Dr Ali Eshragh
Honorary Senior Lecturer
School of Information and Physical Sciences
- Email:ali.eshragh@newcastle.edu.au
- Phone:(02) 4921 5127
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.
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.”
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.
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 |
---|---|---|
490511 | Time series and spatial modelling | 30 |
490108 | Operations research | 30 |
490510 | Stochastic analysis and modelling | 40 |
Professional Experience
UON Appointment
Title | Organisation / Department |
---|---|
Senior Lecturer of Data Science | 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 |
Publications
For publications that are currently unpublished or in-press, details are shown in italics.
Journal article (24 outputs)
Year | Citation | Altmetrics | Link | ||||||||
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2024 |
Sierra-Altamiranda A, Charkhgard H, Dayarian I, Eshragh A, Javadi S, 'Learning to project in a criterion space search algorithm: an application to multi-objective binary linear programming', OPTIMIZATION LETTERS, [C1]
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2022 |
Eshragh A, Howley P, Stoja-novski E, 'Modeling the dynamics of the COVID-19 population in Australia: A probabilistic analysis (vol 15, e0240153, 2022)', PLOS ONE, 17 (2022)
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2022 |
Eshragh A, Roosta F, Nazari A, Mahoney MW, 'LSAR: Efficient Leverage Score Sampling Algorithm for the Analysis of Big Time Series Data', JOURNAL OF MACHINE LEARNING RESEARCH, 23 1-36 (2022) [C1]
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Nova | |||||||||
2022 |
Eshragh A, Ganim B, Perkins T, Bandara K, 'The Importance of Environmental Factors in Forecasting Australian Power Demand', ENVIRONMENTAL MODELING & ASSESSMENT, 27 1-11 (2022) [C1]
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Nova | |||||||||
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]
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Nova | |||||||||
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]
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Nova | |||||||||
2020 |
Abolghasemi M, Hurley J, Eshragh A, Fahimnia B, 'Demand forecasting in the presence of systematic events: Cases in capturing sales promotions', International Journal of Production Economics, 230 (2020) [C1]
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Nova | |||||||||
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]
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Nova | |||||||||
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]
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Nova | |||||||||
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]
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Nova | |||||||||
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] 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 ... [more] 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.
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Nova | |||||||||
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] 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... [more] 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.
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Nova | |||||||||
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] Greenhouse gas emissions are receiving greater scrutiny in many countries due to international forces to reduce anthropogenic global climate change. Industry and their supply chai... [more] 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.
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Nova | |||||||||
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]
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Nova | |||||||||
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]
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Nova | |||||||||
Show 21 more journal articles |
Conference (3 outputs)
Year | Citation | Altmetrics | Link | ||
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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] | Nova | |||
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]
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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 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 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, 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 | Emeritus Professor Rick Middleton, Professor Regina Berretta, Professor Michael Bowyer, Doctor Ali Eshragh, Associate Professor Behnam Fahimnia, Doctor Simon Dunstall, Mrs Carlee McGowan, Mr Robert McMahon, Mr Tim Norris, Mr Robert Scoines, Professor Christopher Scarlett, Doctor Quan Vuong, Professor John Bartholdi, Professor Natashia Boland, Professor Michael Bowyer, Professor Mathieu Savelsbergh, Dr Costas Stathopoulos, 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, Associate Professor Yangfeng Ouyang, Steve Straughan, Doctor Ali Eshragh, Mr Michael Backhouse, Professor Natashia Boland, Moffiet, Chad, Associate Professor Yangfeng Ouyang, Professor Mathieu Savelsbergh |
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, Professor Mathieu Savelsbergh, Professor Natashia Boland, Associate Professor Yangfeng Ouyang, Associate Professor Yangfeng Ouyang, Moffiet, Chad, Steve Straughan, Doctor Ali Eshragh, Ouyang, Yanfeng, Mr Chad Moffiet |
Scheme | Linkage Projects Partner Funding |
Role | Lead |
Funding Start | 2014 |
Funding Finish | 2017 |
GNo | G1301276 |
Type Of Funding | C3100 – Aust For Profit |
Category | 3100 |
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 John Bartholdi, Doctor Simon Dunstall, Mrs Carlee McGowan, Mr Robert McMahon, Mr Tim Norris, Mr Robert Scoines, Emeritus Professor Rick Middleton, Professor Regina Berretta, Professor Michael Bowyer, Doctor Ali Eshragh, Professor John Bartholdi, Professor Christopher Scarlett, Doctor Masoud Talebian, Associate Professor Behnam Fahimnia, Professor Mathieu Savelsbergh, Professor Mathieu Savelsbergh, Professor Natashia Boland, Professor Natashia Boland, Dr Costas Stathopoulos |
Scheme | Industrial Transformation Training Centres Partner Funding |
Role | Investigator |
Funding Start | 2014 |
Funding Finish | 2016 |
GNo | G1301129 |
Type Of Funding | C3100 – Aust For Profit |
Category | 3100 |
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 John Bartholdi, Doctor Simon Dunstall, Mrs Carlee McGowan, Mr Robert McMahon, Mr Tim Norris, Mr Robert Scoines, Emeritus Professor Rick Middleton, Professor Regina Berretta, Professor Michael Bowyer, Doctor Ali Eshragh, Professor John Bartholdi, Professor Christopher Scarlett, Doctor Masoud Talebian, Professor Natashia Boland, Associate Professor Behnam Fahimnia, Professor Mathieu Savelsbergh, Professor Mathieu Savelsbergh, Professor Natashia Boland, Dr Costas Stathopoulos |
Scheme | Industrial Transformation Training Centres Partner Funding |
Role | Investigator |
Funding Start | 2014 |
Funding Finish | 2016 |
GNo | G1301130 |
Type Of Funding | C3200 – Aust Not-for Profit |
Category | 3200 |
UON | Y |
Food & Beverage Supply Chain Optimisation Industrial Transformation Training Centre$30,000
Funding body: Sunrice
Funding body | Sunrice |
---|---|
Project Team | Professor John Bartholdi, Doctor Simon Dunstall, Mrs Carlee McGowan, Mr Robert McMahon, Mr Tim Norris, Mr Robert Scoines, Emeritus Professor Rick Middleton, Professor Regina Berretta, Professor Michael Bowyer, Doctor Ali Eshragh, Professor John Bartholdi, Professor Christopher Scarlett, Doctor Masoud Talebian, Professor Natashia Boland, Associate Professor Behnam Fahimnia, Professor Mathieu Savelsbergh, Professor Mathieu Savelsbergh, Professor Natashia Boland, Dr Costas Stathopoulos |
Scheme | Industrial Transformation Training Centres Partner Funding |
Role | Investigator |
Funding Start | 2014 |
Funding Finish | 2016 |
GNo | G1301131 |
Type Of Funding | C3100 – Aust For Profit |
Category | 3100 |
UON | Y |
Research$30,000
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 |
Research Supervision
Number of supervisions
Current Supervision
Commenced | Level of Study | Research Title | Program | Supervisor Type |
---|---|---|---|---|
2023 | PhD | Reinforcement Learning Algorithms for Combinatorial Optimisation Problems | PhD (Statistics), College of Engineering, Science and Environment, The University of Newcastle | Co-Supervisor |
2022 | PhD | New Algorithms for Analysing Big Time Series Data: Nexus Between Classical Statistical Models and Modern Data Science Methods | PhD (Statistics), College of Engineering, Science and Environment, The University of Newcastle | Co-Supervisor |
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), College of Engineering, Science and Environment, 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 |
News
News • 29 Jan 2018
Rising Star Award 2017
Congratulations to Dr Ali Eshragh from the School of Mathematical and Physical Sciences (pictured right), who was awarded the Australian Society for Operations Research (ASOR) Rising Star Award 2017. The award was presented at the International Congress on Modelling and Simulation Conference’s Gala Dinner in Hobart in December.
Dr Ali Eshragh
Position
Honorary Senior Lecturer
School of Information and Physical Sciences
College of Engineering, Science and Environment
Contact Details
ali.eshragh@newcastle.edu.au | |
Phone | (02) 4921 5127 |
Office
Room | SR-119 |
---|---|
Building | Social Sciences Building |