Hernández-Gress, Neil

Positions

Adjunct researchers, Machine Learning , School of Engineering and Sciences
Machine Learning, School of Engineering and Sciences , Tecnológico de Monterrey

Motivated leader with proven ability to manage all the innovation chain: academic programs, research, innovation, tech-transfer and tech-based entrepreneurship projects. Self-starter and independent. Research interests include Big Data, Data-Science, Machine Learning and Processing of Information. Specialist in Innovation Systems: deployment, management and evaluation of the different levels such as: Academic programs development, continue-education programs development, applied research, Technological packaging, Fund rising, research development, Intellectual property, Technological commercialization, Business Plans and Tech-Observatories, University Rankings. High adaptable to any change circumstances. Key member in any team. Fluent speaking in Spanish, English and French.

Research Areas

Contact

ngress@tec.mx

Websites

Publications
Research
Background
Campus
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Selected publications

academicarticlesco
- Research collaboration recommendation for universities based on data science 2018
- Clustering model for in-N-out products in retail: A case study. Proceedings of International Conference on Computers and Industrial Engineering, CIE. 2018-December. 2018
- Review of the Principal Indicators and Data Science Techniques Used for the Detection of Financial Fraud and Money Laundering 2017
- Socio-Demographic Factors and Data Science Methodologies in Type 2 Diabetes Mellitus Analysis 2017
- Scalability of multiclass simulation of spiking neural networks on GPUs 2015
- Quadratic optimization fine tuning for the Support Vector Machines learning phase. Expert Systems with Applications. 41:886-892. 2014
- Fuzzy hyperheuristic framework for GA parameters tuning 2013
- Towards a ubiquitous user model for profile sharing and reuse. Sensors. 12:13249-13283. 2012
- An ontology driven multi-agent system for client assignment in a bank queue 2010
- Business process mining and rules detection for unstructured information 2010
- Towards a ready-to-use drivers' vigilance monitoring system 2008
- Hybrid fuzzy-SV clustering for heart disease identification 2006
- A comparison of road departure warning systems on real driving conditions 2004
- SVM clustering for identification of Takagi-Sugeno fuzzy models. IFAC Proceedings Volumes (IFAC-PapersOnline). 36:209-214. 2003
- Discriminating sensors for driver's impairment detection 2000
chapter
- Los agentes sociales de la política científica: el caso de México. . 3-57.
chapter in scopus
- Early Anomalous Vehicular Traffic Detection Through Spectral Techniques and Unsupervised Learning Models. Lecture Notes in Computer Science. 162-175. 2019
- Predictive analytics with factor variance association. Lecture Notes in Computer Science. 346-359. 2019
- Bayesian Based Approach Learning for Outcome Prediction of Soccer Matches. Lecture Notes in Computer Science. 269-279. 2018
- Statistical and Multivariate Analysis Applied to a Database of Patients with Type-2 Diabetes. Lecture Notes in Computer Science. 191-201. 2018
- Molecular docking based on Ligand by complexity LMC. Lecture Notes in Computer Science. 425-436. 2017
- Classification of Database by Using Parallelization of Algorithms Third Generation in a GPU. Communications in Computer and Information Science. 25-38. 2014
- Pattern recognition with spiking neural networks. Lecture Notes in Computer Science. 279-288. 2013
- Generation and exploitation of semantic information using an epidemiological relational database as a primary source of information. Lecture Notes in Computer Science. 446-457. 2013
- Support vector optimization through hybrids: Heuristics and math approach. Lecture Notes in Computer Science. 237-245. 2009
- A comparison of different initialization strategies to reduce the training time of support vector machines. Lecture Notes in Computer Science. 613-618. 2005
- Comparison of SVM-fuzzy modelling techniques for system identification. Lecture Notes in Computer Science. 494-503. 2005
- Increasing the training speed of SVM, the Zoutendijk algorithm case. Lecture Notes in Computer Science. 312-320. 2005
- Quadratic optimization fine tuning for the learning phase of SVM. Lecture Notes in Computer Science. 347-357. 2005