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NEW: Since July 2026, I am an Assistant Professor at UCLA Neurobiology where I lead a computational neuroscience and machine learning group. Please see the lab's website at triplett-lab.org for more information.
We will have a range of positions available focused on statistical machine learning methods for neural data analysis, as well as for studying computation in artificial and biological neural systems. Please reach out if interested!
Bio: I'm a postdoctoral research scientist at Columbia University working with Liam Paninski on computational methods for high-throughput optical interrogation of neural circuits. I also develop statistical techniques for high-dimensional neural data analysis and mechanistic models of neural circuits. Previously, I completed my PhD in Computational Neuroscience with Geoff Goodhill at the University of Queensland, Australia. Before that, I obtained a BSc in Computer Science and Mathematics at the University of Auckland, New Zealand, where my primary research interest was in logic.
Support: I'm currently funded by an NIH BRAIN Initiative K99/R00 Advanced Postdoctoral Career Transition Award to continue developing computational methods for holographic optogenetics and voltage imaging.
Columbia University Affiliations:
Grossman Center for the Statistics of Mind
Center for Theoretical Neuroscience
Zuckerman Mind Brain Behavior Institute
Department of Statistics
Publications
Publications
M.A. Triplett & K. Kay. (2026). Emergent representations of graphical structure in mechanistic neural models of causal judgment. bioRxiv 2026.05.13.724819. [Link]
M. A. Triplett, E. Bäumler, A. Prodan, R. Stonis, D. S. Peterka, M. Häusser & L. Paninski. (2026). Computational optimization of two-photon holographic stimulation sites in vivo. Journal of Neural Engineering 23 026015. [Link]
M. A. Triplett*, M. Gajowa*, B. Antin, M. Sadahiro, H. Adesnik & L. Paninski. (2025). Rapid learning of neural circuitry from holographic ensemble stimulation enabled by model-based compressed sensing. Nature Neuroscience. doi: 10.1038/s41593-025-02053-7. [Link]
B. Antin*, M. Sadahiro*, M. Gajowa, M. A. Triplett, H. Adesnik & L. Paninski. (2024). Removing direct photocurrent artifacts in optogenetic connectivity mapping data via constrained matrix factorization. PLOS Computational Biology 20(5): e1012053. [Link]
M. A. Triplett, M. Gajowa, H. Adesnik & L. Paninski. (2023). Bayesian target optimisation for high-precision holographic optogenetics. Advances in Neural Information Processing Systems (spotlight award). [Link]
M. A. Triplett & G. J. Goodhill (2022). Inference of multiplicative factors underlying neural variability in calcium imaging data. Neural Computation 34(5), 1-27. [Link]
A. Nies, M. A. Triplett & K. Yokoyama. (2021). The reverse mathematics of theorems of Jordan and Lebesgue. Journal of Symbolic Logic 86(4), 1657-1675. [Link]
M. A. Triplett, Z. Pujic, B. Sun, L. Avitan & G. J. Goodhill. (2020). Model-based decoupling of evoked and spontaneous neural activity in calcium imaging data. PLOS Computational Biology 16(11): e1008330. [Link]
M. A. Triplett & G. J. Goodhill. (2019). Probabilistic encoding models for multivariate neural data. Frontiers in Neural Circuits 13:1. [Link]
M. A. Triplett, L. Avitan & G. J. Goodhill. (2018). Emergence of spontaneous assembly activity in developing neural networks without afferent input. PLOS Computational Biology 14(9): e1006421. [Link]
P. Girard & M. A. Triplett. (2018). Prioritised ceteris paribus logic for counterfactual reasoning. Synthese 195:1681. [Link]
F. Abbas, M. A. Triplett, G. J. Goodhill & M. P. Meyer. (2017). A three-layer network model of direction selective circuits in the optic tectum. Frontiers in Neural Circuits 11:88. [Link]
M. R. Robinson, G. English, G. Moser, L. R. Lloyd-Jones, M. A. Triplett, Z. Zhu et al. (2017). Genotype-covariate interaction effects and the heritability of adult body mass index. Nature Genetics 49, 1174-1181 . [Link]
P. Girard & M. A. Triplett. (2016). Ceteris paribus logic in counterfactual reasoning. Proceedings Fifteenth Conference on Theoretical Aspects of Rationality and Knowledge (TARK). Electronic Proceedings in Theoretical Computer Science 215. [Link]
Dissertations
Dissertations
Neural encoding models for multivariate optical imaging data. (2020). PhD thesis. University of Queensland, Australia. [Link]
Computable functions of bounded variation and the complexity of Jordan decomposition. (2015). Honours dissertation. University of Auckland, New Zealand. [Link]
Software
Software
CAVIaR (Coordinate-ascent variational inference and isotonic regularization)
https://github.com/marcustriplett/circuitmap
Bataro (Bayesian target optimisation)
https://github.com/marcustriplett/bataro
Related pages
Related pages
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