Publications

[C1] Muhammad Osama, Dave Zachariah, Thomas B. Schön, Learning Localized Spatio-Temporal Models From Streaming Data. Proceedings of the 35th International Conference on Machine Learning, Stockholm, 2018. PDF on PMLR. Code on GitHub. Video at [1]. Poster. Presentation.

[C2] Muhammad Osama, Dave Zachariah, Thomas B. Schön, Inferring Heterogeneous Causal Effects in Presence of Spatial Confounding.Proceedings of the 36th International Conference on Machine Learning, Long Beach, California, PMLR 97, 2019. PDF on PMLR. Code on GitHub

[C3] Muhammad Osama, Dave Zachariah, Peter Stoica, Prediction of Spatial Point Processes: Regularized Method with Out-of-Sample Gaurantees, 33rd Conference on Neural Information Processing Systems, Vacouver, Canada, 2019. Video at [2]. Code at Github. PDF at NeurIPS

[C4] Muhammad Osama, Dave Zachariah, Peter Stoica, Learning Robust Decision Policies from Observational Data, 34th Conference on Neural Information Processing Systems, 2020. PDF at NeurIPS. Video at [3]. Code at Github.

[J1] Muhammad Osama, Dave Zachariah, Peter Stoica, Robust Risk Minimization for Statistical Learning from Corrupted Data, IEEE Open Journal of Signal Processing. PDF at IEEEXplore.

[J2] Muhammad Osama, Dave Zachariah, Satyam, Dwivedi, Peter Stoica, Robust localization in wireless networks from corrupted signals, EURASIP Journal on Advances in Signal Processing. PDF at [4].

Coursework

1) Sequential Monte Carlo (6 credits)
2) Matrix Algebra (10 credits)
3) Probabilistic Machine Learning (5+3 credits)
4) Statistical Estimation Theory (9+3 credits)
5) Reinforcement Learning (5 credits)
6) Convex Optimization (6 credits)
7) Deep Learning (5 credits)
8) Reinforcement Learning (offered at group) (5 credits)
9) Ethics of Technology and Science (2 credits)
10) Matrices and Statistics with Applications (5 cr)
11) Causal Inference (5 cr)