Palo Alto Networks

From personally building PAN's first production deep learning model to its current reputation as an AI-first company, I have played a pivotal role in successfully transforming many of PAN's cybersecurity operations with artificial intelligence -- helping to catalyze PAN ahead of countless adversaries and its industry competitors.

Selected contributions:

- Designed, deployed, and published a novel adversarial learning framework called "Innocent Until Proven Guilty" (IUPG) originally designed to overcome a pervasive threat to ML-based malware classification.
- Designed, deployed, and maintained a JS malware classification pipeline featuring a patented architecture that extends stacked generalization by ensembling deep CNN feature extractors of multiple data representations with XGBoost. Also ideated a solution for and integral to the building of an ultra-low memory consumption version that supports packet-by-packet streaming of inputs for use inline on a firewall.
- Designed, deployed, and maintained a URL classification pipeline featuring a patented deep net architecture. The IUPG-trained model extends state-of-the-art with feature source orthogonality constraints and an expanded token encoding with HMM-based randomness scores.
- Designed, deployed, and maintained a deep learning solution for data loss prevention. Several iterations included dataset engineering, making ethical use of private data, and building patented dynamic compression layers.
- Designed, deployed, and published a novel algorithm for reliable and robust semi-supervised learning (SSL) called Contrastive Credibility Propagation (CCP). Integrated this solution into production models that required robust SSL beyond what known solutions in literature could offer.
- Leading a research effort to build discriminative models capable of explaining their verdicts in natural language without loss of discriminative power.
- Leading two company-wide recurring forums to discuss and share AI research.

Sr. Principal Machine Learning Engineer

Principal Machine Learning Engineer

Senior Machine Learning Engineer

Machine Learning Engineer

Rochester Institute of Technology

- Research interests include computational models in theoretical neuroscience and streaming anomaly detection.
- The grant-funded research culminated in a successful MS thesis that redefined state-of-the-art performance in a streaming anomaly detection benchmark and introduced the "High Order Prior Belief" algorithm.

AI Scientist

Personal mathematics and computer science tutor for underprivileged students through the Collegiate Science Technology Entry Program (CSTEP).

Tutor

- Built and prepared a malicious JavaScript dataset with several million entries.
- Designed and deployed into production a new malicious JS classification pipeline that improved both performance and latency.

Data Science Intern

Florida Institute of Technology

- Discovered and described clusters of melanoma from ultra-high dimensional genomic data using a custom machine learning pipeline composed of clustering and feature selection techniques. Later published this work in Frontiers of Genetics.
- Developed a stochastic model of tumor aggressiveness to better estimate mitotic index.

Biomathematics Research Intern

Enel X

- Built a new internal database framework using Hadoop and Impala.
- Built general-purpose data fitting library code using Gauss-Newton nonlinear regression. Demonstrated the code to data scientists in the company making use of their data stores to provide new insights.

Big Data Software Engineering Intern

A curious, self-driven, visionary, and multi-talented individual with a passion for cognitive science and artificial intelligence. I enjoy conducting bio-inspired artificial intelligence research geared toward solving hard, real-world problems that require the sophisticated cognitive capabilities observed uniquely in animal brains. I believe emulating the principles and functional properties of the primate brain, particularly in the cerebral cortex and related mammalian-brain structures, is the most straightforward path toward strong AI beyond what is capable with traditional machine learning and deep learning. In industry, I currently work as a machine learning engineer building AI to provide practical solutions in various domains. I am also a self-taught pianist, guitarist, banjo player, accordion player, aspiring writer, and sketch artist who has made a habit of accomplishing whatever I set my mind on.

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