Dr Ali Eshragh

Dr Ali Eshragh

Senior Lecturer

School of Mathematical and Physical Sciences

Forecasting and the power of big data

Dr Ali Eshragh is finding new ways to effectively analyse big data for forecasting. In doing so, he is setting the stage to markedly advance crucial industry practices.

Ali Eshragh

Dr Ali Eshragh predicts the future, not as a fortune-teller but as a statistician who uses past data to forecast future values.

"For instance, I analyse the daily peak electricity power demand of the city of Newcastle over the past 10 years to predict the demands for the next 30 days,” explains Ali. “Knowing these forecasts in advance is crucial to the planning of generators and distributors.”

Ali’s current area of research is in statistical modelling, in conjunction with advanced machine learning techniques, to analyse big data problems.

Though several classic forecasting models still exist today, the availability of large quantities of data, referred to as “big data”, means a completely different approach to analysis is required.

But working with big data presents two main challenges: data storage limits and computational time limits. Put simply, the amount of storage needed for big data and the inordinate amount of time needed to analyse them.

“Our main goal in our projects is to develop state-of the-art machine learning techniques to overcome these two main challenges in dealing with big time series data.

“Our high-level goals are to develop some fully/semi-automated forecasting systems to analyse big time series data and generate accurate predictions for the future values.”

Forecasting and Big Data

It was Ali’s research mentor Professor Michael Mahoney, a leading scholar in the field of big data and machine learning at the University of  California, Berkeley, who inspired and supported Ali to shift his research focus towards the challenges of big data.

“His mentoring role to move my research direction towards big data problems has been significant and I always appreciate it.”

Since 2014, Ali has led several demand forecasting projects in consultation with Australian food and beverage supply chains. Industry, he believes, is good for inciting research.

“Dealing with challenges involved in those practical projects has been the main motivation for my academic research projects.”

In a proactive step to leverage collective expertise to address these problems, Ali has formed a research group called ForBiD (Forecasting and Big Data).

“Data science is a subject of ongoing intense study. Although our research team is very young, we have already developed novel results for analysing big time series data.”

In a short space of time, the team has rapidly grown and already has active members from the University of Newcastle, other universities in Australia such as the University of Queensland and Monash, and universities in the US such as the University of California Berkeley, and Yale University. In the near future, Ali hopes to establish the ForBiD Cooperative Research Centre at the University of Newcastle.

Advancing operations and performance

Lack of access to real-world big time series data has been an ongoing impediment to Ali’s research efforts, but an alliance with researchers at Yale University is changing that.

“During my recent visit to the US, I could establish a collaboration with the School of Management at Yale University, which led to a couple of joint research projects on a real-world big time series database. Our collaborators at Yale have agreed to share their big database for our joint projects.”

Forecasting, especially when involved with big data, has broad applications that can instigate personal, medical, scientific and engineering advancements. But there’s very little currently available to assist with accurate forecasting, which means Ali’s research could radically transform the way individuals and businesses operate.

“Due to the lack of a user-friendly automated forecasting system, people usually use their own knowledge and experience to predict future events for planning their lives. However, it has been shown that such judgmental forecasts include high errors in practice.”

On an organisational level, big retailers such as David Jones, Amazon and Woolworths could potentially optimise their supply chain and inventory planning logistics with a systematic approach to predicting demand for their products.

“Our research outcome could be immediately applied to any of these personal or business scenarios to improve their performance.”

Improving industry through research

In 2017, Ali was invited to attend a two-day "Science Meets Parliament” workshop in Canberra. He recalls it as a “brilliant event” through which he learnt that among 32 developed countries in the world, Australia ranked last in connections between industries and universities. It’s a position the Australian Government is motivated to improve by fervently supporting initiatives that leverage university-based research to improve industrial practice—initiatives like Ali’s.

“I am so proud that our research outcome can be directly implemented in practice and solve some industry problems. Hopefully, such research projects will pave the road towards improving the ranking between industries and universities.”

Dr Ali Eshragh

Forecasting and the power of big data

Dr Ali Eshragh is finding new ways to effectively analyse big data for forecasting. In doing so, he is setting the stage to markedly advance crucial industry practices.

Read more

Career Summary

Biography

Please see my personal website for correct and up-to-date information: 

http://www.alieshragh.info


Qualifications

  • Doctor of Philosophy, University of South Australia
  • Bachelor of Science, Sharif University of Technology - Iran

Keywords

  • Markov Decision Processes
  • Randomized Numerical Linear Algebra
  • Reinforcement Learning
  • Stochastic Modeling
  • Supply Chain Optimization
  • Time Series Forecasting

Languages

  • Persian (excluding Dari) (Fluent)
  • English (Fluent)

Fields of Research

Code Description Percentage
010406 Stochastic Analysis and Modelling 50
010206 Operations Research 20
010405 Statistical Theory 30

Professional Experience

UON Appointment

Title Organisation / Department
Senior Lecturer University of Newcastle
School of Mathematical and Physical Sciences
Australia

Awards

Prize

Year Award
2017 Australian Society for Operations Research 2017 Rising Star Award
Australian Society for Operations Research
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Publications

For publications that are currently unpublished or in-press, details are shown in italics.


Journal article (16 outputs)

Year Citation Altmetrics Link
2020 Eshragh A, Alizamir S, Howley P, Stojanovski E, 'Modeling the dynamics of the COVID-19 population in Australia: A probabilistic analysis', PLOS ONE, 15 (2020) [C1]
DOI 10.1371/journal.pone.0240153
Co-authors Elizabeth Stojanovski, Peter Howley
2020 Eshragh A, Filar JA, Kalinowski T, Mohammadian S, 'Hamiltonian Cycles and Subsets of Discounted Occupational Measures', Mathematics of Operations Research, 45 713-731 (2020) [C1]
DOI 10.1287/moor.2019.1009
Citations Scopus - 1
Co-authors Thomas Kalinowski
2020 Eshragh A, Esmaeilbeigi R, Middleton R, 'An analytical bound on the fleet size in vehicle routing problems: A dynamic programming approach', Operations Research Letters, 48 350-355 (2020) [C1]
DOI 10.1016/j.orl.2020.04.007
Co-authors Richard Middleton
2019 Sierra-Altamiranda A, Charkhgard H, Dayarian I, Eshragh A, Javadi S, 'Learning to Project in Multi-Objective Binary Linear Programming.', CoRR, abs/1901.10868 (2019)
2019 Charkhgard H, Eshragh A, 'A NEW APPROACH TO SELECT THE BEST SUBSET OF PREDICTORS IN LINEAR REGRESSION MODELLING: BI-OBJECTIVE MIXED INTEGER LINEAR PROGRAMMING', ANZIAM JOURNAL, 61 64-75 (2019) [C1]
DOI 10.1017/S1446181118000275
Citations Web of Science - 1
2018 Fahimnia B, Darvarzani H, Eshragh A, 'Planning of complex supply chains: A performance comparison of three meta-heuristic algorithms', Computers and Operations Research, 89 241-252 (2018) [C1]
DOI 10.1016/j.cor.2015.10.008
Citations Scopus - 20Web of Science - 13
2016 Avrachenkov K, Eshragh A, Filar JA, 'On transition matrices of Markov chains corresponding to Hamiltonian cycles', Annals of Operations Research, 243 19-35 (2016) [C1]

© 2014, Springer Science+Business Media New York. In this paper, we present some algebraic properties of a particular class of probability transition matrices, namely, Hamiltonian... [more]

© 2014, Springer Science+Business Media New York. In this paper, we present some algebraic properties of a particular class of probability transition matrices, namely, Hamiltonian transition matrices. Each matrix P in this class corresponds to a Hamiltonian cycle in a given graph G on n nodes and to an irreducible, periodic, Markov chain. We show that a number of important matrices traditionally associated with Markov chains, namely, the stationary, fundamental, deviation and the hitting time matrix all have elegant expansions in the first n- 1 powers of P, whose coefficients can be explicitly derived. We also consider the resolvent-like matrices associated with any given Hamiltonian cycle and its reverse cycle and prove an identity about the product of these matrices. As an illustration of these analytical results, we exploit them to develop a new heuristic algorithm to determine a non-Hamiltonicity of a given graph.

DOI 10.1007/s10479-014-1642-2
Citations Scopus - 4Web of Science - 3
2016 Bean NG, Eshragh A, Ross JV, 'Fisher Information for a partially observable simple birth process', Communications in Statistics - Theory and Methods, 45 7161-7183 (2016) [C1]

© 2016 Taylor & Francis Group, LLC. In this paper, we study the Fisher Information for the birth rate of a partially observable simple birth process involving n observations... [more]

© 2016 Taylor & Francis Group, LLC. In this paper, we study the Fisher Information for the birth rate of a partially observable simple birth process involving n observations. We suppose that at each observation time, each individual in the population can be observed independently with known fixed probability p. Finding an analytical form of the Fisher Information in general appears intractable. Nonetheless, we find a very good approximation for the Fisher Information by exploiting the probabilistic properties of the underlying stochastic process. Both numerical and theoretical results strongly support the latter approximation and confirm its high level of accuracy.

DOI 10.1080/03610926.2014.978024
Citations Web of Science - 1
2015 Fahimnia B, Sarkis J, Choudhary A, Eshragh A, 'Tactical supply chain planning under a carbon tax policy scheme: A case study', International Journal of Production Economics, 164 206-215 (2015) [C1]

© 2014 Elsevier B.V. All rights reserved. Greenhouse gas emissions are receiving greater scrutiny in many countries due to international forces to reduce anthropogenic global clim... [more]

© 2014 Elsevier B.V. All rights reserved. Greenhouse gas emissions are receiving greater scrutiny in many countries due to international forces to reduce anthropogenic global climate change. Industry and their supply chains represent a major source of these emissions. This paper presents a tactical supply chain planning model that integrates economic and carbon emission objectives under a carbon tax policy scheme. A modified Cross-Entropy solution method is adopted to solve the proposed nonlinear supply chain planning model. Numerical experiments are completed utilizing data from an actual organization in Australia where a carbon tax is in operation. The analyses of the numerical results provide important organizational and policy insights on (1) the financial and emissions reduction impacts of a carbon tax at the tactical planning level, (2) the use of cost/emission tradeoff analysis for making informed decisions on investments, (3) the way to price carbon for maximum environmental returns per dollar increase in supply chain cost.

DOI 10.1016/j.ijpe.2014.12.015
Citations Scopus - 78Web of Science - 63
2015 Bean N, Elliott R, Eshragh A, Ross J, 'On Binomial Observation of Continuous-Time Markovian Population Models', Journal of Applied Probability, 52 457-472 (2015) [C1]
DOI 10.1017/S0021900200012572
Citations Scopus - 1Web of Science - 1
2015 Fahimnia B, Sarkis J, Eshragh A, 'A Tradeoff Model for Green Supply Chain Planning: A Leanness-versus-Greenness Analysis', OMEGA International Journal of Management Science, 1-28 (2015) [C1]
Citations Scopus - 119Web of Science - 78
2011 Eshragh A, Filar J, Nazar A, 'A Projection-Adapted Cross Entropy (PACE) method for transmission network planning', ENERGY SYSTEMS-OPTIMIZATION MODELING SIMULATION AND ECONOMIC ASPECTS, 2 189-208 (2011) [C1]
DOI 10.1007/s12667-011-0033-x
Citations Scopus - 13Web of Science - 3
2011 Eshragh A, Filar JA, Haythorpe M, 'A hybrid simulation-optimization algorithm for the Hamiltonian cycle problem', ANNALS OF OPERATIONS RESEARCH, 189 103-125 (2011) [C1]
DOI 10.1007/s10479-009-0565-9
Citations Scopus - 10Web of Science - 7
2011 Eshragh A, Filar J, 'Hamiltonian Cycles, Random Walks, and Discounted Occupational Measures', MATHEMATICS OF OPERATIONS RESEARCH, 36 258-270 (2011) [C1]
DOI 10.1287/moor.1110.0492
Citations Scopus - 4Web of Science - 3
2009 Eshragh A, Modarres M, 'A New Approach to Distribution Fitting: Decision on Beliefs', Journal of Industrial and Systems Engineering, 3 56-71 (2009)
2008 Eshragh A, Mahlooji, Abouee Mehrizi, Izady, 'Uniform Fractional Part: A Simple Fast Method for Generating Continuous Random Variates', Scientia Iranica: international journal of science and technology, 15 613-622 (2008)
Show 13 more journal articles

Conference (3 outputs)

Year Citation Altmetrics Link
2017 Esmaeilbeigi R, Eshragh A, Garcia-Flores R, Heydar M, 'Whey Reverse Logistics Network Design: A Stochastic Hierarchical Facility Location Model', 22nd International Congress on Modelling and Simulation (MODSIM2017), Hobart, Tasmania (2017) [E1]
2015 Eshragh A, 'Fisher Information, Stochastic Processes and Generating Functions', Proceedings of the 21st International Congress on Modelling and Simulation (MODSIM2015), Gold Coast (2015)
2011 Avrachenkov K, Eshragh A, Filar JA, 'Hamiltonian transition matrices', VALUETOOLS 2011 - 5th International ICST Conference on Performance Evaluation Methodologies and Tools, Cachan, France (2011) [E1]
DOI 10.4108/icst.valuetools.2011.245841
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Grants and Funding

Summary

Number of grants 15
Total funding $3,619,951

Click on a grant title below to expand the full details for that specific grant.


20202 grants / $18,470

Big Time Series Data and Randomised Numerical Linear Algebra$11,580

In problems involving big time series data, selecting an appropriate Autoregressive (AR) model amounts to computing the solutions of many potentially large-scale ordinary least squares (OLS) problems, which can be the main bottleneck of computations. Here is where randomized sub-sampling algorithms can be used to greatly speed-up such mode selection procedures. For computations involving large matrices in general, and large-scale OLS problems in particular, randomized numerical linear algebra (RandNLA) has successfully employed various random sub-sampling and sketching strategies. We have recently developed a highly efficient RandNLA algorithm to fit an appropriate AR model on big time series data and provided theoretical error bounds with high probability. In this project, we aim to extend the approach taken in our recent work for more general time series models, the so-called VAR and ARMA models.

Funding body: The Australian Research Council Centre of Excellence for Mathematical and Statistical Frontiers (ACEMS)

Funding body The Australian Research Council Centre of Excellence for Mathematical and Statistical Frontiers (ACEMS)
Project Team

Ali Eshragh, Fred Roosta

Scheme External
Role Investigator
Funding Start 2020
Funding Finish 2020
GNo
Type Of Funding External
Category EXTE
UON N

Stochastic Analysis of the COVID-19 Population$6,890

Funding body: The Australian Research Council Centre of Excellence for Mathematical and Statistical Frontiers (ACEMS)

Funding body The Australian Research Council Centre of Excellence for Mathematical and Statistical Frontiers (ACEMS)
Project Team

Ali Eshragh; Joshua Ross; Saed Alizamir; Peter Howley; Elizabeth Stojanovski; Judy-anne Osborn

Scheme External
Role Lead
Funding Start 2020
Funding Finish 2020
GNo
Type Of Funding External
Category EXTE
UON N

20193 grants / $26,350

Approximate Solutions to Large Markov Decision Processes$12,000

Randomised Numerical Linear Algebra (RandNLA) is an interdisciplinary research area that exploits randomisation as a computational resource to develop improved algorithms for large scale linear algebra problems. In this project, we utiliseRandNLA methods to develop a new algorithm to solve Markov Decision Processes (MDPs) with large state space approximately. More precisely, we develop a non-uniform sampling algorithm for a large state space MDP to construct a reasonably smaller MDP such that the former can effectively be approximated by solving the latter. These results would shed a light on the literature of not only MDPs, but also machine learning, in particular reinforcement learning.

Funding body: The Australian Research Council Centre of Excellence for Mathematical and Statistical Frontiers (ACEMS)

Funding body The Australian Research Council Centre of Excellence for Mathematical and Statistical Frontiers (ACEMS)
Project Team

Ali Eshragh, Fred Roosta

Scheme External
Role Investigator
Funding Start 2019
Funding Finish 2019
GNo
Type Of Funding External
Category EXTE
UON N

Data Science Down Under Workshop$7,350

Funding for the Data Science Down Under Workshop, will be held in Newcastle, 8-12 December 2019

Funding body: The Australian Research Council Centre of Excellence for Mathematical and Statistical Frontiers (ACEMS)

Funding body The Australian Research Council Centre of Excellence for Mathematical and Statistical Frontiers (ACEMS)
Project Team

Ali Eshragh, Fred Roosta

Scheme External
Role Lead
Funding Start 2019
Funding Finish 2019
GNo
Type Of Funding Aust Competitive - Commonwealth
Category 1CS
UON N

Data Science Down Under Workshop$7,000

Funding for the Data Science Down Under Workshop, will be held in Newcastle, 8-12 December 2019

Funding body: AMSI Intern Australian Mathematical and Physical Sciences

Funding body AMSI Intern Australian Mathematical and Physical Sciences
Project Team

Ali Eshragh, Fred Roosta, Natalie Thamwattana, Ricardo Gabrielli Barreto Campello, Elizabeth Stojanovski

Scheme Small Event Funding
Role Lead
Funding Start 2019
Funding Finish 2019
GNo
Type Of Funding Aust Competitive - Commonwealth
Category 1CS
UON N

20161 grants / $161,151

Building statistical literacy for success in higher education$161,151

Funding body: Department of Education

Funding body Department of Education
Project Team Professor Peter Howley, Associate Professor Elena Prieto-Rodriguez, Doctor Ali Eshragh, Doctor Elizabeth Stojanovski, Associate Professor Erica Southgate, Professor Michael Martin, Associate Professor Peter Dunn, Professor Kathryn Holmes
Scheme Higher Education Participation and Partnerships Programme
Role Investigator
Funding Start 2016
Funding Finish 2016
GNo G1600141
Type Of Funding C2110 - Aust Commonwealth - Own Purpose
Category 2110
UON Y

20149 grants / $3,413,980

Food & Beverage Supply Chain Optimisation Industrial Transformation Training Centre$2,221,092

Funding body: ARC (Australian Research Council)

Funding body ARC (Australian Research Council)
Project Team Professor Rick Middleton, Professor Regina Berretta, Professor Michael Bowyer, Doctor Ali Eshragh, Associate Professor Behnam Fahimnia, Professor Mathieu Savelsbergh, Professor Natashia Boland, Dr Costas Stathopoulos, Professor John Bartholdi, Doctor Simon Dunstall, Mrs Carlee McGowan, Mr Robert McMahon, Mr Tim Norris, Mr Robert Scoines, Professor Christopher Scarlett, Doctor Quan Vuong, Professor Michael Bowyer, Doctor Masoud Talebian
Scheme Industrial Transformation Training Centres
Role Investigator
Funding Start 2014
Funding Finish 2016
GNo G1301004
Type Of Funding Aust Competitive - Commonwealth
Category 1CS
UON Y

Maintenance Optimisation in Rail Infrastructure Systems for Coal and Iron Ore Exports$592,729

Funding body: ARC (Australian Research Council)

Funding body ARC (Australian Research Council)
Project Team Doctor Thomas Kalinowski, Professor Mathieu Savelsbergh, Professor Natashia Boland, Associate Professor Yangfeng Ouyang, Steve Straughan, Doctor Ali Eshragh, Mr Michael Backhouse, Moffiet, Chad, Ouyang, Yanfeng
Scheme Linkage Projects
Role Lead
Funding Start 2014
Funding Finish 2017
GNo G1301225
Type Of Funding Aust Competitive - Commonwealth
Category 1CS
UON Y

Maintenance Optimisation in Rail Infrastructure Systems for Coal and Iron Ore Exports$408,159

Funding body: Aurizon Network Pty Ltd

Funding body Aurizon Network Pty Ltd
Project Team Doctor Thomas Kalinowski, Professor Mathieu Savelsbergh, Professor Natashia Boland, Associate Professor Yangfeng Ouyang, Steve Straughan, Doctor Ali Eshragh, Moffiet, Chad, Ouyang, Yanfeng
Scheme Linkage Projects Partner Funding
Role Lead
Funding Start 2014
Funding Finish 2017
GNo G1301276
Type Of Funding C3111 - Aust For profit
Category 3111
UON Y

Food & Beverage Supply Chain Optimisation Industrial Transformation Training Centre$90,000

Funding body: Coca Cola Amatil (Australia)

Funding body Coca Cola Amatil (Australia)
Project Team Professor Rick Middleton, Professor Regina Berretta, Professor Michael Bowyer, Doctor Ali Eshragh, Professor Christopher Scarlett, Doctor Masoud Talebian, Associate Professor Behnam Fahimnia, Professor Mathieu Savelsbergh, Professor Natashia Boland, Dr Costas Stathopoulos, Professor John Bartholdi, Doctor Simon Dunstall, Mrs Carlee McGowan, Mr Robert McMahon, Mr Robert Scoines, Mr Tim Norris
Scheme Industrial Transformation Training Centres Partner Funding
Role Investigator
Funding Start 2014
Funding Finish 2016
GNo G1301129
Type Of Funding C3111 - Aust For profit
Category 3111
UON Y

Food & Beverage Supply Chain Optimisation Industrial Transformation Training Centre$30,000

Funding body: Sanitarium Health and Wellbeing Company

Funding body Sanitarium Health and Wellbeing Company
Project Team Professor Rick Middleton, Professor Regina Berretta, Professor Michael Bowyer, Doctor Ali Eshragh, Professor Christopher Scarlett, Doctor Masoud Talebian, Associate Professor Behnam Fahimnia, Professor Mathieu Savelsbergh, Professor Natashia Boland, Dr Costas Stathopoulos, Professor John Bartholdi, Doctor Simon Dunstall, Mrs Carlee McGowan, Mr Robert McMahon, Mr Robert Scoines, Mr Tim Norris
Scheme Industrial Transformation Training Centres Partner Funding
Role Investigator
Funding Start 2014
Funding Finish 2016
GNo G1301130
Type Of Funding C3112 - Aust Not for profit
Category 3112
UON Y

Food & Beverage Supply Chain Optimisation Industrial Transformation Training Centre$30,000

Funding body: Sunrice

Funding body Sunrice
Project Team Professor Rick Middleton, Professor Regina Berretta, Professor Michael Bowyer, Doctor Ali Eshragh, Professor Christopher Scarlett, Doctor Masoud Talebian, Associate Professor Behnam Fahimnia, Professor Mathieu Savelsbergh, Professor Natashia Boland, Dr Costas Stathopoulos, Professor John Bartholdi, Doctor Simon Dunstall, Mrs Carlee McGowan, Mr Robert McMahon, Mr Robert Scoines, Mr Tim Norris
Scheme Industrial Transformation Training Centres Partner Funding
Role Investigator
Funding Start 2014
Funding Finish 2016
GNo G1301131
Type Of Funding C3111 - Aust For profit
Category 3111
UON Y

Research$30,000

This is a small research grant over three years 2014-2016 granted by Laureate Professor Jon Borwein.

Funding body: Priority Research Centre for Computer-Assisted Research Mathematics and its Applications (CARMA), The University of Newcastle

Funding body Priority Research Centre for Computer-Assisted Research Mathematics and its Applications (CARMA), The University of Newcastle
Scheme Research Purposes
Role Lead
Funding Start 2014
Funding Finish 2016
GNo
Type Of Funding Internal
Category INTE
UON N

New Staff Grant$10,000

The PVC of the University of Newcastle offered an AU$10K start up grant in 2014. This provided me with the opportunity to visit A./Prof. Catherine Greenhill at the University of New South Wales in May 2014 and Prof. Martin Dyer at the University of Leeds in September-October 2014 to elaborate my research findings in detail and secure their participation in a Discovery Project. In 2015, we submitted a DP-proposal including myself as the lead-CI, Catherine as the co-CI and Martin as the PI.

Funding body: Faculty of Science and Information Technology,The University of Newcastle

Funding body Faculty of Science and Information Technology,The University of Newcastle
Scheme Faculty PVC Conference Funding
Role Lead
Funding Start 2014
Funding Finish 2014
GNo
Type Of Funding Internal
Category INTE
UON N

Faculty PVC Conference Assistance Grant 2014$2,000

Funding body: University of Newcastle - Faculty of Science & IT

Funding body University of Newcastle - Faculty of Science & IT
Project Team Doctor Ali Eshragh
Scheme PVC Conference Assistance Grant
Role Lead
Funding Start 2014
Funding Finish 2014
GNo G1401187
Type Of Funding Internal
Category INTE
UON Y
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Research Supervision

Number of supervisions

Completed3
Current5

Current Supervision

Commenced Level of Study Research Title Program Supervisor Type
2020 Honours Empirical comparison of four algorithms for solving Toeplitz least square problems with applications in big time series data analysis Statistics, Faculty of Science | University of Newcastle Co-Supervisor
2020 Honours A novel algorithm to fit an ARMA model on big time series data Statistics, Faculty of Science | University of Newcastle Principal Supervisor
2020 PhD Autoregressive Moving Average Model, Big Time Series Data, and Randomised Numerical Linear Algebra PhD (Statistics), Faculty of Science, The University of Newcastle Principal Supervisor
2019 Masters Utilising RandNLA Methods to Solve Markov Decision Processes M Philosophy (Statistics), Faculty of Science, The University of Newcastle Co-Supervisor
2018 PhD Policy optimisation in reinforcement learning Mathematics, The University of Queensland Co-Supervisor

Past Supervision

Year Level of Study Research Title Program Supervisor Type
2019 Honours A new state-aggregation algorithm to approximately solve large Markov decision processes Statistics, The University of Newcastle Principal Supervisor
2018 Honours Exploration of flu-tracking approaches using time series methods Statistics, The University of Newcastle Principal Supervisor
2017 Honours Optimal observation times, the Fisher Information and generating functions Statistics, The University of Newcastle Principal Supervisor
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News

Rising Star Award 2017

January 29, 2018

UON's Ali Eshragh wins the 2017 ASOR Rising Star Award

Dr Ali Eshragh

Position

Senior Lecturer
School of Mathematical and Physical Sciences
Faculty of Science

Contact Details

Email ali.eshragh@newcastle.edu.au
Phone (02) 4921 5127

Office

Room SR119
Building Social Sciences Building
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