Technical Leadership & Capability Building
Two decades of experience mentoring quantitative talent, architecture of data science curricula, and instructing advanced ML and statistical theory.
Core Areas of Technical Instruction
1. Machine Learning & Computational AI
- Supervised Learning & Predictive Analytics: Taught advanced predictive modeling, high-dimensional classification/regression, ensemble methods, and neural network foundations.
- Algorithms & Optimization: Instructed graduate-level algorithm design, spatial/temporal complexity analysis, graph algorithms, and dynamic programming.
- Discrete Systems & Linear Algebra: Covered formal discrete structures, graph theory, matrix decompositions, and vector spaces essential for high-performance ML architectures.
2. Applied Statistics & Experimental Design
- Experimental Design & A/B Testing Frameworks: Specialized in advanced design and analysis of experiments, causal inference, and statistical hypothesis testing critical for data-driven product decisions.
- Predictive Modeling with Big Data: Formulated coursework on handling high-volume datasets, statistical feature selection, and sample survey design.
- Biostatistics & Stochastic Modeling: Taught applied biostatistical methods, queueing theory computations, and probabilistic inference.
Technical Program Architecture & Leadership
Co-Chair of Program Development | MS in Data Science
University of Southern Maine * Co-designed and spearheaded the foundational curriculum for the Master of Science in Data Science. * Defined core technical competencies bridging computational programming, statistical inference, and big data infrastructure for industry readiness.
Graduate Program Coordinator | MS in Statistics
University of Southern Maine * Oversaw technical track standards, mentored incoming cohorts, and aligned graduate coursework with modern analytical tools and data science methodologies.
Applied R&D Supervision & Mentorship
Directly mentored advanced researchers in translating theoretical models into practical computational solutions:
- PhD Dissertation Advisory Committee: Cancer prognosis prediction using multi-omics data on both pathway and gene-level (Dartmouth College, QBS, 2021) — Advised on high-dimensional data integration and predictive pathway modeling.
- MS Thesis Advisory: An Examination of Computational Methods Related to G/M/c Queueing (University of Southern Maine, 2019) — Guided numerical analysis and stochastic modeling for complex queueing networks.
Summary of Taught Subject Area Breadth
| Domain Category | Core Topics Covered |
|---|---|
| Machine Learning & AI | Supervised Learning, Predictive Analytics, Big Data Modeling, Feature Selection |
| Algorithms & Computing | Graduate Algorithms, Discrete Structures, Numerical Analysis, C++/Python Computation |
| Statistical Foundations | Probability Theory, Statistical Inference, Linear Algebra, Multivariable Calculus |
| Experimental Design | Advanced A/B Testing, Factorial Design, Biostatistics, Sample Survey Analysis |