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 ground I stand on
Why →The world is complex — interacting parts, feedback, adaptation, emergence. Complexity science is the ground: the superset of dynamics, evolution, entropy and computation, and the interactions between them. Not a lens I picked, but how the world works — and where advances in AI, including AGI, have to be understood from. It is also why I think across disciplines rather than inside one.
- Dynamics & controlHow systems change over time, and how feedback holds them together or tears them apart.
- Information & computationWhat can be known, compressed, learned and computed — and at what limit.
- Entropy & energyWhat order costs, where systems tend, and why some structures persist.
- Evolution & adaptationHow systems vary, select, learn and keep working in a world that moves.
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?
- Sep 2026Preferred Problem Spaces — Building My Career Around What Keeps Me CuriousMusing
- Sep 2026The Next Starting LineMusing
- Sep 2026Complex Systems, Not Integrations: What Forward-Deployed/AI Engineering Is MissingArticle
- Jul 2026Memories 1: Growing up in SingalandapuramMusing
- Jul 2026Pre-NEET: the story the data missed, and how people misread Tamil NaduMusing
- Jul 2026Curriculum as a Living SystemMusing
- Jul 2026How I Built Myself a Curriculum From Zero (thinking out loud with an AI)Musing
- Jul 2026A State of Me, and What's NextMusing
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