Vishal Gupta is an Associate Professor of Data Sciences and Operations at the USC Marshall School of Business. Because of his research interests and expertise, he also holds a courtesy appointment in USC Viterbi’s School of Engineering in Industrial and Systems Engineering and is an affiliate faculty with USC’s Center for AI and Society.
Before joining USC, Vishal Gupta completed his B.A. in Mathematics and Philosophy at Yale University, graduating Magna Cum Laude with honors, and completed Part III of the Mathematics Tripos at the University of Cambridge with distinction. He then spent four years working as a “quant” in finance at Barclays Capital, focusing on commodities modeling, derivatives pricing, and risk management.
Eventually, Vishal realized how much he missed working towards a larger mission of impact, and left the private sector to complete his Ph.D. in Operations Research at MIT in 2014.
Vishal’s research focuses on data-driven decision-making and optimization, particularly in settings where data are scarce. Such settings are common in applications that rely on personalization (like precision healthcare) and real-time decision-making (like risk management). Consequently, his research spans a wide variety of areas including revenue management, education, healthcare, and artificial intelligence.
Vishal has received a number of recognitions for his work, including the Wagner Prize for Excellence in the Practice of Advanced Analytics and Operations Research, the Pierskalla Best Paper Prize, the Jagdish Sheth Impact of Research on Practice Award.
On his research:
“Although we’ve seen tremendous strides in using machine learning and artificial intelligence for prediction and generation tasks where source data is plentiful (like ChatGPT and DallE), high-stakes decision-making problems typically entails settings where data is scarce. We might want to personalize an educational curriculum for a particular student, but have limited data about students like that student. Or we might want to design emergency medical and disaster response protocols but such large scale disasters are (thankfully) rare. My research focuses on these high-stakes, scarce data decision-making settings and developing algorithms that provably and practically use what little data we have to make the best decisions we can.”