Dr Lim Wern Han

Lecturer
School of IT

lim.wern.han@monash.edu
+603 5515 9662
Room 2-4-21
ORCID

Personal statement

Wern Han Lim (Ian) has been teaching computer science with the School of Information Technology since 2015; achieving teaching recognition from student association, school, faculty and campus such as:

  • 2020 PVC Awards for Excellence in Education: Outstanding Educator
  • 2019 Faculty of Information Technology Teaching Award: Teaching Excellence
  • 2018 Monash Student Association (MSA) Teaching Awards - Faculty Teaching Excellence Award: Information Technology.
  • 2017 Faculty of Information Technology Teaching Award 2017: Citation for Outstanding Contribution to Student Learning.

His teaching experience includes:

  • FIT1008 Introduction to Computer Science
  • FIT2004 Algorithms and Data Structures
  • FIT2014 Theory of Computation - FIT3140 Advanced Programming
  • FIT3152 Data Science
  • FIT3155 Advanced Data Structures and Algorithms

Ian is also the student experience coordinator for the school. He often initiate, coordinates and act as the advisor for student enhancement programs -- having mentored student teams successfully in various local, regional and international events particularly hackathons and datathons. Besides that, he also contributes to community outreach programs such as the Campus Community Engagement (CCE) as one of the academic advisors.

On the research front, Ian’s area of interest and expertise includes (not limited to):

  • Information Retrieval (IR) and Knowledge Discovery particularly on User-Generated Content (UGC) platforms. His work in this area also includes Natural Language Processing (NLP), Data Science (DS) and Machine Learning (ML).
  • Graph Algorithms for a wide range of use cases such as knowledge graph, social engineering and flow optimization with capacity constraint.

To date, he has been involved in a number of research projects in these research areas including postgraduate supervision, industry collaborations and industry consultancy.

Academic degrees

  • Doctor of Philosophy in Computer Science, Monash University, 2018
  • Degree in Computer Science, Monash University, 2009

Professional affiliations

Member of International Professional Bodies

  • Association for Computing Machinery, Member

Research Interests

Ian has a strong research interest to bring order to the World Wide Web (WWW) today – estimating the reliability and information quality of User-Generated Content (UGC). He aims to enhance the information retrieval (IR) experience on the WWW, leveraging the Wisdom of the Crowd (WotC). To do so, he utilizes various data science techniques in conjunction with machine learning (ML) to produce powerful models such as:

  • Classification of WWW resources using user annotations/ tags.
  • Identifying experts on community question-answering (CQA) platforms and reliable product reviewers for improved recommender systems.
  • Prediction of best answer in a CQA environment.
  • Identifying high quality comments on Reddit and filtering out unwanted comments/ fake news.
  • Identifying malicious users/ spammers on various UGC platforms including fake news.
  • Extraction of tacit knowledge from UGC platforms, towards enhancing existing knowledge graphs.

Besides that, he also work on other areas such as:

  • Analytics and design of novel flow networks (humans and vehicles) for route optimization with capacity constraints and a wide range of use cases.

Research Projects

Title: Knowledge Extraction from User-Generated Content (UGC)

User-Generated Content (UGC) has the information potential that rivals established expert-generated content; while maintaining the advantage of being highly relevant (up-to-date), diverse and robust to the current happenings. Amongst this lie tacit knowledge which would greatly enhance currently know explicit knowledge repository such as the Wiki Knowledge graph. Thus, this research project focus on the discovery and extraction of knowledge from UGC platforms. As a by-product, users are profiled to identify both reliable (trustworthy) contributors while filtering out malicious users (spammers, fake news spreader etc.).

Title: Flow Optimization with Novel Graph Modelling, Analyics and Algorithms.

Flow optimization is a constant challenge in our daily lives. For example, traffic congestion from improper traffic planning or unwanted incidents greatly affect the our travel route options. This research aims to alleviate this challenge by exploring novel graph data structures that models and encapsulate significant features valuable for a wide range of applications including context-sensitive routing and strategic hub placements. A goal of the project is ensure a time-space complexity for the proposed data strutures and algorithms. The project is further optimized through accelerated computing frameworks.

Title: Big Data Analytics to Critical Problems, Reception and Responses.

This project aims to policy makers in making informed decisions with calculated risks by aggregating data from a wide range of publicly available data sources with an emphasis on social platforms. The collected data is fed into an analytics engine with derived features (of high significance). For example, how are the tourism industry affected by COVID-19, what are the response undertaken by them and how is it received?

Education

Units taught

  • FIT1008 Introduction to Computer Science
  • FIT2004 Algorithms and Data Structures
  • FIT2014 Theory of Computations
  • FIT3152 Data Science
  • FIT3155 Advanced Data Structures and Algorithms

Local grants

Current supervision

Adi Azri bin Daman Huri
Knowledge Extraction from User-Generated Content
4 (honour)
Monash University Malaysia

Sahinya Akila, Ishfak bin Munsur, Yobeena Vencatasamy, Vihara Kadawathaarachi
User Profiling and Recommendation System using User-Generated Content
3 (final year)
Monash University Malaysia

Suvashish Chakraborty
Reddit Fortification: Enriching Wikipedia
3 (final year)
Monash University Malaysia

Errystio Rizky Tendean
What Do I Buy? Trusted Recommendations on Reddit
3 (final year)
Monash University Malaysia

Local Award/Recognition/Exhibition/Stewardship

  • 2020 PVC Awards for Excellence in Education: Outstanding Educator, Monash University Malaysia, 2020

International Award/Recognition/Exhibition/Stewardship

  • 2019 Faculty of Information Technology Teaching Award: Teaching Excellence, Faculty of Information Technology, Monash University, 2019
  • 2018 Monash Student Association (MSA) Teaching Awards - Faculty Teaching Excellence Award: Information Technology, Monash Student Association (MSA), Monash University Clayton, 2018
  • 2017 Faculty of Information Technology Teaching Award 2017: Citation for Outstanding Contribution to Student Learning,  Faculty of Information Technology, Monash University,  2017

Journal

Lim, Wern Han; Carman, Mark James (2017) Annotator expertise and information quality in annotation-based retrieval, ACM International Conference Proceeding Series, Volume: 2017-December, 10.1145/3166072.3166075

Lim, Wern Han; Carman, Mark James; Wong, Sze Meng Jojo (2017) Estimating relative user expertise for content quality prediction on Reddit, HT 2017 - Proceedings of the 28th ACM Conference on Hypertext and Social Media, (55-64), 10.1145/3078714.3078720

Lim, Wern Han; Carman, Mark James; Wong, Sze Meng Jojo (2016) Estimating domain-specific user expertise for answer retrieval in community question-answering platforms, ACM International Conference Proceeding Series, (33-40), 10.1145/3015022.3015032

Lim, Wern Han; Alhashmi, Saadat M.; Siew, Eu Gene (2011) Personalized information retrieval: User profiling with web 2.0 folksonomy, Innovation and Knowledge Management: A Global Competitive Advantage - Proceedings of the 16th International Business Information Management Association Conference, IBIMA 2011, (1808-1820), Volume: 4, https://www.scopus.com/record/display.uri?eid=2-s2.0-84905111146&origin=resultslist