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Ram is a Data Cowboy in Azure Security at Microsoft, working in the intersection of Machine Learning and Security. At Microsoft, his primary focus is modeling massive amounts of security logs to surface malicious activity. For instance, how do you detect an attacker is moving through the system when you have to analyze billions of events per second?

Another area of focus, is the use of machine learning systems for offense - for instance, what does automatic attack planning and automatic attack execution look in the context of red teaming? His work has appeared in industry conferences like BlueHat, DerbyCon, MIRCon, Infiltrate, Strata+Hadoop World Practice of Machine Learning as well as academic conferences like NIPS, IEEE Usenix, ACM - CCS.

At Berkman, he is broadly investigating two questions: How do we assess the safety of ML systems? What are the policy and legal ramifications of AI, in the context of security? Ram graduated from Carnegie Mellon University with a Masters in Electrical and Computer Engineering and a separate Masters in Innovation Management focusing on Telecom Policy. If you are working in Machine Learning or Security, he wants to hear from you! Always reachable on twitter @ram_ssk


Community

Bloomberg

Artificial Intelligence vs. the Hackers

Machine-learning algorithms watch hackers’ behavior and adapt to their evolving tactics.

A profile of "Data Cowboy" Ram Shankar Siva Kumar, who trains security algorithms

Thursday, Jan 3, 2019
Medium

Law and Adversarial Machine Learning

A survey of existing legal remedies for attacks that have been demonstrated on machine learning systems, and suggests some potential areas of exploration for machine learning…

Thursday, Dec 20, 2018
arXiv

Law and Adversarial Machine Learning

When machine learning systems fail because of adversarial manipulation, how should society expect the law to respond?

Friday, Oct 26, 2018