Ali Eshragh

Professor Ali Eshragh, who has visited ICSI previously, is a Senior Lecturer in Data Science (equivalent to tenured Associate Professor in the US system) at the University of Newcastle in Australia. Ali’s main expertise is in modeling and optimizing real-world complex systems in the presence of uncertainty to provide optimal decisions for managers and policy makers. He publishes regularly in optimization and machine learning journals including Mathematics of Operations Research, Annals of Operations Research, and Journal of Machine Learning Research. Ali has been involved as a Principal Investigator (PI or Co-PI) in 12 funded research projects totaling USD 1,987,407. Ali is part of the Big Data group and works with Prof. Michael Mahoney. 

Part of Ali's research with the group involves developed models and algorithms for the analysis of large-scale time series data. Such models have many applications, from supply chains and queuing networks to cognitive science and epidemiology. They apply methods from randomized numerical linear algebra (RandNLA) to develop a new fast algorithm to estimate the leverage scores of an autoregressive (AR) model in big data regimes. They show that the accuracy of approximations lies within (1 + O (ε)) of the true leverage scores with high probability. These theoretical results are subsequently exploited to develop an efficient algorithm, called LSAR, for fitting an appropriate AR model to big time series data. Their proposed algorithm is guaranteed, with high probability, to find the maximum likelihood estimates of the parameters of the underlying true AR model and has a worst case running time that significantly improves those of the state-of-the-art alternatives in big data regimes. Empirical results on large-scale synthetic as well as real data highly support the theoretical results and reveal the efficacy of this new approach.