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: podcast@galois.com

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.

We chat with Alex Malozemoff and Marc Rosen about a recently published paper, "Balboa: Bobbing and Weaving Around Network Censorship," on a novel system for censorship circumvention and its corresponding implementation. The paper authors also include James Parker.

Alastair Reid describes Google's efforts to bring formal methods to developers so that they can be useful today. We cover a recent publication describing their approach, Alastair's project to document all of the papers he read for a year, and a prototype tool that they've been building to demonstrate formal verification tools in rust.

Gregory Malecha talks with Joey and Shpat about Bedrock, a startup bringing systems engineers together with formal methods engineers to build some of the most secure and correct systems in the world.

Tycho Andersen shares lessons that Linux kernel developers have learned from decades of open-source interactions. We discuss how the open-source community works together to make the Linux kernel better for everyone, and also what it's like to work debugging the kernel.

Dr. Eric Davis walks us through what it means for a data model to be trustworthy, what common pitfalls predictive models run into, reproducibility issues, and what can be done. We chat about how subject area experts are expected to be many things: statisticians, computer scientists, and mathematicians, and how that can sometimes lead to mistakes. We also look at the COVID-19 pandemic and how data models affect decision-making.

Dr. Aditya Thakur, a computer science professor at U.C. Davis, walks us through his work on developing analysis tools that he wished he had while working in industry at places like Google. Aside from program analysis, we talk about making a research group successful by exposing them to industry. Towards the end, he shares his work on techniques and tools for repairing a trained deep neural network once a mistake has been discovered. Along the way, we learn about things like abstract interpretation, non-determinism, the trickiness of parallelism, and other concepts pertinent to analysis in an approachable way.

Dan Guido, CEO of Trail of Bits, walks us through how they work with customers to make long-term improvements in security and software quality. He also describes what blockchain has done right, and how the rest of the software world should learn from them.

Talia Ringer, a Ph.D. candidate at University of Washington, explains how they do deep people-centric PL research. We discuss proof repair, UX for software correctness, and how to ask users of tools for feedback to react to.

Principal Researcher, Alex Malozemoff, walks us through what homomorphic encryption is, what CKKS is, and how a recent new attack on CKKS will impact progress on homomorphic encryption.

The founders of MuseDev discuss making modern static analysis usable and leveraging the latest promising research for automatic bug finding. MuseDev is a spin-off of Galois.