About me

I work at the intersection of data and meaning.
My background is in computational modeling, time-series causality, and large-scale population analytics — but my real passion is understanding the hidden forces that shape human lives: ideas, emotions, beliefs, memories, and the “abstract beings” living in the collective mind.

I don’t see research as just numbers or methods. I see it as a way to reveal the invisible structures behind society — poverty patterns, inequality, health risks, human behavior, and the quiet signals inside time.

Over the years, I’ve learned that solving problems matters, and clarity matters more than credentials. My work now blends science with philosophy, data with storytelling, and analytical reasoning with poetic reflection.

On this site, you’ll find a mix of my research, software tools, articles, and creative writings about meaning, identity, sorrow, hope, and the evolving architecture of the human mind.

Whether you come from academia, curiosity, or imagination — you’re welcome here.

Talks

• Collective behaviors, coordination, and leadership inference in time series.  PDF

• Is A Given Statement True From Data? Statistics, Machine Learning, And Causal Inference Approaches. PDF

• Introduction to Learning from Data. PDF 

• Introduction to statistics Modeling & Inference. PDF

Pop science articles

• What Makes Something Precious: The Quiet Equation Behind What We Cannot Bear to Lose link

• How to Build a Healthy Community — for Both Members and Leaders link

• เราทำนาย ชีวิตของคนในมิติต่างๆ ด้วย AI ได้จริงหรือ? link

• เราจะรู้ได้อย่างไรว่าลิงปกครองด้วยเผด็จการหรือประชาธิปไตย สืบจาก time series หรรษา!! link

MISC

Heart - Fiction: The Traveler of Counterfactual Stars or link

Mind - Paper: Interpretation as Linear Transformation: A Cognitive-Geometric Model of Belief and Meaning link

R CRAN packages

Currently, I'm the R CRAN package maintainer that take care the following packages.

• VLTimeCausality: Variable-Lag Time Series Causality Inference Framework. https://cran.r-project.org/package=VLTimeCausality

• EDOIF: Empirical Distribution Ordering Inference Framework (EDOIF). https://cran.r-project.org/package=EDOIF

• mFLICA: Leadership-Inference Framework for Multivariate Time Series: 'mFLICA'. https://cran.r-project.org/package=mFLICA

• ipADMIXTURE: Iterative Pruning Population Admixture Inference Framework. https://cran.r-project.org/package=ipADMIXTURE

• MRReg: MDL Multiresolution Linear Regression Framework. https://cran.r-project.org/package=MRReg

• BiCausality:  Binary Causality Inference Framework. https://cran.r-project.org/package=BiCausality