bayesian workflow
prior elicitation, generative model design, posterior predictive checks, full lifecycle from problem specification through decision analysis. Stan, PyMC, probabilistic programming.
about.
matrices is a consulting practice that builds infrastructure for adaptive decision-making.
the practice of sam cialek
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the practice
01
we help organizations reason under uncertainty: designing systems that update beliefs as new data arrives, quantify causal effects from observational data, and simulate complex behavior at population scale. our work spans sectors but shares a common thread: replacing intuition with calibrated, reproducible inference.
founded by sam cialek, the practice draws on 15 years of experience in quantitative finance, data science, and software engineering. past and current engagements include consumer health AI, sustainability analytics, political modeling, and hedge fund research infrastructure.
credentials
02
degrees
B.A. Economics & Mathematics
nyu, 2011
MBA
columbia business school, 2019
M.S. Computer Science
georgia tech, 2024
certifications
ESG Investing Certification
cfa institute
Corporate Value Chain (Scope 3) Certification
ghg protocol
Product Life Cycle Certification
ghg protocol
domains
01–05
causal inference
DAG specification, observational study design, treatment effect estimation, sensitivity analysis. bridging the gap between prediction and intervention.
emergent simulation
agent-based modeling, synthetic populations, scenario planning. modeling complex adaptive systems where macro behavior emerges from micro-level rules.
sustainability & esg
emissions accounting (scope 1/2/3), regulatory compliance (LL97, SB253), decarbonization planning, GRESB benchmarking.
health technology
clinical + wearable data integration, causal health models, EHR middleware, personalized intervention design.