Teaching Experience

Teaching Assistant, Intro to Construction Management

University of Wyoming
Fall 2026

Supported instruction in an undergraduate Construction Management course, assisting with course delivery, student guidance, and evaluation activities, including grading, course material preparation, and administering assignments and quizzes through Canvas.

Engineering Drawing (Undergraduate Level)

Institute of Engineering, Pulchowk Campus, Tribhuvan University, Nepal
Winter 2020

Provided supplemental instruction to first-year civil engineering students, covering technical drawing principles, geometric construction, and projection techniques.

Coursework (Construction Management Focus)

Relevant Coursework

• Heavy Construction Methods
• Construction Scheduling
• Building Information Modeling (BIM)
• Construction Safety
• Construction Documents
• Construction Economics

Technical Competencies

Project Planning and Controls

• Scheduling using Primavera P6 and MS Project
• Construction sequencing and resource allocation
• RFI and submittal tracking using Procore

Digital Construction and Modeling

• BIM modeling using Revit
• Construction drawing interpretation and plan reading
• As-built documentation and record drawing preparation

Field Operations and Equipment Analysis

• Earthwork planning and quantity takeoff
• Equipment productivity and fleet optimization analysis
• Crane load chart interpretation

Surveying and Reality Capture

• Operation of Theodolite and Total Station
• Experience with 3D laser scanning systems (Topcon GLS-2000, Leica BLK2FLY, Leica BLK360, Matterport Pro3)

Coursework (Artificial Intelligence and Data Analytics Focus)

Relevant Coursework

• Introduction to Artificial Intelligence
• Introduction to Deep Learning
• Machine Learning and Data Analysis

Technical Competencies

Artificial Intelligence and Intelligent Systems

• Fundamentals of artificial intelligence and intelligent agents
• Search algorithms and problem-solving methods
• Agent-based systems and introductory agentic AI concepts

Machine Learning and Statistical Analysis

• Probability and statistics for data analysis
• Loss functions and optimization methods
• Cluster analysis and unsupervised learning techniques
• Principal Component Analysis (PCA) and dimensionality reduction

Deep Learning and Neural Networks

• Shallow neural networks and deep neural networks (DNNs)
• Model regularization and overfitting prevention techniques
• Convolutional Neural Networks (CNNs)
• Introduction to graph neural networks