Gonsalge Almeida

Lecturer (Full-time), Department of Mathematics, Applied Mathematics, and Statistics

Case Western Reserve University
Office: Sears Library 569
Email: gsa19@case.edu
Phone: (216) 816-0342

Biography

I am a full-time Lecturer in the Department of Mathematics, Applied Mathematics, and Statistics at Case Western Reserve University (CWRU), beginning Fall 2025. I teach STAT 312/312R (Basic Statistics for Engineering and Science) and MATH 201 (Introduction to Linear Algebra for Applications). My teaching and research span statistics, applied mathematics, data science, and trading education, with a particular emphasis on financial modeling using Lévy processes and applications to the equity markets.

I hold a Ph.D. in Applied Mathematics (concentration in Statistics) and have also pursued Ph.D.-level study in Mathematics (concentration in Statistics) at Central Michigan University. I have taught in the United States for more than 13 years. In all of my courses, I integrate real-world datasets, especially from finance, business, and data science, so that students develop modeling fluency, a strong grasp of statistical concepts and their visualization, computational literacy, and the ability to translate applied-mathematics concepts into practical tools for data science.

I also completed a Data Engineering Fellowship at The Data Incubator, focusing on practical machine-learning workflows, data engineering, and end-to-end analytics.

Areas of Interest

Statistical modeling and inference for finance and data science
Financial mathematics: Lévy processes, stochastic modeling, and risk analytics
Time series and predictive modeling for markets and operations
Applied probability and stochastic processes
Trading education: integrating Lévy processes into stochastic dynamic trading models (strategy design and risk management)

Teaching at CWRU

STAT 312/312R — Basic Statistics for Engineering and Science (with R practice)
MATH 201 — Introduction to Linear Algebra for Applications

Course pages, schedules, syllabi, and assignment repositories are linked from the Courses section of this site.

Teaching Philosophy

My approach is problem-driven and computation-forward. Students learn theory in tandem with practice: deriving results, implementing them in code, and validating conclusions with data. I emphasize clear communication of statistical evidence and ethical use of data. In STAT 312/312R, I align R assignments with Montgomery, Runger, and Hubele so that learners build both conceptual and computational mastery.

Education