Tag: avi rubin

Johns Hopkins Prof Invited to Appear on “Poker Night in America”



When you tune into Poker Night in America, a new cable TV show debuting this spring, keep an eye out for a guy who looks a little bit more like a computer science professor than a professional card shark. He’ll probably be wearing wire frame glasses and a huge grin, since playing poker with seasoned professionals has been Hopkins prof (and poker obsessive) Avi Rubin’s dream ever since he started getting serious about the game a few years ago.

“I have to say, I’m pretty excited about this,” Rubin told the Hopkins Hub. “It’s been a dream of mine to play with some of these guys whom I’ve seen on TV many times.” It’ll be a match to watch, for sure – how often do you see a poker player who correctly uses “whom”?

JHU Prof’s Second Life as a Poker Obsessive

Photo by Todd Klassy

By day, Avi Rubin is a computer security consultant and a professor of computer science at Johns Hopkins’ Whiting School of Engineering. But by night — and on the weekends, and whenever his wife doesn’t mind too much — Rubin is a poker aficionado, one of those real obsessives with his sights set on the World Series of Poker in Las Vegas. And with a talent for numbers and an obsessive focus on probabilities, he just might end up making it there.

Rubin sees the game in terms of his academic interests — game theory, machine learning, combinatorics. A true computer scientist at heart, he envisioned a poker hand as a finite, countable structure. Once the first cards are dealt (in Texas Hold-Em, each player is dealt two cards face down; five communal cards are dealt faceup), the rest of the game unfolds like a kind of decision tree. Rubin crafted a program to determine the relative probabilities of success given particular variables — his position in the betting, number of opponents, etc. “For any given spot in the decision tree,” he told Johns Hopkins Magazine, “I could come up with a probability distribution of different plays. Then I could write a learning program that I could use as a simulator on the computer and play a thousand times with particular settings, then tweak the settings and run it again to see if I do better, and work backward from it to infer why that was a better play in that situation.”

But because computers can’t yet model the full complexity of playing with real human beings, who bluff/get bored/try to show off/otherwise behave unpredictably, Rubin sat in on weekly games with friends, many of whom were doctors and lawyers. “The lawyers tend to be better,” he says. “The math in poker is basic arithmetic, it’s not that hard. But you still have people, like a lot of the doctors that I play with, who’d rather not bother with all the math. They feel that they have enough intuition for the game.” But that intuitive technique tends to backfire, in Rubin’s experience:  “The fundamental math is much more important. If you’re a solid mathematical player, in the long run you’re going to kill the intuitive player.”