PraCha
Prabakaran Chandran · பிரபாகரன் சந்திரன்
AI Research · Engineering · Data Science · Writing · Decision Systems
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
Five Modes of Work
- Research FAIRE sprints — causal-continual learning, C-GRPO, IRM-NECIL, multi-agent RL; TA in Causal Inference and Applied Risk Analytics at Columbia, shaping how graduate students approach ML rigorously
- Engineering 6.5 yrs production ML — satellite imagery, aquaculture AI, document understanding, enterprise agents; systems that shipped, scaled, and changed operational decisions at real companies
- Data Science DIPP analytics stack — forecasting, experimentation, A/B systems, and decision loops across industries; turning data into decisions that others act on and build upon
- Writing Essays on AI, learning, systems, and culture — 37+ musings and growing; Thursday Learning Hours seminars published weekly so others can follow the intellectual thread
- Building BroCoDDE · nthExperiment series · Thursday Learning Hours — open artifacts others can use; working toward a decision-engineering company
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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