Summer 2015: Fellow at Insight Data Science
2015-2016: Data Scientist at Custora (acquired by Amperity)
2017: Data Scientist at Atlassian
2018: Postdoctoral Researcher at University of Technology Sydney
2019: Postdoctoral Researcher at Harvard University
2021-current: Data Scientist at Grubhub
Groen, L., Joseph, A., Black, E., Menictas, M., Tam, W. and Gabor, M., 2010. Optimal location of Tsunami warning buoys and sea level monitoring stations in the Mediterranean Sea. Science of Tsunami Hazards.
Menictas, M. and Wand, M.P., 2013. Variational inference for marginal longitudinal semiparametric regression. Stat, 2(1), pp.61-71.
Menictas, M. and Wand, M.P., 2015. Variational inference for heteroscedastic semiparametric regression. Australian & New Zealand Journal of Statistics, 57(1), pp.119-138.
Menictas, M., 2015. Variational inference for heteroscedastic and longitudinal regression models (Doctoral dissertation).
Menictas, M., Rabbi, M., Klasnja, P. and Murphy, S., 2019. Artificial intelligence decision-making in mobile health. The biochemist, 41(5), pp.20-24.
Menictas, M., Tomkins, S. and Murphy, S.A., 2020. Streamlined Empirical Bayes Fitting of Linear Mixed Models in Mobile Health. arXiv preprint arXiv:2003.12881.
Carpenter, S.M., Menictas, M., Nahum-Shani, I., Wetter, D.W. and Murphy, S.A., 2020. Developments in mobile health just-in-time adaptive interventions for addiction science. Current addiction reports, 7, pp.280-290.
Nolan, T.H., Menictas, M. and Wand, M.P., 2020. Streamlined variational inference with higher level random effects. Journal of Machine Learning Research, 21(157), pp.1-62.
Menictas, M., Nolan, T.H., Simpson, D.G. and Wand, M.P., 2021. Streamlined variational inference for higher level group-specific curve models. Statistical Modelling, 21(6), pp.479-519.
Battalio, S.L., Conroy, D.E., Dempsey, W., Liao, P., Menictas, M., Murphy, S., Nahum-Shani, I., Qian, T., Kumar, S. and Spring, B., 2021. Sense2Stop: a micro-randomized trial using wearable sensors to optimize a just-in-time-adaptive stress management intervention for smoking relapse prevention. Contemporary Clinical Trials, 109, p.106534.
Menictas, M., Di Credico, G. and Wand, M.P., 2023. Streamlined variational inference for linear mixed models with crossed random effects. Journal of Computational and Graphical Statistics, 32(1), pp.99-115.
Menictas, M., Oates, C.J. and Wand, M.P., 2025. Online Semiparametric Regression via Sequential Monte Carlo. Australian and New Zealand Journal of Statistics, full publication details pending.