Sosa-Hernández, Víctor Adrián

I received a Ph.D. in computer science from the Centre for Research and Advanced Studies of the National Polytechnic Institute, Mexico, in 2017 and a postdoctoral stay with the Advanced Artificial Intelligence group at Tecnológico de Monterrey, Campus Estado de México, Mexico, in 2018. I have more than six years of experience in the academy and ten years in industry and government within the field of computer science. I have collaborated with Amazon, Danone, and the secretary of public security in Mexico. My work as a researcher includes more than ten publications in conferences, books, and international journals with strict arbitration. Currently, I´m the Director of the computer science postgraduate program at Tecnológico de Monterrey and a Professor of the computer science department at the same institution. I´m an adherent member of the Mexican Academy of computing and conduct theoretical and applied research in machine learning, deep learning, neuro-evolution, and multi-objective evolutionary optimization.

I received my B. S. degree in computer systems from the Instituto Politécnico Nacional (IPN), in 2011, studying at the Escuela Superior de Cómputo (ESCOM). In 2013, I received my master's degree in computer science from the Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional (CINVESTAV-IPN), specializing in the area of ¿¿multi-objective optimization and evolutionary algorithms. In 2017, I obtained my Ph. D. degree doing my studies in the computer science department of the CINVESTAV-IPN. I did a postdoctoral stay in the computer science department of the Instituto Tecnológico y de Estudios Superiores de Monterrey, campus Estado de México (ITESM-CEM), focusing my research on the machine learning area. Currently, I am part of the professors' team of the institute at the Computer Science department. My main research interests are the design of local search techniques based on performance indicators for multi-objective optimization evolutionary algorithms, memetic strategies, algorithms for the treatment of dynamic multi-objective optimization problems and machine learning techniques.