CV

Jorge Mario Cruz-Duarte

Summary

Jorge M. Cruz-Duarte (b. 1990 Colombia) is a Research Professor (since 2021) in the Research Group on Advanced Artificial Intelligence at the Tecnológico de Monterrey (TEC) (opens in a new tab). He is a Level I member of the CONACHyT (opens in a new tab) Mexican National System of Researchers, IEEE (opens in a new tab) Senior Member, member of the Mexican Mathematical Society (SMM) (opens in a new tab), and member of the Mexican Academy in Computer Sciences (AMEXCOMP) (opens in a new tab). Jorge is also a reviewer of several scientific journals, e.g., Applied Soft Computing, Swarm Evolutionary and Computation, Applied Thermal Engineering, IEEE Access, and Mathematical Reviews.

Jorge received his B.Sc. and M.Sc. degrees in Electronic Engineering from the Universidad Industrial de Santander (UIS) (opens in a new tab) and his Ph.D. in Electrical Engineering from the Universidad de Guanajuato (UGto) (opens in a new tab). He was in a Short Research Stay at the University POLITEHNICA of Bucharest (UPB) (opens in a new tab). From 2019 to 2021, he was on a Postdoctoral Stay at the Research Group with Strategic Focus on Intelligent Systems at the Tecnológico de Monterrey (TEC) (opens in a new tab) in collaboration with the Chinese Academy of Sciences (CAS) (opens in a new tab) for the Integration of Data Science and Optimisation.

Experience

Tecnológico de Monterrey, Research Professor

(July 2021 - Present)

• Co-author of more than 30 scientific papers related with Artificial Intelligence, Data Science, Optimisation, and Applied Engineering. Further information can be found here.
• Lecturer of courses related with Artificial Intelligence such as Design of Neural Networks, Introduction to Programming Languages, Modeling Multi-agent Systems with Computational Graphics, Laboratory of Microcontrollers.
• Adviser to three master's students working on Machine Learning algorithms (Neural Networks and Hyper-heuristics) for generating optimal techniques to solve practical engineering problems, such as tumour detection in medical image processing.

Tech Stack: Python (Pandas, Numpy, TensorFlow, Keras, SciPy, ScikitLearn, Seaborn, Matplotlib), Matlab/Octave, Assembly, MPLAB, KiCad, PSpice, Unity, C/C++, C#, R, LaTeX, Inkscape, Affinity Designer, MS Office.

Tecnológico de Monterrey, Postdoctoral Researcher

(June 2019 - June 2021)

• Developed a cutting-edge Python framework for studying and designing metaheuristic-based algorithms for practical engineering problems called CUSTOMHyS. I've proposed the theoretical foundations for combining and studying the existing metaheuristics in the literature.
• Awarded the National Researcher member (Level 1) from the National System of Researcher at the National Council of Science and Technology (CONACyT) of Mexico.
• Volunteered as proctor to guide and oversee competing teams for the IEEExtreme in the 14.0 programming competition, 2020.

Tech Stack: Python (Pandas, Numpy, TensorFlow, Keras, SciPy, ScikitLearn, Seaborn, Matplotlib), LaTeX, Inkscape, MS Office.

Projects

Synergistic Integration of Data Science and Optimisation for Tailoring Meta-heuristics to Solve Continuous Problems

Tecnológico de Monterrey and Chinese Academy of Sciences, (June 2019 - June 2021)

• I was the principal researcher and developer of this project. (See the repository here) (opens in a new tab)
• I reviewed the literature about Optimisation and Data Science, proposed a structured and systematic model for designing metaheuristics from existing methods, and developed a framework to facilitate the study and generation of these algorithms for practical engineering problems.
• As a result, I've published six (6) scientific articles in different fora and advised two (2) master's degree theses.

Tech Stack: Python (Pandas, Numpy, TensorFlow, Keras, SciPy, ScikitLearn, Seaborn, Matplotlib), LaTeX, Inkscape, MS Office.

Feature transformation for improving characterisation of combinatorial optimisation problems

CONACyT, (2019 - 2023)

• I was a researcher and developer of this project where different techniques based on hyper-heuristics have been implemented in various problems. I contributed to developing three repositories associated with this project, such as
  • A Matlab-based Hyper-Heuristic framework (opens in a new tab),
  • An Object-oriented Class for Handling Job-Shop Scheduling Problems (opens in a new tab), and
  • A Set of Instances for Different Problem Domains (opens in a new tab).
• As a result, I've participated in co-authoring more than three (3) scientific articles published in different fora.

Tech Stack: Matlab, LaTeX, Inkscape, MS Office.

Skills

• Programming Languages: Python, Matlab/Octave, LaTeX, R, C/C++, C#, Javascript.
• Frameworks and Tools: Azure, Watson, Tensorflow, PyCharm, DataSpell, Spyder, Pandas, NumPy, Keras, SciPy, ScikitLearn, Matplotlib, Seaborn, MS Office, Git, Github, COMSOL, SPICE, Inventor, Inkscape, Affinity, Unity, MPLAB, VS Code, VIM, Bash.
• Data Base: SQL, NOSQL, MongoDB, Cassandra.
• Human Languages: English (Fluent), French (Intermediate), and Spanish (Native).

Education

• Universidad de Guanajuato – Salamanca, México. Doctor of Philosophy, Electrical Engineering, Dec. 2018. Average Score: 9.96/10.0; Summa Cum Laude.
• Universidad Industrial de Santander – Bucaramanga, Colombia. Master of Science, Electronic Engineering, Jun. 2015 Average Score: 4.67/5.0.
• Universidad Industrial de Santander – Bucaramanga, Colombia. Bachelor of Science, Electronic Engineering, Dec. 2012 Average Score: 3.86/5.00.

Academic Advising

• One Student – Tecnológico de Monterrey – Doctoral Degree Program on Computer Sciences, 2022 – present.
• Four Students – Tecnológico de Monterrey – Master’s Degree Program on Computer Sciences, 2021 – present.
• Two Students – Universidad de Guanajuato – Master’s Degree Program on Electrical Engineering, 2018 & 2020.
• Four Students – Universidad Industrial de Santander – Bachelor’s Degree Program on Electronic Engineering, 2013, 2015 & 2016.

Further details can be found here.

Certifications

• Data Science Foundations - IBM. 2022
• Data Science Methodologies - IBM. 2022
• Data Science Tools - IBM. 2022
• Multi-agent Systems and Computational Graphics (CADi) – Tecnológico de Monterrey. 2021.
• Grammar and Punctuation – University of California, Irvine. 2021.
• Learning Regular Expressions – LinkedIn Learning. 2021.
• Python Data Analysis – LinkedIn Learning. 2021.
• Data Visualization: Best Practices & Storytelling – LinkedIn Learning. 2021.
• Data Visualization Tips and Tricks – National Association of State Boards of Accountancy (NASBA). 2021.
• Technology and Knowledge Transfer Training – University of Oxford / ISIS Innovation. 2014.
• Asynchronous Hybrid and Heterogeneous Parallel Programming with MPI/OmpSs – Barcelona Supercomputing Center. 2013.