Stanford GSB | Mr. Marine Corps
GMAT 600, GPA 3.9
MIT Sloan | Mr. AI & Robotics
GMAT 750, GPA 3.7
MIT Sloan | Ms. MD MBA
GRE 307, GPA 3.3
Stanford GSB | Mr. Fundraising Educator
GMAT 510, GPA 2.89
Berkeley Haas | Mr. Work & Family
GMAT No GMAT Yet, GPA 4
Cornell Johnson | Mr. Fintech Startup
GMAT 570, GPA 3.4
Kellogg | Ms. Ukrainian Techie
GMAT 700 (ready to take it again), GPA 3.6
Kellogg | Mr. Pretty Bland
GMAT 710, GPA 3.5
Harvard | Ms. Sales & Trading
GMAT 730, GPA 3.5
NYU Stern | Mr. Long Shot
GRE 303, GPA 2.75
INSEAD | Mr. Consulting Dream
GMAT 760, GPA 3.1
Columbia | Mr. Alien
GMAT 700, GPA 3.83
Harvard | Mr. Veteran
GRE 331, GPA 3.39
Wharton | Mr. Naval Submariner
GMAT 760, GPA 3.83
Wharton | Mr. Second MBA
GMAT Will apply by 2025, GPA 7.22/10
IU Kelley | Mr. Builder
GMAT 620, GPA 3.3
Stanford GSB | Mr. Supply Chain Data Scientist
GMAT 730, GPA 3.9
Stanford GSB | Ms. Aspiring Entrepreneur
GMAT 750, GPA 3.8 (Highest Honor)
Yale | Mr. Environmental Sustainability
GRE 326, GPA 3.733
Yale | Mr. Project Management
GRE 310, GPA 3.3
Harvard | Mr. Samaritan Analyst
GMAT 690, GPA 3.87
MIT Sloan | Ms. Physician
GRE 307, GPA 3.3
Chicago Booth | Mr. Cal Poly
GRE 317, GPA 3.2
HEC Paris | Ms Journalist
GRE -, GPA 3.5
IU Kelley | Mr. Educator
GMAT 630, GPA 3.85
IU Kelley | Mr. Tech Dreams
GMAT 770, GPA 3
Tuck | Mr. Strategic Sourcing
GMAT 720, GPA 3.90

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.

MASTER’S TO INTERNSHIP TO JOB

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.