BIML in the Barn is our keynote speaker presentation series, covering topics across the MLsec landscape by leading researchers and experts in the field.

Our latest talk is “The Challenges of Machine Learning in Adversarial Settings” by Prof. Patrick McDaniel, the Tsun-Ming Shih Professor of Computer Sciences at the University of Wisconsin’s School of Computer, Data & Information Sciences and a fellow of the IEEE, ACM, and the AAAS.

David Evans, professor of computer science and head of the Security Research Group at the University of Virginia, talks about data leakage risk in ML systems and different approaches used to attack – and secure – models and datasets.

Ram Shankar Siva Kumar, a Data Cowboy at Microsoft’s Azure Trustworthy ML, talks about important Machine Learning risks, how an overabundance of user trust multiplies them, and proposes standards and certifications as a solution to strive toward.

Gary McGraw, CEO of the Berryville Institute of Machine learning, talks about the BIML 78, threats to generic ML models that BIML has researched and published in our Attack Taxonomy.

Maritza Johnson of the University of San Diego talks about the pitfalls of mistaken assumptions in ML, and the importance of centering ML product design in human experience.

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