Ernest Fokoue Headshot

Ernest Fokoue

Professor

School of Mathematics and Statistics
College of Science
epfeqa@rit.edu
585-475-7525
Office Hours
Tuesday, 3:30pm - 4:30pm Thursday, 3:30pm - 4:30pm Wednesday, 4:00pm - 5:00pm
Office Location
HLC-2517

Ernest Fokoue

Professor

School of Mathematics and Statistics
College of Science

Education

BSc, University of Yaounde; Maitrise, University of Yaounde; MSc, Aston University; Ph.D., University of Glasgow

Bio

Ernest Fokoué is a professor of statistics at Rochester Institute of Technology in the School of Mathematical Sciences. His research interests include statistical learning theory, bayesian statistics, theoretical statistics, statistical machine learning, ring theoretic learning, computational statistics, philosophy,  epistemology, and metaphysics. He leads the statistical machine learning and data science lab as well as the data science research group, which spearheads the creation and development of state of the art and avant-garde algorithms, learning machines and methods for knowledge discovery.

Ernest Fokoué is currently exploring the interface of differential equations and empirical processes, with the finality of reconciling first principles traditional mathematical modeling with empirical data driven approach of statistics and statistical machine learning.

epfeqa@rit.edu
585-475-7525
Personal Links
Personal Website
ORCID
Publication Database
Curriculum Vitae
Google Scholar
Areas of Expertise
Statistical Learning Theory
Theoretical Statistics
Bayesian Statistics
Statistical Machine Learning
Computational Statistics
Ring Theoretic Learning
Philosophy and Epistemology
Metaphysics

Select Scholarship

Featured Papers

Mixtures of factor analysers. Bayesian estimation and inference by stochastic simulation
E. Fokoué, DM. Titterington
Machine Learning 50, 73-94

Efficient approaches to Gaussian process classification
L. Csató, E. Fokoué, M. Opper, B. Schottky, O. Winther
Advances in neural information processing systems 12

Mixtures of factor analyzers: an extension with covariates
E. Fokoué
Journal of Multivariate Analysis 95 (2), 370-384

Model selection for optimal prediction in statistical machine learning
E Fokoué
Not. Am. Math. Soc 67 (2)

Dropout fails to regularize nonparametric learners
RW. Murray, E. Fokoué
Journal of Statistical Theory and Practice 15, 1-20

Estimation of atom prevalence for optimal prediction
EP. Fokoue
Contemporary Mathematics 443, 103-130

Books 

Principles and theory for data mining and machine learning
B. Clarke, E. Fokoue, HH. Zhang

Springer Science & Business Media

Currently Teaching

ISTE-600
Foundations of Data Mining
3 Credits
This course provides students with exposure to foundational data mining techniques. Topics include analytical thinking techniques and methods, data/exploring data, classification algorithms, association rule mining, cluster analysis and anomaly detection. Students will work individually and in groups on assignments and case study analyses.
MATH-498
Independent Study in Mathematical Sciences
1 - 3 Credits
This course is a faculty-guided investigation into appropriate topics that are not part of the curriculum.
MATH-790
Research & Thesis
0 - 9 Credits
Masters-level research by the candidate on an appropriate topic as arranged between the candidate and the research advisor.
STAT-305
Regression Analysis
3 Credits
This course covers regression techniques with applications to the type of problems encountered in real-world situations. It includes use of the statistical software SAS. Topics include a review of simple linear regression, residual analysis, multiple regression, matrix approach to regression, model selection procedures, and various other models as time permits.
STAT-547
Data Mining
3 Credits
The use of statistical models in computer algorithms allows users to make decisions and predictions, and to perform tasks that traditionally require human cognitive abilities. Data mining and Machine learning are interdisciplinary fields at the intersection of statistics, computer science, applied mathematics which develops such statistical models and interweaves them with computer algorithms. It underpins many modern technologies, such as speech recognition, Internet search, bioinformatics and computer vision. The course will provide an introduction to Statistical Machine Learning and its core models and algorithms.
STAT-747
Principles of Statistical Data Mining
3 Credits
This course covers topics such as clustering, classification and regression trees, multiple linear regression under various conditions, logistic regression, PCA and kernel PCA, model-based clustering via mixture of gaussians, spectral clustering, text mining, neural networks, support vector machines, multidimensional scaling, variable selection, model selection, k-means clustering, k-nearest neighbors classifiers, statistical tools for modern machine learning and data mining, naïve Bayes classifiers, variance reduction methods (bagging) and ensemble methods for predictive optimality.

In the News

  • November 21, 2024

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    In and out of Africa

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