Ali Eshragh CV

Ali Eshragh Curriculum Vitae

Contact

Information

Carey Business School

Johns Hopkins University

Washington DC – Baltimore Area, United States

Ali.Eshragh@jhu.edu

Website Homepage

Google Scholar

Current

Position

Associate Professor in Business Analytics and Operations Management

Carey Business School

Johns Hopkins University, United States

Research

Affiliation

Institute for Data Intensive Engineering & Science (IDIES)

Johns Hopkins University, United States

Faculty Member

International Computer Science Institute (ICSI)

University of California at Berkeley, United States

Aﬃliated Researcher

Research

Interests

Advanced Computing (Data Processing and Analysis Techniques), Artiﬁcial Intelligence

(Machine Learning and Reinforcement Learning), Probabilistic Operations Research

(Statistical Modeling and Stochastic Optimization), Business Analytics (Supply Chain

Analytics)

Education Johns Hopkins University, USA August 2027 (Expected)

M.Sc. in Artiﬁcial Intelligence at Whiting School of Engineering

University of South Australia, Australia August 2011

Ph.D. in Applied Mathematics, Stochastic Operations Research

Minor Applied Probability and Optimization

• Thesis Topic: Hamiltonian Cycles and the Space of Discounted Occupational Measures

Sharif University of Technology, Iran January 2004

M.Sc. in Industrial Engineering, Stochastic Operations Research

Minor Statistical Modeling and Stochastic Optimization – GPA: 89.0%

• Thesis Topic: Application of Decision on Beliefs in Response Surface Methodology

Sharif University of Technology, Iran September 2001

B.Sc. in Industrial Engineering , Stochastic Operations Research

Minor System Analysis – GPA: 87.5%

• Thesis Topic: A New Approach to Distribution Fitting: Decision on Beliefs

Training and

Certificates

School of Computer Science, Carnegie Mellon University

Machine Learning: Fundamentals and Algorithms

• 10–week online course

November 2021

Overall Grade: 100%

1 of 11

Honors and

Awards

Research

Grants:

AU$2,752,088

(equivalent to

US$1,987,407)

Total Awarded

Funds

DeepLearning.AI, Coursera September 2020

Deep Learning Specialization – 5-month online program including ﬁve :

• Neural Networks and Deep Learning

• Improving Deep Neural Networks

• Structuring Machine Learning Projects

• Convolutional Neural Networks

• Sequence Models

Overall Grade: 100%

1. Staﬀ Excellence Award–Values Award Category, University of Newcastle, 2020.

2. Australian Society for Operations Research Rising Star Award, Australia, 2017.

3. Teaching Excellence and Contribution to Student Learning Team Award – Runner-

up, University of Newcastle, 2016.

4. South Australia Science Excellence Award in the Category of PhD Research Excellence–

Physical Sciences, Mathematics and Engineering – Runner-up, Government of

South Australia, Australia, 2011.

5. B.H. Neumann Prize for the Best Student Talk – Runner-up, The 54

Annual

Australian Mathematical Society Conference, Australia, 2010.

6. Endeavour International Postgraduate Research Scholarship Award (Covering Tuition

Fees, Family Insurance and a Tax-Free Living Allowance of AU$27,222 per annum

Over the Course of PhD Study), The Australian Government, Australia, 2008-

2011.

7. Best Paper Award, The 5

International Industrial Engineering Conference, Iran,

2005.

8. Best Bachelor Final Project Award, Awarded by the R&D Department of Schlumberger

Company (US$1,000), France, 2002.

9. Ranked First (out of Approximately 5000 Entrants) in the Highly Competitive

National Masters Entrance Exam of Iranian Universities, 2001.

10. Best Student Award, Sharif University of Technology, Iran, 2001.

1. Lead-Chief Investigator, Large Markov Decision Processes and Combinatorial

Optimization, Australian Research Council (ARC) Discovery Project, AU$383,000,

2022–2024.

2. Lead-Chief Investigator, Stochastic Analysis of the COVID-19 Population, ARC

Centre of Excellence for Mathematical and Statistical Frontiers (ACEMS), AU$6,890,

2020–2022.

3. Chief Investigator, Big Time Series Data and Randomized Numerical Linear Algebra,

ACEMS, AU$11,580, 2020.

4. Chief Investigator, Approximate Solutions to Large Markov Decision Processes,

ACEMS, AU$12,000, 2019.

2 of 11

Research

Outcomes:

Submitted and

Working Papers

5. Chief Investigator, The Higher Education Participation and Partnerships Grant,

Australian Government, Department of Education and Training, AU$161,151,

2016.

6. Chief Investigator, The Industrial Transformation Training Centre for Food and

Beverage Supply Chain Optimization, ARC Industrial Transformation Training

Centre, AU$2,119,872, 2016-2020.

7. Lead-Chief Investigator, Rapidly Mixing Markov Chains and the Hamiltonian

Cycle Problem, The Priority Research Centre for Computer-Assisted Research

Mathematics and its Applications (CARMA), AU$30,000, 2014-2016.

8. Lead-Chief Investigator, The New Staﬀ Grant, University of Newcastle, AU$10,000,

2014.

9. Chief Investigator, Application of Simulation-Based Optimization Algorithms in

Sustainable Logistic and Supply Chain Management, School of Management, University

of South Australia, AU$10,000, 2011.

10. Chief Investigator, Application of Non-Smooth Optimization Methods for Hamiltonian

Cycle Problem, Barbara Hardy Institute, University of South Australia, AU$3,500,

2010.

11. Lead-Chief Investigator, An International Travel Grant, University of South Australia,

Australia, AU$4,095, 2009.

1. A. Fattahi, A. Eshragh, B. Aslani, and M. Rabiee, Ranking Vectors Clustering:

Theory and Applications, 2024.

2. E. Harris, A. Eshragh, B.P. Lamichhane, and E. Stojanovski, Eﬃcient Leverage

Score Sampling Algorithm for the Minimum Volume Covering Ellipsoid Problem

in Big Data, 2024.

3. G. Dunn, H. Charkhgard, A. Eshragh, and S. Mahmoudinazlou, Deep Reinforcement

Learning for Picker Routing Problem in Warehousing, arXiv preprint arXiv:2402.03525,

2024.

4. A. Eshragh, M.P. Skerrittd, B. Salvye, and T. McCallum, Optimal Experimental

Design for a Partially Observable Pure Birth Process, arXiv preprint arXiv:2402.09772,

2024.

5. A. Eshragh, L. Yerbury, A. Nazari, F. Roosta, and M.W. Mahoney, SALSA:

Sequential Approximate Leverage-Score Algorithm with Application in Analyzing

Big Time Series Data, arXiv preprint arXiv:2401.00122, 2023.

6. V. Dewanto, G. Dunn, A. Eshragh, M. Gallagher and F. Roosta, Average-

reward Model-free Reinforcement Learning: A Systematic Review and Literature

Mapping, arXiv preprint arXiv:2010.08920, 2023.

7. H. Charkhgard, .H Rastegar Moghaddam, A. Eshragh, and S Mahmoudinazlou,

Solving Hard Bi-Objective Knapsack Problems Using Deep Reinforcement Learning,

SSRN preprint ssrn.4585010, 2023.

8. S. Alizamir, A. Eshragh, K. Bandara, and F. Iravani, A Hybrid Statistical-

Machine Learning Approach for Analyzing Online Customer Behavior: An Empirical

Study, arXiv preprint arXiv:2212.02255, 2023.

3 of 11

Research

Outcomes:

Published

Papers

9. A. Eshragh, G. Livingston, T.M. McCann and L. Yerbury, Rollage: Eﬃcient

Rolling Average Algorithm to Estimate ARMA Models for Big Time Series Data,

arXiv preprint arXiv:2103.09175, 2023.

10. A. Eshragh, O.D. Pietro and M. Saunders, Toeplitz Least Squares Problems,

Fast Algorithms and Big Data, arXiv preprint arXiv:2112.12994, 2021.

1. A.S. Altamiranda, H. Charkhgard, I. Dayarianb, A. Eshragh and S. Javadia,

Learning to Project in Multi-objective Binary Linear Programming, To Appear

in Optimization Letters, 2024.

2. A. Eshragh, F. Roosta, A. Nazari and M. Mahoney, LSAR: Eﬃcient Leverage

Score Sampling Algorithm for the Analysis of Big Time Series Data, Journal of

Machine Learning Research, 23:1-36, 2022.

3. A. Eshragh, B. Ganim, T. Perkins and K. Bandara, The Importance of Environmental

Factors in Forecasting Australian Power Demand, Environmental Modeling &

Assessment, 27:1–11, 2021.

4. M. Abolghasemi, J. Hurley, A. Eshragh and B. Fahimnia, Demand Forecasting

in the Presence of Systematic Events: Cases in Capturing Sales Promotions,

International Journal of Production Economics, 230:107892, 2020.

5. A. Eshragh, S. Alizamir, P. Howley and E. Stojanovski, Modeling the Dynamics

of the COVID-19 Population in Australia: A Probabilistic Analysis, PLOS-One,

15(10):e0240153, 2020.

6. A. Eshragh, R. Esmaeilbeigi and R. Middleton, An Analytical Bound on the

Fleet Size in Vehicle Routing Problems: A Dynamic Programming Approach,

Operations Research Letters, 48(3):350-355, 2020.

7. A. Eshragh, J. Filar, T. Kalinowski and S. Mohammadian, Hamiltonian Cycles

and Subsets of Discounted Occupational Measures, Mathematics of Operations

Research, 45(2):403-795, 2020.

8. H. Charkhgard and A. Eshragh, A New Approach to Select the Best Subset

of Predictors in Linear Regression Modeling: Bi-Objective Mixed Integer Liner

Programming, ANZIAM Journal, 62(1):64-75, 2019.

9. B. Fahimnia, H. Davarzani and A. Eshragh, Performance Comparison of Three

Meta-Heuristic Algorithms for Planning of a Complex Supply Chain, Computers

and Operations Research, 89:241-252, 2018.

10. R. Esmaeilbeigi, A. Eshragh, R. Garcia-Flores and M. Heydar, Whey Reverse

Logistics Network Design: A Stochastic Hierarchical Facility Location Model,

Proceedings of the 22

International Congress on Modeling and Simulation, Hobart,

Australia, December 2017.

11. K. Avrachenkov, A. Eshragh and J. Filar, On Transition Matrices of Markov

Chains Corresponding to Hamiltonian Cycles, Annals of Operations Research,

243(1):19-35, 2016.

12. N.G. Bean, A. Eshragh and J.V. Ross, Fisher Information for a Partially-

Observable Simple Birth Process, Communications in Statistics: Theory and

Methods, 45(24):7161-7183, 2016.

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Talks in

International

Conferences

13. N.G. Bean, R. Elliott, A. Eshragh and J.V. Ross, On Binomial Observation of

Continuous-Time Markovian Population Models, Journal of Applied Probability,

52:457-472, 2015.

14. B. Fahimnia, J. Sarkis, A. Choudhary and A. Eshragh, Tactical Supply Chain

Planning Under a Carbon Tax Policy Scheme: A Case Study, International Journal

of Production Economics, 164:206-215, 2015.

15. B. Fahimnia, J. Sarkis and A. Eshragh, A Trade-oﬀ Model for Green Supply

Chain Planning: A Leanness-Versus-Greenness Analysis, OMEGA, 54:173-190,

2015.

16. A. Eshragh, Fisher Information, Stochastic Processes and Generating Functions,

Proceedings of the 21

International Congress on Modeling and Simulation, Gold

Coast, Australia, December 2015.

17. A. Eshragh and J. Filar, Hamiltonian Cycles, Random Walks and the Geometry

of the Space of Discounted Occupational Measures, Mathematics of Operations

Research, 36(2):258-270, 2011.

18. A. Eshragh, J. Filar and M. Haythorpe, A Hybrid Simulation-Optimization

Algorithm for the Hamiltonian Cycle Problem, Annals of Operations Research,

189:103–125, 2011.

19. K. Avrachenkov, A. Eshragh and J. Filar, Markov Chains and Hamiltonian

Transition Matrices, Proceedings of the 5

International ICST Conference on

Performance Evaluation Methodologies and Tools, Paris, France, 2011.

20. A. Eshragh, J. Filar and A. Nazari, A Projection-Adapted Cross Entropy (PACE)

Method for Transmission Network Planning, Energy Systems, 2(2):189-208, 2011.

21. A. Eshragh and M. Modarres, A New Approach to Distribution Fitting: Decision

on Beliefs, Journal of Industrial and Systems Engineering, 3(1):56-71, 2009.

22. H. Mahlooji, A. Eshragh, H. Abouee Mehrizi and N. Izady, Uniform Fractional

Part: A Simple Fast Method for Generating Continuous Random Variates, Scientia

Iranica, 15(5):613-622, 2008.

1. An Eﬃcient Algorithm for Approximating ARMA Model Fitting in Large-scale

Time Series Data, The 43

International Symposium on Forecasting (ISF), Virginia,

USA, 2023.

2. Eﬃcient Leverage Score Sampling Algorithm for the Analysis of Big Time Series

Data, The 2021 INFORMS Annual Meeting, Anaheim/Online, USA, 2021.

3. A New Fast Algorithm to Approximate the Leverage Scores of Big Time Series

Data: Theory and Application, The 20

INFORMS Applied Probability Society

Conference, Brisbane, Australia, 2019.

4. Optimal Experimental Design For a Partially Observable Simple Birth Process,

The 2018 INFORMS Annual Meeting, Phoenix, USA, 2018.

5. A New Approach to Select the Best Subset of Predictors in Linear Regression

Modeling, The 61

Australian Mathematical Society Conference, Sydney, Australia,

2017.

6. Fisher Information, Stochastic Processes and Generating Functions, The 18

INFORMS Applied Probability Conference, Istanbul, Turkey, 2015.

5 of 11

7. Fisher Information, Stochastic Processes and Generating Functions, The 21

International Congress on Modeling and Simulation, Gold Coast, Australia, 2015.

8. Approximating the Fisher Information for a Partially-Observable Growing Population,

ICERM Workshop on Challenges in 21

Century Experimental Mathematical

Computation, Providence, USA, 2014.

9. Random Walks, Polyhedra and Hamiltonian Cycles, CARMA Workshop on Optimization,

Nonlinear Analysis, Randomness & Risk, Newcastle, Australia, 2014.

10. On Binomial Observations of Continuous-Time Markov Chains, The 57

Australian

Mathematical Society Conference, Sydney, Australia, 2013.

11. Fisher Information for a Partially-Observable Simple Birth Process, Australia and

New Zealand Applied Probability Workshop, Brisbane, Australia, 2013.

12. Optimal Observation Times for a Partially-Observable Pure Birth Process, The

European Conference on Operational Research, Rome, Italy, 2013.

13. Hamiltonian Cycles, Extreme Points and Rapidly Mixing Markov Chains, Hamiltonian

Cycle, Traveling Salesman and Related Optimization Problems Workshop, Adelaide,

Australia, 2012.

14. Optimal Experimental Design for a Pure Birth Process with Incomplete Information,

The 25

European Conference on Operational Research, Vilnius, Lithuania, 2012.

15. A Modiﬁed Cross Entropy Method for the Optimization of an Environmentally

Sustainable Supply Chain, The 25

European Conference on Operational Research,

Vilnius, Lithuania, 2012.

16. Optimal Observations of a Growing Population, The 48

Australian and New

Zealand Industrial and Applied Mathematics Conference, Warrnambool, Australia,

2012.

17. Polynomial Limit Control Algorithm to Identify Nearly all Cubic, non-Hamiltonian,

Graphs, The 19

Triennial Conference of the IFORS, Melbourne, Australia, 2011.

18. On Random Graphs, Random Walks and the Hamiltonian Cycle Problem, The

Annual Australian Mathematical Society Conference, Brisbane, Australia,

2010.

19. A Random Pivoting Algorithm for the Hamiltonian Cycle Problem, The 24

European Conference on Operational Research, Lisbon, Portugal, 2010.

20. Investigating Hamiltonian Cycles through Random Walks, The 46

Australian

and New Zealand Industrial and Applied Mathematics Conference, Queenstown,

New Zealand, 2010.

21. A New Random Algorithm for the Hamiltonian Cycle Problem, The 23

European

Conference on Operational Research, Bonn, Germany, 2009.

22. A Hybrid Simulation-Optimization Algorithm for the Hamiltonian Cycle Problem,

The 45

Australian and New Zealand Industrial and Applied Mathematics Conference,

Caloundra, Australia, 2009.

23. A New Approach to Response Surface Methodology, The 5

International Industrial

Engineering Conference, Tehran, Iran, 2005.

24. A New Approach to Distribution Fitting: Decision on Beliefs, The 53

Session

of International Statistical Institute, Seoul, South Korea, 2001.

25. Order Statistics and Their Applications, The 1

Iranian Statistical Student Conference,

Tehran, Iran, 1999.

6 of 11

Invited Seminars

25. Eﬃcient Models and Algorithms for the Analysis of Big Time Series Data, International

Computer Science Institute, University of California at Berkeley, USA, April 13,

2022.

26. Randomized Numerical Linear Algebra and the Analysis of Big Time Series Data,

Simons Institute for the Theory of Computing, University of California at Berkeley,

USA, December 16, 2019.

27. Eﬃcient Leverage Score Sampling for the Analysis of Big Time Series Data, School

of Mathematics and Statistics, University of Melbourne, Australia, October 21,

2019.

28. Hamiltonian Cycles, Polytopes and Random Walks, Colloquium–School of Mathematics

and Physics, University of Queensland, Australia, February 18, 2019.

29. Hamiltonian Cycles and Subsets of Discounted Occupational Measures, Linear

Algebra and Optimization Seminars–Institute for Computational & Mathematical

Engineering, Stanford University, USA, October 25, 2018.

30. Hamiltonian Cycles, Polytopes and Markov Chains, Simons Institute for the Theory

of Computing, University of California at Berkeley, USA, February 19, 2016.

31. Fisher Information, Stochastic Processes and Generating Functions, P, Colloquium–

School of Mathematics and Statistics, University of New south Wales, Australia,

October 8, 2015.

32. Computational Complexity of the Fisher Information, INRIA–Paris, France, October

6, 2014.

33. Binomial Observations, Fisher Information and Optimal Sampling Times, Colloquium–

School of Mathematical and Physical Sciences, University of Newcastle, Australia,

November 14, 2013.

34. P or NP: That Is the Question, Undergraduate Seminar–School of Mathematical

Sciences, University of Adelaide, Australia, May 22, 2012.

35. Can Hamiltonian Cycle Problem on a Random Graph be Solved with High Probability

in a Polynomial Time?, Colloquium–Faculty of Information Technology, Monash

University, Australia, February 29, 2012.

36. Hamiltonian Cycles and Random Walks, Colloquium–School of Computer Science,

University of Adelaide, Australia, December 7, 2011.

37. Hybrid Simulation-Optimization Algorithm for Combinatorial Optimization Problems,

Divisional Research Day–University of South Australia, Australia, September 10,

2010.

38. Hamiltonian Cycles, Random Walks and Discounted Occupational Measures, Department

of Applied Mathematics, University of Twente, The Netherlands, June 22, 2010.

39. Decision on Beliefs: Concepts and Applications, Indian Statistical Institute–New

Delhi, India, March 2004.

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Teaching

Experience

List of Courses:

• Johns Hopkins Carey Business School: Statistical Analysis, Simulation for

Business Applications

• University of Newcastle: Business Decision Making, Supply Chain Optimization,

Forecasting with Linear Time Series Models, Deterministic and Stochastic

Optimisation, Data Analytics for Business Intelligence, Markov Chains and

Their Applications, Engineering Statistics, and Statistical Reasoning and Literacy.

Course Development:

• Designing and creating all teaching and assessment materials for a newly

established elective course titled Forecasting Models for Business Intelligence,

intended for Master of Business Analytics and Risk Management students,

Johns Hopkins Carey Business School, 2024.

• Developing all teaching and assessment materials for the core course, Statistical

Analysis, oﬀered to Business students, Johns Hopkins Carey Business School,

2022.

• Designing and creating all teaching and assessment materials for the newly

established third-year core course, Deterministic and Stochastic Optimization,

oﬀered to Mathematics, Statistics, Engineering, and Business students, University

of Newcastle, 2021.

• Designing and creating all teaching and assessment materials for the newly

established second-year core course, Engineering Statistics, oﬀered to Electronic

and Electrical Engineering students, University of Newcastle, 2018-2019.

• Designing all teaching and assessment materials for the ﬁrst-year core course,

Business Decision Making, oﬀered to Business students in the blended mode,

University of Newcastle, 2016-2017.

• Developing the course syllabus, as well as all teaching and assessment materials,

for the elective third-year/graduate course, Forecasting with Linear Time Series

Models, oﬀered to Mathematics, Statistics, Engineering, and Business students,

University of Newcastle, 2016-2021.

• Developing all teaching and assessment materials for the second-year core

course, Engineering Mathematics and Statistics, oﬀered to Electrical Engineering

and Computer Science students, University of Newcastle, 2014-2015.

• Developed all teaching and assessment materials for the compulsory graduate

course Statistics in Engineering, oﬀered to Engineering students, University of

Adelaide, 2013.

Supervising

Experience

• PhD Student 2022-2025

Thesis Title: New Algorithms for Analysing Big Time Series Data: Nexus Between

Classical Statistical Models and Modern Data Science Methods

• PhD Student 2022-2025

Thesis Title: Deep Reinforcement Learning for Compbintorial Optimization Problems

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• PhD Student 2020-2024

Positions and

Professional

Experiences

Thesis Title: Eﬃcient Algorithms to Detect Outliers in Big Data

• Honours Student 2021-2022

Thesis Title: Solution Algorithms for Large Markov Decision Processes

• Honours Student 2021

Thesis Title: Toeplitz Least Squares Problems, Fast Algorithms and Big Data

• Honours Student 2020

Thesis Title: A New Algorithm for Fitting ARMA Models to Big Time Series Data

• Honours Student 2020

Thesis Title: Rollage: Eﬃcient Rolling Average Algorithm to Estimate ARMA

Models for Big Time Series Data

• Honours Student 2019

Thesis Title: A New State Aggregation Algorithm to Solve Large Markov Decision

Processes

• PhD Student 2018-2021

Thesis Title: Policy Optimization in Reinforcement Learning

• Honours Student 2017-2018

Thesis Title: Exploration of Flu-tracking Approaches Using Time Series Models

• Honours Student 2016-2017

Thesis Title: Optimal Observation Times, Fisher Information and Generating Functions

• Associate Professor in Business Analytics and Operations Management 2022-Present

Carey Business School, Johns Hopkins University, United States

• Senior Lecturer in Data Science – Honorary 2022-Present

School of Information and Physical Sciences, University of Newcastle, Australia

• Senior Lecturer in Data Science – Ongoing 2018-2022

(Equivalent to Tenured Associate Professor in the U.S. System)

School of Information and Physical Sciences, University of Newcastle, Australia

• Lecturer in Statistics and Optimization – Ongoing 2014-2017

School of Mathematical and Physical Sciences, University of Newcastle, Australia

• Lecturer in Stochastic Operations Research – Fixed Term 2013-2014

School of Mathematical Sciences, University of Adelaide, Australia

• Postdoctoral Research Associate 2011-2013

Working on the Australian Research Council (ARC) Discovery Project Entitled ‘New

Methods for Improving Active Adaptive Management in Biological Systems’

School of Mathematical Sciences, University of Adelaide, Australia

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Professional

Services

Professional

Affiliations

• Consultant

Several Industries and Organizations Including Coca-Cola Amatil, Nestl´e, and Sanitarium

Health & Wellbeing Australia

• Program Director, Graduate Certiﬁcate in Data Analytics/Science, University of

Newcastle. 2021

• Associate Editor and Member of Editorial Board, Environmental Modeling &

Assessment, Springer Journal. 2020-Present

• Chair and Organizer, Data Science Down Under International Workshop, Newcastle.

8-12 December, 2019

• Deputy Head of School – Industry and Engagement Coordinator, School of Information

and Physical Sciences, University of Newcastle. 2019-2021

• Academic Representative on the Organizing Committee, Quarterly Central Coast

and Hunter Area Supply Chain & Logistics Forum. 2019-2021

• Member of the Faculty of Science Board, University of Newcastle. 2019-2020

• Chair and Organizer, Applied Probability, Combinatorics and Optimization Workshop,

Newcastle. 17 December, 2016

• Member of the Faculty of Science Board, University of Newcastle. 2016-2017

• Member of Progress and Appeals Committee, Faculty of Science, University of

Newcastle. 2016-2021

• Ph.D. Students Coordinator, School of Mathematical and Physical Sciences, University

of Newcastle. 2014-2017

• Organizer, Hamiltonian Cycle and Traveling Salesman Problems: Theory and

Computation Workshop, Adelaide. 14-15 December, 2012

• Convener, Stochastic Lunch: Fortnightly Research Presentations Meetings, School

of Mathematical Sciences, University of Adelaide. 2012

• Returning Oﬃcer, Australian and New Zealand Industrial and Applied Mathematics

(ANZIAM) Division. 2012-2014

• Refereeing for Journals: Annals of Applied Statistics, Annals of Operations Research,

ANZIAM Journal, Environmental Modeling & Assessment, International Journal

of Production Economics, International Journal of Production Research, Journal

of Applied Probability, Journal of the American Statistical Association, Management

Science, Mathematics of Operations Research, Operations Research, Queuing Systems,

Random Structures and Algorithms. 2009-Present

• Refereeing for Conferences: International Conference on Machine Learning (ICML)

2018-Present

• Institute for Operations Research and the Management Sciences (INFORMS)

• International Institute of Forecasting (IIF)

• Australian Society for Operations Research (ASOR)

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Special Skills

Software Skills:

• Mathematical Packages: Matlab, Mathematica

• Statistical Packages: R, SPSS

• Optimization Packages: CPLEX, Lingo

• Discrete-event Simulation Packages: Arena, Enterprise Dynamics

• Programming Languages: C, Python

• Others: L

X, MS-Oﬃce

Languages:

• Persian (Native)

• English (Fluent)

• French (Elementary)

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