Prabakaran Chandran
Engineer, generalist, problem solver.
பிரபாகரன் சந்திரன் · Tamil Nadu → New York
Intelligence is becoming abundant. I work on what happens downstream of it — which problems are now worth attempting, and what it takes to build the systems that attempt them. The ones I keep returning to are the hard ones: human–AI coexistence, complex systems, safety and prosperity, and advanced AI turned on disease, pandemics, wellbeing and social problems.
Six and a half years from control engineering through data science and machine learning to research — Mu Sigma, Captain Fresh, Informatica, now Nile — carrying one conviction the whole way: anything that can be seen as a system can be modelled, understood and acted upon. Finishing an MS in Data Science at Columbia.
Open to Applied Scientist and Research Engineer roles from December 2026.
The work divides three ways
- ChoosingWhich problems are consequential enough to attempt, and how we would know. Problem Solving →
- BuildingThe systems that attempt them, deployed where decisions actually get made. Builds →
- UnderstandingThe research underneath — probabilistic and causal inference, complex systems, safety. AI →
Questions I am working on
- What does a generalist become in the age of AI, and where does that still matter?
- Which problems are worth focusing on under superintelligence — and how would we know?
- Where do misalignment, malfunction and security threats actually emerge in advanced systems?
- What holds epistemic sanity in place when knowledge gets cheap to produce?
Musings
All 55 →- Jul 2026Memories 1: Growing up in Singalandapuram
- Jul 2026Pre-NEET: the story the data missed, and how people misread Tamil Nadu
- Jul 2026Curriculum as a Living System
- Jul 2026How I Built Myself a Curriculum From Zero (thinking out loud with an AI)
- Jul 2026A State of Me, and What's Next
- Jun 2026A Letter to a Fellow Mu Sigman — Don't You Think You Should Quit the Job?
Builds
All →- 2026Energy-Based Port-Hamiltonian Neural Networks
- 2026HMV-CRL: Separating Platform Influence from Preference
- 2025–2026Zero-Shot Coordination in Multi-Agent RL
- 2025–2026No-Exemplar Continual Learning via Causal Invariance
- 2025–2026Causal State-Space Models for Time Series
- 2025Generative Social Network Simulation
Studies
All →- Short, grounded studies begin here.