Prabakaran Chandran
MS Data Science @ Columbia · ML Engineering Lead · Causal Inference & Agentic AI
About Me
I am a graduate student at Columbia University pursuing a Master of Science in Data Science. My work spans the intersection of causal inference, machine learning, and artificial intelligence—with a deep commitment to understanding how agents, both human and artificial, make decisions under uncertainty.
Before Columbia, I led machine learning engineering at a stealth startup building agentic AI systems, and worked as a Senior ML Engineer at Informatica on enterprise AI solutions. My journey began at Mu Sigma, where I spent over three years as a Decision Scientist working across energy, manufacturing, pharma, retail, and smart cities.
I believe that the most meaningful problems lie at the convergence of human behavior and intelligent systems. My research interests include causal inference, probabilistic machine learning, reinforcement learning, and the design of AI systems that augment rather than replace human judgment.
What I Believe
Learning is Reconstruction
True understanding comes not from accumulation, but from the willingness to unlearn and rebuild from first principles. Like a diffusion process, we must sometimes embrace noise before clarity emerges.
Decisions Shape Destiny
Every meaningful outcome traces back to a decision made under uncertainty. Understanding the science of decisions—how agents choose, learn, and adapt—is understanding the substrate of progress itself.
Prosperity as Purpose
The ultimate measure of our work is whether it contributes to human flourishing. Technology, theory, and method are means; prosperity—broadly conceived—is the end that gives them meaning.
Patience and Craft
Great work emerges from sustained attention and care. There are no shortcuts to mastery. The willingness to sit with difficulty, to iterate, to refine—this is the price of work worth doing.
Areas of Focus
Data Science
- Statistical Modeling & Inference
- Causal Inference
- Analytics Engineering
Machine Learning
- Causal and Probabilistic ML
- Reinforcement Learning
- Theory of Interactive Decision Making
- Modern Deep Learning
Artificial Intelligence
- Continual Learning
- Human-Centered AI
- Foundation Models
- Alignment, Safety & Interpretability
Agentic Decision Sciences
A canvas for understanding, modeling, and nurturing agents—human, artificial, and hybrid—from the atomic individual to the coexisting ecosystem, with prosperity as the through-line.
Agentic Decision Sciences is not a method or a technique. It is a canvas—a way of seeing the problem space that emerges when we take seriously the idea that the world consists of agents making decisions under uncertainty, and that these agents are increasingly both human and artificial, existing in shared ecosystems where their fates intertwine.
Read the full convergence document →Current Work
Teaching at Columbia
CA for Prof. Adam Kelleher's graduate course—a rigorous treatment of modern causal inference spanning potential outcomes, structural causal models, quasi-experimental methods, and ML for causal inference.
TA for advanced econometrics and applied causal inference bridging statistics and social science research.
Research & Experiments
Zero-Shot Coordination in Multi-Agent Systems — Formalized cross-play generalization as an objective in Dec-POMDPs. Built visuomotor policy architecture with residual CNN encoders, multi-head spatial attention, and GRU recurrence.
Hierarchical Causal State-Space Modeling — Developed a hierarchical causal recurrent SSM for retail basket time series enabling counterfactual simulation of marketing interventions.
Energy-Based Port-Hamiltonian Neural Networks — Designed differentiable physics engines that parameterize Hamiltonian energy for structure-preserving dynamics.
Education
Master of Science, Data Science
Analysis of Algorithms · Causal Inference · Probabilistic Machine Learning · Reinforcement Learning · Advanced Deep Learning & Generative AI
Bachelor of Engineering, Control Systems · GPA: 8.63
Control Theory · Dynamical Systems · Signal Processing · Neural Networks & Evolutionary Algorithms
Explore
Agentic Decision Sciences
The convergence document—a philosophical framework for understanding agents, decisions, and ecosystems.
Data Science
Writings on statistical modeling, causal inference, analytics engineering, and the craft of working with data.
Machine Learning
Explorations in probabilistic ML, reinforcement learning, deep learning theory, and interactive decision making.
Artificial Intelligence
Thoughts on foundation models, AI alignment, human-centered AI, and the future of intelligent systems.
Library
Books that have shaped my thinking—with notes, highlights, and reflections from each reading.
People
Thinkers, researchers, and mentors whose work has shaped my understanding.
Experiments
Personal experiments on habits, productivity, and self-improvement—with tracked results.
Archive
Personal reflections, grad school notes, and contemplations on learning, growth, and the examined life.
Labs ↗
Experimental projects, prototypes, and tools under development.
Daily ↗
Daily notes, quick thoughts, and micro-reflections.