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Theodore Allen

Theodore “Ted” Allen

Associate Professor at The Ohio State University

Theodore (Ted) Allen is an Associate Professor in the Integrated Systems Engineering department at the Ohio State University. He is also the President and founder of factSpread, a nonprofit which uses advertising to inform swing state voters about public policy facts. He received his B.A. from Princeton, his M.S. from UCLA, and his Ph.D. from the University of Michigan (1997). He is currently the president of the Social Media Analytics section of INFORMS and the simulation area editor of Computers & Industrial Engineering (IF: 3.2). He has published over 60 refereed publications and received over 25 grants as PI including from NSF, ARCYBER, Ford, and GE Appliances. His research on simulation optimization for voting machine allocation has received national attention and he has contributed to millions of voters avoiding hours of waiting and effective or actual law changes in North Carolina, Ohio, and Michigan. He has also served as associate editor for the Journal of Manufacturing Systems and Quality Approaches in education and as a reviewer for Operations Research, EJOR, Technometrics, and many other journals.

Track: Decision & Risk Analysis

Tuesday, April 16, 4:40–5:30pm

Analyzing Social Media Data To Identify Cybersecurity Threats: Decision Making With Real-time Data

In 2018, 27.9% of businesses experienced a cybersecurity breach, losing over 10,000 documents and $3M according to the Ponemon Institute. Of breaches known to Ponemon, 77% involve the exploitation of existing bugs or vulnerabilities. In our work, we found that incidents occur in narrow time windows around when vulnerabilities are publicized. Can you optimally adjust your cybersecurity policies and decisions to address emerging threats? Analyzing social media will help you preemptively identify major medium-level vulnerabilities, which managers often ignore, but which contribute to a large fraction of the incidents and warnings. Success requires transforming textual information into numbers, and I present a method, called K-means latent Dirichlet allocation, that identified the Heartbleed virus. I will describe a Bayesian approach as well, and with both methods, you can adjust your cybersecurity as social media identifies new hazards. Related opportunities for closed loop control using Fast Bayesian Reinforcement Learning are also briefly described. The qualitative benefit of experimentalism of these methods enables improved maintenance options.