Bedrock Global A.I. is your on-call data science team originating out of the University
of California San Diego. We are a mix of Ph.D. and M.S. graduates that have strong backgrounds
in Applied Mathematics, Mechanical Engineering, Machine Learning, and Deep Learning. Our team
has had past engineering experience with companies such as: FICO, NASA, and Autodesk.
Brian Whiteaker M.S.
Brian has partaken in multiple at the NASA Glenn Research
Center as a researcher and expert in Machine Learning. Brian developed text mining topic modeling
tools and created support vector machine based meta-classifiers as part of text
mining tools and pipeline. At NASA, he's modeled aircraft surface
acoustic interactions with the atmosphere using vibrational data and state-of-the-art
statistical learning methods. Outside of NASA, Brian also has experience in the
marketing domain by creating contextual reinforcement learning algorithms for use
in adset optimization.
Jeremy Schmitt Ph.D.
Jeremy is a Machine Learning researcher and Data Scientist with a Ph.D in Mathematics
from the University of California, San Diego. As a Ph.D candidate, he served as a
referee for the Elsevier journal in Applied Mathematical Modeling, and researcher for
the Center of Computational Mathematics (CCOM). He is an Analytic
Scientist at FICO he is working on anomaly detection algorithms for credit card fraud
detection. Jeremy received the FICO Spot Award for developing the first production-ready
Falcon Fraud model that integrated real-time collaborative filtering with a feedforward
Premanand Kumar M.S.
He is an experienced data scientist with expertise in
time series data generated from IoT sensors. In his most recent work, Premanand has
developed algorithms to identify cyclic and seasonal patterns for energy consumption.
He has created metrics that have been shown to be effective in optimization over highly
variational energy consumption and complex pricing strategies from the San Diego Gas and
Electric Company (SDGE). He developed machine learning algorithms based on decision
trees, regression, and deep neural networks that outperformed existing models by seven
percent in the deployment to production.
William Mendoza Gopar M.S.
William is a Data Scientist and Machine Learning researcher with experience in initiating and deploying
massive data mining and machine learning projects.
At Autodesk, William initiated a long term project for the Subscription Platform Group
during their current transition into a subscription-based cloud company. There, William took
on a Machine Learning engineering role and worked on automating the extraction and
pre-processing of more than a million log records where he then
developed metrics that are indicative of larger, more complex measures of platform
health that can be monitored in real-time for anomaly detection systems.