SMU Professor of Computer Science David Lo is honoured with IEEE Fellow, the highest grade of membership in the IEEE, for his contributions to “synergising software engineering and data mining”. IEEE is the world’s largest technical professional organisation dedicated to advancing technology for the benefit of humanity.
The Board of Directors confers it upon a person with an extraordinary record of accomplishments in any IEEE field of interest. Less than 0.1% of voting members are selected for this member grade elevation each year. The number of newly elevated IEEE Fellows under age 40 is scarce, and Prof Lo belongs to this youngest category of new IEEE Fellows this year.
Prof Lo belongs to the IEEE Computer Society (IEEE CS), an international organisation with more than 373,000 individuals, which focuses on advancing all aspects of Computer Science and Engineering. He is one of the 56 IEEE CS members worldwide – the only one in Singapore – elevated to the IEEE Fellow status in 2022.
He is also among the Fellows of Automated Software Engineering (ASE). ASE Fellows are deemed to “have rendered significant and sustained contributions to the ASE Community, through their scientific accomplishments and service to the ASE Conferences, ASE Journal, and the ASE research community.”
Prof Lo said, “I am honoured and humbled to receive these recognitions. I want to thank my PhD advisor, mentors, mentees, and collaborators for their help, support, and contributions, without which these recognitions would not have been possible. I am grateful to the School of Computing and Information Systems (SCIS) and SMU for providing me with much support in the last 13 years to grow as a researcher and mentor. These recognitions are a great encouragement for me to continue contributing to the research community, especially to the software engineering and data science fields, as a member of a vibrant group of researchers at SCIS and SMU.”
Prof Lo obtained his Bachelor degree in Computer Engineering from Nanyang Technological University in 2004 and his PhD degree in Computer Science from the National University of Singapore in 2008. He joined the School of Information Systems (former name of SCIS) as a Lecturer in May 2008.
His research work has created an impact in several ways. He has published more than 400 papers in refereed conferences and journals. They have attracted vast interest from the research community and inspired many subsequent studies that push the frontiers of software engineering and data science knowledge. As of April 2022, Google Scholar has more than 19,000 citations listed, with an H-index of 75.
Among his research are works conducted with industry partners, leading to papers presenting state-of-the-art solutions deployed in practice and unique insights into industrial software systems and processes. In a recent paper, he and his co-authors presented a state-of-the-art system deployed in practice to detect emerging issues of the WeChat app by analysing a stream of user feedback.
Prof Lo has successfully trained, as main advisor, 12 PhD students at SMU who have secured employment at high-tech companies and world-class universities across the globe. His research work has also created collaborations between SMU and other universities in over 20 countries, resulting in works published in renowned conferences and journals across various areas of computer science research.
In addition, he has contributed to the research community by co-organising conferences and workshops and serving in the programme boards, programme and steering committees, editorial boards of many top-tier and leading conferences and journals. He served/currently serving as the General Chair/Programme Co-Chair of 11 international conferences, including the 31st IEEE/ACM International Conference on Automated Software Engineering, held at SMU in 2016. He is also serving on the editorial board of IEEE Transactions on Software Engineering, Empirical Software Engineering, IEEE Transactions on Reliability, Automated Software Engineering, Journal of Software: Evolution and Process, Information and Software Technology, Journal of Software Engineering Research and Development, Information Systems, and Neurocomputing.
In April 2020, he co-founded the Research Lab for Intelligent Software Engineering (RISE), which researches the intersection of Software Engineering, Artificial Intelligence, and Cybersecurity to improve software quality and reduce software cost.
Prof Lo was awarded the 2021 IEEE CS TCSE Distinguished Service Award for his “extensive and outstanding service to the software engineering community in his many roles in major software engineering conferences and journals”. He is the first in Singapore and second in Asia to have received this prestigious award.
He received several SMU research awards: the Lee Foundation Fellowship in 2009, the Lee Kong Chian Fellowship in 2018, and the Lee Kuan Yew Fellowship in 2019. He has also received 17 international research and service awards, including 12 most influential/best/distinguished paper awards.
He designs data science solutions that transform passive data into tools which improve developer productivity and system quality and generate new insights. His research is at the intersection of software engineering and data science (software analytics), combining socio-technical aspects and analysing different software artefacts (code, execution traces, bug reports, Q&A posts, user feedback, and developer networks) and their interplay.
In addition to his current research work on software analytics, Prof Lo is keen to solve an emerging problem - how best to adapt software engineering processes and tools currently used to design conventional software for AI system development.
The Association for Computing Machinery, ACM is the world’s largest educational and scientific computing society. In 2019, Prof Lo was named ACM Distinguished Member for his outstanding scientific contributions to computing. He was among the 62 members worldwide to achieve this recognition.
AI is advancing rapidly and incorporated into many systems that humans interact with daily, such as self-driving cars. One immediate goal is to investigate and characterise the limits of current software engineering best practices and tools to AI system development and design novel solutions that address those limitations.