Research Supervisors and their Research Interests

I have two main research areas which I will be happy to supervise EngD students, namely Data & Decision Analytics and Learning Analytics. In Data & Decision Analytics, we first look at identifying the actual cause of business problems by collecting, preparing, and exploring data to gain business insights, before proposing what objectives and solutions can and should be done to solve the problems using decision analytics (optimization). Typical problems will be in supply chain and logistics, where I have done work in ATM field engineer optimization, large supermarket queue optimization, distribution network optimization, manpower scheduling, airport queue management and operations.

The second area in Learning Analytics, where we are interested to design, develop and implement systems which will complement students’ learning in-class and out-of-class. By using the systems, we can collect data related to student interactions and determine if the learning outcomes have been achieved. Recently, we have developed an integrated Telegram and Web-based Q&A Forum, capable of automatic thoughtfulness assessment of posts, using text mining and Natural Language Processing methodologies, coupled with intelligent engagement features, to facilitate student-driven participatory and collaborative learning.

My main research interests are in data science, machine learning and decision analytic. These include (1) machine learning with application in learning analytics: how to better represent knowledge using machine learning techniques to improve learning quality, (2) machine learning with application in natural language understanding: how to better answer questions with contextual information discovered from earlier conversations, and (3) machine learning with application in decision analytics: how to apply machine learning techniques for better optimization and decision making.

My research interests are mainly on the application of Artificial Intelligence in enhancing Teaching and Learning (T&L). One area of my research is on Affective Tutoring System (ATS) which senses the learner’s affect on a moment-by-moment basis with the use of unobtrusive sensors. The ATS senses academic emotions e.g. boredom and frustration of the learner, and adapts its tutoring response dynamically. This research spans multiple disciplines, namely education, psychology and AI. Another of my project employs the use of Natural Language Processing (NLP) to illuminate the key topics and sentiments surrounding Problem Based Learning (PBL) with the goal of enhancing an educational institution’s PBL pedagogy and processes. The analysis of these reflections and logs of the learners (digital learning traces) can uncover learners’ learning strategies and preferences in a complex problem-solving scenario, and ultimately for curriculum enhancements. A third area is in applied computer vision projects in collaboration with industry. This includes the development of Smart Glasses with facial recognition capabilities for use in a health care scenario, document digitalization using scene text detection, and recognition algorithms for crop protection and regulation scenario. Prior to teaching, I was working at the Monetary Authority of Singapore (MAS) where I developed payments and bond auction systems.

My research area is in information system education. My current work is to create an experiential learning environment where students work on real projects thru learning activities. There are many facets to this work. The main focus is on understanding factors that influence success such as real world issues and information system problems, industry sponsor and active mentoring relationship, team and project management and motivation, project innovation and design, and learning outcome and assessment. Another focus is on designing learning activities that work for Design thinking. A third area is on designing global competence learning outcome for short-term faculty led study missions. As academic director of the DHL-SMU Analytics lab, my research has included logistic data visualization system and optimizing the vehicle routing problem. My prior experience in supply chain included e-Business cases from Asia. My past research area is in Object Oriented programming languages.

The main focus of my research is the application of disruptive technologies to maximise business benefit, especially in financial services. The two main technologies I am currently researching are blockchain and quantum computing. Blockchain from the perspective of decentralised networks of nodes and the application of quantum computing in finance and decentralised consensus.

I spent over 15 years in R&D of quantum devices then another 15 years running IT in large global banks with the last 8 years working on decentralized systems including blockchains. One of the main objectives of my research is to produce meaningful solutions that are useful to industry and students on the school's EngD Programme will be trained in analysing and solving real industry problems with leading companies. For example, current research is being carried out with global industry partners including OneConnect (a subsidiary of Ping-An) and Kratos (a partner of R3).

I work in the areas of Operations Research and Artificial Intelligence. I received my Ph.D in Industrial and Systems Engineering from the National University of Singapore.

My main research interests include operations research, algorithm design, and data analytics that relate to metaheuristics, algorithm configuration, design of experiments, combinatorial optimization, and automated planning/scheduling in logistics, transportation and supply chain management. My past studies have been published in top conferences and journals in Operations Research. In addition, I have also been serving as a committee member of the Operational Research Society of Singapore.

With past industry, research and teaching experience in the areas of digital transformation and organisational change management, I would like to apply these knowledge and skills to educate and train a new generation of digital leaders and digital managers. I am keen to explore further on the topics of digitalisation, process transformation and people management. My current research interest is focused on people and information technology. The main motivation arises from the belief that people, through culture, is one of the most critical factors driving digital transformation success. Therefore, my on-going investigations have been centred on questions related to smart health and smart nation. I also have a keen interest on the subjects of information and knowledge management, learning organisation and competency development. For some of the previous works, I have used a combination of quantitative/qualitative and conceptual research to provide advice on how to better manage information and knowledge management processes.

My research area is in applied analytics using social media and textual resources. With social media as a source, I have worked on various national grant research projects in target audience profiling, mining opinion from social media, multilingual polarity detection and topic identification. I am currently working on an upcoming research project titled ‘Actionable situational intelligence for urban events using social media’.

I am keen to create value using small data by leveraging pre-train models and hybrid approaches to uncover evolving relationship that can potentially be transformative. Using small data and word embedding approach, I have studied students’ informal reflection and implemented a doubt identification approach to aid in personalised learning. Besides that, I am also looking at methods in analysing trends and development in digital business and transformation through cases and news sources. Prior to my teaching and research experience, I was a senior consultant and analyst for the legal, government and financial sectors in the areas of digitalization, application development and delivery.

My research is in the area of Digital Banking and FinTech. I am the architect of SMU Teaching Bank (SMU tBank), a digital banking platform developed by students for teaching and research purposes. My related research questions include; (1) How can microservices architecture minimize the impact of large scale change? (2) What are the best practice methods for migrating from a monolithic (all in one) core banking system to a microservices-based (coreless) banking system? (3) How are non-bank FinTech alternatives disrupting traditional banks?

SMU tBank is used to support course modules covering; retail banking, corporate banking, payments, and solution / enterprise architecture related courses. As a benefit of developing banking applications (e.g. Internet/Mobile Banking, Payments Gateway, Trade Finance, Conversational Banking, AI-driven Chat-bot, etc.), as well as developing the underlying microservices, students gain a deep technical understanding of how a bank works.

Prior to joining the faculty at SMU-SIS, my roles in the banking industry included; Vice President and Head of Service-Oriented Architecture at OCBC Bank, Senior Enterprise Architect at ANZ Bank, and Chief Technology Officer at TIBCO Software Asia supporting over 20 banks across the region

My research revolves around empirical software engineering in areas of software designs and software reuse. I focus on methods, techniques and tools that enable software solutions to provide impactful outcomes. These outcomes can be business-related such as profitability or quality related such as security and productivity. I am also interested to apply machine learning techniques to improve student’s learning outcomes and design methods to improve the curriculum of design-related courses for lifelong learning.

I have a total of over two decades of industry experiences implementing large-scale IT projects and academic experiences delivering courses for adult learners. My involvement in both academic and industry enabled me to appreciate the academic rigour and the challenges of industry projects.

My research broadly addresses the problem of engineering correct and secure software/systems. I have co-developed techniques for testing critical infrastructure (e.g. water treatment plants) based on fuzzing and machine learning, tools for analysing execution models of concurrency APIs, and theories for proving the functional correctness of programs.

I am keen to supervise projects that touch upon these domains, or that aim to implement practical testing/analysis techniques for other ‘non-traditional‘ systems, such as cyber-physical systems, microservice architectures, or smart contracts. Prior to joining SMU, I also held posts at ETH Zürich and SUTD.

I have two main areas of work. The first one is Data and AI for education. This work includes research on the use of data analytics for learner profiling, development of algorithms for the personalization of learning and recommendation systems. Typical problems in this domain will be in the field of learning management systems and in the design of mass open online courses.

I am also deeply interested in Education on Environmental Sustainability and technology. I am interested in various aspects of this area addressing questions around the following – (a) The design of Problem based Learning scenarios that allow students to develop a systems thinking approach to Sustainability Education, (2) Tools and Technologies to address sustainability related issues, including but not limited to Artificial Intelligence, Internet of Things, Immersive environments, Design thinking and prototyping (3) Scalability of the proposed pedagogies via digital platforms (4) Learning analytics, evaluation and gleaning of insights into learner methods and learning processes.

Prior to teaching, I was involved in the development and evaluation of STEM programmes addressing the UN sustainable development goals and also in the development and use of learning technologies in large scale settings.

My current areas of research interests include digital business technologies (e.g. analytics, cloud, AI, IoT) and transformation, enterprise systems and integration, and education pedagogy. Following is a brief overview of my recent research interests.

By effectively leveraging digital technologies namely cloud computing, big data and analytics, mobile networks, social media, artificial intelligence, blockchain and the Internet of Things organizations can go beyond boosting efficiency and drive new business models, develop new revenue streams, or drive other material changes that lead to an increase in the top or bottom lines. My research in this area focuses on developing decision frameworks, methodologies and architecture patterns for enabling digital transformation and building smart processes.

Technology Enhanced Personalized Learning (TEPL) is focused on enhancing student learning experience through the use of technology. If used effectively, technology can enhance both live and online learning sessions. My research in this area focuses on applying analytics, artificial intelligence and other technologies to enhance student learning experience. We have built and evaluated a number of tools that help to promote personalized learning.

My research focus is on security testing and analysis of web and mobile applications.

I have worked on both industrial and academic research projects with industrial partners and European universities. One of the major project is access control testing of distributed information sharing platform in collaboration with HITEC, International Emergency Response and Crisis Management Centre based in Luxembourg. I also worked on requirements-driven security testing of mobile applications in collaboration with university of Geneva and industrial partners from UK and Switzerland.

Currently I am working on malware analysis of mobile and IoT apps. My research leverages on program analysis, constraint solving, search-based testing, and machine learning techniques.

My current research focuses on large scale social sensing in popular social networking platforms (e.g. Reddit, YouTube, Twitter, Instagram, etc.) and multi-player video games (Mobile Legends Bang Bang, League of Legends, and a number of other Massively Multiplayer Online Games). Using statistics, machine learning and social science theories, I research how people connect, collaborate, mentor, exchange and spread information across large social networks, build long-term relationships, and form communities. My research seeks to capture and analyze social conversations about a wide range of social topics such as politics, health (e.g. epidemics, pandemics, mental health), economy, entertainment, sports, etc.

I am an industry practitioner with 15 years of experience in Software Development and Data Science in a variety of domains such as computational biology, computational social science, healthcare, legal, video gaming, marketing, and logistics & supply chain management. I build data solutions - crawlers, scrapers, databases, ETL, models, visualization tools and analytics pipelines in the cloud. As an Amazon Web Services Academy instructor and a strong cloud computing advocate, I incorporate cloud computing into my teaching and research. I work with student organizations to offer cloud workshops on SMU campus. I am an avid video game player, and I actively research ways to make learning “game-like” - fun and engaging. Prior to joining SMU, I worked at IBM, Thomson Reuters and Ninja Metrics.

My current interests are in the areas of novel blockchain applications, cloud/edge computing security, and distributed machine learning. More details are as follows: Collaborative, privacy-preserving machine learning enabled by private blockchain systems (e.g., Hyperledger Fabric). There are many interesting practical applications in the research area. Recently, my team successfully implemented a Hyperledger-based prototype system for decentralized, regional air traffic flow management (project supported by CAAS and NTU). My team also investigated similar approaches in the areas of financial fraud detection, and other industrial problems, e.g., microgrid and power system management (in collaboration with NTU, supported by EMA).

Efficient and secure resource optimization approaches for cloud and distributed systems - my recent work include resource management for machine learning applications, and secure cloud virtual machine placement.

I also aim to develop autonomous agents for teaching and learning of computer science fundamentals for AI in Education. My team started by investigating neural approaches for automatic assessment of textual answers in a computer science course.

My research interests span across two main areas: healthcare analytics and learning analytics. My healthcare research examines healthcare processes by taking the data-driven approach. I analysed and addressed operational issues involving patient flow and test procedures in the emergency room, downstream bed management and surgical scheduling processes. The techniques I applied include simulation, machine learning algorithms and optimization. Over the last decade, I worked directly with healthcare institutions, bringing research to practice.

My learning analytics research involves analysis of dual-perspectives feedback mechanism and its impact on student learning. The mechanism consists of learner-centric reflection and instructor-centric analytics which provide guiding principles for interventions required to help students learn. An on-going project is an automated text-mining approach to scoring students‘ understanding and identifying doubts for personalized learning.

Prior to my career in SMU, I spent seven years implementing large-scale enterprise systems as an IT consultant. My experiences allow me to appreciate how impactful research is adopted in the industry and how industry problems define impactful research.

The current focus of my research is in the practical and pedagogical applications of data analytics and other software tools, as well as their theoretical and mathematical foundations. Some of my ongoing projects include applications of text analytics in legal documents and statistical analyses of student participation and performance to track learning. As a physicist from CERN, I get fascinated by the more foundational (or even philosophical) aspects of some of the algorithms and tools we use. One such ongoing project is to define and defend metrics for clustering quality, providing a basis by which we can consider the output of one clustering run better or worse than another one. Another one is the development of a simple, but statistically rigorous, classification algorithm for pedagogical purposes. Such algorithms, which take the purely statistical concepts to practical applications, are crucial in teaching the foundations of analytics. On a different front, I have been recently working on implementing secure online assessments and automated grading tools: a purely pedagogical application that the current COVID-19 situation rendered relevant.

My research areas are in Natural Language Processing (NLP) and Natural Language Understanding (NLU), Machine Learning, Data Analytics, Text Mining, Social Media Content Mining and Analysis, Fine-grained Sentiment and Emotion Analysis, Opinion-based Stock Market Trending Analysis, Deep Language Understanding and Intelligent Robots, Casual Reasoning and Language Understanding, Image Processing, Safety and Risk Analysis, Artificial Intelligence (AI) & Computational Intelligence (CI) and their Applications. Future plans include research in Next-Generation AI which integrates vision, language understanding, reasoning, and learning capabilities to build a unified framework for intelligent computer systems and next-generation intelligent robots for real world applications.

Prior to joining SMU, I was a Senior Scientist at the Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR), Singapore, for more than 8 years. At IHPC, I spearheaded the filing of more than 5 IPs as the first inventor which generated a number of commercial licenses. In addition, I have 15+ years of R&D experience as a scientist and academic in big data and AI technologies. I have published 60+ papers in international journals and conferences.