About Us

Warren Pies

Warren Pies, E.R.P

Cofounder & Strategist

Prior to founding 3Fourteen Research, Warren led Ned Davis Research’s Energy and Commodity strategy. In that role, he built the firm’s commodity-related studies, models, and unique indicators. His research combines proprietary fundamental, technical and macro indicators to identify major investment themes and market trends affecting capital markets.

Warren is a frequent contributor to the media including participating in the 2013 Barron’s MLP Roundtable discussion, the Wall Street Journal, CNBC, and RealVision. In 2014, Euromoney Institutional Investor awarded Warren the Padraic Fallon award for his research on the Shale Revolution. Outside of the research world, Warren has worked as a practicing attorney specializing in regulatory approvals for various industrial activities and projects. He has also owned, operated, and sold a portfolio of self-storage properties to a national REIT.

He earned both his Bachelor of Science and Juris Doctorate from the University of Florida. Warren is an Energy Risk Professional – Certified by the Global Association of Risk Professionals.

At 3Fourteen, Warren works closely with a team of analysts, data scientists, and developers whose expertise ranges from machine learning, time series analysis, and application development.

Fernando Vidal | 3Fourteen Research Data Scientist

Fernando Vidal

Cofounder & Chief Data Scientist

Fernando spent 7 years working as a quantitative analyst at Ned Davis Research’s consulting group conducting research, building and testing models and studies for institutional investors. Following this work in the investment space, he spent 6 years founding and leading a Data Science team at SauceLabs, a VC-backed fast growing market leader in software testing based out of San Francisco. His team worked on AI and Machine Learning applications involving large-scale time series analysis and classification models.

At 3Fourteen, Fernando leads our model development process and brings machine learning research into our mix of qualitative analysis and quantitative rigor. His many years of experience implementing machine learning models at scale has provided him a combination of healthy skepticism and deep appreciation for how these techniques can add value to the investment process.

He earned a Master of Science in Machine Learning from Georgia Tech and a Bachelor of Science in Finance and Economics from the University of South Florida. He is also the lead inventor on multiple patents related to machine learning for time series analysis and unsupervised learning.