I will have multiple openings for PhD and postdoc positions in 2025 as part of my research group at NTU Singapore (in the School of Physical and Mathematical Sciences).
The positions pertain to the project "Learning high-dimensional (networked) dynamical systems" (broadly defined) and will be funded by a NAP grant from NTU. There is ample room for exploring sub-topics depending on the interest of the candidate. The focus will be on deriving efficient algorithms with provable statistical guarantees, using tools from: high-dimensional statistics, optimization, probability theory, approximation theory etc.
The duration of the PhD will typically be 4 years, while the postdoc positions will be for 2 years. Interested candidates who fit the criteria outlined below are encouraged to contact me via email along with their CV.
Please note:
Due to a large number of emails, I will only be able to respond to candidates whose profile is potentially of interest
Some sample papers related to the theme of the project are listed at the end of this page. These are representative of the type of analysis that is expected to be carried out in this project
These positions would be especially relevant for those with a background/interest in time-series analysis , theoretical machine learning on networks, and high-dimensional statistics
In your email, clearly outline how your background is potentially suitable for these positions. For PhD candidates, this would comprise of relevant coursework (probability, statistics, optimization, linear algebra...) and projects/internships. For postdoc candidates, this would comprise of relevant research work within the domain of statistical learning problems, with a strong theoretical component
Bachelor's degree (or above) in Mathematics/Computer Science/Statistics or related fields
Strong background in Probability/Statistics with an interest in statistical learning problems -- especially from a theoretical perspective -- as evidenced by relevant coursework and projects
Online application deadline: 31 July 2025; Starting date : January 2026.
Before applying, you should first email me sufficiently in advance (with your CV) so that there is time to arrange for an interview in case your profile appears to be a good fit.
PhD in Mathematics/Computer Science/Statistics or related fields
Strong research background in statistical learning problems -- especially from a theoretical perspective -- as evidenced by publications
Knowledge of at least one of: MATLAB, R, Python or similar languages
The earliest starting date possible will be around August 2025 (but this is flexible) . In general, administrative formalities can take up to 4-5 months (in the worst case) from the time an offer is made, so the exact starting date will depend on this.
H. Tyagi and Denis Efimov, Learning linear dynamical systems under convex constraints, 2023, arxiv:2303.15121
Ingvar M Ziemann, Henrik Sandberg, Nikolai Matni, Single Trajectory Nonparametric Learning of Nonlinear Dynamics, Thirty Fifth Conference on Learning Theory, PMLR 178:3333-3364, 2022
Tuhin Sarkar, Alexander Rakhlin, Near optimal finite time identification of arbitrary linear dynamical systems, arxiv:1812.01251, 2018
E. Karlé and H. Tyagi, Dynamic Ranking with the BTL Model: A Nearest Neighbor based Rank Centrality Method, Journal of Machine Learning Research (JMLR), 24(269):1-57, 2023
E. Araya, M. Cucuringu and H. Tyagi, Dynamic angular synchronization under smoothness constraints, 2024, arxiv:2406.04071
Marianna Pensky, Teng Zhang, Spectral clustering in the dynamic stochastic block model, arXiv:1705.01204