E-mail address



Teaching activities

Quality systems engineering

Management & entrepreneurship 

Advanced modelling and simulation methodologies  

Applied statistics

Signal and Image Analysis and Processing

Process Improvement Management

Research interests

Industrial Statistics (Multivariate data analysis, SPC, DOE, Measurement uncertainty and uncertainty propagation)

Process Systems Engineering (fault detection & diagnosis, process monitoring, modeling and control)

Data-Driven Multiscale Methodologies (applications in process monitoring, system identification, image analysis, pattern recognition)

Image Analysis (MIA)

Quality Improvement (Lean, Six Sigma, TOC)

Pulp and Paper Technology (Dimensional stability, Curl of paper and paperboard)

Key publications

Reis, M.S., G. Gins, Industrial Process Monitoring in the Big Data/Industry 4.0 Era: From Detection, to Diagnosis, to Prognosis. Processes. 5(3), 35, (2017), p.1-16.

Rendall, R., A.C. Pereira, M.S. Reis, Advanced Predictive Methods for Wine Age Prediction: Part I - A Comparison Study of Single-Block Regression Approaches based on Variable Selection, Penalized Regression, Latent Variables and Tree-based Ensemble Methods. Talanta. 171 (2017), p. 341-350.

Campos, M., R. Sousa, A.C. Pereira, M.S. Reis, Advanced Predictive Methods for Wine Age Prediction: Part II - A Comparison Study of Multiblock Regression Approaches. Talanta. 171 (2017), p. 132-142.

Rato, T.J., J. Blue, J. Pinaton, M.S. Reis, Translation Invariant Multiscale Energy-based PCA for Monitoring Batch Processes in Semiconductor Manufacturing. IEEE – Transactions on Automation Science and Engineering. 14(2) (2017), p. 894-904.

Reis, M.S., R.S. Kenett, A Structured Overview on the Use of Computational Simulators for Teaching Statistical Methods. Quality Engineering. (2016), p. 1-16.

Coleman, S., R. Goeb, G. Manco, A. Pievatolo, X. Tort-Martorell, M.S. Reis, How can SMEs benefit from Big Data? Challenges and a Path Forward. Quality and Reliability Engineering International. 32(6) (2016), p. 2151-2164.

Rato, T.J., M.S. Reis, On-line process monitoring using local measures of association. Part I: Detection performance. Chemometrics and Intelligent Laboratory Systems. 142 (2015), p. 255-264.

Rato, T.J., M.S. Reis, On-line process monitoring using local measures of association. Part II: Design issues and fault diagnosis. Chemometrics and Intelligent Laboratory Systems. 142 (2015), p. 265-275.

Rato, T.J., M.S. Reis, Fault detection in the Tennessee Eastman benchmark process using dynamic principal components analysis based on decorrelated residuals (DPCA-DR). Chemometrics and Intelligent Laboratory Systems. 125 (2013), p. 101-108. 

Reis, M.S., Network-Induced Supervised Learning: Network-Induced Classification (NI-C) and Network-Induced Regression (NI-R). AIChE Journal. 59(5) (2013), p. 1570-1587.

Other information

ORCID ID: 0000-0002-4997-8865

Process Chemometrics Laboratory (http://www.eq.uc.pt/~marco/research/pclab/)

personal page: https://www.eq.uc.pt/~marco/