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Faculty Uncovered: Farahat

by Esohe Denise Odaro '09

Issue date: 4/14/08 Section: Features
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Welcome to the very first of the Faculty Uncovered Series! The Cornell Business Journal is excited to present this series of informal interviews with the faculty who you know but don't really know. The idea is to get you to know your faculty better.

This month's interviewee is the highly revered Statistician who has been known to work until the wee hours of the morning, I once found him in his office at 2am when I was slipping a deliverable underneath his door. He holds a PhD in Operations Research from MIT, has worked at McKinsey, Unilever, and British Telecom, and has been awarded the UPS Fellowship from MIT and the Chevening Scholarship from the UK Foreign and Commonwealth Office. If you hadn't guessed by now, it is Professor Amr Farahat. A search under his name on Facebook yields 5 locked profiles and with only 6 conections on LinkedIn, this interview is probably the only way you will get to know him better…

CBJ: Professor, the class of 2009 reveres you as the Stats guru of Sage. What do you think about this?

AF: As you take more courses and meet more of the School's faculty you will find that Sage has several Stats gurus!

CBJ: What do you enjoy most about teaching?

AF: The energy in the classroom; observing some students come in thinking that 501 is a course in Sadistics only to find out that Statistics is sometimes subtle but never malicious, and widely applicable to business and life; astute questions that get me thinking about advanced topics; students sending me pictures of Halloween costumes decorated with decision trees and others telling me about regressions they ran before buying engagement rings.

CBJ: Your first section began at 8.40 am, so did you ever have time to have breakfast before teaching and if so what is your regular breakfast?

AF: If coffee counts as breakfast then of course I have never missed breakfast.

CBJ: How would you say the class of '09 differs to '08?

AF: Well, like in sampling, every observation can be unique but the average tends to be pretty much similar. That is a good thing.
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