Most of the codes related to my papers can be accessed below. Contact me for additional resources.

- McTorch, a Python toolbox for manifold constrained deep learning.
- GeoMM to learn multilingual word embeddings. (comming soon)
- Domain adaptation using metric learning on manifolds.
- Side Information infused Incremental Analysis.
- Low-rank geometric mean metric learning.
- A dual framework for tensor completion.
- Structured matrix learning.
- R-SVRG, accelerated stochastic gradients on manifolds.
- Decentralized subspace learning with gossiping.
- Sparse and low-rank index coding with Riemannian optimization.
- Scaled SGD for low-rank matrix completion.
- Symmetry-invariant optimization in deep networks.
- Riemannian preconditioning for tensor completion.
- Sparse plus low-rank autoregressive identification in neuroimaging time series.
- A Riemannian approach to low-rank algebraic Riccati equations.
- R3MC: A Riemannian three-factor algorithm for low-rank matrix completion.
- qGeomMC: A quotient geometric approach to low-rank matrix Completion.
- It also contains a number of computationally efficient Riemannian algorithms for fixed-rank (low-rank) matrix completion for different matrix factorizations.

- Trace norm minimization of the form f(X) + λ || X ||
_{*}, where f is a smooth convex function, || X ||_{*}is the trace (or nuclear) norm, and λ >= 0 is a regularization parameter. - Euclidean distance matrix (EDM) completion.