Teaching

Program: Food Technology
Institution: Singapore Institute of Technology
Role: Course Developer and Sole Instructor
Period: 2026–Present

This course introduces students to the complete data analytics workflow, from problem formulation and data preparation to analysis, visualization, interpretation, and communication of insights.

Topics include data quality, descriptive and comparative analysis, data visualization, relationships and patterns, Python fundamentals, and responsible use of AI. Students work with Excel and Jupyter/Python and apply analytics to food-technology datasets and real-world problems.

Teaching approach: Applied Data Analysis · Project-Based Learning · Excel · Python · Responsible AI

Programs: Pharmaceutical Engineering and Chemical Engineering
Institution: Singapore Institute of Technology
Role: Co-Instructor
Period: 2026–Present

Contributes to undergraduate teaching in process safety, including lectures and assessments on process hazards, toxic release modeling, risk assessment, and safety management systems.

The course emphasizes quantitative analysis and engineering judgment for identifying, evaluating, and managing risks in chemical and process industries.

Teaching areas: Process Hazards · Risk Assessment · Consequence Analysis · Safety Management

Type: Interdisciplinary industry-based AI project course
Institution: Singapore Institute of Technology
Role: Co-Instructor
Period: 2026–Present

Students work in interdisciplinary teams with industry partners to develop practical AI-enabled solutions to real-world problems.

Teaching and supervision cover problem definition, data analytics, machine learning, natural language processing, Generative AI, feasibility assessment, responsible AI, technical reporting, and industry presentation.

Teaching approach: Industry-Based Learning · Machine Learning · NLP · Generative AI · Responsible AI · Team Projects

Audience: Food Technology Undergraduate Students
Format: Full-Day Hands-On Workshop
Role: Workshop Developer and Instructor
Period: 10/2026

Designed and delivered an intensive hands-on workshop introducing students to the end-to-end data analytics workflow, from problem formulation and data preparation to analysis, visualization, interpretation, and communication of actionable insights.

Through guided exercises and real-world datasets, participants learn how to formulate analytical questions, collect and clean data, explore patterns and trends, create effective visualizations, interpret results, and communicate evidence-based recommendations.

The workshop combines foundational concepts with practical applications, enabling participants to develop data literacy, analytical thinking, and problem-solving skills relevant to academic, professional, and real-world decision-making.

Teaching approach: Hands-On Learning · Data Analytics · Excel · Data Visualization · Analytical Thinking · Problem Solving · Real-World Applications

Audience: University Students
Institution: New Mexico Institute of Mining and Technology
Format: Hands-On Technical Workshop
Role: Workshop Developer and Instructor
Period: 2026

Designed and delivered a hands-on technical workshop on CO₂ transportation and infrastructure modeling using SimCCS, introducing students to computational approaches for planning and evaluating carbon capture, transport, and storage systems.

Students used SimCCS to explore CO₂ source–sink networks, transportation infrastructure design, pipeline routing, network optimization, transport capacity, and system-level cost analysis. The workshop connected fundamental CO₂ transportation concepts with practical modeling and infrastructure planning.

Through guided modeling exercises and applied case studies, participants gained experience in translating engineering assumptions and spatial data into CO₂ transport-network models and interpreting optimization results for CCUS infrastructure decision-making.

Teaching approach: Hands-On Modeling · SimCCS · CO₂ Transportation · CCUS Infrastructure · Network Optimization · Techno-Economic Analysis · Decision Support

Project-based learning and student supervision are important components of teaching and student development in A-SMART Lab. Students apply analytical, computational, and engineering methods to authentic problems in engineering, food technology, sustainability, energy, and industry.

Student projects include:

• Final Year Projects
• Bachelor Thesis Projects
• Industry Team Projects
• Independent Research Projects
• Interdisciplinary AI and Data Analytics Projects

Projects emphasize problem formulation, data analysis, computational modeling, critical interpretation, responsible use of AI, and communication of actionable insights.

Program: Petroleum Engineering
Institution: University of Alberta, Canada
Role: Teaching Assistant
Years: 2014, 2015, and 2017
Enrollment: Approximately 70 students per year

Supported undergraduate teaching in applied reservoir engineering, including course instruction, student learning activities, and technical problem solving in petroleum reservoir engineering.

Program: Petroleum Engineering
Institution: University of Alberta, Canada
Role: Teaching Assistant
Year: 2014
Enrollment: Approximately 60 students

Supported undergraduate instruction in petroleum reservoir fluids and the application of fluid-property concepts to reservoir engineering problems.