Pedagogical Portfolio • NUS School of Computing

Teaching Dossier & Student Evaluations

Instruction, tutorial leadership, and curriculum design spanning 8 academic modules at NUS School of Computing (National University of Singapore). Select any course below to explore its curriculum, quantitative ratings, and qualitative student feedback.

Students Mentored 905+

Undergrad & Grad • 52+ projects

Tutorials Conducted 107

880+ classroom tutorial hours

Academic Modules 8

NUS Master's & Undergrad

Quantitative Rating 4.3 / 5.0

Surpasses Benchmark (4.1)

Course Index Swipe

FT5003 Master's Level Master's Level

Blockchain Innovations & Decentralized Systems

Graduate Instructor / TA • NUS School of Computing

Key Contributions & Instructional Delivery

• Co-developed comprehensive course syllabus and pedagogical architecture with Prof. Jungpil Hahn.
• Curated cutting-edge industry blockchain use cases and university data banks.
• Formulated hands-on project briefs illustrating enterprise distributed ledger implementations.

Verified Student Testimonials

“ Extensive research and resource curation ensuring cutting-edge coverage of decentralized financial technology.

Strengths Highlighted

• Comprehensive coverage of blockchain consensus, cryptographic primitives, and smart contracts
• Strong grounding in real-world decentralized finance and enterprise adoption

Course Syllabus & Curriculum Architecture

National University of Singapore (NUS) • Official Module Outline

4 Core Units

Course Pedagogical Focus

A core graduate module in the Master of Science in Digital Financial Technology (MSc DFinTech) program, exploring the disruptive architectural, economic, and institutional dimensions of blockchain technologies. Students analyze cryptographic foundations, distributed consensus algorithms, smart contract programming in Solidity, decentralized finance (DeFi) protocols, zero-knowledge scalability mechanisms, and enterprise DLT architectures across global financial systems.

Modular Curriculum Structure

Unit 1 Unit 1 of 4

Cryptographic Foundations & Distributed Ledger Architecture

Mathematical and cryptographic fundamentals underpinning immutable distributed ledgers, peer-to-peer network topologies, and transaction verification.

Topics Taught:

• Cryptographic hash primitives (SHA-256, Keccak), Merkle trees, and Patricia tries for state verification
• Asymmetric public-key cryptography, ECDSA curve secp256k1, and hierarchical deterministic key generation (BIP-32/39/44)
• Peer-to-peer (P2P) network topologies, gossip propagation protocols, and transaction mempool management

Unit 2 Unit 2 of 4

Consensus Algorithms, Fault Tolerance & Network Security

Rigorous comparative analysis of consensus protocols, Byzantine fault tolerance, game-theoretic security, and network fork economics.

Topics Taught:

• Nakamoto consensus: Proof of Work (PoW), difficulty adjustment, 51% attack vectors, and selfish mining incentives
• Proof of Stake (PoS), slashing conditions, Casper FFG, and modern Byzantine Fault Tolerant (PBFT / Tendermint) engines
• MEV (Maximal Extractable Value), front-running, sandwich arbitrage, and transaction ordering dynamics

Unit 3 Unit 3 of 4

Smart Contracts, EVM & Decentralized Finance (DeFi) Protocols

Engineering autonomous programmatic financial contracts on the Ethereum Virtual Machine (EVM) and dissecting decentralized capital markets.

Topics Taught:

• EVM execution semantics, gas computation mechanics, and Solidity design patterns (proxy upgrades, access control)
• Token standards and economics: ERC-20 fungible tokens, ERC-721/1155 NFTs, and algorithmic tokenomics models
• DeFi protocol architectures: Automated Market Makers (Uniswap constant product curves), collateralized lending (Aave/Compound), and flash loans

Unit 4 Unit 4 of 4

Enterprise Blockchains, Zero-Knowledge Scaling & FinTech Governance

Evaluating permissioned enterprise ledgers, Layer-2 rollup scalability, Zero-Knowledge privacy proofs, and regulatory frameworks.

Topics Taught:

• Permissioned enterprise DLT: Hyperledger Fabric channel architectures, endorsement policies, and private data collections
• Layer-2 scaling solutions: Optimistic rollups, ZK-Rollups (STARKs / SNARKs), state channels, and cross-chain bridge security
• Cross-border settlement, Central Bank Digital Currencies (CBDCs), Project Ubin/Dunbar, and regulatory frameworks (MAS PSA / MiCA)

What Students Master in this Module

Evaluate cryptographic primitives and distributed consensus mechanisms to design fault-tolerant FinTech architectures.

Architect, develop, and audit production-grade smart contracts on EVM networks following security and gas-efficiency standards.

Deconstruct and model decentralized finance (DeFi) protocols, automated market makers, and tokenomic incentive structures.

Formulate strategic enterprise blockchain implementations addressing scalability, regulatory compliance, and cross-border settlement.

Assessment & Grading Breakdown

Graduate Capstone FinTech DApp Project 40%

End-to-end design, smart contract deployment, and security audit of an original DeFi or enterprise blockchain application

Technical Case Studies & Protocol Critiques 30%

In-depth evaluations of major protocol failures, smart contract exploits (reentrancy, oracle manipulation), and DeFi architectures

Applied Laboratory Milestones (Solidity / Hardhat) 20%

Hands-on implementation of token standards, multi-sig vaults, and decentralized oracle integrations

Seminar Discussion & Protocol Governance Review 10%

Active seminar participation, DAO governance analysis, and presentation of emerging cryptographic breakthroughs

Primary Software & Environments

Solidity Hardhat / Foundry Ethereum / EVM Ethers.js / Web3.js Hyperledger Fabric MetaMask OpenZeppelin Contracts Remix IDE

Curriculum Verified NUS School of Computing

BT3103 Undergraduate / Upper Division 02 Semesters (2017-18 & 2022-23)

Application Systems Development for Business Analytics

Teaching Assistant & Project Mentor • NUS School of Computing

Key Contributions & Instructional Delivery

• Instructed undergraduate students on relational database design, SQL queries, and application architecture.
• Evaluated final semester group projects and provided iterative technical reviews.

Quantitative Ratings vs Department Benchmark

Evaluation Scale: 1.0 (Low) – 5.0 (High)

Effectiveness 2.2/5.0

Thinking Ability 2.3/5.0

Timely Feedback 2.3/5.0

Subject Interest 2.3/5.0

Verified Student Testimonials

“ Provided constructive guidance on database design and backend architecture.

Strengths Highlighted

• Committed to student learning and providing code-level guidance
• Thorough evaluation of final group web applications and database designs

Areas for Improvement

• Improve technical knowledge to address student queries effectively
• Be better equipped to handle subject-specific questions
• Ensure continuous engagement throughout the tutorial sessions
• Create a structured approach for explaining concepts clearly

Course Syllabus & Curriculum Architecture

National University of Singapore (NUS) • Official Module Outline

4 Core Units

Course Pedagogical Focus

A pivotal upper-division engineering module that bridges software engineering and business analytics through embedded analytics. Students learn to architect, build, and deploy production-grade data-driven web applications that embed statistical models, machine learning pipelines, and interactive visual dashboards directly into operational business workflows.

Modular Curriculum Structure

Unit 1 Unit 1 of 4

Full-Stack Web Architecture & Embedded Analytics Paradigms

Designing modern multi-tier web architectures tailored for real-time analytics delivery, responsive UI layouts, and user-centric data workflows.

Topics Taught:

• Client-server architectures, RESTful API design principles, and single-page application (SPA) paradigms
• Responsive user interface engineering using modern JavaScript frameworks (Vue.js / React), HTML5, and modern CSS
• Embedded analytics design patterns: Transitioning from offline business intelligence to real-time interactive decision tools

Unit 2 Unit 2 of 4

Backend Engineering, Relational Database Modeling & ORM

Engineering reliable server-side services, relational schema normalization, query optimization, and secure API gateways.

Topics Taught:

• Server-side runtime engineering with Node.js and Express.js, routing patterns, and middleware pipelines
• Relational database modeling in MySQL/PostgreSQL: 3NF normalization, foreign keys, indexing, and transactional integrity
• Object-Relational Mapping (ORM) abstractions, parameterized SQL execution, and connection pooling

Unit 3 Unit 3 of 4

Analytics Pipeline Integration & Interactive Data Visualization

Connecting client-facing applications with analytical backends, automated data pipelines, and reactive chart libraries.

Topics Taught:

• Exposing machine learning inferences and statistical models via asynchronous microservice REST endpoints
• Real-time client-side data visualization: Integrating Chart.js, D3.js, and dynamic KPI dashboards
• Authentication and data access control: JWT token validation, role-based access control (RBAC), and multi-tenant data segregation

Unit 4 Unit 4 of 4

Agile Delivery, Cloud Deployment & Application Lifecycle Management

Practicing iterative agile delivery, version control branching strategies, automated testing, and cloud infrastructure hosting.

Topics Taught:

• Agile Scrum execution: Sprint planning, user story grooming, burn-down tracking, and continuous integration workflows
• Cloud hosting and serverless backends: Deploying to cloud platforms (AWS, Firebase, Vercel/Render) and managing persistent databases
• Production verification: Automated API endpoint testing, performance profiling, error monitoring, and post-launch analytics telemetry

What Students Master in this Module

Architect and develop full-stack data-driven web applications integrating backend databases with dynamic user interfaces.

Embed predictive machine learning models and analytical dashboards directly into real-time operational web systems.

Design normalized relational databases and implement secure, high-throughput REST APIs with authentication and authorization.

Execute end-to-end agile software development lifecycles with collaborative Git workflows, automated testing, and cloud deployment.

Assessment & Grading Breakdown

Team Analytics Capstone Web Application 40%

Full-lifecycle development and live client demonstration of an operational business analytics web application

Individual Practical Coding Assignments 30%

Hands-on milestones assessing responsive UI construction, REST API development, database querying, and model integration

Mid-Term Technical & Architectural Examination 15%

Rigorous evaluation of full-stack design patterns, database normalization, API security, and analytics workflows

Agile Sprint Participation & Code Reviews 15%

Continuous evaluation of Scrum ceremony engagement, sprint retrospectives, and GitHub pull request reviews

Primary Software & Environments

JavaScript / TypeScript Vue.js / React Node.js / Express.js MySQL / PostgreSQL Firebase Chart.js / D3.js Git / GitHub Postman

Curriculum Verified NUS School of Computing

IS4302 Undergraduate / Senior Capstone Spring Semesters

Blockchain and Distributed Ledger Technologies

Teaching Assistant • NUS School of Computing

Key Contributions & Instructional Delivery

• Designed and delivered lab sessions on smart contract architectures and token standard implementations.
• Mentored student project groups through decentralized application (DApp) development and deployment.

Quantitative Ratings vs Department Benchmark

Evaluation Scale: 1.0 (Low) – 5.0 (High)

Effectiveness 2.6/5.0

Thinking Ability 2.6/5.0

Timely Feedback 2.4/5.0

Subject Interest 2.3/5.0

Verified Student Testimonials

“ Deep technical knowledge and expertise in blockchain development.

Strengths Highlighted

• Technical knowledge and expertise in blockchain development
• Dedication to assisting students through complex distributed ledger architectures

Areas for Improvement

• Conduct live coding demonstrations instead of relying on static slides or screenshots
• Prepare more thoroughly before lab sessions to anticipate technical edge-cases
• Provide clearer, more detailed responses to advanced student queries

Course Syllabus & Curriculum Architecture

National University of Singapore (NUS) • Official Module Outline

4 Core Units

Course Pedagogical Focus

An advanced capstone module examining the technological, economic, and organizational implications of blockchain and distributed ledger technologies (DLT). Students master the computational mechanisms of decentralized consensus, evaluate public versus permissioned ledger architectures (Ethereum vs. Hyperledger Fabric), analyze digital token standards, and assess enterprise adoption across finance, government, logistics, and supply chain domains.

Modular Curriculum Structure

Unit 1 Unit 1 of 4

Distributed Systems Architecture & Cryptographic Foundations

Fundamental principles of distributed state machines, cryptographic security primitives, and decentralized trust mechanisms.

Topics Taught:

• Distributed state replication, CAP theorem, crash vs. Byzantine fault tolerance, and network partition resilience
• Cryptographic primitives: Collision-resistant hash functions, asymmetric digital signatures, and Merkle tree state proofs
• The Bitcoin protocol: UTXO transaction model, script validation, peer-to-peer network gossip, and halving economics

Unit 2 Unit 2 of 4

Public Blockchains & Programmable Smart Contract Platforms

Dissecting account-based public ledgers, the Ethereum Virtual Machine, smart contract execution, and decentralized applications (DApps).

Topics Taught:

• Account-based distributed ledgers: Ethereum state storage, world state trie, and gas metering economics
• Smart contract lifecycle: Compilation, bytecode verification, EVM execution, and decentralized application (DApp) architectures
• Token standards (ERC-20, ERC-721, ERC-1155) and their role in programmable asset ownership and digital governance

Unit 3 Unit 3 of 4

Permissioned Enterprise DLT & Hyperledger Fabric

Designing privacy-preserving enterprise consortium networks with role-based identities and modular consensus architectures.

Topics Taught:

• Enterprise requirements: Confidentiality, regulatory compliance, finality, and high transaction throughput
• Hyperledger Fabric architecture: Membership Service Provider (MSP), CA infrastructure, and cryptographically verified identities
• Channels, chaincode execution lifecycle, endorsement policies, ordering services (Raft), and private data collections

Unit 4 Unit 4 of 4

DLT Security, Governance & Enterprise Transformation

Analyzing blockchain security vulnerabilities, decentralized identity (DID) standards, and strategic enterprise implementation frameworks.

Topics Taught:

• Smart contract security vulnerabilities: Reentrancy bugs, integer overflows, oracle manipulation, and auditing toolchains
• Decentralized Identifiers (DIDs), Verifiable Credentials (VCs), and self-sovereign identity ecosystems
• Enterprise business model innovation: Multi-party supply chain traceability, cross-border payments, and digital asset custody

What Students Master in this Module

Compare and contrast consensus algorithms, fault-tolerance models, and public versus permissioned blockchain paradigms.

Architect and deploy distributed ledger solutions utilizing Ethereum smart contracts and Hyperledger Fabric enterprise channels.

Identify and remediate critical security vulnerabilities and economic game-theoretic exploit vectors in decentralized protocols.

Formulate strategic, regulatory-compliant DLT transformation roadmaps for complex multi-enterprise business environments.

Assessment & Grading Breakdown

Enterprise DLT Group Capstone Project 40%

Design, implementation, and demonstration of a functional decentralized enterprise application with architecture whitepaper

Individual Smart Contract & DLT Programming Labs 30%

Hands-on laboratory exercises covering smart contract programming, chaincode deployment, and cryptographic verification

Mid-Term Technical Examination 15%

Comprehensive assessment of distributed systems theory, cryptographic proofs, and consensus mechanics

Tutorial Case Presentations & Cohort Discussions 15%

Active debate and presentation on real-world DLT industry case studies, tokenomics, and regulatory frameworks

Primary Software & Environments

Ethereum / Solidity Hyperledger Fabric Go / Node.js Docker Web3.js / Ethers.js MetaMask Ganache / Truffle Postman

Curriculum Verified NUS School of Computing

BT4221 Undergraduate / Honors Fall Semesters

Big Data Techniques and Technologies

Teaching Assistant • NUS School of Computing

Key Contributions & Instructional Delivery

• Delivered hands-on tutorials on distributed cluster management and MapReduce job orchestration.
• Mentored students on data processing pipelines using HBase, Cassandra, and Dynamo.
• Guided practical big data analytics implementations using R.

Verified Student Testimonials

“ Students gained hands-on experience with Hadoop, MapReduce, and R for big data analytics, effectively analysing large-scale datasets.

Strengths Highlighted

• Hands-on walkthroughs for distributed cluster management and MapReduce jobs
• Real-time debugging support during complex big data infrastructure setups

Areas for Improvement

• Expand practice datasets for real-time streaming analytics

Course Syllabus & Curriculum Architecture

National University of Singapore (NUS) • Official Module Outline

4 Core Units

Course Pedagogical Focus

An intensive honors-level course covering scalable computational architectures and data engineering pipelines required to process, store, and analyze massive volumes of structured, semi-structured, and streaming data. Students gain deep hands-on expertise with distributed computing paradigms, Apache Hadoop, Apache Spark, real-time stream processing with Kafka, and distributed NoSQL storage engines.

Modular Curriculum Structure

Unit 1 Unit 1 of 4

Distributed Computing Principles & Big Data Storage Architectures

Foundational cluster computing architectures, distributed file systems, and the MapReduce computational paradigm.

Topics Taught:

• The Big Data paradigm (Volume, Velocity, Variety, Veracity) and horizontal scaling economics vs. vertical scale-up
• Hadoop Distributed File System (HDFS): NameNodes, DataNodes, block replication, fault tolerance, and rack awareness
• The MapReduce programming paradigm: Map, shuffle, sort, and reduce execution phases on distributed commodity clusters

Unit 2 Unit 2 of 4

Large-Scale Distributed Data Processing with Apache Spark

In-memory distributed computing, resilient distributed datasets, DAG optimization, and Spark SQL query engines.

Topics Taught:

• Apache Spark architecture: Driver, cluster manager (YARN / Standalone), executors, and in-memory execution advantages
• Resilient Distributed Datasets (RDDs): Transformations, actions, lineage graphs, lazy evaluation, and partition tuning
• Spark SQL, DataFrames, and Datasets: Catalyst query optimizer, Tungsten execution engine, and distributed joins

Unit 3 Unit 3 of 4

Real-Time Event Streaming & Stream Processing Pipelines

Architecting real-time streaming pipelines for low-latency analytics, event-driven architectures, and windowing operations.

Topics Taught:

• Event streaming fundamentals with Apache Kafka: Topics, partitions, consumer groups, offsets, and high-throughput messaging
• Spark Structured Streaming: Continuous processing vs. micro-batching, streaming DataFrames, and event-time watermarking
• Stream analytics windowing techniques: Tumbling, sliding, and session windows; handling out-of-order and late-arriving events

Unit 4 Unit 4 of 4

Distributed NoSQL Systems & Cloud Data Warehousing

Distributed non-relational database architectures, data partitioning, indexing, and modern cloud analytics warehouses.

Topics Taught:

• NoSQL database taxonomies: Key-value, column-family (Apache HBase/Cassandra), document (MongoDB), and graph stores
• Distributed database mechanics: Consistent hashing, sharding strategies, CAP theorem trade-offs, and eventual consistency
• Modern cloud data lakes and warehouses: Delta Lake, Apache Parquet columnar storage, AWS S3/EMR, and distributed OLAP query design

What Students Master in this Module

Architect distributed storage and processing pipelines capable of processing terabyte-to-petabyte scale datasets.

Implement and optimize complex distributed data transformations and analytic queries using Apache Spark and PySpark.

Design and deploy low-latency real-time stream processing workflows using Apache Kafka and Spark Structured Streaming.

Select, configure, and query distributed NoSQL databases and columnar data lakes matching specific enterprise access patterns.

Assessment & Grading Breakdown

Distributed Big Data Systems Capstone Project 35%

End-to-end implementation of a distributed batch or streaming analytics pipeline on authentic large-scale datasets

Hands-On Cluster Laboratory Assignments 30%

Practical coding labs evaluating PySpark data processing, Kafka stream ingestion, and distributed NoSQL modeling

Comprehensive Technical Examination 25%

Rigorous evaluation of distributed computing theory, MapReduce mechanics, Spark DAG optimization, and storage architectures

Lab Quizzes & Technical Participation 10%

Continuous assessment of weekly distributed systems problem sets and active participation in lab walkthroughs

Primary Software & Environments

Apache Spark / PySpark Apache Hadoop (HDFS) Apache Kafka Python Apache HBase / Cassandra AWS S3 / EMR Parquet Docker

Curriculum Verified NUS School of Computing

IS4301 Undergraduate Annual Sessions

Agile IT with DevOps

Teaching Assistant • NUS School of Computing

Key Contributions & Instructional Delivery

• Guided student cohorts in architecting end-to-end continuous deployment pipelines for banking services.
• Mentored teams through Agile sprint cycles, code reviews, and Git workflows.

Quantitative Ratings vs Department Benchmark

Evaluation Scale: 1.0 (Low) – 5.0 (High)

Effectiveness 3.8/5.0

Thinking Ability 3.7/5.0

Timely Feedback 4.1/5.0

Subject Interest 3.7/5.0

Verified Student Testimonials

“ Very fast in replying and very helpful for tutorials when we face difficulties.

“ Knowledgeable and willing to help!

“ Passionate in what he does.

“ Technically strong.

Strengths Highlighted

• Very fast in replying and helpful during tutorials
• Knowledgeable and technically strong across modern DevOps stacks
• Passionate about teaching and assisting students with debugging pipelines

Areas for Improvement

• Improve clarity in tutorial instructions to reduce ambiguity
• Enhance tone and warmth in email responses
• Ensure greater familiarity with all tutorial programs to address edge cases effectively

Course Syllabus & Curriculum Architecture

National University of Singapore (NUS) • Official Module Outline

4 Core Units

Course Pedagogical Focus

An advanced upper-division module preparing computing students for enterprise-scale Agile IT transformations. Covers modern software delivery paradigms, continuous integration/continuous deployment (CI/CD) pipelines, containerized microservices, and automated testing applied within complex financial and banking services contexts.

Modular Curriculum Structure

Unit 1 Unit 1 of 4

Agile Methodologies & Enterprise Transformation

Comparative study of software development lifecycles, Scrum ceremonies, and lean agile practices in regulated enterprise environments.

Topics Taught:

• Predictive (Waterfall) vs. Empirical (Agile) paradigms; iterative incremental delivery economics
• Scrum framework: Sprint planning, backlog grooming, daily standups, sprint reviews, and retrospectives
• User story authoring, acceptance criteria (Gherkin/BDD), story point estimation, and velocity tracking

Unit 2 Unit 2 of 4

Continuous Integration (CI) & Automated Quality Gates

Designing automated build and test validation pipelines to detect regressions early and enforce code quality.

Topics Taught:

• Branching topologies: Trunk-based development vs. GitFlow and pull request review workflows
• Automated build systems, unit/integration testing harnesses, and test automation frameworks
• Static code analysis, code coverage enforcement, and automated security vulnerability scanning with SonarQube

Unit 3 Unit 3 of 4

Containerization, Microservices & Infrastructure as Code

Architectural decomposition of monolithic systems into containerized microservices managed via declarative infrastructure.

Topics Taught:

• Monolithic vs. Microservices architecture: Service boundaries, decoupled databases, and API gateways
• Containerization with Docker: Dockerfiles, multi-stage compilation, image minimization, and container registries
• Infrastructure as Code (IaC) principles: Declarative configuration, reproducibility, and environment parity

Unit 4 Unit 4 of 4

Continuous Delivery, Cloud Orchestration & Production SRE

Automated zero-downtime release strategies, container orchestration fundamentals, and production site reliability engineering.

Topics Taught:

• Continuous Delivery (CD) pipelines, automated artifact promotions, and staging gate approvals
• Zero-downtime deployment patterns: Blue-Green deployments, Canary releases, and feature flags
• Container orchestration fundamentals with Kubernetes, PaaS hosting, and observability/monitoring toolchains
• Applied capstone: Architecting and deploying a resilient, audited retail banking digital service

What Students Master in this Module

Lead and participate in Agile Scrum team ceremonies using industry-standard backlog management systems.

Architect and automate end-to-end Continuous Integration (CI) pipelines with integrated quality and security gates.

Package, containerize, and deploy microservice architectures using Docker and modern container registries.

Design zero-downtime deployment strategies and configure production monitoring for mission-critical enterprise systems.

Assessment & Grading Breakdown

Enterprise DevOps Capstone Project & Live Demo 40%

Complete end-to-end automated deployment pipeline for an enterprise banking service, verified via live demo

Individual Architecture & Strategy Assignments 30%

Technical case studies on legacy modernization, microservice decoupling, and pipeline security

Tutorial Leadership & Sprint Demonstrations 20%

Leading tutorial cohort demonstrations on container debugging, pipeline troubleshooting, and Scrum roles

Peer Review & Agile Sprint Contributions 10%

Evaluation of collaborative teamwork, code review thoroughness, and sprint retrospectives

Primary Software & Environments

Docker Jenkins GitHub Actions Kubernetes SonarQube Jira / Confluence Linux CLI PaaS / Cloud

Curriculum Verified NUS School of Computing

IS1103 Undergraduate Core Annual Sessions

Ethics in Computing & Technology Law

Teaching Assistant • NUS School of Computing

Key Contributions & Instructional Delivery

• Facilitated interactive case study seminars examining real-world ethical dilemmas in emerging tech.
• Provided thorough commentary and grading on policy analysis essays and team presentations.

Quantitative Ratings vs Department Benchmark

Evaluation Scale: 1.0 (Low) – 5.0 (High)

Effectiveness 3.6/5.0

Thinking Ability 3.5/5.0

Timely Feedback 3.7/5.0

Subject Interest 3.5/5.0

Verified Student Testimonials

“ He promptly replies to emails and clarifies doubts effectively.

“ Provides timely feedback and is always open to aiding students through emails.

“ Explanations for questions are clear and helpful.

“ Very critical of opinions and provides salient feedback in class.

Strengths Highlighted

• Responsive and approachable; answers emails promptly
• Clear and timely communication with constructive feedback
• Very critical of opinions in a constructive manner, providing salient insights in class discussions
• Prepares diligently for every tutorial session and addresses student questions effectively
• Honest, transparent, and easy to speak with

Areas for Improvement

• Increase student interaction and engage students more actively during discussions
• Develop greater classroom enthusiasm and optimize tutorial time control to avoid rushing

Course Syllabus & Curriculum Architecture

National University of Singapore (NUS) • Official Module Outline

4 Core Units

Course Pedagogical Focus

This foundational module examines the complex interactions between computing technologies, legal statutes, and societal welfare. Students analyze how automated digital paradigms transform human institutions, studying statutory frameworks (e.g. Singapore PDPA, EU GDPR, Computer Misuse Act), algorithmic bias, intellectual property, and professional codes of conduct governing computing engineers.

Modular Curriculum Structure

Unit 1 Unit 1 of 4

Ethical Frameworks & Moral Philosophy in Computing

Establishes foundational moral philosophies and translates ethical theories into actionable software engineering decision frameworks.

Topics Taught:

• Classical ethical theories: Deontology, Utilitarianism, Virtue Ethics, and Social Contract Theory
• Professional codes of ethics (ACM, IEEE-CS) and personal accountability in engineering
• Whistleblowing protocols, moral courage, and navigating corporate dilemmas in tech

Unit 2 Unit 2 of 4

Information Privacy, Surveillance & Data Governance

Analyzes the erosion of informational privacy in the age of big data, comparing regulatory frameworks and privacy-preserving architectures.

Topics Taught:

• Philosophy of digital privacy: Informational self-determination vs. surveillance capitalism
• Statutory compliance: Singapore Personal Data Protection Act (PDPA) vs. European Union GDPR
• Biometric identification, location telemetry, data brokers, and cross-border transfer laws

Unit 3 Unit 3 of 4

Algorithmic Bias, AI Ethics & Autonomous Systems

Investigates societal harm, discriminatory bias, and accountability deficits in automated machine learning systems and AI.

Topics Taught:

• Algorithmic fairness, dataset bias, and opacity in automated credit, hiring, and judicial algorithms
• Moral responsibility and liability in autonomous decision-making (e.g. autonomous vehicles, healthcare AI)
• Generative AI ethics, deepfakes, synthetic media, and the proliferation of digital misinformation

Unit 4 Unit 4 of 4

Intellectual Property, Open Source & Cyberlaw

Explores legal protections for digital assets, open-source licensing regimes, and computer crime jurisprudence.

Topics Taught:

• Software intellectual property: Copyrights, patents, trade secrets, and Digital Rights Management (DRM)
• Open source licensing taxonomy: Permissive (MIT, Apache 2.0) vs. Copyleft (GPL, LGPL)
• Cybercrime statutes: Computer Misuse and Cybersecurity Act, ethical hacking, and software liability

What Students Master in this Module

Critically evaluate complex moral and ethical dilemmas arising from emerging computing technologies.

Apply statutory provisions of the Singapore PDPA and global privacy frameworks to enterprise architectures.

Diagnose automated systems and machine learning pipelines for algorithmic bias and ethical vulnerabilities.

Formulate legally and ethically sound corporate policies for data governance, compliance, and IP protection.

Assessment & Grading Breakdown

Case Study Analysis & Position Papers 35%

In-depth written analyses of landmark tech ethical dilemmas and regulatory inquiries

Continuous Assessment & Blended Missions 25%

Weekly interactive scenario evaluations and online conceptual assessments

Seminar Discussion & Ethical Debates 20%

Active cohort participation, dialectical argumentation, and tutorial presentations

Final Synthesis Examination 20%

Comprehensive scenario-based assessment evaluating regulatory and ethical reasoning

Primary Software & Environments

Singapore PDPA Jurisprudence ACM Code of Ethics Coursemology Platform Case Law Archives

Curriculum Verified NUS School of Computing

BT1101 Undergraduate Foundation Year

Introduction to Business Analytics

Teaching Assistant • NUS School of Computing

Key Contributions & Instructional Delivery

• Conducted interactive lab sessions translating statistical theorems into practical R code.
• Held regular office hour consultations clarifying homework questions and modeling assignments.

Quantitative Ratings vs Department Benchmark

Evaluation Scale: 1.0 (Low) – 5.0 (High)

Effectiveness 3.3/5.0

Thinking Ability 3.4/5.0

Timely Feedback 3.5/5.0

Subject Interest 3.4/5.0

Verified Student Testimonials

“ Extremely clear in explanations and engaging.

“ Great at applying context to theory and simplifying concepts.

“ Actively engages the class with clear instructions and helpful feedback.

Strengths Highlighted

• Extremely clear in explanations and engaging classroom presence
• Provides timely, high-quality feedback and actively facilitates student participation
• Great at applying real-world context to complex statistical theory and simplifying challenging concepts
• Actively engages the class with clear, structured instructions

Areas for Improvement

• Improve clarity in addressing questions and proactively correcting student misconceptions
• Provide step-by-step guided walkthroughs for difficult exercises to reduce initial confusion
• Focus on creating a relaxed, welcoming atmosphere to encourage hesitant students to speak up

Course Syllabus & Curriculum Architecture

National University of Singapore (NUS) • Official Module Outline

4 Core Units

Course Pedagogical Focus

The foundational core course for NUS Business Analytics students, synthesizing computational statistical inference, exploratory data wrangling, and predictive modeling in R to empower data-driven organizational decision-making.

Modular Curriculum Structure

Unit 1 Unit 1 of 4

Exploratory Data Analysis & Statistical Computing

Data preparation, wrangling, transformation pipelines, and high-impact visual analytics in the R programming environment.

Topics Taught:

• Data cleaning, reshaping, and transformation pipelines using R (Tidyverse: dplyr, tidyr, readr)
• Exploratory Data Analysis (EDA): summary statistics, skewness, outlier detection, and correlation metrics
• Data visualization and visual storytelling using ggplot2 (scatter, boxplots, histograms, facets)

Unit 2 Unit 2 of 4

Probability Foundations & Inferential Hypothesis Testing

Rigorous statistical inference, distribution theory, and hypothesis testing frameworks for business research.

Topics Taught:

• Discrete and continuous distributions (Binomial, Poisson, Normal) and sampling theory
• Central Limit Theorem, point estimation, and confidence interval interpretation
• Parametric and non-parametric tests: One/Two-sample t-tests, ANOVA, and Chi-Square tests of independence

Unit 3 Unit 3 of 4

Multivariate Predictive Regression Modeling

Building, evaluating, and diagnosing multivariate econometric and linear models for predictive business insights.

Topics Taught:

• Simple and multiple linear regression: Ordinary Least Squares (OLS) estimation and coefficient interpretation
• Categorical variables, dummy coding, interaction terms, and non-linear polynomial transformations
• Model diagnostics: Multicollinearity (VIF), heteroscedasticity, normality of residuals, and influential outliers

Unit 4 Unit 4 of 4

Classification, Logistic Regression & Business Decision Trees

Discrete choice classification modeling and algorithmic decision trees for customer and organizational analytics.

Topics Taught:

• Binary classification: Log-odds, logistic sigmoid function, and maximum likelihood estimation
• Model validation metrics: Confusion matrices, sensitivity/specificity, precision/recall, and ROC/AUC curves
• Classification and Regression Trees (CART), pruning, and translating models into executive recommendations

What Students Master in this Module

Proficiently clean, transform, and visualize complex real-world datasets in R and RStudio.

Formulate, execute, and interpret rigorous inferential statistical hypothesis tests to validate business hypotheses.

Construct and diagnose multivariate linear and logistic regression models with thorough residual checks.

Communicate statistical modeling conclusions effectively through reproducible RMarkdown business reports.

Assessment & Grading Breakdown

Group Real-World Analytics Case Project 30%

End-to-end data analytics project on an authentic business dataset with empirical modeling report

Comprehensive Final Examination 30%

Rigorous evaluation of probability, hypothesis testing, regression mathematics, and modeling concepts

Applied Practical Coding Exam 20%

Hands-on timed laboratory exam testing data manipulation, visualization, and modeling in RStudio

Weekly Problem Sets & Coursemology Missions 20%

Regular coding challenges reinforcing statistical theorems and tidyverse workflows

Primary Software & Environments

R / RStudio Tidyverse (dplyr, ggplot2) RMarkdown Coursemology Platform Applied Business Datasets

Curriculum Verified NUS School of Computing

IS3106 Undergraduate Semester Elective

Enterprise Systems Interface Design & Development

Teaching Assistant • NUS School of Computing

Key Contributions & Instructional Delivery

• Authored and conducted hands-on tutorials on React component lifecycles and modern UI state management.
• Evaluated capstone team web applications in coordination with Dr. Shalinda based on rigorous UI/UX rubrics.
• Conducted formal examination invigilation ensuring academic integrity.

Verified Student Testimonials

“ Guided students through building responsive, user-friendly React interfaces seamlessly integrated with enterprise backend systems.

Strengths Highlighted

• Hands-on tutorial creation with React and modern enterprise front-end patterns
• Consistent, fair grading and actionable feedback on full-stack architecture

Course Syllabus & Curriculum Architecture

National University of Singapore (NUS) • Official Module Outline

4 Core Units

Course Pedagogical Focus

The core front-end engineering module for NUS Information Systems undergraduates, complementing IS2103 (Server-Side Design). Focuses on architecting and delivering high-performance, modular, accessible, and scalable client-side interfaces and design systems for enterprise software.

Modular Curriculum Structure

Unit 1 Unit 1 of 4

Enterprise Front-End Architecture & Design Systems

Introduces modular UI architecture, enterprise design systems, and human-computer interaction (HCI) heuristics for business software.

Topics Taught:

• Component-driven software development and scalable front-end directory structures
• Nielsen Norman Group usability heuristics, cognitive load reduction, and workflow optimization
• Creating unified design systems, design tokens, and atomic UI libraries using Figma

Unit 2 Unit 2 of 4

Modern Reactive Frameworks & Component Lifecycles

Deep dive into reactive front-end programming, single-page application (SPA) paradigms, and component state encapsulation.

Topics Taught:

• Reactive UI architecture (React / React Native / Angular) and virtual DOM reconciliation
• Component lifecycle hooks, state and prop immutability, and custom hook abstractions
• MVVM (Model-View-ViewModel) architectural patterns and declarative UI bindings

Unit 3 Unit 3 of 4

Enterprise State Management & Asynchronous Data Flow

Mastering complex global application state, asynchronous microservice API consumption, and caching strategies.

Topics Taught:

• Global state management (Redux Toolkit / Context API) across multi-step enterprise workflows
• Consuming RESTful and GraphQL enterprise microservices with robust error boundaries
• Optimistic UI updates, client-side caching, data persistence, and offline capabilities

Unit 4 Unit 4 of 4

Enterprise Form Handling, Security & Accessibility

Building production-grade data collection interfaces with robust validation, security protections, and WCAG accessibility compliance.

Topics Taught:

• Complex enterprise form validation, multi-page transactional wizards, and dynamic data grids
• Front-end security: Cross-Site Scripting (XSS) defenses, CSRF token validation, and JWT session handling
• Web Content Accessibility Guidelines (WCAG 2.1 AA) conformance and responsive cross-platform layouts

What Students Master in this Module

Architect and engineer scalable, component-based enterprise web and mobile user interfaces.

Integrate front-end applications with distributed enterprise backend APIs and manage global asynchronous state.

Implement industry-standard client security defenses (XSS/CSRF mitigations) and validation schemes.

Conduct rigorous heuristic usability audits and produce accessible interfaces complying with WCAG standards.

Assessment & Grading Breakdown

Team Enterprise Capstone Application 40%

Full-scale enterprise web/mobile application with end-to-end API integration and live demonstration

Individual Technical Lab Milestones 30%

Hands-on programming assignments covering reactive components, state stores, and form validation

Mid-Term Technical Examination 15%

Assessment of front-end architecture, lifecycle hooks, and design pattern mastery

Peer Code Reviews & Class Participation 15%

Active engagement in tutorial code walkthroughs, design crits, and sprint reviews

Primary Software & Environments

React / React Native TypeScript Redux Toolkit Figma RESTful APIs Tailwind CSS Git / GitHub

Curriculum Verified NUS School of Computing

Welcome to the New Academic Presence

Teaching Dossier & Student Evaluations

Blockchain Innovations & Decentralized Systems

Key Contributions & Instructional Delivery

Verified Student Testimonials

Strengths Highlighted

Course Syllabus & Curriculum Architecture

Modular Curriculum Structure

Cryptographic Foundations & Distributed Ledger Architecture

Consensus Algorithms, Fault Tolerance & Network Security

Smart Contracts, EVM & Decentralized Finance (DeFi) Protocols

Enterprise Blockchains, Zero-Knowledge Scaling & FinTech Governance

What Students Master in this Module

Assessment & Grading Breakdown

Primary Software & Environments

Application Systems Development for Business Analytics

Key Contributions & Instructional Delivery

Quantitative Ratings vs Department Benchmark

Verified Student Testimonials

Strengths Highlighted

Areas for Improvement

Course Syllabus & Curriculum Architecture

Modular Curriculum Structure

Full-Stack Web Architecture & Embedded Analytics Paradigms

Backend Engineering, Relational Database Modeling & ORM

Analytics Pipeline Integration & Interactive Data Visualization

Agile Delivery, Cloud Deployment & Application Lifecycle Management

What Students Master in this Module

Assessment & Grading Breakdown

Primary Software & Environments

Blockchain and Distributed Ledger Technologies

Key Contributions & Instructional Delivery

Quantitative Ratings vs Department Benchmark

Verified Student Testimonials

Strengths Highlighted

Areas for Improvement

Course Syllabus & Curriculum Architecture

Modular Curriculum Structure

Distributed Systems Architecture & Cryptographic Foundations

Public Blockchains & Programmable Smart Contract Platforms

Permissioned Enterprise DLT & Hyperledger Fabric

DLT Security, Governance & Enterprise Transformation

What Students Master in this Module

Assessment & Grading Breakdown

Primary Software & Environments

Big Data Techniques and Technologies

Key Contributions & Instructional Delivery

Verified Student Testimonials

Strengths Highlighted

Areas for Improvement

Course Syllabus & Curriculum Architecture

Modular Curriculum Structure

Distributed Computing Principles & Big Data Storage Architectures

Large-Scale Distributed Data Processing with Apache Spark

Real-Time Event Streaming & Stream Processing Pipelines

Distributed NoSQL Systems & Cloud Data Warehousing

What Students Master in this Module

Assessment & Grading Breakdown

Primary Software & Environments

Agile IT with DevOps

Key Contributions & Instructional Delivery

Quantitative Ratings vs Department Benchmark

Verified Student Testimonials

Strengths Highlighted

Areas for Improvement

Course Syllabus & Curriculum Architecture

Modular Curriculum Structure

Agile Methodologies & Enterprise Transformation

Continuous Integration (CI) & Automated Quality Gates

Containerization, Microservices & Infrastructure as Code

Continuous Delivery, Cloud Orchestration & Production SRE

What Students Master in this Module

Assessment & Grading Breakdown

Primary Software & Environments

Ethics in Computing & Technology Law

Key Contributions & Instructional Delivery

Quantitative Ratings vs Department Benchmark

Verified Student Testimonials

Strengths Highlighted

Areas for Improvement