Chicago Booth | Mr. Overrepresented Indian Engineer
GMAT 740, GPA 8.78/10
Berkeley Haas | Mr. Biz Human Rights
GRE 710, GPA 8/10
Darden | Mr. Program Manager
GRE 324, GPA 3.74
Harvard | Mr. The Builder
GMAT 740, GPA 4.0
Harvard | Mr. Consulting To Emerging Markets Banking
GRE 130, GPA 3.6 equivalent
Harvard | Mr. Comeback Kid
GMAT 770, GPA 2.8
Stanford GSB | Mr. Greek Taverna
GMAT 730, GPA 7.03/10
Harvard | Ms. Biotech Ops
GMAT 770, GPA 3.53
NYU Stern | Mr. Development
GMAT 690, GPA 2.5
Chicago Booth | Mr. Energy Operations
GRE 330, GPA 3.85
Harvard | Mr. Big 4 To Healthcare Reformer
GRE 338, GPA 4.0 (1st Class Honours - UK - Deans List)
Wharton | Mr. Steelmaker To Consultant
GMAT 760, GPA 3.04/4.0
Duke Fuqua | Mr. Indian Quant
GMAT 745, GPA 9.6 out of 10
Stanford GSB | Mr. Food & Education Entrepreneur
GMAT 720, GPA 4.0
Harvard | Mr. Standard Military
GMAT 700, GPA 3.74
Harvard | Ms. Gay Engineer
GMAT 730, GPA 3.6
Harvard | Mr. International Oil
GMAT 710, GPA 3.7
Harvard | Mr. Lieutenant To Consultant
GMAT 760, GPA 3.7
Duke Fuqua | Mr. IB Back Office To Front Office/Consulting
GMAT 640, GPA 2.8
Tuck | Mr. Infantry Officer To MBA
GRE 314, GPA 3.4
Rice Business | Mr. Future Energy Consultant
GRE Received a GRE Waiver, GPA 3.3
Berkeley Haas | Mr. Campaigns To Business
GMAT 750, GPA 3.19
MIT Sloan | Mr. Special Forces
GMAT 720, GPA 3.82
Columbia | Mr. Fingers Crossed
GMAT 730, GPA 3.2
Harvard | Ms. Egyptian Heritage
GRE 320, GPA 3.7
Harvard | Mr. Investor & Operator (2+2)
GMAT 720, GPA 3.85
Harvard | Ms. Harvard Hopeful
GMAT 750, GPA 3.7

2015 Best 40 Under 40 Professors: Cynthia Rudin, MIT Sloan School

Cynthia Rudin

Associate Professor of Statistics

MIT Sloan School of Management



From the top persuasive words used in meetings to using data to predict your future health, MIT Sloan School’s Cynthia Rudin is an expert in using big data and machine learning to improve human decision making. This associate professor of statistics applies her research interests to the areas of healthcare, computational criminology, and energy grid reliability. Most recently, she’s received widespread media coverage for assisting NYC to predict manhole incidents using statistical modeling. Outside of teaching and researching, Rudin’s service contributions to her field includes her current role as chair-elect for the INFORMS Data Mining Section. She also serves on committees for DARPA, the National Academy of Sciences, the US Department of Justice, and the American Statistical Association. Her most recent awards include the NSF CAREER Award, a Solomon Buchsbaum Research Fund grant, and a best poster award at the International Conference on Machine Learning and Applications.

Age: 38

At current institution since: 2009

Education: PhD; Applied and Computational Mathematics; Princeton University; 2004

Courses currently teaching: Alternate between a PhD course in machine learning, data mining, and statistics, an undergraduate course in statistics, and an MBA class about data and modeling

Professor you most admire: Jerry Friedman at Stanford – he’s invented about half of the data mining methods in use.

“I knew I wanted to be a b-school professor when” I realized that I wanted to be more than just an “academic.” I want to do work that has real impact beyond the academic world – I want to help power companies prevent power outages, I want to help police identify patterns of crime that they didn’t know about, I want to help race car drivers win races, and I want to help doctors predict medical outcomes – actually, these are all things that my students and I participate in. It isn’t just about producing theory or proof of concept. If we create the right machine learning methods, we can move rapidly from the theoretical stage to using them directly in practice.

“If I weren’t a b-school professor” Well, if I couldn’t be a professor I’d design puzzles for a living. These are physical puzzles that might be easy for someone in middle school but hard for an adult. I recently designed a “room escape” game at home where each puzzle led to a key that unlocked a box with the next puzzle inside. The very first clue was a block of ice with a golden key embedded in it – I put strawberries around it and served it as dessert.

Most memorable moment in the classroom or as a professor: Probably the reaction I got for the hip hop glides I did during class – I’m pretty nerdy, so I couldn’t tell whether they were laughing with me or at me. I sort of thought they were laughing with me, but I truly don’t mind either way.

What professional achievement are you most proud of? Building interpretable predictive models. Machine learning methods have a reputation of being “black box” methods – they just tell you what will happen but they can’t tell you why. Our methods aren’t black box; they tell you the reasons behind each prediction, and then you can determine for yourself whether to trust them. These are models small enough to put on an index card, but it takes a lot of math and computation to solve for them.

What do you enjoy most about teaching? I like giving lessons that can be told as stories. The story of vaccines and autism is a super interesting story for teaching hypothesis testing!

What do you enjoy least? I don’t like teaching topics that I personally think are boring. Luckily that doesn’t happen too often.

Fun fact about yourself: I can make a pretty mean batch of ice cream. My “mocha chocolate” flavor and “lemon – lemon peel – toasted sesame” flavor are yummy.

Favorite book: The Neverending Story

Favorite movie: “Big Fish” and “O Brother, Where Art Thou”

Favorite type of music: Bluegrass, Irish and French (Ravel and Debussy)

Favorite television show: White Collar and NUMB3RS

Favorite vacation spot: Marlborough New Zealand – wineries, gorgeous coastlines and islands

What are your hobbies? Designing puzzles, playing room escape video game apps, hip hop dancing (occasionally semi-coordinated), and eating expensive food

Twitter handle: I don’t have one! I like to analyze data, but apparently I don’t like to generate it!

“If I had my way, the business school of the future would have” Lots of analytics and Big Data. How else are managers going to be sure they are making the right decisions?

DON’T MISS: Poets&Quants 2015 Best 40 Under 40 Professors