PraCha
Prabakaran Chandran · பிரபாகரன் சந்திரன்
Systems · Complexity · Data Science · ML Engineering · AI · Decision Engineering
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 gave a lens: everything is a system. Mu Sigma confirmed it through complexity science, behavioral analytics, and enterprise intelligence — that frame has organized everything since.
- Data Science DIPP analytics across industries — forecasting, experimentation, A/B systems, decision loops. Turning data into decisions that organizations act on, not just reports that sit in decks.
- ML Engineering 6.5 years of production ML: satellite imagery, aquaculture AI, document understanding, enterprise agents. Systems that shipped, scaled, and changed real operational decisions at real companies.
- AI Research FAIRE sprints at Columbia — causal-continual learning, C-GRPO, multi-agent RL. TA in Causal Inference and Applied Risk Analytics. Theory filling the gaps industry doesn’t have patience for.
- Decision Engineering The destination. Where systems thinking, AI, and complexity science converge into products that change how decisions get made at scale. The company is ahead.
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
Convention emergence via IPPO. When does ad-hoc teamwork fail — and what enables it without prior agreement?
Causal State-Space Models
SCM-augmented Mamba for counterfactual forecasting. Granger causality vs. do-calculus interventions.
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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