By Associate Professor Zhang Meilin, Head, Master of Analytics and Visualisation Programme

1. What inspired you to pursue research in your specific field, and how has your focus evolved over time?
   My journey into research started with a background in Computer Science, which trained me in technology fundamentals but left me searching for ways to apply these skills in impactful, interdisciplinary ways. I was drawn to Operations Research (OR) because it blends applied mathematics with business insights to address complex, real-world problems. Although I began with limited business and advanced math knowledge, I was inspired by OR’s potential to solve social issues, particularly within computational social science and economic contexts. Over time, my focus has evolved from traditional frameworks to exploring realistic, data-driven approaches that address decision-making under uncertainty, which is increasingly vital for applications in manufacturing, transportation, healthcare, and the sharing economy.


2. Can you describe a time when your research led to unexpected findings or hiccups, and how did you handle it?
   One memorable challenge involved a large healthcare analytics project that aimed to streamline operations in Singapore’s Emergency Departments by examining doctors’ picking habits and scheduling. Initially, we anticipated certain behavioral patterns based on theoretical models, but our data revealed unexpected variability in decision-making among doctors. Instead of dismissing these anomalies, I shifted our approach to incorporate robust optimization techniques that could accommodate such variability. This pivot required additional time and resources, but it allowed us to present more realistic models that better represented the complexities of medical operations. It was a powerful reminder of the importance of flexibility and adaptability in research.


3. How do you see the practical implications of your research affecting your field or society at large?
   The practical implications of my research lie in creating more adaptable and realistic solutions for pressing issues in sectors like healthcare, transportation, and the sharing economy. By developing decision-making frameworks that embrace uncertainty and variability, my work enables organizations to improve service quality while also addressing social benefits, such as enhanced accessibility and responsiveness. This is particularly relevant for IoT networks, where robust optimization can help balance efficiency with quality of service, benefiting both providers and consumers. Ultimately, my research supports decision-makers in making data-informed choices that consider both economic and social dimensions.


4. What emerging trends or areas of study in your field are you most excited about, and why?
   I’m particularly excited about the intersection of robust optimization and AI-driven decision-making in the big data era. As data becomes more granular and complex, developing methods that integrate reinforcement learning and large-scale computing with robust optimization holds immense promise. These advancements are opening up new possibilities for on-demand services in the sharing economy, such as ride-sharing and on-demand delivery, where we can directly impact service quality and user satisfaction. Additionally, I’m thrilled to explore ways to make robust optimization more accessible, potentially developing user-friendly tools that democratize decision-making in uncertain, dynamic environments.