nthexperiment

lab for agentic decision science

my personal lab

my personal lab serves as an enabler for my confidence and continuous learning. within it, i experiment, encounter failure, and improve—learning from my thought processes and challenges, which guide me towards my destiny.

over the past six years, i have formally and iteratively learned how businesses excel through data-centric problem-solving. as ai evolves and opens many doors, i find myself increasingly drawn towards the intersection of systems, intelligence, and the behaviour of humans and machines.

about me

prabakaran chandran

with a keen interest in applied science, particularly data and ai, i've spent the last six years navigating the dunning-kruger curve, gaining experience at the "slope of enlightenment." my journey has spanned fortunes, enterprises, and startups, where i've contributed at the idea, implementation, and capability levels.

timeline:

musigma (2019-2022) captainfresh (2022-2023) informatica (2024-2025)

my north-star

now, i have a north-star that guides me, which is: "how can human and business decisions be nudged and improved with ai and data science?" this principle sets the focus for my learning and experimentation. recognizing that agency is key to the future, i am centering my work on what i term "agentic decision science."

hence, i am trying to build nthexperiment, my lab – a space where thoughts, ideas, and excitement related to agentic decision science can take shape, be put into action, and lead to tangible outcomes. it is an applied science focused self-initiative.

experiment streams

the experiments within nthexperiment will follow two main streams:

  • applied level: exploring how ai/ml algorithms can be used for specific decision-making problems.
  • foundational level: conducting experiments around the building blocks and break-through concepts in ai/ml.

on-going experiments

as these are experiments, they keep changing as sometime i dont feel i can stick with it due to reasons like its boring / very naive / maybe i am distracted.

  • bandits & open policy evaluation - an experiment on open bandit dataset

my writings

notes, essays, and thoughts on various topics within agentic decision science and beyond.

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few of my favorite books

  • thinking, fast and slow - daniel kahneman
  • misbehaving: the making of behavioral economics - richard h. thaler
  • reinforcement learning: an introduction - richard s. sutton and andrew g. barto
  • the book of why: the new science of cause and effect - judea pearl & dana mackenzie
  • artificial intelligence: a modern approach - stuart russell and peter norvig
  • range: why generalists triumph in a specialized world - david epstein
  • understanding deep learning - simon j.d. prince
  • probabilistic machine learning: an introduction - kevin p. murphy
  • a brief history of intelligence - max bennett

recent thoughts & shares

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