About Us

Detectica Leadership




Josh manages the technology and data science decisions for Detectica. Josh was the Director of Data Science at Etsy, on the founding team of Integral Ad Science, and was a Research Scientist at Yahoo! Research.

He received his Ph.D. in Computer Science from New York University, focusing on applications of machine learning.

He is an Adjunct Professor at the NYU Stern School of Business, teaching data science, data mining, and machine learning.




Panos is a Professor of Data Science at the NYU Stern School of Business.

Panos served as an early advisor of Integral Ad Science, and has worked as a Visiting Scientist for Google, the World Bank and oDesk.

His research has received nine “Best Paper” awards and nominations, a CAREER award from the National Science Foundation.

Panos was the recipient of the prestigious 2015 Lagrange Prize in Complex Systems.




Foster is a Professor of Data Science at the NYU Stern School of Business.

He helped form Dstillery and Integral Ad Science. Foster was the Editor-in-Chief of Machine Learning and co-chaired ACM SIGKDD.

He also advises the NSF, NASA, DARPA, and others on policy and investments in data mining research.

Foster is coauthor of the best-selling Data Science for Business.

Josh, Panos & Foster have been working together on systems that tightly integrate human intelligence and AI/ML for a decade. They specialize in machine learning problems that have very rare positive examples and are not “self-revealing” – i.e., where historical data does not naturally reveal training data labels.

They designed and built the founding data science architecture for Integral Ad Science, originally founded as AdSafe Media. The AdSafe problem involved scouring the web to identify (among other things) web pages that contain ultra-rare objectionable content like hate speech, and blocking ads from appearing there.

The trio introduced a variety of methods for training that are stronger than traditional machine learning “labeling”, including new active learning strategies, active feature labeling, search-aided training, Beat the Machine, integrating expert knowledge with training data to do better than either, and integrating multiple sources of evidence—for example evidence from text and evidence from network interconnections.

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