Harvard | Mr. Finance in Tech
GMAT 760, GPA 3.9
Wharton | Ms. Female Engineer
GRE 323, GPA 3.5
Foster School of Business | Mr. Tesla Gigafactory
GMAT 720, GPA 3.0
Stanford GSB | Mr. Global Innovator
GMAT 720, GPA 3.99
Stanford GSB | Mr. Global Energy
GMAT 760, GPA 7.9/10
London Business School | Mr. CFA Charterholder
GMAT 770, GPA 3.94
Tuck | Mr. Federal Civilian
GMAT 780, GPA 3.4
Kellogg | Mr. Texan Adventurer
GMAT 740, GPA 3.5
London Business School | Mr. Impact Financier
GMAT 750, GPA 7.35/10
Kellogg | Mr. Class President
GRE 319.5, GPA 3.76
Harvard | Ms. Media Entertainment
GMAT 740, GPA 3.3
Berkeley Haas | Mr. Upward Trend
GMAT 730, GPA 2.85
Kellogg | Mr. Defense Contractor
GMAT 730, GPA 3.2
Berkeley Haas | Mr. Work & Family
Berkeley Haas | Mr. Navy Electronics
GRE 316, GPA 3.24
Kenan-Flagler | Ms. Big Pharma
GRE 318, GPA 3.3
Wharton | Mr. Big 4
GMAT 770, GPA 8/10
Berkeley Haas | Mr. 1st Gen Grad
GMAT 740, GPA 3.1
Berkeley Haas | Mr. Career Coach
GRE 292, GPA 3.468
Kellogg | Mr. Indian Globetrotter
GMAT 750, GPA 4.0
Stanford GSB | Mr. S.N. Bose Scholar
GMAT 770, GPA 3.84
INSEAD | Mr. Indian In Cambodia
GMAT 730, GPA 3.33
Duke Fuqua | Mr. O&G Geoscientist
GRE 327, GPA 2.9
Chicago Booth | Mr. Indian O&G EPC
GMAT 730, GPA 3.75
Wharton | Ms. Product Manager
GMAT 730, GPA 3.4
Berkeley Haas | Ms. Jill Of All Trades
GRE 314, GPA 3.36
Berkeley Haas | Ms. Midwest Startup
GRE 328, GPA 3.51

B-School Master’s: Spotlight On Carnegie Mellon Tepper

Carnegie Mellon Tepper School of Business

Carnegie Mellon Tepper School of Business

It’s not everyone who can say that their introduction to stochastic calculus was a formative experience. In fact, it’s probably fair to say that the vast majority of people have never even heard of this somewhat obscure and incredibly difficult branch of mathematics. But for Calvin Zhu, learning about stochastic calculus during his undergraduate math program at NYU meant more than merely deepening his skill in complex calculations – it showed him how math was applied in the real world, in the finance sector. Using stochastic calculus to model probabilities, he learned, could provide an advantage in the stock market. “The idea really hit me, like, ‘Wow, here I thought stocks were really, really random, no one knows what to do with them, but you can gain a little edge . . . by knowing this information.’ I was hooked after that,” Zhu says.

Still, his grounding in math was mostly around theory, and he was going to need to find a job. Next stop after NYU was another school, Carnegie Mellon, for a master’s degree in computational finance (financial engineering) at the Tepper School of Business. “The MFE has been a great launchpad for my career, coming straight out of undergrad . . . with basically no relevant work experience,” says Zhu, 22. “It’s been a great experience and a great opportunity for me.”

Tepper School master's student Calvin Zhu

Tepper School master’s student Calvin Zhu

The program’s emphasis on applying math in the finance industry complemented Zhu’s background in theory, he says. But it was the discipline required in the courses, and the thought processes required for completing the tasks assigned in class, that gave Zhu the most value, he says. “The most important thing is not really the material but the process of going through a very challenging problem and solving it in a very systematic but very creative way,” says Zhu, who was born and raised in New York City and took the program there. Tepper also offers the degree at its Pittsburgh campus.


The program – which attracts about 1,000 applicants for each class of about 100 – led to an internship at AQR Capital Management, where he performed so well he’s been offered a job. After he finishes at Tepper in a month, he’s going back to AQR, as a portfolio manager on the stock-selection team.

The interdisciplinary program that propelled Zhu from being a young and inexperienced student to a portfolio manager was the brainchild of a Tepper professor who identified a need in the financial services industry for employees with powerful math and statistics skills, who could also “talk like an MBA” when it came to standard business matters, says Rick Bryant, executive director of the master’s in computational finance program. When the big banks were asked whether they’d hire such people, “The banks said, ‘Absolutely,’” Bryant says. The Carnegie Mellon MS in Computational Finance was born.