Manuel Chica Serrano

Manuel Chica Serrano

Ramon y Cajal Senior Researcher

University of Granada

Biography

Manuel Chica is Ramon y Cajal Senior Researcher (RYC-2016-19800), granted by the Spanish Government, at the University of Granada (Computer Science and AI Deparment). He is also Chief AI Officer at ZIO Analytics and Conjoint Lecturer at the University of Newcastle, Australia.

His current research interests include agent-based modeling, evolutionary game theory, single and multi-objective evolutionary optimization, data science, and complex systems. His research is mainly applied and has been applied to a diverse range of fields: from industrial engineering and marketing, to healthcare systems and climate change.

Interests

  • Artificial Intelligence
  • Social Simulation
  • Optimization
  • Data Science

Education

  • PhD in Artificial Intelligence and Computer Science (cum laude), 2011

    University of Granada

  • Master in Soft Computing and Intelligent Systems, 2009

    University of Granada

  • MEng in Computer Science, 2006

    University of Jaen

  • BSc in Computing Engineering (Management), 2004

    University of Jaen

Publications

89

JCR-indexed papers

39

Q1 ranked papers

30

Citations

More than 1K (h-index = 18)

R&D projects

18

Patents

2

Academic Positions

 
 
 
 
 

Ramon y Cajal Senior Researcher

University of Granada

Jun 2018 – Present Granada, Spain
Researcher at the Computer Science and AI Department. Research, R&D project, and teaching
 
 
 
 
 

Conjoint Lecturer

University of Newcastle, Australia

Aug 2017 – Present Callaghan, Australia
Supervision of Master and PhD students, research collaboration, and teaching support
 
 
 
 
 

Senior Researcher

Internet Interdisciplinary Institute (IN3)

Jun 2016 – Feb 2017 Barcelona, Spain
Scientific research using optimization algorithms and metaheuristics, complex systems, and projects’ writing.
 
 
 
 
 

Visiting Researcher

Wroclaw University of Technology

Sep 2015 – Jan 2016 Wroclaw, Poland
Collaborative work with other members of the EU ENGINE project (social network analysis, simulation, diffusion of innovations)
 
 
 
 
 

Visiting Scholar

R.H. Smith School of Business, University of Maryland

Aug 2014 – Dec 2014 College Park, Maryland, US
Research stay, working at the Marketing Department and Center for Complexity in Business
 
 
 
 
 

Visiting Researcher

University of Auckland

Sep 2012 – Dec 2012 Auckland, New Zealand
Basic research about metaheuristics, radiation therapy optimization, mathematical programming
 
 
 
 
 

Research Assistant

University of Jaen

Aug 2005 – Jun 2006 Jaen
Research on data mining and pre-processing techniques. Develop of feature selection algorithms for the Keel project (a Java open-source data mining software).

Experience in Industry

 
 
 
 
 

Chief AI Officer

ZIO Analytics

Apr 2017 – Present Madrid, Spain
Partner, scientific and technological direction, projects’ support.
 
 
 
 
 

Deputy Principal Researcher

European Centre for Soft Computing

Feb 2012 – May 2016 Asturias, Spain
R&D projects management. Leading actions on agent-based modelling, system dynamics and artificial economics for brand management and marketing . Applied research on multicriteria decision making. Unit management and preparation of FP7 proposals.
 
 
 
 
 

R&D Engineer

Inspiralia

Jul 2008 – Jan 2012 Madrid, Spain
Delivery of FP7 projects related with intelligent systems, image analysis, pattern recognition, data mining and and software development. Technical management of the projects.
 
 
 
 
 

Research Assistant

European Centre for Soft Computing

Jun 2007 – Jun 2008 Asturias, Spain
R&D projects about metaheuristics, optimization and production.
 
 
 
 
 

Internship

Apple Computer

Mar 2007 – May 2007 Cork, Ireland
Testing of software and hardware devices on Windows over a Mac architecture.

Recent Posts

Science Daily covers our JMR paper

New technique could boost online word-of-mouth marketing: Researchers have developed a technique for creating complex predictive tools that can be used to make effective decisions about word-of-mouth marketing for online products and services, Science Daily informed here.

Eposbed on Euronews, rTVE and EU-Bulletin

After two years’ work on an EU-funded project, a Spanish-based company has come up with a new prototype of a bed that could make life more comfortable for both patients and nurses, Euronews published:

Massive media coverage about our study on 11M attacks in Madrid

The results of our paper, recently published in the Knowledge-based Systems journal, have appeared in many Spanish newspapers: La Vanguardia Eldiario.es El Economista IDEAL Noticias de Guipuzkoa Canal UGR Government, politicians, and mass media generated a large quantity of information after the bombing attacks in Madrid on the 11th of March 2004.

New publications in California Management Review

Authors from US, the Netherlands, and Spain have published a practical guide for the adoption of AI technologies in Marketing, following the data science standard CRISP-DM. The paper was published in California Management Review (CMR) in July 2019, one of the most relevant publications in management (impact factor of 5.

Nature Scientific Reports publishes our study on trust for the sharing economy

We present an evolutionary trust game, taking punishment and protection into consideration, to investigate the formation of trust in the so-called sharing economy from a population perspective. Our results show that each player type influences the existence and survival of other types of players, and untrustworthy players do not necessarily dominate the population even when the temptation to defect (i.

Awards and Grants

Endeavour Research Fellowship

Best PhD in Engineering and Architecture

Best Master degree curriculum (course 2001-2006)

Featured Projects

Multi-manned assembly line balancing

Metaheuristics and mixed integer linear models (MILP) for multi-manned assembly line balancing (MALBP)

PhD Thesis

Ant Colony Optimization (ACO) and Genetic Algorithms to solve the Time and Space Assembly Line Balancing Problem (TSALBP).

SIMARK

Massive Agent-based Simulations and Explainable Artificial Intelligence in Marketing

Zio

A cutting-edge Web application for marketing using agent-based modeling and AI

Toxdtect

The aim of FP7 TOXDTECT project is to develop an intelligent packaging solution for fresh bovine meat products. The technology is based on the identification of specific volatile organic compounds (VOCs) produced inside the packaging during microorganisms’ growth. The signal coming from the interaction between VOCs and polymer inks is recorded by an external device where data are processed. A predictive software based on data mining correlates the sensors signal with the remaining shelf-life of meat (i.e., number of days during which meat is still safe to be consumed).

Identimod

A decision support system based on computational intelligence and system dynamics for branding and marketing

Apifresh

A European project funded by the European Commission 7th Framework Program. The objective of the Apifresh project is to provide European beekeepers with the scientific and technological aids necessary to improve the quality of European pollen and royal jelly and also to promote the regulatory means that will allow European bee products compete under fair conditions against lower quality or adulterated products.

Eposbed

This FP7 for SMEs project was developed in Inspiralia to create a medical device which automatically recognizes patient’s intentions. Manuel led the intelligent system based on neural networks to identify the patient’s movements

Recent Publications

Quickly discover relevant content by filtering publications.

A collective risk dilemma for tourism restrictions under the COVID-19 context

The current COVID-19 pandemic has impacted millions of people and the global economy. Tourism has been one the most affected economic sectors because of the mobility restrictions established by governments and uncoordinated actions from origin and destination regions. The coordination of restrictions and reopening policies could help control the spread of virus and enhance economies, but this is not an easy endeavor since touristic companies, citizens, and local governments have conflicting interests. We propose an evolutionary game model that reflects a collective risk dilemma behind these decisions. To this aim, we represent regions as players, organized in groups; and consider the perceived risk as a strict lock-down and null economic activity. The costs for regions when restricting their mobility are heterogeneous, given that the dependence on tourism of each region is diverse. Our analysis shows that, for both large populations and the EU NUTS2 case study, the existence of heterogeneous costs enhances global agreements. Furthermore, the decision on how to group regions to maximize the regions’ agreement of the population is a relevant issue for decision makers to consider. We find out that a layout of groups based on similar costs of cooperation boosts the regions’ agreements and avoid the risk of having a total lock-down and a negligible tourism activity. These findings can guide policy makers to facilitate agreements among regions to maximize the tourism recovery.

An Evolutionary Game Model for Understanding Fraud in Consumption Taxes

This paper presents a computational evolutionary game model to study and understand fraud dynamics in the consumption tax system. Players are cooperators if they correctly declare their value added tax (VAT), and are defectors otherwise. Each player’s payoff is influenced by the amount evaded and the subjective probability of being inspected by tax authorities. Since transactions between companies must be declared by both the buyer and seller, a strategy adopted by one influences the other’s payoff. We study the model with a well-mixed population and different scale-free networks. Model parameters were calibrated using real-world data of VAT declarations by businesses registered in the Canary Islands region of Spain. We analyzed several scenarios of audit probabilities for high and low transactions and their prevalence in the population, as well as social rewards and penalties to find the most efficient policy to increase the proportion of cooperators. Two major insights were found. First, increasing the subjective audit probability for low transactions is more efficient than increasing this probability for high transactions. Second, favoring social rewards for cooperators or alternative penalties for defectors can be effective policies, but their success depends on the distribution of the audit probability for low and high transactions.

Coral reefs optimization algorithms for agent-based model calibration

volume 100 104170 Impact factor: 4.201. Journal rank: Q1, 13/91 (Engineering, Multidisciplinary)

Others' fortune in online vs. offline settings: how envy affects people's intention to share information

volume xx yyy Impact factor: 4.708. Journal rank: Q1, 25/156 (Computer Science - Information Systems)

Understanding the dynamics of inter-provincial migration in the Mekong Delta, Vietnam: An agent-based modeling study

volume 22 (3) 1 Impact factor: 1.090. Journal rank: Q4, 82/108 (Computer Science, Software Engineering)

Current tasks & skills

Modeling and scientific writing

40%

Project management

30%

Analytics and UX design

20%

Programming

10%

Contact

  • Periodista Fernando Gomez de la Cruz, no. 61, Granada, 18014
  • Second floor, Office 1 (ZIO)
  • Skype Me
  • Email me