UTS site search

Dr Chris Oates


Chris is a statistician currently working as a Research Fellow at the ARC Centre of Excellence for Mathematical & Statistical Frontiers (ACEMS), University of Technology Sydney.

From mid-2017 Chris will join Newcastle University, UK, as a Senior Lecturer and be seconded as a Group Leader for the Program on Data-Centric Engineering at the Alan Turing Institute, UK.


Chris is an Associate Editor for Statistics and Computing (Springer) and Program Committee member for AISTATS 2016, 2017, NIPS 2016. Chris is also a Group Leader for the working group on Probabilistic Numerics at SAMSI (Statistical and Applied Mathematical Sciences Institute, USA).
Image of Chris Oates
Postdoctoral Research Fellow, School of Mathematical and Physical Sciences

Journal articles

Friel, N., Mira, A. & Oates, C. 2016, 'Exploiting Multi-Core Architectures for Reduced-Variance Estimation with Intractable Likelihoods', Bayesian Analysis, vol. 11, no. 1, pp. 215-245.
View/Download from: Publisher's site
Many popular statistical models for complex phenomena are intractable, in the sense that the likelihood function cannot easily be evaluated. Bayesian estimation in this setting remains challenging, with a lack of computational methodology to fully exploit modern processing capabilities. In this paper we introduce novel control variates for intractable likelihoods that can dramatically reduce the Monte Carlo variance of Bayesian estimators. We prove that our control variates are well-defined and provide a positive variance reduction. Furthermore, we show how to optimise these control variates for variance reduction. The methodology is highly parallel and offers a route to exploit multi-core processing architectures that complements recent research in this direction. Indeed, our work shows that it may not be necessary to parallelise the sampling process itself in order to harness the potential of massively multi-core architectures. Simulation results presented on the Ising model, exponential random graph models and non-linear stochastic differential equation models support our theoretical findings.
Oates, C., Papamarkou, T. & Girolami, M. 2016, 'The controlled thermodynamic integral for Bayesian model evidence evaluation', Journal of the American Statistical Association.
View/Download from: Publisher's site
Oates, C., Smith, J.Q., Mukherjee, S. & Cussens, J. 2016, 'Exact Estimation of Multiple Directed Acyclic Graphs', Statistics and Computing, vol. 26, no. 4, pp. 797-811.
View/Download from: Publisher's site
This paper considers structure learning for multiple related directed acyclic graph (DAG) models. Building on recent developments in exact estimation of DAGs using integer linear programming (ILP), we present an ILP approach for joint estimation over multiple DAGs. Unlike previous work, we do not require that the vertices in each DAG share a common ordering. Furthermore, we allow for (potentially unknown) dependency structure between the DAGs. Results are presented on both simulated data and fMRI data obtained from multiple subjects.
Oates, C.J., Smith, J.Q. & Mukherjee, S. 2016, 'Estimation of Causal Structure Using Conditional DAG Models', Journal of Machine Learning Research, vol. 17, pp. 1-23.
This paper considers inference of causal structure in a class of graphical models called conditional DAGs. These are directed acyclic graph (DAG) models with two kinds of variables, primary and secondary. The secondary variables are used to aid in the estimation of the structure of causal relationships between the primary variables. We prove that, under certain assumptions, such causal structure is identi able from the joint observational distribution of the primary and secondary variables. We give causal semantics for the model class, put forward a score-based approach for estimation and establish consistency results. Empirical results demonstrate gains compared with formulations that treat all variables on an equal footing, or that ignore secondary variables. The methodology is motivated by applications in biology that involve multiple data types and is illustrated here using simulated data and in an analysis of molecular data from the Cancer Genome Atlas.
Oates, C.J., Girolami, M. & Chopin, N. 2016, 'Control Funtionals for Monte Carlo Integration', Journal of the Royal Statistical Society Series B: Statistical Methodology.
Harjanto, D., Papamarkou, T., Oates, C.J., Rayon-Estrada, V., Papavasiliou, F.N. & Papavasiliou, A. 2016, 'RNA editing generates cellular subsets with diverse sequence within populations', Nature Communications, vol. 7.
View/Download from: Publisher's site
© The Author(s) 2016.RNA editing is a mutational mechanism that specifically alters the nucleotide content in transcribed RNA. However, editing rates vary widely, and could result from equivalent editing amongst individual cells, or represent an average of variable editing within a population. Here we present a hierarchical Bayesian model that quantifies the variance of editing rates at specific sites using RNA-seq data from both single cells, and a cognate bulk sample to distinguish between these two possibilities. The model predicts high variance for specific edited sites in murine macrophages and dendritic cells, findings that we validated experimentally by using targeted amplification of specific editable transcripts from single cells. The model also predicts changes in variance in editing rates for specific sites in dendritic cells during the course of LPS stimulation. Our data demonstrate substantial variance in editing signatures amongst single cells, supporting the notion that RNA editing generates diversity within cellular populations.
Oates, C., Costa, L. & Nichols, T.E. 2015, 'Toward a Multisubject Analysis of Neural Connectivity', Neural Computation, vol. 27, no. 1, pp. 151-170.
View/Download from: Publisher's site
Oates, C.J. 2015, 'Accelerated Nonparametrics for Cascades of Poisson Processes', Stat, vol. 4, no. 1, pp. 183-195.
View/Download from: Publisher's site
Cascades of Poisson processes are probabilistic models for spatio-temporal phenomena in which (i) previous events may trigger subsequent events and (ii) both the background and triggering processes are conditionally Poisson. Such phenomena are typically "data rich but knowledge poor," in the sense that large datasets are available, yet a mechanistic understanding of the background and triggering processes that generate the data is unavailable. In these settings, non-parametric estimation plays a central role. However, existing non-parametric estimators have computational and storage complexity O(N2), precluding their application on large datasets. Here, by assuming the triggering process acts only locally, we derive non-parametric estimators with computational complexity O(NlogN) and storage complexity O(N). Our approach automatically learns the domain of the triggering process from data and is essentially free from hyperparameters. The methodology is applied to a large seismic dataset where estimation under existing algorithms would be infeasible
Korkola, J.E., Collisson, E.A., Heiser, L., Oates, C., Bayani, N., Itani, S., Esch, A., Thompson, W., Griffith, O.L., Wang, N.J., Kuo, W.L., Cooper, B., Billig, J., Ziyad, S., Hung, J.L., Jakkula, L., Feiler, H., Lu, Y., Mills, G.B., Spellman, P.T., Tomlin, C., Mukherjee, S. & Gray, J.W. 2015, 'Decoupling of the PI3K Pathway via Mutation Necessitates Combinatorial Treatment in HER2+ Breast Cancer.', PloS one, vol. 10, no. 7, p. e0133219.
We report here on experimental and theoretical efforts to determine how best to combine drugs that inhibit HER2 and AKT in HER2(+) breast cancers. We accomplished this by measuring cellular and molecular responses to lapatinib and the AKT inhibitors (AKTi) GSK690693 and GSK2141795 in a panel of 22 HER2(+) breast cancer cell lines carrying wild type or mutant PIK3CA. We observed that combinations of lapatinib plus AKTi were synergistic in HER2(+)/PIK3CA(mut) cell lines but not in HER2(+)/PIK3CA(wt) cell lines. We measured changes in phospho-protein levels in 15 cell lines after treatment with lapatinib, AKTi or lapatinib + AKTi to shed light on the underlying signaling dynamics. This revealed that p-S6RP levels were less well attenuated by lapatinib in HER2(+)/PIK3CA(mut) cells compared to HER2(+)/PIK3CAwt cells and that lapatinib + AKTi reduced p-S6RP levels to those achieved in HER2(+)/PIK3CA(wt) cells with lapatinib alone. We also found that that compensatory up-regulation of p-HER3 and p-HER2 is blunted in PIK3CA(mut) cells following lapatinib + AKTi treatment. Responses of HER2(+) SKBR3 cells transfected with lentiviruses carrying control or PIK3CA(mut )sequences were similar to those observed in HER2(+)/PIK3CA(mut) cell lines but not in HER2(+)/PIK3CA(wt) cell lines. We used a nonlinear ordinary differential equation model to support the idea that PIK3CA mutations act as downstream activators of AKT that blunt lapatinib inhibition of downstream AKT signaling and that the effects of PIK3CA mutations can be countered by combining lapatinib with an AKTi. This combination does not confer substantial benefit beyond lapatinib in HER2+/PIK3CA(wt) cells.
Oates, C., Amos, R. & Spencer, S.E.F. 2014, 'Quantifying the multi-scale performance of network inference algorithms', Statistical Applications in Genetics and Molecular Biology, vol. 13, no. 5.
View/Download from: Publisher's site
Oates, C., Korkola, J., Gray, J.W. & Mukherjee, S. 2014, 'Joint estimation of multiple related biological networks', The Annals of Applied Statistics, vol. 8, no. 3, pp. 1892-1919.
View/Download from: Publisher's site
Oates, C., Dondelinger, F., Bayani, N., Korkola, J., Gray, J.W. & Mukherjee, S. 2014, 'Causal network inference using biochemical kinetics', Bioinformatics, vol. 30, no. 17, pp. i468-i474.
View/Download from: Publisher's site
Oates, C. & Mukherjee, S. 2014, 'Joint Structure Learning of Multiple Non-Exchangeable Networks', Journal of Machine Learning Research (W&CP), vol. 33, pp. 687-695.
Casale, F.P., Giurato, G., Nassa, G., Armond, J.W., Oates, C., CorĂ¡, D., Gamba, A., Mukherjee, S., Weisz, A. & Nicodemi, M. 2014, 'Single-Cell States in the Estrogen Response of Breast Cancer Cell Lines', PLoS ONE, vol. 9, no. 2, pp. e88485-e88485.
View/Download from: Publisher's site
Armond, J.W., Saha, K., Rana, A.A., Oates, C., Jaenisch, R., Nicodemi, M. & Mukherjee, S. 2014, 'A stochastic model dissects cell states in biological transition processes', Scientific Reports, vol. 4.
View/Download from: Publisher's site
Oates, C. & Mukherjee, S. 2012, 'Network inference and biological dynamics', The Annals of Applied Statistics, vol. 6, no. 3, pp. 1209-1235.
View/Download from: Publisher's site
Oates, C., Hennessy, B.T., Lu, Y., Mills, G.B. & Mukherjee, S. 2012, 'Network inference using steady-state data and Goldbeter-koshland kinetics', Bioinformatics, vol. 28, no. 18, pp. 2342-2348.
View/Download from: Publisher's site