Yuyan Bao
Augusta University, GA·Computer Science
Software now steers cars and runs medical devices, so a single bug can injure someone. One way to rule bugs out is deductive verification: instead of testing a program on examples, you write down mathematical statements about what it should do and have a tool prove the program always satisfies them.
- Fields
Computer Science
Mathematics & Statistics
- Topics
formal verification
deductive verification
program verification
- Grants
- 1 known grant, through Apr 2027
What they work on
- Develop verification methodologies for reasoning about function values — functions handled as data — in industrial programming languages, using first-order assertions and auxiliary logical variables suited to automated reasoning.
- Formulate reasoning rules for common programming patterns that interleave program statements with specifications, so these patterns can be handled by Satisfiability Modulo Theories (SMT) solvers, the engines underlying automatic verifiers.
- Use the resulting theory to build specification synthesis techniques that generate annotations automatically, cutting the manual annotation overhead that currently requires deep formal-methods expertise.
- Implement the methodologies in a prototype automatic deductive program verifier, released open source and used as a tool in graduate and undergraduate curriculum development.
More about the research
The catch is that the proofs are done automatically only when the properties are expressed in a form the underlying prover can handle, and the programmer must write a lot of these statements, called annotations, by hand. Modern industrial programming languages also contain features that today's verification tools simply cannot reason about.
Why it matters
Safety- and security-critical software, from self-driving cars to digital medical devices, is growing faster than the ability to prove it correct. Reducing the manual annotation burden and supporting language features current tools cannot handle could make mathematical verification practical for working developers rather than specialists, improving software quality, safety and security.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Andrew Hirsch
University at Buffalo, NY·Computer Science
Develop open choreographies, choreographic programs whose co-designed components can communicate safely with independently written, off-the-shelf software.
Xiaokang Qiu
Purdue University, IN·Computer Science·Early-career
Develop program synthesis algorithms that infer objective functions, library models and task decomposition themselves, so programmers need not hand-craft them, enabling scalable library-based synthesis.
Binoy Ravindran
Virginia Polytechnic Institute and State University, VA·Computer Science
Develop methods that lift binary code into a provably over-approximate high-level model, one that captures both intended and unintended program behavior, for reasoning about exploitable flaws.
The school
Augusta University: 11 labs on PI Pages, $166M a year on research, 37 research doctorates a year.
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