Why Bedrock?

The cost of having full-time staff dedicated to data science in small to mid-size companies is too expensive and risky. Figuring out how machine learning will fit into your business and keeping the data science team busy during down-time is too much of an investment to take.

We will work on your projects and build off of your vision for your business and act as a modular extension to an existing engineering or data science team for temporary projects.

About the team

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 neural networks

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.

Get in touch and learn more!