EngD Students

“Business transformation and innovation” has been a consistent theme throughout my career, and continues to be an area which I remain passionate about. I am currently heading up the Transformation Office at the Maritime and Port Authority of Singapore (MPA), and am excited to be part of the machinery that will shape the future of Maritime Singapore to remain as global frontrunners. With digitalisation and decarbonisation key drivers shaping the next frontiers of transformation for the maritime industry, I was looking at a doctorate programme to add rigour to how I can better untangle system complexities to drive digital transformation.

Pursuing the EngD program with SMU was a straightforward decision. I completed my MBA with SMU many years back, and thoroughly enjoyed that experience. I truly believe and appreciate the global perspectives, industry connections and broad holistic education I received then, and am hoping that my EngD journey will also be an exciting and fulfilling journey of self-discovery.

I am particularly interested in the adoption of AI in the maritime industry, in particular to increase the capacity of our limited sea space for maritime activities, as well as to transform jobs to make maritime a “port of call” for talent. I believe we must effectively realise the value of AI for successful digital transformation to strengthen Singapore’s position as the premier global hub port and maritime centre.

As Greek philosopher Heraclitus quoted the saying of “change is the only constant in life”, we see that technology has also been constantly changing through the many decades. With this evolution in technology, the usage of AI has accelerated greatly with the advancement of AI models and programming. Not only does it enhance creativity in all possible areas, but also allows for the purse of innovative solutions which might not have been possible in the past.

From a student studying AI programming in the 1990s during my tertiary days, to a student studying AI in the 2020s in my postgraduate days, I observe a huge significant change in AI coding as well as its application in modern days. With Generative AI gaining tremendous popularity, the trajectory of AI usage will propel to a whole new level.

Medical science has been evolving with technology, and with the increasing usage of AI, they allow uncharted grounds of the medical field to be explored and researched. In today’s world, AI has been greatly used in the different fields in medical science such as diagnosing heart diseases, discovering of lung cancer, radiology, diagnosing stroke in emergency department, supporting healthcare systems to react and manage COVID-19 situations, as well as analysing images and medical data, and so on.

Embarking on my newfound journey with the SMU Doctor of Engineering, I am looking forward to research on the AI methodologies as well as its different techniques. The target domain of my research is medical data analysis, especially in the field of TCM (Traditional Chinese Medicine). I believed that this program will hone my research skills and grow my technical knowledge in the practical applications of AI in real life scenarios.

With a career dedication that spans over 20 years in network engineering, I have cultivated a deep-seated expertise in orchestrating the design, research, and implementation of large-scale data centers, backbones, and metropolitan networks. In recent years, my work has been particularly focused on large-scale high-performance network technologies, which are the primary means of communication technology during GPU training. Throughout this experience, the objective has been to provide the most robust infrastructure support for advanced AI/ML applications. It became apparent that a significant challenge was in ensuring optimal integration of AI within our existing infrastructure.

My research will canter on enhancing the acceleration ratio and utilization rate of AI clusters. This involves optimizing the cluster architecture, allocating computing and storage resources most effectively, and improving the training processes' affinity with physical infrastructure. I aspire to continue to be an innovator at the vanguard of technology, tackling the evolving engineering challenges presented by ML/AI fields. The academic rigor and the focus on practice research at SMU’s Doctor of Engineering program align perfectly with my goal to develop groundbreaking solutions. I am eager to commence this new chapter of academic exploration.

Pursuing a Doctoral Degree was something I had been considering for a while. I wanted to pick up skills in Data Analytics (DA) and Machine Learning (ML) because I think there are boundless opportunities to make work safer, more efficient, and more effective in many different industries.

Being an electrical engineer by training and having worked in the engineering field for the last 20 odd years as a naval systems engineer, I have learnt that understanding DA and ML at a superficial level will be insufficient for me to evaluate DA/ML products and make design or acquisition recommendations professionally. Thus, pursuing the SMU’s Doctor of Engineering Degree is a natural decision.

My research interest will be on uncovering the latent power of Small Data. There are many segments of industry where Big Data is widely deployed. At the same time, there are others where Big Data is not effective due to limited data volume or computing power. By harnessing the latent power of Small Data, we can push ML capabilities to the edge and in niche domains that have yet to be explored.

I like to see Small Data-optimised techniques to advance ML deployment to areas where current Big Data solutions could not adequately serve. Some examples of these are embedded medical implants, remote exploration or military applications where high bandwidth and intensive computing power might not be available.

Over the course of my career, I have been actively blending the use of technology with investment and business functions to design and deploy solutions that create a lasting impact.

My research centers around employing and enhancing appropriate machine learning algorithms such as neural networks for process analysis, optimisation, remediation and predictive capabilities to improve process flows. Under the mentorship of a world-class faculty, I endeavor to create a solution that can be scalable to a variety of workflow scenarios.

With my engineering and econometrics background, I believe that SMU's Doctor of Engineering will allow me to deliver proper industrial solutions necessary in furthering our digital economy to the next level. The various centres, labs and initiatives that SMU SCIS has will also provide excellent insights and exploration to how we can further optimise the financial technology scene using artificial intelligence.

During my 7+ years working in the banking industry, I had the chance to realize the importance of new financial technologies and data analytics in solving the industry’s problems, improving business processes, creating new, innovative products and solutions for customers. As an MITB program graduate, I was well equipped with practical trainings in financial technologies, novel business models, statistical and data analysis, as well as independent research skills. These have laid a solid academic foundation to pursuing the EngD program.

Sustainable finance has attracted great interests from investors, companies, financial institutions, and regulators. Many global banks have adopted Environmental, Social and Governance (ESG) criteria in their lending decision making, while more and more companies are keen on monetary incentives of sustainable financing besides long-term benefits of sustainability. As a relatively new area, sustainable finance has many intrinsic technical problems that have not been tackled yet.

During the EngD program, I will conduct research on how digital technologies, such as blockchain, internet of things, and machine learning, can foster the development and adoption of sustainable finance. SCIS is home to a world-renowned faculty, and there are many professors possessing very strong expertise in research fields that are relevant to my proposed topic. Moreover, Singapore is a global financial center. Its government has also set a vision for the country to become a regional hub for sustainable finance. Consequently, there have been schemes rolled out to promote this emerging form of financing. Doing my proposed research in Singapore, thus, would benefit from a spillover of such promotions.

I am the Systems Security Group Leader, Cybersecurity department with the Institute for Infocomm Research (I2R). In this role, I lead a team of scientists and engineers to solve problems in 5G Cybersecurity and AI for Cybersecurity. I am also a co-Investigator in the National Research Foundation (NRF) funded Singapore Blockchain Innovation programme. Prior to these engagements, I have co-founded and exited a venture-capital backed startup, worked in two other startups and constantly exploring similar opportunities. I am innovation-centric and aim to bring impactful technologies for commercial use.

Impact requires innovation, and successful innovation must integrate knowledge not just from technology, but also from diverse domains such as economics, law, public policy as well as perspectives from social sciences and humanities. I believe that SMU is the best place to pursue interdisciplinary engineering and truth be told, I like the SMU campus!

With over two decades of technical experience in Cybersecurity and AI across multiple stints in startups and R&D centric organisations such as A*STAR, my key strength lies in identifying good commercial opportunities in technology. I am an engineer, scientist and entrepreneur rolled into one.

Machine learning (ML) inference is probabilistic, not deterministic - so ML will not be correct all the time. Second, ML models are created using historical data and past data is most likely not a good indicator of future data - so ML will deteriorate over time. Third, many ML systems use multiple ML models chained together. Each of these models are likely to be trained independently, so the overall system could have unknown and unexpected behaviour. Professor Michael Jordan, a world renowned professor at Berkeley says that a new engineering discipline in decision making is emerging. I want to contribute to this new emerging discipline by developing engineering best practices for managing ML degradation and failure. I am specifically interested in developing rigorous and quantitative metrics for ML requirement specification and ML testing for safety-critical systems in Cybersecurity (anomaly detection) and Healthcare (imaging).

Having been in the Information & Communications Technology (ICT) industry for over 20 years, I constantly keep myself relevant through structured learning by attaining 14 ICT/cybersecurity certifications, as well as two Master’s degrees (MSc and MBA). An opportunity came knocking on my door to begin a full-time career in academic teaching, with a focus on Cybersecurity. Through teaching, I caught the interest in pedagogy and many education related subjects. As a practicing professional, applied research is preferred over basic research, so pursuing the SMU Doctor of Engineering (EngD) programme is a natural progression for me.

SMU’s EngD programme offers ‘Computing Practice & Education’ as one of its integrative research areas, where I established my interest in Technology-Enhanced Learning and Pedagogy. The workload for the part-time EngD programme is well spread out, making it suitable for working professionals.

Lifelong learning is important to ICT professionals as technology is constantly evolving. Through the deeper study of Technology-Enhanced Learning and Pedagogy, my proposed research intends to enhance the learning effectiveness of ICT professionals, integrating training activities between classroom, workplace and personal learning space. The outcome of this research could serve as a compass that helps stakeholders better calibrate the use of appropriate tools and resources, depending on digital workplace maturity and employee readiness.

I am currently working at Grab as a data scientist, focusing on pricing services for the company. My work encompasses applying reinforcement learning techniques to optimize pricing strategies that requires one to have strong math background, computer science capabilities and research experience. To acquire such deep knowledge and skills, I have decided to pursue a professional doctoral degree. Additionally, I am also interested in equipping myself with more research training as I would like to contribute more practice research to the industry.

As I have completed my Master of IT in Business (MITB) programme and worked as a research engineer at SMU SCIS, the SCIS Doctor of Engineering (EngD) became my first choice naturally. I believe this programme has the right balance in academic rigor and practical industry applications.

Through my education and work experience, I have acquired a solid foundation in math, software engineering and IT project management. This has led me to propose my research topic - “Designing an artificial environment with state transition to solve the real world pricing problems” as it is closely related to my daily work.

In my opinion, there is an inclination in the industry to apply reinforcement learning to adjust pricing strategies as an alternative to seeking economic experts’ inputs. This will result in reduced cost and better agility in terms of adjustments in pricing strategies in response to market dynamics.

A frequent challenge we face at work is the absence of state transition using simple artificial environment when we try to define and test different pricing strategies. As a result, this inhibits our understanding of the potential long-term impact to the market if we apply the strategies without the state transition interface. My research topic aims to solve this challenge by using state transition techniques to enable an artificial environment to be “self-evolving”, much like a real market situation where we interact with it with proposed strategies.

With over 25 years of diverse experience, my career commenced in software development and engineering, and later expanded into data communication network engineering and distributed systems. Progressing through roles as a sysadmin, network manager, pre-sales and Senior IT Architect, I eventually shifted my focus to regional business development, driving sales and business growth while providing solution consulting in various industries. My expertise spans telecommunications, multimedia platforms, IT optimizations, and cutting-edge technologies such as 5G, SDWAN, Security, Network Infrastructure, and MultiCloud Solutions in driving technology sales. Having completed two engineering and one business Master's, I now brace myself to take the next step – pursuing the Terminal Degree.

As an enterprising and aspiring doctoral candidate, I look forward to embarking on the distinctive SMU Engineering Doctorate’s practice-led approach to applied research. My transformative research endeavors venture into harnessing the potential of the dynamic convergence of AI/ML, cybersecurity, and quantum information science, charting a course towards cutting-edge technology to reshape the digital landscape and foster innovation. I am driven by a passion for industry transformation, discovering opportunities by solving real-world challenges, and creation of meaningful impact across various industry domains.

My research will be primarily focused on industry applications, delving into AI/ML techniques inspired by cutting-edge developments in deep learning, natural language processing, computer vision, and hybrid supervised and unsupervised machine learning algorithms. I aim to explore how AI/ML can enhance and integrate cybersecurity measures, encompassing anomaly detection, intrusion prevention, threat intelligence, network security, and vulnerability assessment. Additionally, my research will emphasize the significance of human elements, particularly human behavior, and its implications for cybersecurity.

With a hands-on approach, I am committed to understanding the seamless integration of AI/ML technologies into existing security practices, empowering industries to respond swiftly and effectively to cyber threats. Moreover, recognizing the real-world impact of AI/ML technologies, I prioritize ensuring the security and reliability of AI/ML systems themselves. A comprehensive understanding of designing and implementing robust and trustworthy AI systems is crucial to instilling confidence in AI-powered applications within the cybersecurity realm.

The results of these studies have the potential to be generalized and extended, providing valuable solutions applicable to data science and big data engineering in general.

To further enrich my research, I plan to infuse it with quantum information science, exploring the transformative potential of quantum computing and cryptography. Envisioning a future where industries can harness the power of quantum technologies in cybersecurity, such as data privacy, integrity, and secure communications, as well as highperformance computing platforms, excites me. This forward-looking perspective promises to revolutionize digital transformation in high performance computing, next generation AI/ML, and digital security across industries, advancing fields like finance, healthcare, telecommunications, and beyond.

With more than 24 years of experience in technology and software development, I am currently pursuing a Professional Doctoral Degree in the SCIS EngD programme, with a focus on the application of generative AI technologies within the financial services industry. In my current role as VP R&D at Mastercard, I bring invaluable practical knowledge to my research.

My proposed research focuses on revolutionizing the financial sector by harnessing the power of generative AI to improve decision-making processes and enhance customer experiences. My primary research interests include risk management, investment strategy, and customer engagement. By leveraging technologies such as machine learning, artificial intelligence, and data analytics, I aim to develop advanced generative AI algorithms that can simulate various market scenarios, predict financial market trends, and build personalized chatbots and recommendation systems.

Centred at the intersection of AI, financial services, and software development, my research is aimed at creating adaptive, resilient systems to address complex challenges and explore innovative solutions. Ultimately, I aspire to develop innovative AI-driven solutions that address critical challenges and capitalize on new opportunities. By combining a robust academic foundation with extensive industry experience, I am poised to make a meaningful impact in the field, driving the responsible and sustainable use of AI in financial services.

Pursuing a Professional Doctoral Degree in the SCIS EngD programme empowers me to drive the advancement of cyber-resilient systems in critical infrastructure. My passion lies in conducting ground-breaking research focused on designing and developing cyber-resilient systems, with a specific emphasis on cyber-physical systems and proactive defence mechanisms. The SCIS EngD programme stood out to me due to its commitment to interdisciplinary collaboration, strong industry partnerships, and seamless integration of academic rigor with real-world experience.

Drawing upon a robust academic foundation and 24 years of hands-on experience as a cybersecurity professional, including my current role as a Group Chief Information Security Officer, I bring invaluable practical knowledge to my research.

In my proposed research, I aim to leverage technologies such as machine learning, artificial intelligence, and data analytics to enhance the detection and mitigation of cyber threats in critical infrastructure systems. My ultimate goal is to develop proactive defence mechanisms and incident response strategies that exhibit both robustness and adaptability. By effectively bridging the gap between theoretical knowledge and practical insights, my work seeks to create a meaningful impact that extends beyond the confines of academia.

Through improving the detection and mitigation of evolving cyber threats, my work will significantly improve cyber resilience, thus ensuring uninterrupted continuity of essential services in critical infrastructure systems. Moreover, the development of proactive defense mechanisms and incident response strategies will empower organisations to effectively combat cyber-attacks and minimise their potential impact.

In conclusion, my pursuit of a Professional Doctoral Degree in the SCIS EngD programme enables me to make substantial contributions to the advancement of cyber-resilient systems. By combining my strong academic foundation with extensive practical experience, I strive to explore innovative approaches and address the complex challenges faced by critical infrastructure systems. Through my research, I am determined to strengthen the detection and mitigation of cyber threats while ensuring the adaptability and robustness of systems. Ultimately, the value of this research lies in safeguarding critical infrastructure and ensuring the seamless continuity of essential services in the face of emerging cyber threats.

As an Australian who has lived here for over two decades in Singapore, I am most familiar with how vibrant the technological eco-systems have been in this country. Even though I have access to free post graduate education in Australia, the SMU Engineering Doctoral (EngD) program so impressed me that I opted to continue to stay in Singapore to be part of this program.

What drew me to the SMU professional doctoral degree is firstly, the teaching pedagogy - which adopts a more blended approach of academic coursework coupled with practical industry / project-based research training. I find this approach best suited for my learning preferences than the more theoretical and research-oriented approach of the standard PhD program. Secondly, I also like the focus on solving practical problems in a real-world context more appealing than the focus on originality in research and evaluation of theory that is more typical of an academic PhD. Third, it is my long-term career interest to set up a technology start-up of my own at some point in my life. To this end, I believe a EngD doctoral program will serve my long-term career aspirations better.

My interests lie in researching natural language processing and how ChatGPT or any generative AI (GAI) can be applied in the real world. Specifically, utilizing GAI to create language models with a focus on a particular industry or domain, and developing the ability to comprehend and produce text unique to that field. This would entail calibrating the model to the particular language and terminology used in that domain by training it on a sizable quantity of text data unique to that domain. This research has practical implications as generative AI has the potential to benefit multiple industries.

After my graduate studies in Electronics and Computer Science, my career took me into Finance and Banking where I developed expertise in Markets and Trading. But beneath this, my passion for technology remained and the emerging developments in Artificial Intelligence and Distributed Ledgers had me captivated and yearning for more. I was keen to engage in applied research at the rich intersection of Markets and Technology. I embarked on this quest by ‘dipping my toes’ into the MTech program at NUS. While meant to serve as a litmus test of my aspirations, that journey became my inspiration, and by the time I completed the degree in 2022, the destination had changed to a milestone with a longing to continue the exploration and adventure.

While there are several options today for open learning in almost every subject, when pursuing areas of learning outside one’s immediate domain, it helps to be part of an environment that is stimulating and a program that provides a structured approach towards this endeavour. The industry-experienced faculty and student cohort combined with a city campus readily accessible after work made SMU the institute of choice, and the EngD program with its balanced focus on academia and practice research helped seal the decision.

My interests are diverse and varied across convergence across Markets and Trading. Current areas of active research include ‘Smart Beta Factor Allocation, and Best Execution Algorithms using Deep Learning & Evolutionary Optimization’ and ‘Real-time Clearing and Settlement of Derivatives using Decentralized Autonomous Organization’.

I have 22+ years of experience in Banking Technology and Operations, delivering transformation programs and, building, leading and motivating cross-cultural teams. I am currently working in United Overseas Bank Ltd, implementing tranformation projects in the Singapore Industry Integration Initiatives for Trade and Supplier Financing domain. Prior to this role, I worked in Citi’s Technology and Operations division, with teams across multiple geographies and delivered strategic programs. I am also serving as an adjunct faculty in SMU, lecturing topics on Digital Banking Architecture and Financial Markets for undergraduates.

As a veteran banking professional, I strongly believe constant learning is one of key principles to adopt. As an alumnus and adjunct faculty of SMU, I have first hand experience of knowledge, values and learning experience in SMU. In pursuit of higher educational objective, I decided to join the EngD programme as this provides a well-rounded learning experience covering relevant technology topics and focuses on quality research themes whichleverage my professional expertise.

Financial scams are on the rise.... We read in daily newspapers, watched news clips and repeated broadcast in social media posts about an alarming rise in financial scams in Singapore. In Singapore, we may be assured of personal safety in leaving a S$2000 mobile phone to block a seat in a crowded food-court, but unable to protect ourselves from the prying eyes operating millions of miles away to find a way to get data from the same phone, prey on our emotions, despair or greed and rob us of our hard-earned money and life-savings.

When someone is scammed, it becomes “blame the victim” discussion where lot of feedback were provided and reactive lessons were documented – but do we really learn the lesson here and attempt to prevent such an incident from happening to the next person? What are we doing as an advanced society to prevent these from happening instead of merely repeated warnings shared to the citizens?

I would like to perform a detailed analysis on this topic in Singapore, the extent of reporting and resolutions, and identify ways for new technologies to mitigate the problem. I intend to use my professional experience and learning from the EngD programme into this research and target to recommend systemic measures and controls to mitigate this issue.

I was working as a Deputy Director, Data Analytics for Monetary Authority of Singapore (MAS) leading efforts in Ml Ops and Productionising Machine Learning systems on premise and the Cloud. I have 12+ years of experience in software development and building data analytics systems which will help in solving complex research problems using AI applications.

I completed my Bachelors degree in Information Technology and my Master degree in Knowledge Engineering from Institute of Systems Science at NUS. During my undergraduate studies, I have implemented and published a paper on Bandwidth Optimization using Genetic Algorithms. My strong inclination towards research has driven me to implement a Gesture Recognition system using Machine Learning techniques as a final year project in my M.Tech Knowledge Engineering degree programme, resulting in a paper publication.

Therefore, I have the intent to formalize my research interests through a Professional Doctoral degree programme.

With AI adoption across industries, explainability of an AI system is important. There is an increase in automation using AI and the explanations of the decisions made by the AI system is essential to attain human trust. The inability to interpret the black boxes representing the Machine Learning and Deep Learning algorithms, will slow down the AI adoption. Therefore, the outcomes of the proposed research aim to increase the trustworthiness of the AI systems, achieve better understanding of the decisions made, and transferring the knowledge to other applications.

I am currently working as a Data Scientist within the FMCG industry where decision making processes are increasingly data driven. My research interest focuses on the integration of traditional data sources with geospatial analytics to drive smarter decisions. Geospatial Data Science is a niche field which is often less understood compared to other mainstream data science resources. As part of the programme, I hope to contribute to the field by enhancing the understanding of data science practitioners towards its applications and benefits within the industry.

SMU School of Computing and Information Systems has a team of high calibre faculty members with an established track record of developing solutions that are critical to industry partners. The Part-Time EngD Programme is especially suited for individuals who aspire to make significant contributions to businesses and societies by taking on wicked problems which are technically complex and also require deep business domain knowledge. It is also an attractive programme for working professionals like myself, allowing me to develop deep technical knowledge without disrupting my career plans.