Elliot Shin Oblander
Elliot Shin Oblander first became interested in marketing after taking Peter Fader’s course “Applied Probability Models in Marketing” as an undergraduate at the Wharton School of the University of Pennsylvania. “This course first taught me to think about modeling individual-level behavior and inspired me to take doctoral-level courses in statistics and economics for the remainder of my undergraduate career. I decided to pursue my PhD in Marketing where I can continue conducting research on modeling consumers’ preferences and decisions.”
Oblander chose Columbia Business School’s PhD program because it offers a great research environment and tight-knit community. “Within my department, the faculty are very accessible to students and are happy to engage in research with students even from their first year. Faculty in other departments are also very welcoming to marketing students and I have had the great opportunity to get primary training in econometrics and machine learning.”
Oblander found the curriculum to be rigorous, especially in the first year, but also learned substantially from it – both in terms of breadth and depth. “The comprehensive coursework allows for broad exploration of the field of marketing, while still allowing us flexibility to get training and work with faculty in economics, statistics, and computer science. I have very broad interests, and Columbia’s large and research-active marketing faculty provided me with the option to explore a variety of research areas. I now have much better awareness of different research areas within marketing, and have also greatly expanded my methodological toolkit.”
One of Oblander’s favorite courses is “Computational Models in Perception and Choice,” taught by Michael Woodford in the Economics Department. “This course has taught me a whole new perspective on quantitative models of decision making and has challenged me to think more deeply about the implications of standard assumptions in economic modeling.”
At Columbia, Oblander is exploring a number of different research areas but is particularly interested in exploring the intersection of econometrics and causal inference with machine learning methods. “Modern machine learning methods have increased our capacity to handle complex, unstructured data to extract interpretable features. I am exploring research that takes such methods beyond descriptive interpretation and incorporates unstructured data into econometric models. My hope is that this fusion of methodological approaches can help us answer new questions about how consumers make decisions based on complex information obtained through channels like online review content and customer service interactions.”
After completing the PhD program, Oblander intends to become a professor. “I plan to stay in marketing as my academic field, but am interested in collaborating with researchers in economics and other disciplines. The openness of Columbia’s faculty, both in my department and beyond, to work with a relatively junior PhD student has allowed me to jumpstart my research career and foster connections, allowing me to bounce around research ideas that I can pursue in the future.”