Research & Publications
Advancing the frontiers of AI, causal inference, and optimization through rigorous research
Our Research Focus
We pursue fundamental and applied research across key areas of AI and optimization
Causal Inference
Developing methods for identifying and estimating causal effects from observational and experimental data, with applications to product analytics and decision-making.
Experimentation Methods
Advancing the theory and practice of randomized experiments, including sequential testing, adaptive designs, and methods for complex experimental scenarios.
Optimization Algorithms
Creating efficient algorithms for solving complex optimization problems in real-time systems, including methods for stochastic and robust optimization.
Machine Learning
Exploring novel approaches to learning from data, including representation learning, reinforcement learning, and methods that combine ML with causal reasoning.
Applied Research
Translating theoretical advances into practical applications across industries, focusing on real-world impact and scalability.
Methodological Advances
Developing new statistical and computational methods that enable better decision-making under uncertainty and complexity.
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Interested in research collaboration or want to discuss our work? Get in touch.