As the director of the University of Florida Informatics Institute, George Michailidis, who is also an Amazon Scholar on the Supply Chain Optimization Technologies (SCOT) team, leads a diverse community of data scientists with training in engineering, statistics, applied math, and other sciences. He notes that assortment of backgrounds is important in data science.
“In addition to statistics, there are a number of other disciplines that data scientists need to be aware of, such as programming, algorithms, optimization, and of course, some subject matter expertise because you don’t do data science in a vacuum,” he says.
Michailidis was trained in applied mathematics and statistics, with a PhD thesis focused on optimization problems and its applications to statistical problems. His postdoc was in operations research, which introduced him to a different class of problems. “Some of them come about in Amazon’s supply chain, for example, such as problems of how to schedule the jobs on the machine, or how to route the traffic in the network, and so forth.”
For about 17 years, Michailidis was a faculty member at the University of Michigan in statistics with a joint appointment in electrical engineering. “I combined my statistical training with my interest in engineering types of problems.”
Data integration
Since then, his research agenda at the University of Florida has had strong theoretical components, but he remains very interested in practical applications. One of his current interests is data integration, and its many potential uses. For example, when it comes to the study of diseases, there is a wealth of molecular-level data from patients’ samples. At the same time, there is information on the patient’s clinical records and demographics.
“How do you create models to try to identify key drivers, for example, for disease progression by combining all these different data sources,” is one of the questions that motivates Michailidis’ work. With these models, he tries to provide insights both for prognostic or diagnostic purposes, but also for the understanding of the biological mechanisms that lead to that disease.
Another large component of Michailidis’ research relates to a problem known as anomaly detection. “This is an old problem that has been going on for more than 60 years,” he says. To a large extent, it originated in manufacturing, where people were interested in finding defects in the manufacturing process and fixing them. As the technology evolved, similar questions have been arising in many other fields.
This is broadly the theme of a paper published by Michailidis and his colleagues Hossein Keshavarz, a senior data scientist at relationalAI, and Yves Atchadé, a professor of statistics at Boston University, entitled “Sequential change-point detection in high-dimensional Gaussian graphic models.”
Michailidis notes that, as manufacturing processes became more complex, it became necessary to monitor many more metrics.
“A typical example of this complexity is semiconductor manufacturing, where you have to monitor hundreds of little things,” he says.
In more modern applications, the next step is to monitor networks.
“You’re not only monitoring a lot of things. Now these things are interconnected and you’re trying to understand how this network, as an object, changes its structure at some point in time,” Michailidis explains. “And you’re doing that in an online fashion because this process keeps going. You keep observing the network and you’re trying to identify changes as quickly as possible.”
In addition to developing a technique to detect changes, researchers also must establish that their technique is sensitive enough for certain types of changes and determine whether it detects them quickly enough. This is the challenge, in the online realm, that Michailidis and his colleagues attempt to address in their paper. The paper introduces “introduces a novel scalable online algorithm for detecting an unknown number of abrupt changes”.
In the paper, the authors present an application on stock market data, where the network is made of movements of stocks. “We showed how the network changes, for example, during the great financial crisis of 2008, and how the stock market got affected by the European debt crisis in 2012 and so forth.” Michailidis notes that these techniques are especially suited for problems where there are dependencies between observable elements without knowledge of the nature of those dependencies.
“With stocks, whether they are moving together or in different directions, these movements —or lack of movement — is what gives rise to the network structure. And that’s what we are capturing with these graphical models,” he says.
Within the SCOT organization, Michailidis says he has the opportunity to tackle challenging problems at an unprecedented scale. “The problems are much more complex because they’re not as clear cut as they are in academia.” In this interview, he discusses his research on anomaly detection and its potential applications.
