Dr. Sergio Fajardo is a mathematician, professor, and politician. At the beginning of his career, he worked for different scientific institutions in Colombia, such as the National Council for Basic Sciences (transformed in 2019). Later on, in 2016 and 2019, he held the position of visiting professor at the Center for Latin American Studies at the University of California, Berkeley, in the United States.

In 1999, he founded the civic movement Compromiso Ciudadano, with which he was elected mayor of Medellín for the 2004–2007 term with the highest number of votes registered at that time in the history of the city. His administration was characterized by a new form of public management, focused on citizenship, transparency, and social innovation, which resulted in the highest acceptance and popularity rates in Colombia. He led the greatest transformation of Medellín: from a city of inequality and violence to a global example of social change through education and urbanism.

From 2012 to 2015, he was governor of Antioquia, where he worked against inequality, violence, and corruption, and led its economic and social improvement. He was a candidate for the Colombian presidential elections in 2018 and 2022. In 2023, Sergio Fajardo co-founded the political party Dignidad y Compromiso.

As a mathematician, the central topics of his research were mathematical logic and the area of theory of probability models. In politics, his work has focused on urbanism, territorial development, education, and gender equality, to name a few.

Sergio Fajardo joined Tecnológico de Monterrey as a Distinguished University Professor in Public Leadership for the School of Social Sciences and Government and the School of Government and Public Transformation.

• Ph.D., Mathematics, University of Wisconsin-Madison
• Master's, Mathematics, University of Wisconsin-Madison
• B.Sc., Mathematics, University of the Andes
   

• Fajardo, S. (2017). El poder de la decencia. Ideas sensatas para un país en busca de confianza. Colombia. Número de páginas: 202. https://hdl.handle.net/11285/652332

• Fajardo, Sergio & Keisler, H.. (2017). Model Theory of Stochastic Processes. 10.1017/9781316756126.

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Dr. Sergio Fajardo is a mathematician, professor, and politician. At the beginning of his career, he worked for different scientific institutions in Colombia, such as the National Council for Basic Sciences (transformed in 2019). Later on, in 2016 and 2019, he held the position of visiting professor at the Center for Latin American Studies at the University of California, Berkeley, in the United States.

In 1999, he founded the civic movement Compromiso Ciudadano, with which he was elected mayor of Medellín for the 2004–2007 term with the highest number of votes registered at that time in the history of the city. His administration was characterized by a new form of public management, focused on citizenship, transparency, and social innovation, which resulted in the highest acceptance and popularity rates in Colombia. He led the greatest transformation of Medellín: from a city of inequality and violence to a global example of social change through education and urbanism.

From 2012 to 2015, he was governor of Antioquia, where he worked against inequality, violence, and corruption, and led its economic and social improvement. He was a candidate for the Colombian presidential elections in 2018 and 2022. In 2023, Sergio Fajardo co-founded the political party Dignidad y Compromiso.

As a mathematician, the central topics of his research were mathematical logic and the area of theory of probability models. In politics, his work has focused on urbanism, territorial development, education, and gender equality, to name a few.

Sergio Fajardo joined Tecnológico de Monterrey as a Distinguished University Professor in Public Leadership for the School of Social Sciences and Government and the School of Government and Public Transformation.

• Ph.D., Mathematics, University of Wisconsin-Madison
• Master's, Mathematics, University of Wisconsin-Madison
• B.Sc., Mathematics, University of the Andes
   

• Fajardo, S. (2017). El poder de la decencia. Ideas sensatas para un país en busca de confianza. Colombia. Número de páginas: 202. https://hdl.handle.net/11285/652332

• Fajardo, Sergio & Keisler, H.. (2017). Model Theory of Stochastic Processes. 10.1017/9781316756126.

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Dr. Carlos Coello is a professor with distinction (Investigador CINVESTAV 3F) at the Center for Research and Advanced Studies of the Instituto Politécnico Nacional (CINVESTAV-IPN) in Mexico City and visiting professor at the Basque Center for Applied Mathematics in Spain. Has taught master’s and Ph.D. level courses on evolutionary computation, evolutionary multiobjective optimization, programming languages, and engineering optimization at CINVESTAV-IPN. In addition, he has taught short courses in Spain, England, India, U.S., among others.

His research interests are evolutionary computation (genetic algorithms and evolution strategies), as well as engineering optimization. His main contributions have been the design of biologically inspired stochastic algorithms to solve highly complex multi-objective optimization problems (mainly non-linear). He has made pioneering contributions to this area, which is now known as evolutionary multi-objective optimization. For example, he proposed, along with his research group, the first genetic micro-algorithm for multi-objective optimization, which has been used in real-world applications in several countries, like the United States, for the design of supersonic business jets. He is member of the Foundation Advisory Board of the International AIQT Foundation, which strives to establish a highly competitive (international-level) research center in artificial intelligence and quantum technology.

Among his multiple special recognitions and awards, Dr. Carlos Coello was awarded by the International Society on Multi-Criteria Decision Making (MCDM) the 2024 MCDM Edgeworth-Pareto Award. In 2023, he won the Premio Crónica in Science and Technology and was selected by Líderes magazine as part of their "300 most influential leaders in Mexico" list, taking the 69th position. He also won the SIGEVO Outstanding Contribution Award (2023) given by the Association for Computing Machinery (ACM), the IEEE Computational Intelligence Society Evolutionary Computation Pioneer Award (2021), among many others.

Dr. Coello is level 3 in the National System of Researchers (SNII) in Mexico and was also ranked #289 in the world and #1 in Mexico in the 8th Edition of the Guide2Research 2022 Ranking of Top 1000 Scientists in the field of Computer Science and Electronics.

Professor Carlos Coello joined Tecnológico de Monterrey as Distinguished Visiting Professor in Computer Science and Computational Intelligence for the School of Engineering and Sciences.

• Ph.D., Computer Science, Tulane University, New Orleans
• M.Sc., Computer Science, Tulane University, New Orleans
• B.Sc., Civil Engineering, Universidad Autónoma de Chiapas (Summa cum laude)
   

• Sofien Boutaib, Maha Elarbi, Slim Bechikh, Carlos A. Coello Coello, Lamjed Ben Said, Uncertainty-wise Software Anti-patterns Detection: A Possibilistic Evolutionary Machine Learning Approach, Applied Soft Computing, Vol. 129, Article Number: 109620, November 2022.

• Forhad Zaman, Saber Elsayed, Ruhul Sarker, Daryl Essam and Carlos A. Coello Coello, Pro-Reactive Approach for Project Scheduling Under Unpredictable Disruptions, IEEE Transactions on Cybernetics, Vol. 52, No. 11, pp. 11299--11312, November 2022.

• Lingjie Li, Yongfeng Li, Qiuzhen Lin, Zhong Ming, Carlos A. Coello Coello, A Convergence and Diversity Guided Leader Selection Strategy for Many-objective Particle Swarm Optimization<, Engineering Applications of Artificial Intelligence, Vol. 155, Article Number: 105249, October 2022.

• Qiuzhen Lin, Xunfeng Wu, Jianqiang Li, Maoguo Gong and Carlos A. Coello Coello, An Ensemble Surrogate-based Framework for Expensive Multiobjective Evolutionary Optimization, IEEE Transactions on Evolutionary Computation, Vol. 26, No. 4, pp. 631--645, August 2022.

• Qiyuan Yu, Qiuzhen Lin, Zexuan Zhu, Ka-Chun Wong, Carlos A. Coello Coello, A dynamic multi-objective evolutionary algorithm based on polynomial regression and adaptive clustering, Swarm and Evolutionary Computation, Vol. 71, Article No. 101075, June 2022.

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Dr. Carlos Coello is a professor with distinction (Investigador CINVESTAV 3F) at the Center for Research and Advanced Studies of the Instituto Politécnico Nacional (CINVESTAV-IPN) in Mexico City and visiting professor at the Basque Center for Applied Mathematics in Spain. Has taught master’s and Ph.D. level courses on evolutionary computation, evolutionary multiobjective optimization, programming languages, and engineering optimization at CINVESTAV-IPN. In addition, he has taught short courses in Spain, England, India, U.S., among others.

His research interests are evolutionary computation (genetic algorithms and evolution strategies), as well as engineering optimization. His main contributions have been the design of biologically inspired stochastic algorithms to solve highly complex multi-objective optimization problems (mainly non-linear). He has made pioneering contributions to this area, which is now known as evolutionary multi-objective optimization. For example, he proposed, along with his research group, the first genetic micro-algorithm for multi-objective optimization, which has been used in real-world applications in several countries, like the United States, for the design of supersonic business jets. He is member of the Foundation Advisory Board of the International AIQT Foundation, which strives to establish a highly competitive (international-level) research center in artificial intelligence and quantum technology.

Among his multiple special recognitions and awards, Dr. Carlos Coello was awarded by the International Society on Multi-Criteria Decision Making (MCDM) the 2024 MCDM Edgeworth-Pareto Award. In 2023, he won the Premio Crónica in Science and Technology and was selected by Líderes magazine as part of their "300 most influential leaders in Mexico" list, taking the 69th position. He also won the SIGEVO Outstanding Contribution Award (2023) given by the Association for Computing Machinery (ACM), the IEEE Computational Intelligence Society Evolutionary Computation Pioneer Award (2021), among many others.

Dr. Coello is level 3 in the National System of Researchers (SNII) in Mexico and was also ranked #289 in the world and #1 in Mexico in the 8th Edition of the Guide2Research 2022 Ranking of Top 1000 Scientists in the field of Computer Science and Electronics.

Professor Carlos Coello joined Tecnológico de Monterrey as Distinguished Visiting Professor in Computer Science and Computational Intelligence for the School of Engineering and Sciences.

• Ph.D., Computer Science, Tulane University, New Orleans
• M.Sc., Computer Science, Tulane University, New Orleans
• B.Sc., Civil Engineering, Universidad Autónoma de Chiapas (Summa cum laude)
   

• Sofien Boutaib, Maha Elarbi, Slim Bechikh, Carlos A. Coello Coello, Lamjed Ben Said, Uncertainty-wise Software Anti-patterns Detection: A Possibilistic Evolutionary Machine Learning Approach, Applied Soft Computing, Vol. 129, Article Number: 109620, November 2022.

• Forhad Zaman, Saber Elsayed, Ruhul Sarker, Daryl Essam and Carlos A. Coello Coello, Pro-Reactive Approach for Project Scheduling Under Unpredictable Disruptions, IEEE Transactions on Cybernetics, Vol. 52, No. 11, pp. 11299--11312, November 2022.

• Lingjie Li, Yongfeng Li, Qiuzhen Lin, Zhong Ming, Carlos A. Coello Coello, A Convergence and Diversity Guided Leader Selection Strategy for Many-objective Particle Swarm Optimization<, Engineering Applications of Artificial Intelligence, Vol. 155, Article Number: 105249, October 2022.

• Qiuzhen Lin, Xunfeng Wu, Jianqiang Li, Maoguo Gong and Carlos A. Coello Coello, An Ensemble Surrogate-based Framework for Expensive Multiobjective Evolutionary Optimization, IEEE Transactions on Evolutionary Computation, Vol. 26, No. 4, pp. 631--645, August 2022.

• Qiyuan Yu, Qiuzhen Lin, Zexuan Zhu, Ka-Chun Wong, Carlos A. Coello Coello, A dynamic multi-objective evolutionary algorithm based on polynomial regression and adaptive clustering, Swarm and Evolutionary Computation, Vol. 71, Article No. 101075, June 2022.

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Dr. Thomas Kurfess is chief manufacturing officer at the Georgia Institute of Technology (GT) and executive director of the Georgia Tech Manufacturing Institute (GTMI), as well as HUSCO Ramirez Distinguished Chair in Fluid Power and Motion Control at GT. He is also Regents’ Professor at the Georgia Institute of Technology and professor and BMW Chair of Manufacturing Emeritus at Clemson University. Previously, he served as assistant director for the Advanced Manufacturing Office of Science and Technology Policy in the Executive Office of the President of the United States of America
(2012-2013) and as chief manufacturing officer of the Oak Ridge National Laboratory (2019- 2021).

During the early stage of his career, he was an associate professor at Carnegie Mellon University. He has taught courses in subjects such as advanced manufacturing systems; quality control and quality assurance; vehicle testing; automotive electronics; real-time signal processing and control; precision systems and manufacturing; and robotics and advanced dynamics. 

Thomas Kurfess’ research focuses on the design and development of advanced manufacturing systems by integrating new technologies into production operations and on national and international manufacturing policy issues.

He is an elected member of the U.S. National Academy of Engineering; chair of the U.S. National Academy of Engineering (NAE) study called “Options for a National Plan for Smart Manufacturing”; member of the U.S. National Materials and Manufacturing Board; and member of the Board of Governors of the
American Society of Mechanical Engineers (ASME), among others.

Thomas Kurfess joined Tecnológico de Monterrey as a Distinguished Visiting Professor in Advanced Manufacturing for the School of Engineering and Sciences.

• Ph.D., Mechanical Engineering, Massachusetts Institute of Technology (MIT)
•  S.M., Electrical Engineering and Computer Science, MIT
• S.M., Mechanical Engineering, MIT
• S.B., Mechanical Engineering, MIT 
   

• Lu, L. Kurfess, T.R., Saldana, C.J., “Effects of Extrinsic Noise Factors on Machine Learning Based Chatter Detection in Machining,” International Journal of Smart and Sustainable Manufacturing, 2021, accepted.

• Miers, J.C, Tucker, T.M., Kurfess, T.R., Saldana, C.J., “Voxel-Based Modeling of Transient Material Removal in Machining,” International Journal of Advanced Manufacturing Technology, 2021, accepted.

• Parto Dezfouli, M., Urbina Coronado, Saldana, C.J., Kurfess, T.R., “Cyber-Physical System Implementation for Manufacturing with Analytics in the Cloud Layer,” ASME Journal of Computing and Information Science in Engineering, 2021.

• Feldhausen, T., Saleeby, K., Love, L., Kurfess, T.R., “Spinning the Digital Thread with Hybrid Manufacturing,” ASME Smart and Manufacturing Letters, Vol. 29, pp. 15-18, doi.org/10.1016/j.mfglet.2021.05.003, 2021.

• Castro‐Martin, A.P., Ahuett‐Garza, H., Guamán‐Lozada, D., Márquez‐Alderete, M.F., Urbina Coronado, P.D. 1, Orta Castañon, P.A., Kurfess, T.R., González de Castilla, E., “Connectivity as a Design Feature for Industry 4.0 Production Equipment: Application for the Development of an In‐Line Metrology System,” Applied Science, Vol. 11, doi.org/10.3390/app11031312, 2021

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Dr. Thomas Kurfess is chief manufacturing officer at the Georgia Institute of Technology (GT) and executive director of the Georgia Tech Manufacturing Institute (GTMI), as well as HUSCO Ramirez Distinguished Chair in Fluid Power and Motion Control at GT. He is also Regents’ Professor at the Georgia Institute of Technology and professor and BMW Chair of Manufacturing Emeritus at Clemson University. Previously, he served as assistant director for the Advanced Manufacturing Office of Science and Technology Policy in the Executive Office of the President of the United States of America
(2012-2013) and as chief manufacturing officer of the Oak Ridge National Laboratory (2019- 2021).

During the early stage of his career, he was an associate professor at Carnegie Mellon University. He has taught courses in subjects such as advanced manufacturing systems; quality control and quality assurance; vehicle testing; automotive electronics; real-time signal processing and control; precision systems and manufacturing; and robotics and advanced dynamics. 

Thomas Kurfess’ research focuses on the design and development of advanced manufacturing systems by integrating new technologies into production operations and on national and international manufacturing policy issues.

He is an elected member of the U.S. National Academy of Engineering; chair of the U.S. National Academy of Engineering (NAE) study called “Options for a National Plan for Smart Manufacturing”; member of the U.S. National Materials and Manufacturing Board; and member of the Board of Governors of the
American Society of Mechanical Engineers (ASME), among others.

Thomas Kurfess joined Tecnológico de Monterrey as a Distinguished Visiting Professor in Advanced Manufacturing for the School of Engineering and Sciences.

• Ph.D., Mechanical Engineering, Massachusetts Institute of Technology (MIT)
•  S.M., Electrical Engineering and Computer Science, MIT
• S.M., Mechanical Engineering, MIT
• S.B., Mechanical Engineering, MIT 
   

• Lu, L. Kurfess, T.R., Saldana, C.J., “Effects of Extrinsic Noise Factors on Machine Learning Based Chatter Detection in Machining,” International Journal of Smart and Sustainable Manufacturing, 2021, accepted.

• Miers, J.C, Tucker, T.M., Kurfess, T.R., Saldana, C.J., “Voxel-Based Modeling of Transient Material Removal in Machining,” International Journal of Advanced Manufacturing Technology, 2021, accepted.

• Parto Dezfouli, M., Urbina Coronado, Saldana, C.J., Kurfess, T.R., “Cyber-Physical System Implementation for Manufacturing with Analytics in the Cloud Layer,” ASME Journal of Computing and Information Science in Engineering, 2021.

• Feldhausen, T., Saleeby, K., Love, L., Kurfess, T.R., “Spinning the Digital Thread with Hybrid Manufacturing,” ASME Smart and Manufacturing Letters, Vol. 29, pp. 15-18, doi.org/10.1016/j.mfglet.2021.05.003, 2021.

• Castro‐Martin, A.P., Ahuett‐Garza, H., Guamán‐Lozada, D., Márquez‐Alderete, M.F., Urbina Coronado, P.D. 1, Orta Castañon, P.A., Kurfess, T.R., González de Castilla, E., “Connectivity as a Design Feature for Industry 4.0 Production Equipment: Application for the Development of an In‐Line Metrology System,” Applied Science, Vol. 11, doi.org/10.3390/app11031312, 2021

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