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Career Development

Amy Greenwald

Amy Greenwald is a computer scientist whose work focuses on artificial intelligence, specifically multi-agent interactions and game theory. She did her undergraduate work in a dual degree program at the University of Pennsylvania, where she studied both computer science and economics, which, in essence, remain her focus. But, says Dr. Greenwald, "I can't say that early on I knew that was exactly what I wanted to do. Computers and economics made for a great program and a great opportunity, so I took advantage of it, but my career zigzagged around a little bit before I came back to it."

Picture of Dr. Amy GreenwaldThat zigzagging took her first to Oxford, where she had won a scholarship. With the focus on computer theory at Oxford, Dr. Greenwald says that she "veered off from economics and computer science into logic and computer science." She got a master's degree at Oxford and returned to the United States to start her Ph.D. at Cornell. Because Cornell has a very strong group in logic and computer science, she stayed with that focus. After a few years, she realized that combination wasn't what she wanted, and in 1995 she left Cornell and went to New York City.

While interviewing for jobs and considering how best to continue her doctoral studies, Dr. Greenwald sat in on a class at City University of New York because one of her advisors at Cornell had recommended the professor, Dr. Rohit Parikh. "Of all the things I did in that period," says Dr. Greenwald, "I liked the courses at City University the best." Although she decided to go to New York University (NYU) for her Ph.D., Dr. Parikh served on her thesis committee, and the collaboration between the two prospered.

The NYU computer science building happened to be right across the street from the business school. "I was just starting out at NYU," says Dr. Greenwald, "and I was looking for a new thesis topic. I knew I was going to do something with economics and computer science. One day I happened to sit in on a game theory class at the business school, and that completely changed my career interest."

Since then, Dr. Greenwald has been doing research on computer science and game theory. She worked as a postdoc on the KDI project Automated Learning in Network Traffic Control, along with her advisor from NYU, Dr. Bhubaneswar Mishra (the project's principal investigator) and Dr. Parikh (co-principal investigator), among others.

For the project, Dr. Greenwald did work on resource allocation. The team started with a problem called the "Santa Fe bar problem," which assumes that there is a bar in Santa Fe that has live music on Thursday nights. The bar seats 60 people, but every Thursday night 100 people want to go. The problem is to figure out, on any given Thursday night, whether to go—and risk finding out there's not enough room—or stay home, only to learn that there were plenty of seats and then wish you had gone. The team modeled the program game theoretically, and eventually, using low-rationality algorithms, they were able find a way for a different set of 60 people to go to the bar each time. Dr. Greenwald says, "We were viewing this just like sending packets along a network link. It's a similar problem. It's as if you wanted, for example, to send 100 packets and only had capacity for 60."

Today an assistant professor in computer science at Brown University, Dr. Greenwald continues to focus on game theory. She is actively involved in an international forum called Trading Agent Competition (TAC), which promotes research into the trading agent problem. In TAC Classic, a travel agent must put together a travel package for clients that includes everything the clients want (flights, hotels, etc.), but each component is sold separately in simultaneous auctions. "There's a lot of machine learning in this game," says Dr. Greenwald, "because we're trying to make predictions about what prices will be, and in particular we're trying to predict the behavior of the other agents in the game." In the newest version of the game, called TAC SCM (Supply Chain Management), agents must bid to sell their products, while at the same time getting all the components they need and predicting prices. This set of steps duplicates many of the challenges inherent in supporting effective supply chain practices. For more information on this work, visit the TAC Web site at

"This is a very practical and very relevant problem, and a very, very hard one," says Dr. Greenwald. This research can be used by any company that needs to figure out its procurement schedule, as well as how it's going to put together their components, when to sell them, and what the price might be.

To learn more about Dr. Greenwald's work, visit her Web site at:


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