Tuck School of Business | Putting Models into Practice

by Kirk KardashianFeb 07, 2022

This previous summer time, Raghav Singal joined the Tuck school after incomes his Ph.D. in Operations Research in 2020 and spending a yr as an information scientist intern at Amazon. Ever since his undergraduate days on the University of Toronto, Singal, who performed tennis at a nationwide degree as a junior, has been within the intersection of machine studying and utilizing knowledge to assist us make higher choices.

That inclination first manifested for Singal throughout his 14-month internship in finance whereas he was a pupil in Toronto. He spent so much of that point doing portfolio optimization, which is the apply of utilizing knowledge to resolve how a lot cash to spend money on sure belongings when forming a portfolio. Singal had additionally completed some scientific analysis at Toronto, and when he in contrast it to his time in finance, he discovered that the method of gaining new data was extra intellectually stimulating than placing previous data into apply, and he favored the mental freedom of academia.  

Singal pursued his doctorate at Columbia, and thru professor Martin Haugh, began pondering extra about fantasy sports activities—on-line video games utilizing digital groups composed of actual gamers, whose efficiency relies on statistics from their precise efficiency. “Given my background in portfolio optimization, fantasy sports activities simply clicked,” he says. “It’s a really comparable idea: your belongings, as a substitute of being shares or bonds, are athletes in the actual world, and you’ve got historic knowledge on them.” The main distinction is within the definition of successful. With monetary belongings, you don’t have an opponent, so it’s all about absolute efficiency. In fantasy sports activities, nevertheless, you solely must carry out effectively sufficient to beat your competitors. So Singal proposed a mannequin that adapts your athlete portfolio as your opponents kind their very own portfolio. He and Haugh then leveraged that mathematical mannequin to design tractable computational algorithms, which they used to take a position cash in fantasy soccer through the 2017 NFL season. They ended up tripling their cash in simply three months and donated their winnings to charity. The paper that flowed from their analysis—“How to Play Fantasy Sports Strategically (and Win)”—was printed in Management Science in 2020.

Big data-driven organizations have possibly 100 fashions operating their operations. If one thing sudden occurs—say, stock is de facto low—it’s laborious to know if the info was unhealthy, or if the mannequin tousled.

With one other group of advisors at Columbia, Singal started finding out internet marketing. But earlier than he began constructing fashions, he interned at Adobe, in San Jose, CA. Most individuals know Adobe from their suite of inventive software program resembling Photoshop, Illustrator, and InDesign, however the firm additionally has an enormous presence in digital advertising administration, by its Adobe Advertising Cloud software program. The software program helps companies resolve how and the place to promote, and perceive which advertisements are working. In this area, Singal realized about the issue of attribution—determining which advertising channels (emails, social media, search engines like google and yahoo) bore essentially the most duty for a selected sale. Practitioners have shaped heuristics to guess at attribution, giving distinguished weight to both the primary advert a shopper sees, the final advert they see, or making use of uniform weight to all of the advertisements they see. Starting from these heuristics, Singal and his colleagues at Columbia created a mannequin that served as a unifying framework for all such heuristics and allowed the researchers to see each’s strengths and limitations. Then, they used the ensuing insights to suggest a extra justified strategy to attribution. Interestingly, their mathematical modeling led them to advocate for the uniform heuristic, however one which accounts for the chance that some of the advertisements a shopper sees could have been nugatory. Their mannequin is the premise of a paper printed on-line final month by Management Science, “Shapley Meets Uniform: An Axiomatic Framework for Attribution in Online Advertising.”  

Once Singal accomplished his Ph.D., he approached the attribution drawback from a distinct angle: as a provide chain problem for Amazon. Similar to how advertisers wish to attribute specific gross sales to sure advertising actions, Amazon wished to attribute errors from its provide chain forecasts to specific hyperlinks in its community of fashions. “Big data-driven organizations have possibly 100 fashions operating their operations,” Singal explains. “Each mannequin has an output, which turns into the enter of one other mannequin. If one thing sudden occurs—say, stock is de facto low—it’s laborious to know if the info was unhealthy, or if the mannequin tousled.” Singal and his colleagues on the University of Florida and Amazon developed a mannequin to raised perceive how info flows in these sorts of networks, and printed their work with the International Conference on Machine Learning (ICML).  

Singal is co-teaching the core course Analytics I with Professor Jim Smith and is wanting ahead to growing an elective that faucets into his ardour for machine studying and data-driven modeling.   


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