Haiyan Cheng

Education

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

Experience

• 2021-present, Professor, Computer Science Department, Willamette University
• 2015-2021, Associate Professor, Computer Science Department, Willamette University
• 2017-2020 Department Chair, Computer Science Department, Willamette University
• 2009-2015, Assistant Professor, Computer Science Department, Willamette University
• 2003-2004, Instructor,  Computer Science Department, University of Windsor, Canada

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 435 Computational Science and Applications
• CS 451 Topics in Computer Science (World Wide Web Programming) (Issues in Scientific Computing)
• CS 470 Introduction to Data Science
• CS 495W, CS 496W Computer Science Senior Seminar (1)(2)
• GSMDS 5002 Practical Applications of Python for Data Science

All course descriptions are listed in the CAS Course Catalog.

We are offering a new Data Science program.

Publications

• Rowe, P., Fortmann, L., Guasco, Y., Wright, A., Ryken, A., Sevier, E., Stokes, G., Mifflin, A., Wade, R., Cheng, H., Pfalzgraff, W., Beaudoin, J., Rajbhandari, I., Fox-Dobbs, K., and  Neshyba, S. Integrating Polar Research Into Undergraduate Curricula Using Computational Guided Inquiry, Journal of Geoscience Education, June, 2020.
• 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 Driven Problem Solving", University of Puget Sound, January 12, 2021.
• "Scientific Machine Learning and its Potentials." Applied Math and Computer Science Lab, Computer Science Department, Universidad del Norte, Columbia, August 14, 2020.
• 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

• National Center for Women In Information Technology (NCWIT) Mentoring Award for Undergraduate Research (MAUR) recipient, 2021.
• Banff International Research Station for Mathematical Innovation and Discovery (BIRS) "Mathematical Approaches for Data Assimilation of Atmospheric Constituents and Inverse Modeling" workshop, Banff, 2020.
• National Center for Women In Information Technology (NCWIT) Academic Alliance Seed Fund, 2020. 
• Institute for Computational and Experimental Research in Mathematics (ICERM) "Computational Statistics and Data-Driven Models" workshop, Providence, RI, 2020.
• ICERM "Scientific Machine Learning" workshop, Providence, RI, 2019.
• Luce Initiative on Asian Studies and Environment (LIASE) Summer Curricular Development Grant, Willamette University, 2019.
• Computing Research Association for Women (CRA-W) Mid-Career Mentoring Workshop, Phoenix, AZ, 2018.
• iHuman Sciences Initiative (iHSI) interdisciplinary faculty/student collaborative research grant, 2018.
• Big Data to Knowledge (BD2K) Data Science Innovation Lab Workshop: Mathematical Challenges of Single Cell Dynamics, Bend, OR, 2018. 
• ICERM "Women in Data Science" Workshop, Providence, RI, 2017.
• ICERM "Predictive Policing" Workshop, Providence, RI, 2016.
• ICERM "Mathematics in Data Science" Workshop, Providence, RI, 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, 2019, 2020. 
• Willamette Atkinson Research Grant, 2011, 2014.
• NSF TUES Faculty Training Workshop in Teaching the Science of Information, West Lafayette, IN, 2013.
• Society for Industrial and Applied Mathematics (SIAM) Travel Award for SIAM Uncertainty Quantification (SIAM-UQ) Conference, Raleigh, NC, 2012.
• Willamette University Presidential Discretionary Fund, 2015, 2016.
• Willamette University Hewlett Grant for curricular development, 2011, 2012, 2014.
• Willamette University  CS/Math Lilly Project.