I am an Associate Professor of Industrial Engineering with over two decades of experience in teaching, research, and supervision in data-driven decision making, operations research, and computational modeling. My research integrates Multi-Criteria Decision Analysis (MCDA), Agent-Based Modeling (ABM), and Reinforcement Learning (RL) to support strategic decision systems in sustainability, investment, and industrial operations. My recent work explores human-centric and multi-actor systems, leveraging simulation-based optimization and AI-driven analytics to enhance resilience, efficiency, and sustainability in complex socio-technical environments. I have published extensively in international journals, reviewed over 75 manuscripts, and am an active member of INFORMS and the System Dynamics Society. My long-term goal is to bridge industrial systems modeling with intelligent decision support, aligning academic research with real-world sustainability and innovation challenges.

🔹 Experience
Associate Professor — Industrial Engineering, University of Engineering and Technology, Taxila (2018 – Present)• Teach graduate and undergraduate courses in Operations Research, Data Mining, Advanced Statistics, System Simulation, and Soft Computing.• Conduct funded research in agent-based and reinforcement learning models for sustainable and data-driven decision systems.• Supervise doctoral students in decision analytics, multi-agent modeling, and MCDM applications.• Reviewer for international journals including Neural Computing and Applications, the Journal of Cleaner Production, Annals of Operations Research, Environment, Development and Sustainability, Energy for Sustainable Development, Scientific Reports, IEEE Access, Cleaner Energy Systems, Utilities Policy, and Sustainable Futures

🔹 Research Interests•
Data-Driven Decision Making• Agent-Based Modeling (ABM)• Reinforcement Learning (RL)• Multi-Criteria Decision Analysis (MCDA / MAMCA)• Sustainable Supply Chains• System Dynamics; Simulation• E-Health and Humanitarian Systems

🔹 Selected Achievements•
30+ peer-reviewed publications; ~360+ citations• Reviewer for 75+ international journal papers• Completed Coursera Specializations in Machine Learning, Deep Learning, and Reinforcement Learning• 20+ years of experience integrating data science with sustainability modeling

http://learnmem.cshlp.org/content/27/1.cover-expansion
(Cover simulation using NetLogo, January 2020)
Enver Miguel Oruro, Grace V.E. Pardo, Aldo B. Lucion, Maria Elisa Calcagnotto and Marco A. P. Idiart. Maturation of pyramidal cells in anterior piriform cortex may be sufficient to explain the end of early olfactory learning in rats. Learn. Mem. 2020. 27: 20-32 © 2020 Oruro et al.; Published by Cold Spring Harbor Laboratory Press

http://learnmem.cshlp.org/content/27/12.cover-expansion
(paper using NetLogo, December 2020)
Enver Miguel Oruro, Grace V.E. Pardo, Aldo B. Lucion, Maria Elisa Calcagnotto and Marco A. P. Idiart. The maturational characteristics of the GABA input in the anterior piriform cortex may also contribute to the rapid learning of the maternal odor during the sensitive period Learn. Mem. 2020. 27: 493-502 © 2020 Oruro et al.; Published by Cold Spring Harbor Laboratory Press

Enver Oruro, BA Psych. PhD(s).
Computational Psychologist
[email protected]
https://br.linkedin.com/in/enveroruro
Neurocomputational and Language Processing Laboratory, Institute of Physics/ UFRGS
Neurophysiology and Neurochemistry of Neuronal Excitability and Synaptic Plasticity Laboratory, Department of Biochemistry/ UFRGS

Meeting Organization

2009 First Meeting on Complex Systems -Neuroscience and Behavior Laboratory, School of Medicine UPCH Lima

2010 Second Meeting on Complex Systems - College of Psychologists of Peru / Colegio de Psicólogos del Perú (CPsP) Lima

2012 3rd Meeting on Complex Systems – Computational Social Psychology, /Neuroscience and Behavior Laboratory, School of Medicine UPCH Lima February 2012 https://www.comses.net/events/185/
http://www.neurocienciaperu.org/home/3ra-reunion-de-sistemas-complejos-psicologia-social-computacional
2012 4th Meeting on Complex Systems – Cognotecnology and Cognitive Science, Neuroscience and Behavior Laboratory, School of Medicine UPCH Lima July 2012 https://www.comses.net/events/212/

2014 5th Meeting on Complex Systems – Complexity Roadmap. The Imperial City of the Incas, Cusco, April. https://www.comses.net/events/312/

2015 Chair of “e-session on Neuroscience and Behavior” UNESCO UniTwin CS-DC’15
2015 Chair of “e-session on Social Psychology” UNESCO UniTwin CS-DC’15
CS-DC’15 (Complex Systems Digital Campus ’15 – World e-Conference) is organizing the e-satellites of CCS’15, the international Conference on Complex Systems. It is devoted to all scientists involved in the transdisciplinary challenges of complex systems, crossing theoretical questions with experimental observations of multi-level dynamics. CCS’15 is organized by the brand new ASU-SFI Center for Biosocial Complex Systems. Arizona State University, (USA) from Sept 28 to Oct 2, 2015, in close collaboration with the Complex Systems Society and the Santa Fe Institute. from http://cs-dc-15.org/

2018 Seminar in “Mother-Infant Attachment and Supercomputing”, NY. USA and Porto Alegre, Brazil, August 09. https://www.comses.net/events/499/

2019 Seminar in Experimental and Computational Studies on Mother-Infant Relationship October 8 and 15, 2019 ICBS, /Determine the neural pathways by which the nervous system of the neonates establish attachment with their mothers is a problem that has motivated hypothesis and experiments at several scale levels, from neurotransmission to ethological level. UFRGS, Porto Alegre, Brazil. https://www.comses.net/events/549/

2020 Seminar in Maternal Infant Relationship Studies: Neuroscience and Artificial Intelligence March 7 and 9
Goals 1. Discuss a Roadmap for mother-Infant relationship research in the framework of the UNESCO Complex System Digital Campus project. https://www.comses.net/events/570/ https://sites.google.com/view/envermiguel/seminar-in-maternal-infant-relationship-studies?read_current=1

https://drive.google.com/file/d/1-FVQXBXy4RLKIQA-RBx3KFLJxyBsnyCW/view?usp=sharing

In my research I focus on understanding human behaviour in group(s) as a part of a complex (social) system. My research can be characterised by the overall question: ‘How does group or collective behaviour arise or change given its social and physical context?‘ More specifically, I have engaged with: ‘How is (individual) human behaviour affected by being in a crowd?’, ‘Why do some groups (cooperatively) use their resources sustainably, whereas others do not?‘, ‘What is the role of (often implicit simplistic) assumptions regarding human behaviour for science and/or management?’

To address these questions, I use computational simulations to integrate and reflect synthesised knowledge from literature, empirics and experts. Models, simulation and data analysis are my tools for gaining a deeper understanding of the mechanisms underlying such systems. More specifically, I work with agent-based modelling (ABM), simulation experiments and data analysis of large datasets. Apart from crowd modelling and social-ecological modelling, I also develop methodological tools to analyse social simulation data and combining ABM with other methods, such as behavioural experiments.

I have been involved in agent-based modelling since the early nineties with a consistent attention to methdological improvement, institutional development and empirical issues. My mission is that ABM should be a routinely accepted research method (with a robust methodology) across the social sciences. To this end I have built diverse models and participated in research projects across economics, law, medicine, psychology, anthropology and sociology. I took a DPhil in economics on adaptive firm behaviour and then was involved in two research projects on money management and farmer decision making. Since 2006 I have worked at the Department of Sociology (now the School of Media, Communication and Sociology) at the University of Leicester. I was involved in the founding of JASSS and (more recently RofASSS https://rofasss.org) and have regularly served on the review panels for international conferences in the ABM community.

I am currently a Senior Lecturer in Computational Epidemiology at Western Sydney University, School of Computer, Data and Mathematical Science where I am also a member of Translational Health Research Institute (THRI). I am a research associate at the Brain and Mind Centre, Sydney University and Adjunct Senior Lecturer at Psychiatry Monash Health, Monash University.
My work is in the areas of dynamic data-driven computer simulation and systems science. The product of my work is decision and research support software that applies agent and discrete event based models, and metaprogramming techniques to solve complex problems.
I am currently Chief Investigator (CI) on an international grant funded by Botnar foundation as well as on a MRFF funded grant with Brain and Mind Centre, The University of Sydney and an Associate Investigator (AI) on Suicide Prevention Australia funded research at The University of Melbourne. In he last 5 years I have been CI on 7 grants and commissioned research projects and AI on 1 grant with total value of over $8 million AUD.

Ronja Hotz is a PhD student in the Land Use Change & Climate Research Group at the Karlsruhe Institute of Technology, where she has been working since August 2023. Her research focuses on understanding and modelling the social processes underlying land use change using agent-based modelling, with a particular emphasis on the CRAFTY framework. She holds a Bachelor’s degree in Physics from Freie Universität Berlin and a Master’s degree in Theoretical Physics from Technische Universität Berlin.

Prior to her PhD, she worked at the Potsdam Institute for Climate Impact Research, where she implemented a generic decision-making layer for land managers in agent-based socio-ecological models. The framework was analysed in a stylised model to investigate emergent dynamics and critical transitions and was subsequently integrated into the InSEEDS model, which simulates the adoption of conservation agriculture at regional to global scales.

• Since 2010: Associate Professor in Artificial Intelligence at Grenoble-Alpes University. Topic: human behaviour modelling, with a particular focus on emotions, cognitive biases, and their interplay with decision-making; social simulations and serious games for raising awareness about natural disasters and sustainable development, or for increasing civil engagement in urban planning.
• 2008-2010: postdoctoral research fellow at RMIT, Melbourne, Australia. Supervisor: Lin Padgham. Topic: interactive intelligent emotional toy.
• 2007-2008: research engineer at Orange Labs, Lannion, France. Supervisor: Vincent Louis. Topic: institutional logic in JADE for agent-based B2B mediation.
• 2007: PhD in AI from Toulouse University. Supervisors: Andreas Herzig and Dominique Longin. Topic: logical modelling of emotions in BDI for artificial agents.

Lu Ping is a dedicated researcher in interdisciplinary fields including artificial intelligence (AI), digital economy, technological innovation, and industrial economics. Currently serving as an Associate Research Fellow at the China Academy of Information and Communications Technology (CAICT), Lu Ping focuses on examining the impacts of digital technologies (e.g., AI, big data, and IoT) on economic growth, industrial ecosystems, policy formulation, and societal ethics through multidimensional data modeling and empirical research.
Representative Academic Contributions:
1. AI Development and Societal Implications
A Brief History of Artificial Intelligence Development in China (2017): Explored the technological evolution and policy-driven pathways of China’s AI industry.
Ethical Dilemmas Faced by AI Algorithms (2018): Analyzed ethical challenges such as algorithmic bias and data privacy, proposing governance frameworks.
A Brief History of the Evolution of Smart Hardware in China (2018): Systematically reviewed the technological iterations and market dynamics of China’s smart hardware sector.
2.Technological Innovation and Industrial Economics
An Empirical Analysis of Technological Innovation Driving Growth in Internet Companies: Evidence from A-Share Listed Internet Firms in Shanghai and Shenzhen (2019).
Research on Competitiveness Measurement of Frontier Emerging Industries Based on Data Envelopment Analysis (DEA) Models (2019).
3.Digital Economy and Market Behavior
Correlation Analysis of Crowdfunding Behavior and Funding Performance for Internet Products: A Bayesian Approach Based on JD.com Crowdfunding Data (2018): Uncovered nonlinear relationships between user participation and project success rates using crowdfunding platform data.
Analyzing the Effects of Developer and User Behavior on Mobile App Downloads (2019): Built predictive models for app market performance based on user behavior data.
4.Policy Simulation
General Equilibrium Analysis of Beijing’s Water Supply and Consumption Policies: A Computable General Equilibrium (CGE) Model-Based Approach (2015).
Impact Analysis of EU Food Safety Standards on China’s Food Industry: A Dynamic Global Trade Analysis Project (GTAP) Model-Based Study (2015).
Academic Contributions:
Pioneered interdisciplinary paradigms in industrial economics research by integrating perspectives from econometrics, data science, and sociology. Published high-impact research in AI ethics, digital economy policies, and resource-environmental economics, providing decision-making references for academia and policymakers.

My primary research interests lie at the intersection of two fields: evolutionary computation and multi-agent systems. I am specifically interested in how evolutionary search algorithms can be used to help people understand and analyze agent-based models of complex systems (e.g., flocking birds, traffic jams, or how information diffuses across social networks). My secondary research interests broadly span the areas of artificial life, multi-agent robotics, cognitive/learning science, design of multi-agent modeling environments. I enjoy interdisciplinary research, and in pursuit of the aforementioned topics, I have been involved in application areas from archeology to zoology, from linguistics to marketing, and from urban growth patterns to materials science. I am also very interested in creative approaches to computer science and complex systems education, and have published work on the use of multi-agent simulation as a vehicle for introducing students to computer science.

It is my philosophy that theoretical research should be inspired by real-world problems, and conversely, that theoretical results should inform and enhance practice in the field. Accordingly, I view tool building as a vital practice that is complementary to theoretical and methodological research. Throughout my own work I have contributed to the research community by developing several practical software tools, including BehaviorSearch (http://www.behaviorsearch.org/)

• GIS Analyst / GIS Specialist: Experienced in applying GIS tools and spatial analysis techniques to
support decision-making in urban planning, environmental management, transportation, and infrastructure
projects. Skilled in producing high-quality maps, conducting spatial analysis, and delivering actionable
geospatial insights for operational and policy use.
• Geospatial Data Scientist: Specialized in developing spatial predictive models by integrating machine
learning and geospatial data to perform risk assessment, suitability analysis, and forecasting using large-
scale datasets such as satellite imagery, climate variables, and land-use data.
• Spatial Data Engineering & Processing: Strong ability to manage and preprocess complex geospatial
datasets, including raster and vector data, DEMs, remote sensing products, and climate projections, with
rigorous attention to spatial reference systems, accuracy, and data quality control.
• GIS Workflow Automation & Optimization: Proven experience in automating geospatial workflows
using ArcGIS Pro, ArcPy, ModelBuilder, FME (ETL), and Python to improve efficiency in spatial analysis,
data processing, and large-scale mapping tasks.
• Remote Sensing & Earth Observation Analysis: Proficient in satellite imagery processing and analysis,
including cloud masking, spectral analysis, vegetation indices, land cover classification, and temporal
change detection using Google Earth Engine and Python.
• Geospatial Visualization & Cartography: Skilled in producing professional-grade thematic maps, spa-
tial dashboards, and web-based geovisualizations to communicate complex geospatial patterns to both
technical and non-technical stakeholders.
• Cross-Disciplinary Collaboration: Experienced working with multidisciplinary teams across academia,
government, and industry to deliver geospatial solutions for planning, environmental risk assessment, and
policy-driven decision-making.