Antônio Sousa is a biologist with a background in medical entomology, disease ecology, statistical and computational modeling. Antônio has a Ph.D. (2018) and Master (2014) in Science from the School of Public Health at the University of São Paulo, Brazil. Currently, he is a postdoctoral fellow in the same institution.

I graduated Bachelor and Master studies at the University of Warsaw, obtaining the diploma in biology at College of Inter-Faculty Individual Studies in Mathematics and Natural Sciences (MISMaP). After graduation I worked as a freelancer in data science and statistics, then worked for 2 years as a data scientist in an IT startup and now I am working as a statistician in The Polish National Information Processing Institute (OPI PIB) in a group analysing condition of science and higher education in Poland. My interests: agent based modelling, evolutionary ecology, statistics, data science, sociology of science.

I am a Senior Economist in the Capital Markets Division of the Bank of England. I have a PhD in Economics from the joint program at Vilfredo Pareto Doctorate in Economics (University of Turin) and Collegio Carlo Alberto, where I’ve taught graduate level economic courses. Prior to joining the Bank of England, I also worked in the private sector as a quantitative analyst on issues related to different areas including asset management, risk management, and policy implementation.

Eric has graduate degrees in urban planning and policy and sociology and an undergraduate degree in biology. He has worked on multiple collaborative and interdisciplinary projects and is skilled at engaging communities and other stakeholders. He is adept at qualitative research and has earned a Certificate in Geospatial Analysis and Visualization, demonstrating proficiency in Adobe Suite, ArcGIS, agent-based modeling and system dynamics modeling. He is currently writing manuscripts for publication based on his work on motivating energy retrofit decisions, energy-related urban planning, municipal decision-making on infrastructure investments, and other work on resilience and sustainability.

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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

Peter Gerbrands is a Researcher at the of Utrecht University School of Economics, where is develops the data infrastructure FIRMBACKBONE. He teaches data science courses and econometrics as well as supervising bachelor, master, and Ph.D. theses. His research interests are agent-based simulations, social network analysis, complex systems, big data analysis, statistical learning, and computational social science. He applies his skills primarily for policy analysis, especially related to illicit financial flows, i.e. tax evasion, tax avoidance and money laundering and has published in Regulation & Governance, and EPJ Data Science. Prior to becoming an academic, Peter had a long career in IT consulting. In the Fall of 2023, he was a Visiting Research Scholar at SUNY Binghamton in NY.

He is an experienced Lecturer with a demonstrated history of working in the education management industry. He was skilled in Agent-based Modeling and Simulation, Competency Assessing and Fundamental Supply Chain Management. Strong research background and analyst with a Master’s degree focused in Logistics and Supply Chain Management from Institut Teknologi Sepuluh Nopember Surabaya and Certified Supply Chain Analyst from ISCEA International.

As of my incorporation into the Department of Computer Architecture and Operating Systems of the UAB as a postgraduate student, it is possible to divide my scientific-technical career into the following stages:

Simulation of Parallel Applications (1992-99): Focused on the design and development of simulators of parallel applications. This research main objective was the definition of abstractions for parallel programs, based on characterizing tasks and their dependences. Two main abstractions were developed, at first a simpler one, which was easier to parametrize, and, next, a more complex an accurate one. Using these characterizations, several simulation tools were programmed and used in the context of national and European projects. As part of my Master’s thesis, I was involved in the design and development of some of these simulation applications.

National projects: 4, European: 2

International conferences: 3, National: 1, Journal papers: 3

Security in Distributed Systems (2007-12): Focused on the design and development of the FPVA (First Principles Vulnerability Assessment) methodology for the evaluation of vulnerabilities in Grid applications. This methodology clearly defined a set of steps for the assessment of Grid applications vulnerabilities, most of these steps could be automatized or at least supported by specific tools. Jointly with other professors of our group and from the University of Wisconsin, I was involved in the original definition and application of this methodology.

International projects: 2

Master Thesis: 1, Ph.D. Thesis: 1

International conferences: 2, National: 1, Journal papers: 2

Parallel Application Modeling (1999-present): This is my main line of research, aimed at defining high-level performance models for parallel applications. Initially, models were defined for MPI applications with a master-worker and pipeline structure, but later this line has been expanded with the definition of models for memory-intensive OpenMP applications, composed (mix of several structures) applications, applications based on mathematical libraries, distributed data-intensive applications and, finally, applications based on the simulation of agents (ABS) with SPMD structure.

As a result of the work on modeling the performance of ABS parallel systems, we have opened a new line for the definition and implementation of a benchmark for assessing the performance of the parallel simulators generated by well-known platforms, such as FLAME, Repast-HPC or D-Mason. In addition, the knowledge we have gained on this topic has opened new ways of collaboration for optimizing real parallel ABS in the health sciences area (tumor growth and infection spread).

National projects: 12, European: 1

International conferences: 17, National: 4, Journal papers: 11

International Presentations: 4

Parallel Applications Tuning Tools (2010-present): Focused on the design and development of tools for automatic tuning and, in some cases, also dynamic tuning of parallel applications. These tools allow the integration of performance models in the form of external components provided by the analyst. For this reason, this research line is tightly coupled with the Parallel Application Modeling one. The two main tools developed totally or partially by our group are Monitoring Analysis and Tuning Environment-MATE (and its highly scalable evolution ELASTIC) and Periscope Tuning Framework-PTF.

National projects: 2, European: 1

International conferences: 11, Journal papers: 2

Tools: MATE, ELASTIC, PTF

International Presentations: 5