Analytics and machine learning have become key decision-making tools in a wide range of industries, thanks to the vast amount of available data, increase in computational power, and advances in optimization algorithms. In the forefront of this revolution, our research spans a variety of topics across theoretical and applied machine learning, including deep learning, reinforcement learning, online learning, multi-armed bandits, etc. We are working on the development and analysis of machine learning algorithms and their applications in energy, transportation, supply chain management, quality and reliability engineering, service systems, and business analytics.

Faculty members: Chen Nan, Cheung Wang Chi, Kenneth Huang Guang-Lih, Li Xiaobo, Liu Yang, Hieu Pham, Napat Rujeerapaiboon, Wang Guanyi, Ye Zhisheng, Zhang Junyu

Global supply chains, smart cities, infrastructure, and healthcare systems are all complex systems susceptible to abrupt, large-scale, and disruptive dynamics. Systems engineers are tasked with modeling, analyzing, and designing resilient strategies for crisis forecasting and management of complex systems in a wide range of industries. Complex systems engineering is an interdisciplinary field of engineering and management, relying on systems thinking principles to organize a body knowledge from engineering design, economics, finance, operations research, social sciences, etc., and using work-processes, optimization methods, and risk management tools to resolve real-life problems. Complex systems engineering ensures that all likely aspects of a project or system are considered and integrated into a whole.

Faculty members: Chai Kah Hin, Chia Eng Seng Aaron, Ng Tsan Sheng Adam, Li Haobin, Poh Kim Leng, Ye Zhisheng

Data science is an interdisciplinary field at the interface of statistics, computer science, and information science, consisting of a body of tools, concepts, and algorithms for collecting, analyzing, and interpreting data. By gathering and analyzing relevant data, we build statistical models to understand and describe the performance of a broad range of industrial and management systems, which can be used for prediction, control, optimization, and decision making.

Faculty members: Chen Nan, Kenneth Huang Guang-Lih, Ng Szu Hui, Annapoornima M Subramanian, Tan Kay Chuan, Tang Loon Ching, Ye Zhisheng

Energy efficiency and sustainability have become a pressing concern in today’s world due to expanding economies and growing populations. Our research is engaged in a wide range of analysis of energy, industrial, infrastructure, social, and ecological systems, investigating their functioning principles and examining their environmental and economic impacts. We use modeling and quantitative methods to improve the resilience and sustainability of energy, water, transportation, and other infrastructure systems.

Faculty members: Ang Beng Wah, Chai Kah Hin, Ng Tsan Sheng Adam, Poh Kim Leng, Christine Shoemaker, Su Bin, Tan Chin Hon, Tang Loon Ching, Ye Zhisheng

Firms and organizations need sound strategies to give direction and purpose, to deploy resources effectively, and to coordinate the activities within and across organizations and individuals. Innovation strategy is critical for firms seeking to gain competitive advantages in the global marketplace of today. An effective innovation strategy, including but not limited to research and development (R&D) and deployment and configuration of intellectual property rights, can help create value for the new products, services, processes and business models being developed, as well as to capture value and deliver value from these products, services and processes. Increasingly, the locus of R&D and innovative activities has been shifting from developed to emerging economies such as China, India, and ASEAN. Technology and innovation are becoming the cornerstone of the strategies of domestic and global firms operating in these economies. Therefore, it is pertinent for research to shed light on the innovation and technology strategies as well as policies of firms and organizations (including universities and government agencies) in these economies, which face frequent institutional and regulatory changes, weaker IP environments and stronger influence from non-market agents. It is important to understand how firms and organizations adopt a different set of innovation strategies and behaviors to protect and capitalize their knowledge assets across both developed and emerging market environments. A deeper exploration of these issues can provide critical insights to enhance the competitive advantages of firms operating in some of the more volatile and fast-changing environments and a better understanding of the innovative capacity of regional and national economies.

Faculty members: Amit Jain, Kenneth Huang Guang-Lih, Annapoornima M Subramanian, Yu Wei

Faculty members: Cheung Wang Chi, Li Xiaobo, Ng Kien Ming, Ng Tsan Sheng Adam, Napat Rujeerapaiboon, Christine Shoemaker, Wang Guanyi, Zhang Junyu

Stochastic modeling and its primary computational tool, simulation, are built upon probability theory, statistics, and stochastic processes to study complex systems under uncertainties. Such a system often takes the form of a large-scale network of interconnected resources, such as Internet, power grids, supply chains, social networks, and healthcare systems. We design system structures, evaluate system performance, assess risks, and manage resources, using tools and methodologies from business analytics, machine learning, optimization, and computation.

Faculty members: Chew Ek Peng, He Shuangchi, Li Haobin, Ng Szu Hui, Tan Chin Hon, Tang Loon Ching

A supply chain is a set of organizations that work together to design, produce, and deliver a product or service to a market, beginning with procurement of raw materials and ending with distribution of finished products or services to end-users or customers. We are working to improve the effectiveness, efficiency, and resilience of global supply chains, with a focus on port logistics and integrated global logistics.

Faculty members: Chew Ek Peng, Cheung Wang Chi, Li Haobin, Li Xiaobo, Liu Yang, Ng Szu Hui

Technologies play a dominant role in shaping the competitiveness of firms and economies, as well as help individuals have a fulfilling life. The process of coming up with important technologies requires firms to monitor technological trends, identify the right portfolio of technologies to invest in, organize the technological discovery and development processes by orchestrating the broader ecosystem and platform, seek venture capital or other funding, and determine appropriate strategy for wider diffusion and commercialization of the technologies. Consequently, this area of research examines technological and entrepreneurial strategies by closely examining the scientific, technological, organizational, market, environmental and policy level factors that contribute to the successful development and commercialization of technologies in both established companies and startups. The area of research also spans the study of technology’s impact on individuals and teams, and formation of technology-based entrepreneurial firms. The research area adopts cutting-edge quantitative and qualitative methods and approaches in pushing the boundaries of our understanding in this important and growing field.

Faculty members: Kenneth Huang Guang-Lih, Vincent Kuo, Annapoornima M Subramanian, Yu Wei

Research on technology management and change is inherently interdisciplinary. It draws upon management, economic, and sociological disciplines, as well as theories and perspectives derived from an in-depth understanding of pertinent technological and engineering domains to inform large-scale, systematic investigations. These domains include (but are not limited to) information and communications technology, biotechnology, pharmaceutical and life sciences, chemical, energy and clean technology, semiconductor, artificial intelligence, automation, robotics and computer science. The faculty in the department conduct research in technology management using innovative yet careful research designs to improve causal inference and understand relationships among the drivers of technological change. To do this, researchers often need to hand collect and develop novel and large-scale data sets and use approaches such as state-of-the-art econometric techniques, qualitative interviews and fieldwork, supplemented by deep contextual knowledge in management and technology. Research in this area deepens our understanding of the production and use of technology and knowledge management by individuals, firms and regions under different organizational and institutional configurations.

Faculty members: Amit Jain, Kenneth Huang Guang-Lih, Annapoornima M Subramanian, Yu Wei