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Computational Intelligence Laboratory
Department of Computer Science, Stanford University

The Computational Intelligence Laboratory (CIL) investigates the foundations of machine learning and automated reasoning. We are a group of 4 Ph.D. students, 2 postdocs, and several M.S. students working on problems at the intersection of statistics, logic, and computation.

Neural Network Diagram
Fig. 1: Feedforward architecture used in our hybrid neuro-symbolic system

Current Projects
Bayesian Structure Learning Learning the structure of Bayesian networks from sparse, incomplete data. We have developed a novel scoring criterion that incorporates domain-specific structural priors, achieving 40% better reconstruction accuracy than BDe on biomedical datasets.
Funded by NSF IIS-9702847
Hybrid Neuro-Symbolic Reasoning Can neural networks learn to respect logical constraints? We are training feedforward networks augmented with penalty terms derived from first-order logic, enabling a form of "soft theorem proving." Early results on the MYCIN knowledge base are promising.
Funded by DARPA HPKB Program
Knowledge Compilation for Planning Compiling propositional theories into DNNF (Decomposable Negation Normal Form) for efficient online query answering. Applications in autonomous agent planning under uncertainty.
Funded by ONR N00014-96-1-0718
Inductive Logic Programming Extending Progol with statistical significance tests to prevent overfitting in relational learning. Applied to protein structure prediction in collaboration with the Stanford Biochemistry Dept.
Joint work with Prof. R. Altman

Lab Members (1997-98)
Postdocs: Dr. Kenji Takahashi (Bayesian methods), Dr. Ana Soares (ILP)
Ph.D. Students: David Park (structure learning), Sarah Mitchell (neuro-symbolic), Raj Gupta (knowledge compilation), Lisa Wong (planning)
M.S. Students: Tom Bradley, Jennifer Nakamura, Carlos Reyes
Visiting: Prof. Luc De Raedt (K.U. Leuven, Belgium) — Winter Quarter 1998

Interested in joining? I am looking for motivated Ph.D. students for the 1998-99 academic year. Strong mathematical background required. Email me with your CV and a brief description of your research interests.


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