|Manolis Tsakiris, Assistant Professor|
Tel: (021) 20685356
Manolis Tsakiris is an electrical engineering and computer science graduate of the National Technical University of Athens, Greece. He holds an MS degree in signal processing from Imperial College London, UK, and a PhD degree in theoretical machine learning from Johns Hopkins University, USA, under the supervision of Prof. Rene Vidal. Since August 2017 he is an assistant professor at the School of Information Science and Technology (SIST) at ShanghaiTech University. His research focuses on fundamental theoretical aspects of subspace learning methods, with applications in machine learning and data science. Prominent examples include Robust Principal Component Analysis, Subspace Clustering, Shuffled Linear Regression and Matrix Completion. Inspired by the success of the union of subspaces model in machine learning, he is also working on purely mathematical problems concerning algebraic-geometric invariants of related algebraic varieties, such as their Castelnuovo-Mumford regularity.
1. M. C. Tsakiris and L. Peng. Homomorphic sensing, International Conference on Machine Leanring, 2019.
2. M. C. Tsakiris, Linearization of resolutions via products, Journal of Pure and Applied Algebra (to appear), 2019.
3. Z. Zhu, Y. Wang, D. P. Robinson, D. Naiman, R. Vidal, M. C. Tsakiris, Dual principal component pursuit: Improved analysis and efficient algorithms, Neural Information Processing Systems (NIPS), 2018.
4. M. C. Tsakiris and R. Vidal. Dual principal component pursuit, Journal of Machine Learning Research, 2018.
5. M. C. Tsakiris and R. Vidal. Theoretical analysis of sparse subspace clustering with missing entries, International Conference on Machine Leanring, 2018.
6. M. Tsakiris and R. Vidal. Hyperplane clustering via dual principal component pursuit. International Conference on Machine Learning, 2017.
7. M. Tsakiris and R. Vidal. Algebraic clustering of affine subspaces. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017.
8. M. Tsakiris and R. Vidal. Filtrated algebraic subspace clustering. SIAM Journal of Imaging Sciences, 2017.