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Dr Tomas Lagos

Lecturer

 

Business School
Sch of Info Systems & Tech Mgt

Dr Tomas Lagos is a Lecturer in the School of Information Systems and Technology Management (SISTM) at UNSW Sydney.

His research mission is to address pressing environmental and economic challenges, leveraging advanced computational methods to contribute to a more sustainable and resilient future. Tomas’s work bridges the gap between complex algorithmic development and real-world application, aiming to solve critical issues in resource management, climate adaptation, and modern supply chains.

 
  • Journal articles | 2026
    Auad R; Lagos F; Lagos T, 2026, 'Data-Driven Optimization for Meal Delivery: A Reinforcement Learning Approach for Order-Courier Assignment and Routing at Meituan', TRANSPORTATION SCIENCE, http://dx.doi.org/10.1287/trsc.2025.0129
    Journal articles | 2026
    Choi J; Lagos T; Segundo B; Villa-Zapata LM; Prokopyev OA; Ntaimo L; Stripling CW; Gan J, 2026, 'Landscape-level effectiveness of fuel treatments in a forest-dominated ecosystem in the Southern United States', Plos One, 21, http://dx.doi.org/10.1371/journal.pone.0342049
    Journal articles | 2026
    Villa-Zapata LM; Choi J; Lagos T; Prokopyev OA; Gan J; Ntaimo L, 2026, 'Mean-risk stochastic integer programming approach for integrated fuel treatment and wildfire response planning with endogenous uncertainty', Journal of Global Optimization, http://dx.doi.org/10.1007/s10898-026-01604-x
    Journal articles | 2025
    Lagos T; Auad R; Lagos F, 2025, 'The Online Shortest Path Problem: Learning Travel Times Using a Multiarmed Bandit Framework', Transportation Science, 59, pp. 28 - 59, http://dx.doi.org/10.1287/trsc.2023.0196
    Journal articles | 2025
    Lagos T; Choi J; Segundo B; Gan J; Ntaimo L; Prokopyev OA, 2025, 'Bilevel optimization approach for fuel treatment planning', European Journal of Operational Research, 320, pp. 205 - 218, http://dx.doi.org/10.1016/j.ejor.2024.07.014
    Journal articles | 2024
    Lagos T; Prokopyev OA; Veremyev A, 2024, 'Finding groups with maximum betweenness centrality via integer programming with random path sampling', Journal of Global Optimization, 88, pp. 199 - 232, http://dx.doi.org/10.1007/s10898-022-01269-2
    Journal articles | 2023
    Lagos T; Prokopyev OA, 2023, 'On complexity of finding strong-weak solutions in bilevel linear programming', Operations Research Letters, 51, pp. 612 - 617, http://dx.doi.org/10.1016/j.orl.2023.09.011
    Journal articles | 2022
    Lagos T; Armstrong M; Homem-de-Mello T; Lagos G; Sauré D, 2022, 'A framework for adaptive open-pit mining planning under geological uncertainty', Optimization and Engineering, 23, pp. 111 - 146, http://dx.doi.org/10.1007/s11081-020-09557-0
    Journal articles | 2022
    Ostroski A; Lagos T; Prokopyev OA; Khanna V, 2022, 'Consumption-Based Accounting for Tracing Virtual Water Flows Associated with Beef Supply Chains in the United States', Environmental Science and Technology, 56, pp. 16347 - 16356, http://dx.doi.org/10.1021/acs.est.2c03986
    Journal articles | 2021
    Armstrong M; Lagos T; Emery X; Homem-de-Mello T; Lagos G; Sauré D, 2021, 'Adaptive open-pit mining planning under geological uncertainty', Resources Policy, 72, http://dx.doi.org/10.1016/j.resourpol.2021.102086
    Journal articles | 2020
    Lagos T; Moreno R; Espinosa AN; Panteli M; Sacaan R; Ordonez F; Rudnick H; Mancarella P, 2020, 'Identifying Optimal Portfolios of Resilient Network Investments against Natural Hazards, with Applications to Earthquakes', IEEE Transactions on Power Systems, 35, pp. 1411 - 1421, http://dx.doi.org/10.1109/TPWRS.2019.2945316
    Journal articles | 2020
    Moreno R; Panteli M; Mancarella P; Rudnick H; Lagos T; Navarro A; Ordonez F; Araneda JC, 2020, 'From Reliability to Resilience: Planning the Grid against the Extremes', IEEE Power and Energy Magazine, 18, pp. 41 - 53, http://dx.doi.org/10.1109/MPE.2020.2985439
  • Conference Papers | 2018
    Lagos T; Ordonez F; Sacaan R; Rudnick H; Navarro-Espinosa A; Moreno R, 2018, 'Discrete optimization via simulation to determine reliable network investments', in IEEE Power and Energy Society General Meeting, pp. 1 - 5, http://dx.doi.org/10.1109/PESGM.2017.8274589
    Conference Papers | 2017
    NavarroEspinosa A; Moreno R; Lagos T; Ordoñez F; Sacaant R; Espinozat S; Rudnickt H, 2017, 'Improving distribution network resilience against earthquakes', in Iet Conference Publications
    Conference Papers | 2017
    Sacaan R; Rudnick H; Lagos T; Ordonez F; Navarro-Espinosa A; Moreno R, 2017, 'Improving power system reliability through optimization via simulation', in 2017 IEEE Manchester Powertech Powertech 2017, http://dx.doi.org/10.1109/PTC.2017.7981193

The First INFORMS TSL Data-Driven Research Challenge (2024-2025 TSL-Meituan)

Winning material: Data-Driven Optimization for Meal Delivery: A Reinforcement Learning Approach for Order-Courier Assignment and Routing at Meituan

Call: https://connect.informs.org/tsl/tslresources/datachallenge
Outcome: https://www.informs.org/Recognizing-Excellence/Community-Prizes/Transportation-Science-and-Logistics-Section/TSL-Data-Driven-Research-Challenge

Commitment to Impact

A core pillar of Tomas’s academic work is delivering measurable social, environmental, and economic impact. His research is designed to move beyond the theoretical and drive strategic policy changes.

Notable impacts include:

  • Electrical Network Resiliency: Conducted foundational research on enhancing the resilience of the electrical network in Chile. The strategic recommendations arising from these publications have been adopted and are currently being implemented.

  • Wildfire Mitigation Policies: Recently finalized advanced algorithms that provide data-driven recommendations on preventive landscape treatments to mitigate wildfire spread. These models were rigorously calibrated using real-world data from Texas and Australia. The next phase of this project involves applying these algorithms to real-life forestland data to help nations highly vulnerable to climate change craft strategic, effective landscape treatment policies.

Research Interests

Methodologies:

  • Artificial Intelligence & Machine Learning

  • Reinforcement Learning

  • Optimization (Bilevel, Robust, Combinatorial)

  • Online Scheduling

  • Network Theory

Applications:

  • Resources Policy Management

  • Sustainability and Climate Change Adaptation

  • Wildfires & Electrical Networks Resiliency

  • Supply Chain Logistics & Transportation

  • E-Commerce