Xuan (Tan Zhi Xuan)
Xuan (Sh-YEN, IPA: ɕɥɛn) is a PhD student at MIT in the Computational Cognitive Science and Probabilistic Computing research groups. Their current research focuses on inferring the hidden structure of human motivations by modeling agents as probabilistic programs, in the hope of aligning AI with the higher-order goals, values, and principles that humans strive (in part) to live by.
Many Julia libraries implement stochastic simulators of natural and social phenomena, but they are not generally amenable to Bayesian inference. In this talk, we present Genify.jl, which transforms these simulators into the Gen probabilistic programming system via compiler injection, allowing us to compute likelihoods, constrain random variables to specific values, and update these values for Monte Carlo inference, thereby enabling Bayesian inference over a wide range of existing Julia code.
The Julia community aims to be welcoming, diverse, inclusive towards people from all backgrounds. However, the 2020 Julia User & Developer Survey found that only 3% of respondents were women, and reported no respondents who were non-binary or another gender. We, Julia Gender Inclusive, believe this needs to change. In this talk, we will share our ideas and initiatives for improving gender diversity among Julia users and developers, including outreach, community building, and mutual support.
Julia Gender Inclusive is an initiative that came to life from a focus group that has been working on diversity in the Julia community for the last year. We are a group of people whose gender is underrepresented in the community and aim at providing a supportive space for all gender minorities in the Julia community. Through a BoF session we wish to discuss what we are doing and what we hope to do in the future with other people whose gender is underrepresented or allies willing to support us.
Julog.jl is a library and domain-specific language for Prolog-like logic programming in Julia. This lightning will introduce logic programming at a high level, how Julog can be used to solve first-order logic problems, how its functionality can be integrated with custom Julia functions, downstream use cases, and some next steps for making logic and constraint programming fast and accessible for Julia users.