PraCha日月
Prabakaran Chandran · பிரபாகரன் சந்திரன்
AI researcher · engineer · builder — Tamil Nadu → New York
I am interested in intelligence as both a technical and existential problem — which pulls me toward , cognition and philosophy, and equally toward building AI systems that sit where frontier research meets real engineering. Six and a half years across analytics, data science and ML, from sensors to tensors, shaped by . Now finishing an MS in Data Science at Columbia and working toward — systems that keep deciding well after they ship. pracha.me is the visible trace: building is the mechanism of becoming, and every shipped thing is evidence of who I’m turning into.
Now New York Final semester, Columbia MS AI Builder at Nile Building PrachaLabs Open to Applied Scientist & Research Engineer roles · December 2026
Building Right Now
PrachaLabs
An independent, one-person lab, downstream of intelligence. Intelligence is becoming abundant, and the opportunity is not to automate what we already do but to discover what we can now attempt. It works both directions, upstream (how frontier intelligence is made) and downstream (how real value gets created with it), holding research, engineering, product, and strategy as one motion.
Enter the lab → Frontier AI · ResearchResearch Engineering
How I push toward the frontier. Frontier AI research engineering, run as sprints: each begins with a precise question and ends with working code, a finding, or a documented failure. The experiments, deep dives, and independent research that move from reading the frontier to building at it.
See the frontier → Decision Engineering · -1 to 0Evolving Decision Systems
The right answer today is quietly the wrong one tomorrow, yet AI is frozen the moment it ships, and we cover the gap with armies of people rebuilding systems by hand. We’ve gotten good at building AI that’s smart once. The thesis: evolving decision systems from the intersection of AI, complex dynamical systems, and adaptive control, for clinical trials, health, life sciences, finance, and operations.
Explore the thesis →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
FAIRE — Frontiers in AI Research and Engineering
A frontier AI wiki built around curriculum, arcs, concept pages, references, and Minimum Valuable Builds: every pivotal page nudges the reader from understanding into a concrete artifact.
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.
HMV-CRL: Separating What You Want from What You Were Shown
Reward functions conflate two things that shouldn’t be conflated: what the agent actually wants, and the observations it happened to see during training. This disentangles them — so the goal survives when the world changes.
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.
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, without ever having met.
Latest
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