Decision systems for complex domains, in practice.
The applications span decision systems, interactive AI, and scientific machine learning. Each uses the same approach: represent what shapes the outcome, simulate possible actions, decide on the best feasible option, and learn from the result.
Optimizing resources under real-world constraints
How should limited resources be allocated when outcomes depend on many interacting factors and the final plan must obey real constraints?
View workConversational analytics with answers you can verify
How can people ask questions in everyday language and still trust where every number and conclusion came from?
View workPrivate AI for sensitive, long documents
How can people analyze sensitive, very long documents without sending the material to a cloud service?
View workAnalytics that adapt to each organization’s data
How can a proven analytical and optimization method work across teams, brands, regions, and data structures without being rebuilt by hand each time?
View workChoosing which genes to test for oxygen-tolerant nitrogen fixation
Which undercharacterized genes should researchers test next to understand how some cyanobacteria fix nitrogen in the presence of oxygen?
View workChoosing which protein variants to test next
How can researchers choose a small, informative set of Rubisco variants from a protein sequence space too large to test exhaustively?
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