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NEW: I will be starting my computational neuroscience and machine learning research group in the Department of Neurobiology at UCLA this July! We will have a range of positions available focused on statistical machine learning methods for neuroscience, 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 understanding high-dimensional neural population activity, and mechanistic models of neural circuit function. 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, 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 (accepted). bioRxiv 2025.07.31.667911v2. [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]
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. (2017). Prioritised ceteris paribus logic for counterfactual reasoning. Synthese 195:1681. [Link]
P. Girard & M. A. Triplett. (2015). Ceteris paribus logic in counterfactual reasoning. Proceedings Fifteenth Conference on Theoretical Aspects of Rationality and Knowledge. 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
Contact
Contact
mat2245@columbia.edu
Mortimer B. Zuckerman Mind Brain Behavior Institute
Columbia University
3227 Broadway, New York, NY 10027
United States of America
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