Teaching · CSU Bakersfield
Courses built on real data, reproducible workflows, and the judgment that only appears once the analysis is the student’s own — and the undergraduate research that grew out of them.
Teaching at CSUB spans general-education statistics, probability and mathematical statistics, statistical computing, regression, and upper-division data science. Every course is taught computationally, in R and Python, with browser-based access through CSUB JupyterHub so no student is held back by an install or a license.
| Course | Title | Level | Terms taught |
|---|---|---|---|
| MATH 2200 | Introduction to Statistical Concepts and Methods | Lower-division, general-education service | Fall 2023 – Fall 2026, every term |
| MATH 3200 | Probability Theory | Upper-division core | Spring 2024, Fall 2025, Spring 2026, Fall 2026 |
| MATH 3210 | Applied Statistical Computing | Upper-division; graduate cross-list (MATH 5210) | Fall 2023, Fall 2024 |
| MATH 4200 | Mathematical Statistics | Upper-division core | Spring 2025 |
| MATH 4210 | Regression Modeling and Analysis | Upper-division elective | Fall 2026 |
| MATH 4230 | Applied Statistical Methods for Data Science | Upper-division elective; co-developed by the instructor | Spring 2026 |
Each of these is a complete, browser-based coursebook with worked examples, practice and interactive simulations — nothing for a student to install. The AI book needs nothing but a browser; the statistics books run live R on the CSUB JupyterHub; the small language models book is in Python and runs on an ordinary laptop. They are open to anyone, and colleagues are welcome to reuse them.
A complete, practical guide to artificial intelligence for everyone. Fourteen modules, from what AI is and how machine learning works through prompting, research, agents, infrastructure, ethics and careers, with thirteen hands-on labs, interactive quizzes, the BRIEFS and SMARTS frameworks, specialty tracks for educators, business and builders, and a facilitator guide. Free and open access.
Open AI from the Ground UpStudents run small language models on their own machines and learn the probability, linear algebra, numerical precision and statistics underneath, then measure what each run costs in memory and energy. Fifteen chapters in Python, with thirteen interactive simulations and a Math Toolkit that starts from what it means for a letter to stand for a number. Every figure in it was computed by a script in the book, on a documented machine. In development; the course number is not yet assigned.
Open the coursebookFifteen weekly units with embedded R Help (mosaic + BSDA
on the CSUB JupyterHub), worked examples, practice, and simulations.
Thirteen chapters with embedded R Help, worked examples, practice, and live simulations.
Open the MATH 2200 coursebookFoundations, random variables, multivariate distributions and functions of random variables, with worked examples in R.
Open the MATH 3200 coursebookLessons in English and Spanish, in both R and Python, with a game at the end of each chapter.
Open the MATH 4210 coursebookStart R from scratch — install it, or just sign in to the CSUB JupyterHub with a campus account and install nothing. Covers exploratory data analysis, probability, confidence intervals and hypothesis tests, ANOVA with post-hoc comparisons, and simple and multiple regression. A companion to MATH 1209 and MATH 2200, and to anyone learning R.
Open R Help for BeginnersGraduate teaching assistant and instructor · 2017 – 2023
Multiple lecture sections per term, with full responsibility for lectures, assessment, and grading.
Lecture sections, with full responsibility for lectures, assessment, and grading.
Shared instructional responsibility.
Instructor and tutor
I teach statistics as work students do, not a subject they watch me perform. Every course I run puts a computer in front of the student early: R and Python, run through CSUB JupyterHub so no one is held back by an install or a license. The data is real and usually messy, because the judgment calls that matter in statistics only appear once the cleaning is your own problem.
I ask for reproducible work. An analysis that cannot be rerun is an anecdote; I would rather read a notebook that runs than a number that happens to be right.
I treat generative AI the way I treat any other instrument: useful, and only after a student can tell when it is wrong. Its use in my courses is structured and stated outright — what it is for, how it gets disclosed, and which parts of the reasoning have to be the student’s own. My own research on scaffolded AI tutors points the same way: the useful tutor makes the student do the thinking.
I end courses open where I can. MATH 4230 closes with a capstone I do not specify: students choose the question, defend the method, and write the result for someone who was not in the room. That is the part of the course that transfers.
Coordinates MATH 1209: Statistics in the Modern World and MATH 2200: Introduction to Statistical Concepts and Methods across multiple sections, including course expectations, content coverage, shared resources, instructor support, and communication. Built a shared instructional system for both courses: course materials, R resources, assignments, assessments, classroom activities, projects, and browser-based statistical computing through CSUB JupyterHub.
Co-developed with Statistics faculty as an upper-division applied statistics and data science course, and taught in Spring 2026. Built the Spring 2026 implementation end to end — syllabus, instructional materials, computational labs, assessments, and an open-ended capstone centered on real data, reproducible workflows, and statistical communication — using R, Python, JupyterHub, and structured approaches to responsible AI use. Attended the 2026 National Workshop on Data Science Education at UC Berkeley to benchmark the course and the broader curriculum against programs elsewhere.
Works with Statistics and Mathematics faculty on CSUB’s Statistical Data Science curriculum, both the existing concentration and the current effort to develop a standalone B.S. Contributions include degree requirements, prerequisite structure, elective pathways, comparisons with programs at other universities, student-demand survey analysis, and proposal development.
Took part in faculty review and refinement of this new upper-division GE course, led by Dr. Eduardo Montoya, and served as the named proposer responsible for entering and advancing the proposal through CourseLeaf.
A proposed lower-division GE course in Area 2: Mathematical Concepts and Quantitative Reasoning, teaching mathematics and statistics through the inner workings of modern language models: tokens, embeddings, vectors and matrices, attention, probability, sampling, uncertainty, numerical precision, quantization, model size, and statistical evaluation. The concept adapts hands-on small-model teaching ideas encountered through Eric Van Dusen’s UC Berkeley and NWDSE materials to a mathematics-and-statistics GE setting.
A sustained program of workshops, seminars, and training on responsible and useful applications of generative AI in teaching and research, including Teaching With AI: Helping Students Learn Statistics With Custom GPTs and Prompts, AI as a Thinking Partner in Statistics, and Effective Ways to Introduce AI in Your Classroom at CSUB, alongside course-specific AI tutoring approaches for mathematical and statistical reasoning.
Undergraduates have worked with me in sustained individual research supervision since 2024, and several are authors on peer-reviewed journal articles, some as first or co-first author. The work runs from environmental statistics and exposure inequality to machine learning, forecasting, local language models, and cryptography — and it comes with the rest of it: research writing, poster and talk preparation, doctoral applications, and the long conversations about what comes next.
Faculty research mentor. Mentored Tom Regpala in 2024 and Emily DeJesus and Noah Gallego in 2025 through intensive summer research and doctoral-preparation experiences, supervising research design, literature review, data acquisition, statistical and computational analysis, research writing, and poster and presentation development. Several of these collaborations continued past the summer into sustained undergraduate research and publication.
Faculty research mentor. A Customizable AI Pipeline for Academic Excellence: Locally Hosted Language Models in Higher Education. Mentored undergraduate researchers in developing and evaluating locally hosted large language models for higher education, covering retrieval-augmented generation, model fine-tuning, quantization, GPU-based deployment, and comparison with commercial cloud-based AI systems.
Principal investigator and undergraduate research mentor. Air Pollution Disparities in California, 2000–2025: Examining Environmental Inequities Across Income, Poverty, and Minority Populations. Supervised undergraduate research in environmental statistics, air-pollution exposure inequality, statistical computing, and large-scale demographic and environmental data integration. Student research from this project contributed to multiple peer-reviewed publications in environmental statistics and environmental health.
Environmental statistics, air-pollution exposure inequality, statistical computing, and environmental data integration. Began through CV Pathway in 2024 and continued as a sustained research collaboration. Student co-first author on peer-reviewed work published in Atmospheric Environment: X. Continued mentoring covered research writing and doctoral-study preparation, including his 2026–27 CSU Chancellor’s Doctoral Incentive Program application.
Environmental statistics and computational data analysis. Supervised large-scale monitor–satellite air-quality analysis, R programming, statistical methodology, and reproducible workflows. Student co-first author on peer-reviewed work published in Atmospheric Environment: X.
Environmental inequality and statistical data science. Supervised research on income-based pollution-exposure disparities across California counties. Student first author on peer-reviewed work published in Environmental Research: Health.
Environmental data science and applied AI, supported through Chevron SURE and ELEVATE. Student coauthor on peer-reviewed work published in Environmental Research: Health, and co-presenter of undergraduate research on open-source and local large language models.
Machine learning, time-series forecasting, and computational data science; CV Pathway 2025 Research Scholar. Supervised hybrid GRU-CNN influenza forecasting, computational implementation, statistical evaluation, and manuscript development. His work contributed to research presented at the 2025 Joint Statistical Meetings and to a manuscript in progress.
Environmental data science; CV Pathway 2025 Research Scholar. Supervised literature review, environmental and demographic data analysis, interpretation, research writing, and poster preparation, with continued graduate-school and research-pathway mentoring, including support for her Cal-Bridge application.
Applied AI, forecasting, and open-source and local large language models. Supervised research development, computational experimentation, poster preparation, and communication of findings. Co-presented CSUB-GPT: Open-Source Local LLMs for Campus Use at the CSUB Mathematics Seminar.
Undergraduate mathematics research in cryptography and LLM text watermarking; the project is in its initial research-development stage.
Mentored and helped organize the participation of CSUB students, including recruitment, team formation, funding, travel, and lodging. CSUB Team 1 — Juancarlos Sandoval, Krrithik Ezhilarasan, Christine Leanna Bonoan, and Sean Toledo — won Best Overall, Best in Data Analysis, and Best in Data Communication.
Mentored Tom Regpala, Juan Rodriguez Aguilar, and Christian Rodriguez in presenting undergraduate AI and data science research. Aguilar and Rodriguez presented CSUB-GPT: Open-Source Local LLMs for Campus Use.
Undergraduate research with Noah Gallego contributed to presented work on hybrid deep-learning methods for forecasting influenza-like illnesses.
Supervised preparation of undergraduate research reports, posters, and presentations.
Faculty mentor and recommender for Tom Regpala’s 2026–27 doctoral-pathway application.
Supported Emily DeJesus in pursuing Cal-Bridge and corresponded with program faculty about her research preparation and graduate-school pathway.
Graduate and Ph.D. applications, research CVs, statements of purpose, fellowships, internships, employment applications, and letters of recommendation.
Mentor, American Statistical Association.
Recruited the founding student officers and guided the development and registration of the new student organization.
Find my current office hours on your course Canvas page, or email me to set up a time. If you are stuck, come early — I would much rather help before an exam than after.
I am glad to write for students I know well. To write you a strong letter I need at least three weeks’ notice before the deadline, your CV or résumé, a description of the program or position, and a note on the emphasis you would like the letter to take.
Current and former students are welcome to write — about coursework, undergraduate research, or what comes after graduation.