PraCha
Prabakaran Chandran · பிரபாகரன் சந்திரன்
AI researcher · engineer · builder — Tamil Nadu → New York
I am interested in intelligence as both a technical and existential problem. That draws me toward , cognition, consciousness, philosophy, and metaphysics, but also toward building AI and ML systems that live at the intersection of frontier research, applied research, and real engineering. I believe more in learning than in static knowing, in deliberate experimentation over posture, and in interdisciplinary work as a method for discovering better questions and better systems. Each sprint of research is designed to produce a concrete result — working code, a finding, or a documented wall — and everything is published.
I bring 6.5+ years of experience across the stack of analytics and data science, with a career arc that has gone from sensors to tensors and has been shaped strongly by and complexity thinking. I am currently an MS Data Science student at Columbia, running the FAIRE research program in parallel, working toward top AI labs and, in the longer run, toward building a decision-engineering company of my own. pracha.me is where that learning, experimentation, and synthesis become visible — and where it compounds outward.
MS Data Science, Columbia University · Open to research collaborations, consulting, and AI advisory · pc3197@columbia.edu · LinkedIn
Building Right Now
Oyster Club
Two people from different fields. One focused 100-minute session. The most interesting problem is rarely the one you arrived with — it appears when two worlds explain themselves to each other.
Join a session → AI/ML Studio · 125 days · New YorkManhattan Summer 2026
A self-directed AI/ML studio in New York. 125 days of deep work — one serious research paper, one product build, and everything made visible.
Explore themes →The Arc
- Systems & Complexity Control engineering taught me to see everything as a system — state, dynamics, feedback. Mu Sigma widened that to complexity: emergence, nonlinearity, how simple rules produce intricate behavior.
- Data Science Six years turning data into decisions — not reports that sit in decks, but models that organizations actually act on across industries.
- ML Engineering Satellite imagery, aquaculture, document understanding, enterprise AI. Systems that shipped, scaled, and changed how real operations ran.
- AI Research At Columbia: filling the theoretical gaps industry doesn’t have patience for. Causal reasoning, continual learning, reinforcement learning, probabilistic modeling.
- Decision Engineering The destination. Where everything converges — AI, causal thinking, complexity science — into systems that change how decisions get made at scale.
Selected Work
The Philosophical Roots of Frontier AI
A multi-part primer connecting philosophy, ML schools, lab strategies, reasoning systems, and the current frontier of AI research and engineering.
Zero-Shot Coordination in Multi-Agent RL
Agents trained separately fail to coordinate when paired with a stranger — they built different habits. This asks what training structure gives agents conventions general enough to work with anyone.
Causal State-Space Models
Forecasting models predict what will happen — decisions require knowing what would happen. This integrates causal structure into time-series models so they can reason about interventions, not just patterns.
AI / ML Course Atlas Across Leading Universities
A structured reference to public AI, ML, DL, theory, systems, and frontier research courses across leading universities.
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