Data Science" Workshop, 2015.
- National Science Foundation (NSF) Grant: NSF DMS-1217073 "Uncertainty reduction through better nonlinear particle filters," 2012-2015.
- Willamette Science Collaboration Research Program (SCRP), 2010, 2011, 2013, 2014.
- Willamette Liberal Arts Research Collaborative (LARC) project, 2015, 2016.
- Willamette Atkinson Grant, 2011, 2014.
- SIAM Travel Award, SIAM-UQ, 2012.
- Willamette Presidential Discretionary Fund, 2011, 2012, 2014, 2015, 2016.
- Willamette CS/Math Lilly Project.