The Faculty of Mathematics recognizes six graduate students for their outstanding research papers. Andrew Giuliani in the Department of Applied Mathematics, Stefan Sremac in the Department of Combinatorics and Optimization, Ruizhang Jin in the Department of Pure Mathematics, Rui Qiao in the Department of Statistics and Actuarial Science, and Nik Unger and Ahmed Alquraan from the David R. Cheriton School of Computer Science each won a 2019 Huawei Prize for Best Research Paper by a Mathematics Graduate Student.
Giuliani is one of the winners for his paper A moment limiter for the discontinuous Galerkin method on unstructured triangular meshes. Giuliani's work describes a novel approach to limiting on unstructured meshes.
Sremac also received a Huawei Prize. Along with his supervisor Henry Wolkowicz and collaborator Hugo Woerdeman, he characterized the patterns of of a partial Toeplitz matrix for which the maximum determinant positive definite completion is Toeplitz.
Two other prizes went to Unger and Saleh. In Nik’s paper, Improved Strongly Deniable Authenticated Key Exchanges for Secure Messaging, he focused on two-party person-to-person communication where both parties don’t have to be online at the same time. Ahmed’s paper, An Analysis of Network-Partitioning Failures in Cloud Systems, is the first to highlight the catastrophic impact of network faults on modern systems, the first to identify special category of network partitioning called partial partitions, and conclude with a number of recommendations that will directly affect the design of future distributed systems and change current network maintenance practices.
Jin received a Huawei Prize for his paper Constructing types in differentially closed fields that are analysable in the constants. He developed the model-theoretic notion of analysability and establishes criteria for when a canonical analysis exists. He then applied his methods to differential fields exhibiting phenomena that witness the richness of differential-algebraic geometry.
Qiao also received a Huawei Prize. His paper, Deep learning enables de novo peptide sequencing from data-independent-acquisition mass spectrometry, discussed the development of a deep-learning-based model for de novo DeepNovo11 which sequences using data-dependent acquisition data.
We extend our sincere congratulations to these fine researchers and wish them all the best in their continued research pursuits.