معرفی
Abbas Khalili is a Professor in the Department of Mathematics and Statistics at McGill University, Montreal. His research focuses on statistical methodology in data science, particularly high-dimensional statistics, distributed learning in big data, latent variable models (e.g., finite mixtures, hidden Markov models), and time series analysis. He holds a PhD from the University of Waterloo and BSc/MSc degrees from Isfahan University of Technology.
- Education:
- PhD in Statistics, University of Waterloo (supervised by Jiahua Chen)
- MSc in Mathematical Sciences, Isfahan University of Technology
- BSc in Mathematical Sciences, Isfahan University of Technology
His research interests include post-selection inference, neural networks, sparse network analysis, and change point detection. His work has been funded by NSERC (Canada) and Fonds de recherche du Québec-Nature et technologies.
Recent articles focus on mixture models, regularization in autoregressive systems, and hub structure analysis in networks. His work bridges theoretical advancements with practical applications in drug discovery and network science.
Dr. Khalili advises students in PhD, MSc, and undergraduate projects in his research areas. He can be contacted at abbas.khalili@mcgill.ca.
