CV

Education

• B.S. in Mathematical Statistics, University of Technology Sydney, First Class Honours, 2008-2011
  • Honours thesis supervision by Prof. Deborah Street and Dr. Narelle Smith.
• Ph.D in Mathematical Statistics, University of Technology Sydney, 2012-2015
  • PhD supervision by Prof. Matt Wand.

Experience

• Summer 2015: Fellow at Insight Data Science

• 2015-2016: Data Scientist at Custora

• 2017: Data Scientist at Atlassian

• 2018: Postdoctoral Researcher at University of Technology Sydney

• 2019: Postdoctoral Researcher at Harvard University

Publications

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.

Menictas, M. and Wand, M.P. Variational Inference for Marginal Longitudinal Semiparametric Regression.

Menictas, M. and Wand, M.P. Variational Inference for Heteroscedastic Semiparametric Regression.

Menictas, M., Nolan, T.H., Simpson, D.G. and Wand, M.P. Streamlined Variational Inference for Higher Level Group-Specific Curve Models.

Menictas, M., Rabbi, M., Murphy, S.A. Artificial intelligence decision-making in mobile health.

Menictas, M., Di Credico, G. and Wand, M.P. Streamlined Variational Inference for Linear Mixed Models with Crossed Random Effects.

Menictas, M., Tomkins, S., and Murphy, S.A. Streamlined Empirical Bayes Fitting of Linear Mixed Models in Mobile Health.

Carpenter, S.M., Menictas, M., Nahum-Shani, I., Wetter, D.W., and Murphy, S.A. Developments in Mobile Health Just-in-Time Adaptive Interventions for Addiction Science.

Nolan, T.H., Menictas, M. and Wand, M.P. Streamlined Computing for Variational Inference with Higher Level Random Effects.

Download resume here