I’ve been providing analyses, architecting secure environments, and leading security program implementations in IT security and risk since 2011. My career started with solving problems and informing executive decision making with algebraic data analyses for explanation, simulation and attribution (i.e. data driven intelligence analysis, forensics, SOC and CIRT), optimization (i.e. Monte Carlo simulation for risk assessment), descriptive and predictive methods (i.e. machine learning/AI for risk assessment, vulnerability prioritization, and event correlation). My problem-solving toolset has expanded beyond algebraic methods and static probability calculus to using probabilistic programming languages (PPL) like PyMC and Bayesian network models. I’ve packaged these insights into a powerful risk modeling platform, cybersecurity inference engine, and API called Derive that will change the landscape of quantitative risk assessment. I hold a Master of Science in cybersecurity intelligence and forensics, a CISSP, and undergraduate degrees in science and philosophy. Prior to co-founding Derive, I worked as Vice President of Quantitative Risk at Hive Systems, and held cybersecurity engineer and analyst roles at RSA, EMC, HFCU, Dell SecureWorks, Dow Jones, Bloomberg L.P, and NYU.
Listed skills include Information Security, Computer Security, Network Security, Computer Forensics, and 28 others.