In this episode, we're joined by Eric Daimler, CEO & co-founder of Conexus AI, Inc, an MIT spin out. We discuss the Conexus software platform, which is built on top of breakthroughs in the mathematics of Category Theory, and how it guarantees the integrity of universal data models. Eric shares real-world examples of applying this approach to various complex industries, such as transportation and logistics, avionics, and energy. Listen to this episode wherever you listen to podcasts. Eric Daimler: https://www.linkedin.com/in/ericdaimler/ Joey Dodds: https://www.linkedin.com/in/joey-dodds-4b462a41/ Rob Dockins: https://galois.com/team/robert-dockins/ Galois, Inc.: https://galois.com/ Contact us: [email protected]

Today we're joined by Nikhil Swamy, Senior Principal Researcher in the RiSE group at Microsoft Research. We are very excited to hear about what he's been working on. In particular, we're going discuss a language that he's co-created and continually develops called F* (pronounced F star). F* is a dependently typed language that you can both program and prove things about the programs that you write. We'll talk about what makes that language special and unique from other similar languages, as well as some of the applications of F*.

Joey and Shpat talk with Ankush Desai, a Senior Applied Scientist at AWS and one of the primary developers behind the P language. They dig into uses for P, bug finding, and what it takes for formal methods researchers to build useful tools for applied engineers.

In this episode, we talk with Steve Weis, a Senior Staff Security Engineer at Databricks with extensive knowledge of security, cryptography, and software engineering. Steve shares his experience working for large companies like Google and Facebook and how their security needs differ from start-ups and companies trying to scale. He talks about why he thinks companies should share more about how they design their infrastructure and how they can develop a “security mindset” so even non-security-related roles can contribute to building secure systems.

In episode #18, we chat with Jordan Kyriakidis, co-founder and CEO of QRA Corp. QRA is developing QVScribe, a product that helps engineers write requirements and analyze those requirements to gauge whether they are framed well and capture the writer's intent. We discuss the impact of writing good, early-stage design requirements, how they impact your system, how to write better requirements, the state of natural language processing, and machine learning for this use case. We also talk about applying those in situations where you need explainability and where ambiguity is unacceptable.

In this two-part episode, we speak with Iain Whiteside about the challenges and some of the more novel solutions to make autonomous vehicles safer and easier to program. In part 1, we discuss how Ian and his team formalize and check the different actions and situations that a car finds itself in while on the road. In part 2, we discuss how you might validate the accuracy of neural networks that sense the world, and how to mitigate issues that might arise.

In this two-part episode, we speak with Iain Whiteside about the challenges and some of the more novel solutions to make autonomous vehicles safer and easier to program. In part 1, we discuss how Ian and his team formalize and check the different actions and situations that a car finds itself in while on the road. In part 2, we discuss how you might validate the accuracy of neural networks that sense the world, and how to mitigate issues that might arise.

In this episode, we chat with Dr. Kathleen Fisher, who was chair of the Computer Science department at Tufts University at the time of the interview. We talk about Kathleen’s experience in applying formal methods and PL theory to solve significant practical problems throughout her career. Equally important, we discuss how it came to be that she is practically a pro at golf!

In this episode, we talk with Leo de Moura, a principal researcher at Microsoft Research. We’ll dive into his work on Lean, how goals for Lean have evolved, and who can use it. We also discuss how Leo was able to implement such a system without being a programming languages expert

Rod Chapman explains his recent verification of TweetNACL using SPARK/ADA. We discuss how every aspect of his proofs are automated, how the correctness proofs actually enabled better performance after compilation, and higher confidence in some otherwise risky-seeming optimizations.