Grace  Chiu


Email: [[gschiu]]
Office: Andrews Hall 337
Department: Biological Sciences
Phone: (804) 684-7221
Research Lab: {{, Environmental Statistics and Transdisciplinary Data Science (ESTDatS)}}
Curriculum Vitae: {{, URL}}
Google Scholar: {{, URL}}


Subject Areas
  • Bayesian / hierarchical / spatial / temporal / mixed-effects
    modeling of complex 
    environmental / ecological / biological
  • statistical model-based assessment of 
    ecological / biological / policy
  • integration of statistical and
    process / simulation
    models via Bayesian melding
  • non-standard asymptotic theory


Research Interests

I am a transdisciplinary statistician and data scientist who develops integrative, holistic statistical methodologies for multi-faceted problems, mainly from the environmental and biological sciences. Under an integrative methodological framework, one can formulate a single (often complex) model to address multiple scientific questions simultaneously in a unified manner. A statistical framework addresses research questions through statistical inference (data-based estimation of parameters and their uncertainty). Thus, an integrative statistical methodology is a single statistical inference framework that unifies the multi-faceted evidence-based research, with valid uncertainty propagation across facets. Contrast this with a series of standalone statistical analyses (e.g. through off-the-shelf software packages), whereby the parameter and uncertainty estimates from any analysis in the series correspond purely to one narrow aspect of the overarching research while neglecting the crucial role of uncertainty from the other aspects altogether.

I have coined the nomenclature of some of my methodologies, e.g. the bent-cable regression approach, the latent health factor index (LHFI) modeling framework, the assessment of similarity in preference between networked individuals, and the multiresolution heritability measure.

I develop most of my work under the Bayesian inference paradigm — its flexibility facilitates the proper modeling of complex natural phenomena, typically coupled with complex processes under which data are observed, thereby giving rise to “big-data” structures that are convoluted. An extreme example of convoluted structure might be data on the temporal evolution of the 3-dimensional movement of marine plankton, monitored by a coordinated network of sources — e.g. telemetry data, field observations, output from computer simulation models, etc. — which are mutually dependent in space, time, and across the network. In this case, a key challenge is that implementing such a methodology would require highly advanced computational algorithms and hardware, even for “small data.”

Complexity can also arise when subject-matter theoretical models (e.g. process models, computer simulation models) exist alongside data. I have been employing Bayesian melding to integrate the theoretical model as a type of strong prior into the statistical framework. Typically, Bayesian melding is highly computationally intensive. 

Selected Publications

Authors under my supervision marked with *:

  • Wasson, A.P., Chiu, G.S., Zwart, A.B., Binns, T.R.* (2017). “Differentiating wheat genotypes by Bayesian hierarchical nonlinear mixed modeling of wheat root density,” Frontiers in Plant Science 8:282. DOI: 10.3389/fpls.2017.00282

  • Chiu, G.S., Lehmann, E.A., Bowden, J.C. (2013). “A spatial modeling approach for the blending and error characterization of remotely sensed soil moisture products.” Journal of Environmental Statistics 4:9, 1–17. ISSN: 1945-1296.

  • Chiu, G.S., Wu, M.A.*, Lu, L. (2013). “Model-based assessment of estuary ecosystem health using the latent health factor index, with application to the Richibucto estuary,” PLoS One 8:6, e65697, 1–12. DOI: 10.1371/journal.pone.0065697

  • Chiu, G.S., Westveld, A.H. (2011). “A unifying approach for food webs, phylogeny, social
    networks, and statistics.” Proceedings of the National Academy of Sciences of the USA 108, 15881–15886. DOI: 10.1073/pnas.1015359108

  • Chiu, G.S., Gould, J.M.* (2009–2010). “Statistical inference for food webs with emphasis on ecological networks via Bayesian melding,” Environmetrics 21, 728–740. DOI: 10.1002/env.1035

  • Chiu, G., Lockhart, R., Routledge, R. (2006). “Bent-cable regression theory and applications,” Journal of the American Statistical Association 101, 542–553. DOI:

Research Students and Interns
  • Caleb BAKER, Undergraduate Student (current), William & Mary Computational & Applied Mathematics & Statistics (CAMS)
  • Challen HYMAN, PhD student (current), VIMS
  • Swen KUH, PhD Candidate in Statistics (current), Australian National Unversity
  • Kayla RUTHERFORD, REU Summer Internship (2019), VIMS
  • Mark DAWKINS, Honours in Statistics (2017), Australian National Unversity
  • Megan EVANS, PhD in Environmental Policy (2017), Australian National Unversity
  • Timothy BINNS, Summer Vacation Internship (2014–2015), CSIRO
  • Swen KUH, Summer Research Internship (2014–2015), Australian National Unversity
  • Seoyoon CHO, Industrial Research Traineeship (2014), CSIRO
  • Hyangki LEE, Industrial Research Traineeship (2014), CSIRO
  • Shahedul KHAN, PhD (2010) in Biostatistics, University of Waterloo
  • Margaret WU, MMath (2009) in Biostatistics, University of Waterloo
  • Joshua GOULD, MMath (2008) in Statistics, University of Waterloo
  • Joslin GOH, Undergraduate Research Internship (2007) and International Work Study Program (2007), University of Waterloo
Professional Affiliations

# Australian Level D is equivalent to North American Full Professor.