Expert-led R&D
We are the researchers pioneering the science and technology
behind cloud-scalable economic intelligence. We spent our academic
careers designing and building economic, statistic, and AI models;
so we know what solutions will match our clients' capabilities and
needs.
Economic intelligence
Neural networks give black-box answers. Economic intelligence provides
meaningful white-box answers grounded in coherent, structured
understanding. EI can also leverage contextual data and expert
opinion to increase accuracy and calibration beyond what is
possible from training samples alone.
Cloud-scalable
Applications like forecasting, supply chain and operations
optimization, bottom-to-top coherent decision making, and
portfolio selection require fine grained, high dimensional data.
We build models that use multi-GPU and cluster accelerated
computing, and integrate with your data lakes, on premises or in
the cloud.
Product Announcement
Introducing...
Meaningful demand forecasts at any grain.
CatFish is a Bayesian Intelligence forecasting
system. It provides location-specific, consistently aggregatable
and disaggregable, full joint distribution demand inference with
human interpretable factors.
Learn more
Lead Economists
Dr. Ed Egan
Dr. Egan is a serial entrepreneur
and venture capitalist turned academic. He is a leading expert in
ML and economics-based research computing and big data information
systems design. Dr. Egan received his Ph.D. in business economics
from the Haas School, U.C. Berkeley, in 2012. He subsequently held
positions at the NBER, Imperial College London, Rice, and
Georgetown universities.
Prof. John Geweke
Prof. Geweke is an internationally
renowned Bayesian theorist and big data
econometrician. He received his Ph.D. in economics from the University
of Minnesota in 1975. Prof. Geweke is the author of over 100
papers in international refereed journals; the former editor of
the top five journals in econometrics and applied statistics; and
a past president of the International Society for Bayesian
Analysis.
Prof. Garland Durham
Prof. Durham an expert in Bayesian
modeling, forecasting, and simulation using GPUs for massively
parallel computation. He recieved his Ph.D. in economics from the
University of North Carolina in 2001 and is a professor of finance
at California Polytechnic State University. His research has been
published in leading journals, including the Journals of Financial
Economics and Econometrics.
Recent Contract & Grant Proposals
We made an invited submission for Phase I SBIR funding to the National Science Foundation in early 2025. Our project tests whether Bayesian machine learning can scale to solve real-world supply chain optimization problems. It combines full joint distribution demand forecasts from a spatiotemporal economic model with high-dimensional loss functions to optimize inventory placement. This approach could begin a new paradigm of industrial economic decision-making.
We applied to the Defense Logistics Agency's Emergent IV BAA in late 2024. Our proposal develops supply chain optimization tools to meet DLA's need for scalable, interpretable systems that support combat readiness. We model uncertainty and optimize materiel placement under posture, precision, and logistical constraints. The goal is to improve responsiveness, reduce cost, and empower DLA personnel to assess risk and make mission-aligned decisions across tactical, operational, and strategic levels.
The Small Business Administration's Office of Advocacy solicited research proposals from economists in late 2024. Our proposal creates a Bayesian simulation of the U.S. small business economy to evaluate the effects of federal policies and natural disasters at the county level. The project focuses on HUBZones, Opportunity Zones, and rural trends, and supports data-driven, evidence-based policymaking through causal inference and risk assessment.
info@petabyteeconomics.com
www.petabyteeconomics.com
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