Jianyi Lin

Jianyi Lin

Jianyi Lin
Assistant Professor of Mathematics
Department of Mathematics
+971 (0)2 401 8217
jianyi.lin@kustar.ac.ae

Dr. Jianyi Lin graduated with a BSc in Computer Science from the University of Milan, Italy, before obtaining his MSc in Computing Systems Engineering from Politecnico di Milano, Italy, with additional recognition as one of the best graduates for the academic year 2007-2008. After completing his PhD in Mathematics and Statistics for Computational Science at the University of Milan, Italy, in 2012, he worked for five years as a postdoctoral fellow. He was previously a member of the AnacletoLab and the PHuSeLab, Department of Computer Science, the University of Milan, Italy.

During this time, Dr. Lin’s main research topics included computational complexity of geometric clustering, sparse signal modelling, computational methods for bioinformatics and applied dynamical models.

In 2007, Dr. Lin was a temporary research fellow with the Department of Electronics and Information, Politecnico di Milano, Italy, for which he devoted his time to the development of a cell-based model for the simulation of spatially extended forests and their related fire regimes, allowing him to earn the CIRITA Award 2008.

Dr. Lin was previously a visiting scholar at the Bioinformatics Group in the Institute of Molecular Life Science, University of Zurich in 2013, where he focused on semi-supervised methods for the analysis of large-scale protein-protein interaction (PPI) networks and the development of prediction tools. In 2017, Dr. Lin also became a visiting researcher at the Center for Applied Statistics in Business and Economics within the Department of Statistical Sciences, Università Cattolica del Sacro Cuore, Milan, Italy.

Dr. Lin has published more than 20 peer-reviewed papers in international journals and conferences and two monographs. He serves as referee for several journals of international publishers (Elsevier, ACM, IOS Press, etc.) and is member of the European Association for Theoretical Computer Science. He has participated in two basic research projects and one industrial research project. Following the completion of Dr. Lin’s Italian national examination in 2009, he holds the qualification of Senior Information Engineer.

Dr. Lin has teaching experience as temporary lecturer in geometry at Politecnico di Milano and as adjunct faculty in computer science at University of Milan. He has supervised five BSc students and five MSc students during their thesis preparation and has participated as a member of a number of Master’s degree thesis examination committees.
Dr. Lin is an assistant professor in the Department of Mathematics at the Khalifa University of Science and Technology.

Courses taught:

  • MATH213 Probability and Statistics
  • MATH211 Differential Equations and Linear Algebra

Teaching interest: geometry

Research interests:

  • Computational complexity of geometric clustering
  • Sparse representation techniques
  • Computational methods for bioinformatics
  • Stochastic models for pattern statistics
  • M. Goldwurm, J. Lin, and F. Saccà. On the Complexity of Clustering with Relaxed Size Constraints in Fixed Dimension. Theoretical Computer Science, to appear, 2017.
  • Adamo, G. Grossi, R. Lanzarotti, and J. Lin. Sparse Decomposition by Iterating Lipschitzian-type Mappings. Theoretical Computer Science, 664:12–28, 2017.
  • G. Grossi, R. Lanzarotti, and J. Lin. Orthogonal Procrustes analysis for dictionary learning in sparse representation. PLoS One, 12(1):e0169663, 2017.
  • G. Valentini, G. Armano, M. Frasca, J. Lin, M. Mesiti, and M. Re. RANKS: a flexible tool for node label ranking and classification in biological networks. Bioinformatics, 32(18):2872–2874, 2016.
  • Franceschini, J. Lin, C. von Mering, and L. J. Jensen. SVD-phy: improved prediction of protein functional associations through singular value decomposition of phylogenetic profiles. Bioinformatics, 32(7):1085–1087, 2016.
  • G. Grossi, R. Lanzarotti, and J. Lin. Robust face recognition providing the identity and its reliability degree combining sparse representation and multiple features. International Journal of Pattern Recognition and Artificial Intelligence, 30(10):1656007, 2016.
  • G. Grossi, R. Lanzarotti, and J. Lin. High-rate compression of ECG signals by an accuracy-driven sparsity model relying on natural basis. Digital Signal Processing, 45:96–106, 2015.
  • J. Lin, A. Bertoni, and M. Goldwurm. Exact algorithms for size constrained 2-clustering in the plane. Theoretical Computer Science, 629:80–95, 2016.
  • Adamo, G. Grossi, R. Lanzarotti, and J. Lin. ECG compression retaining the best natural basis k-coefficients via sparse decomposition. Biomedical Signal Processing and Control, 15:11–17, 2015.
  • Adamo, G. Grossi, R. Lanzarotti, and J. Lin. Robust face recognition using sparse representation in LDA space. Machine Vision and Applications, 26(6):837–847, 2015.
  • Franceschini, D. Szklarczyk, S. Frankild, M. Kuhn, M. Simonovic, A. Roth, J. Lin, P. Minguez, P. Bork, C. von Mering, and L. J. Jensen. String v9.1: protein-protein interaction networks, with increased coverage and integration. Nucleic Acids Research, 41(D1):D808–D815, 2013.
  • Bertoni, M. Goldwurm, J. Lin, and F. Saccà. Size Constrained Distance Clustering: Separation Properties and Some Complexity Results. Fundamenta Informaticae, 115(1):125–139, 2012.
  • J. Lin and S. Rinaldi. A derivation of the statistical characteristics of forest fires. Ecological Modelling, 220(7):898–903, 2009.
  • H. Kunze, D. La Torre, and J. Lin. IFSM fractal image compression with entropy and sparsity constraints: A sequential quadratic programming approach. In ICNPAA 2016 World Congress, volume 1798 of American Institute of Physics Conference Series, pages 020090–1(7), 2017.
  • J. Lin, M. Mesiti, M. Re, and G. Valentini. Within network learning on big graphs using secondary memory-based random walk kernels. In Complex Networks & Their Applications V, volume 693 of Studies in Computational Intelligence, pages 235–245. 2016.
  • M. Goldwurm, J. Lin, and F. Saccà. On the Complexity of Clustering with Relaxed Size Constraints. In Algorithmic Aspects in Information and Management – 11th International Conference, AAIM 2016, Proceedings, volume 9778 of Lecture Notes in Computer Science, pages 26–38, 2016.
  • G. Grossi, R. Lanzarotti, and J. Lin. A selection module for large-scale face recognition systems. In Image Analysis and Processing – ICIAP 2015, Part II, volume 9280 of Lecture Notes in Computer Science, pages 529–539, 2015.
  • Bertoni, M. Goldwurm, and J. Lin. Exact algorithms for 2-clustering with size constraints in the euclidean plane. In SOFSEM 2015: Theory and Practice of Computer Science, volume 8939 of Lecture Notes in Computer Science, pages 128–139. 2015.
  • R. Storchi, A. Zippo, G. Caramenti, J. Lin, and M. Valente. Modeling neuronal ensemble firing activity through intermittent chaos. In Proc. IEEE 5th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2010, pages 1593–1598, 2010.
  • J. Lin. Exact algorithms for size constrained clustering. Ledizioni Publishing, Italy, 2013.

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