Teaching Experience & Technical Skills

Teaching Experience

Current Teaching Positions (Fall 2025)

Teaching Assistant | BMGT 207: The Ethics of AI

University of Maryland | Instructor: Professor Lauren Rhue

Responsibilities:

  • Co-designed course materials including tutorials, case studies, and in-class activities
  • Led weekly office hours (twice a week, 2 hours each) providing individualized guidance on assignments and ethical frameworks in AI
  • Facilitated small-group discussions and delivered presentations on selected course topics to deepen student engagement

Impact: Supporting 80+ undergraduate students in understanding ethical implications of AI systems across various domains.

Teaching Assistant | BUDT 704: Data Processing and Analysis

University of Maryland | Instructor: Professor Manmohan Aseri

Responsibilities:

  • Led in-class lab sessions guiding students in Python-based data analysis, machine learning, and visualization techniques
  • Designed several homework and project assignments emphasizing applied analytics and reproducible workflows
  • Incorporated AI implementation into assignments to keep curriculum timely and relevant
  • Provided one-on-one support outside scheduled office hours, helping students troubleshoot code and improve project outcomes

Impact: Mentoring 45+ graduate students in advanced data analysis techniques and modern ML applications.

Technical Skills

Programming Languages & Tools

Primary Languages:

  • Python: Advanced proficiency in data science libraries (pandas, numpy, scikit-learn, matplotlib, seaborn)
  • R: Statistical computing and data visualization (ggplot2, dplyr, tidyverse)
  • SQL: Complex query optimization and database design
  • Neo4j: Graph database management and Cypher query language

Additional Languages:

  • STATA: Econometric analysis and statistical modeling
  • JavaScript: Web development and data visualization (D3.js)

Statistical Analysis & Research Methods

Econometric Methods:

  • A/B Testing and experimental design
  • Staggered Difference-in-Differences (DiD)
  • Matching methods (propensity score, exact)
  • Two-Stage Least Squares (2SLS)
  • Regression Discontinuity Design
  • Causal inference frameworks

Research Design:

  • Survey research design and sampling methodologies
  • Statistical modeling and hypothesis testing
  • Natural experiments and observational studies
  • Field studies and data collection protocols

Machine Learning & AI

Core ML Techniques:

  • Supervised and unsupervised learning algorithms
  • Model benchmarking and performance evaluation
  • Cross-validation and hyperparameter tuning
  • Feature engineering and selection

Advanced AI Applications:

  • Large Language Model (LLM) prompting and fine-tuning
  • Retrieval-Augmented Generation (RAG) pipelines
  • Graph Neural Networks (GNNs)
  • Knowledge graph construction and reasoning
  • Neuro-symbolic AI architectures

Model Evaluation:

  • ROC-AUC analysis and precision-recall curves
  • F1-score optimization
  • Bias detection and fairness metrics
  • Interpretability and explainability methods

Data Management & Infrastructure

Database Systems:

  • Relational databases (PostgreSQL, MySQL)
  • NoSQL databases (MongoDB, Neo4j)
  • Data warehouse design and optimization

Big Data Processing:

  • Apache Spark and PySpark
  • Pig Latin for large-scale data processing
  • Distributed computing frameworks

Data Engineering:

  • API integration and web scraping
  • ETL/ELT pipeline development
  • Data cleaning and transformation workflows
  • Cloud platform integration (AWS, Google Cloud)

Software Development & Collaboration

Development Tools:

  • Git version control and collaborative development
  • Jupyter notebooks and reproducible research
  • Docker containerization
  • CI/CD pipeline development

Documentation & Communication:

  • Technical writing and research paper preparation
  • Data visualization and storytelling
  • Presentation design and delivery
  • Code documentation and commenting standards

Teaching Philosophy & Approach

My teaching approach emphasizes:

  • Practical Application: Connecting theoretical concepts to real-world problems and current industry practices
  • Ethical Considerations: Integrating discussions of bias, fairness, and societal impact into technical coursework
  • Reproducible Research: Teaching students to create well-documented, reproducible analytical workflows
  • Individual Support: Providing personalized guidance to help students overcome specific technical challenges
  • Contemporary Relevance: Incorporating current AI developments and emerging technologies into course materials

Professional Development

  • Curriculum Development: Experience in designing course materials and assignments for graduate-level analytics courses
  • Student Mentorship: Track record of supporting student success through office hours and individual consultations
  • Technology Integration: Expertise in incorporating cutting-edge tools and methodologies into educational settings