Ernest Fokoue Headshot

Ernest Fokoue


School of Mathematical Sciences
College of Science

Office Hours
Tuesday, 11:00am - 12:00noon Wednesday, 10:00am - 12:00noon Thursday, 11:00am - 12:00noon
Office Location

Ernest Fokoue


School of Mathematical Sciences
College of Science


I have been around Mathematical Sciences from my very early childhood, and I have always greatly enjoyed the beauty and power that mathematics affords to problem solving. Statistical Science in particular and specifically Statistical Machine Learning and Data Science are my passion, my profession and to some extent my vocation. The flexibility and power of probabilistic reasoning along with the richness and vast applicability of statistical models both fascinate and enthuse me immensely. My mission as a professor of statistical science is to generously, joyfully and rigorously share the full extent of my knowledge and expertise in this exciting field with the ultimate goal of empowering all the students that God graciously sends my way. Indeed, I always envision my students acquiring state-of-the-art knowledge in statistical science and ultimately developing into world class experts and pioneers.


Select Scholarship

Journal Paper
Corsetti, Matthew and Ernest Fokoue. "Nonnegative Matrix Factorization With Zellner Penalty." Open Journal of Statistics 5. 7 (2015): 777-786. Web.
Liu, Bohan and Ernest Fokoue. "Random Subspace Learning Approach to High-Dimensional Outliers Detection." Open Journal of Statistics 5. 6 (2015): 618-630. Web.
Yu, Xingchen and Ernest Fokoue. "Probit Normal Correlated Topic Model." Open Journal of Statistics 4. 11 (2014): 879-888. Web.
Fokoue, Ernest. "A Taxonomy Of Big Data For Optimal Predictive Machine Learning And Data Mining." Serdica Journal of Computing. (2014): 111-136. Print.
Bill, Jo and Ernest Fokoue. "A Comparative Analysis Of Predictive Learning Algorithms On High-Dimensional Microarray Cancer Data." Serdica Journal of Computing. (2014): 137-168. Print.
Ma, Zichen and Ernest Fokoue. "Accent Recognition for Noisy Audio Signals." Serdica Journal of Computing. (2014): 169-182. Print.
Ma, Zichen and Ernest Fokoue. "A Comparison Of Classifiers In Performing Speaker Accent Recognition Using MFCCs." Open Journal of Statistics 4. 4 (2014): 258-266. Web.
Fokoue, Ernest. "Stable Radial Basis Function Selection via Mixture Modelling of the Sample Path." Journal of Data Science 9. 3 (2011): 345-358. Web.
Fokoue, Ernest, Dongchu Sun, and Prem Goel. "Fully Bayesian Analysis of the Relevance Vector Machine With Extended Prior." Statistical Methodology 8. (2011): 83-96. Print.
Fokoue, Ernest and Prem Goel. "An Optimal Experimental Design Perspective on Radial Basis Function Regression." Communications in Statistics: Theory and Methods 40. 6 (2011): 1-12. Print.
Fokoue, Ernest and Bertrand Clarke. "Bias-Variance Trade-off for Prequential Model List Selection." Statistical Papers 52. 4 (2011): 813-833. Print.
Fokoue, Ernest. "Beta Induced Sparsity Algorithm." Advanced and Applications in Statistical Science. (2011): 1-4. Web.
Book Chapter
Dey, Tanujit and Ernest Fokoue. "“Bayesian Variable Selection For Predictively Optimal Regression." Current Trends in Bayesian Methodology with Applications. Ed. Dipak K, Umesh Singh and A. Loganathan. New York, New York: Chapman and Hall, 2015. Print.

Currently Teaching

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.
0 - 9 Credits
Masters-level research by the candidate on an appropriate topic as arranged between the candidate and the research advisor.
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.
1 - 3 Credits
This course is a faculty-directed project that could be considered original in nature. The level of work is appropriate for students in their final two years of undergraduate study.
3 Credits
This course introduces the student to statistical situations not encountered in regular course of study. It integrates and synthesizes concepts in statistical theory with applications. Topics include open-ended analysis of data, current techniques and practice of statistics, development of statistical communication skills and the use of statistical software tools in data analysis. Each student is required to learn and use a statistical technique beyond what is covered in the previous courses. Students are expected to introduce the method in a presentation and to prepare a comprehensive, professional report detailing the statistical method and its application to a data set.
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.
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.
3 Credits
This project-based course introduces students to advanced concepts of statistical computing. We will work in the environment of R—one of the most common and powerful statistical computing languages that are used in professional practice. Topics include: object-oriented features of R, function writing, using environments, non-local assignments (closures), and connections; converting text to code, speeding up processing, advanced features in regular expressions, introduction to the Grammar of Graphics (ggplot2) and lattice methods for graphics, R markdown, computing on large datasets (without reading all data into RAM memory), cleaning and reshaping of messy data, web scraping, interactive web applications (with Shiny), advanced reading from files and writing to files, simulations, select statistical applications.
1 - 3 Credits
This course provides for the presentation of subject matter of specialized value in the field of applied statistics not offered as a regular part of the program.

In the News

  • September 23, 2022

    student wearing a virtual reality headset.

    AI summit brings together an exciting range of research underway

    Applications being developed at RIT using artificial intelligence vary from sophisticated medical monitoring devices to the development of autonomous systems for Indy racecars. These represent some of the exciting and complex work underway at the university that will be featured prominently at the AI@RIT Summit: Discovering and Harnessing the Breadth and Depth of Artificial Intelligence at RIT.