07-23, 11:00–12:00 (US/Eastern), BoF: Room 353
A lot of people are building tooling for differential equation based models in Julia for various domains. DifferentialEquations.jl, DynamicalSystems.jl, PuMaS.jl, Modia.jl, QuantumOptics.jl, etc. and the list goes on. The purpose of this BoF is to gather the developers who are interested in this topic in order to learn about the priorities and gripes within the community in order to plan for the next developments.
Many different aspects of dynamical modeling in Julia have seen a recent boom in popularity. A lot of package development focus has been given to the tooling for simulating dynamical models, such as the differential equation solvers of DifferentialEquations.jl and the relevant underlying pieces like IterativeSolvers.jl and NLsolve.jl. In addition, a community of domain-specific modeling tools such as DynamicalSystems.jl, Modia.jl, PuMaS.jl, QuantumOptics.jl, and more than can be listed have all built their own user bases.
The purpose of this BoF is to gather the developers of these related tooling to discuss the current state of the ecosystem and develop plans and priorities for next steps. A quick overview of the package space and its recent developments will be given to frame the conversion, with most of the time dedicated to discussion. Possible topics include (but are not limited to) understanding the domains most in need of new and more performant solvers, the utilization of parallelism (multithreading, multiprocessing, and GPu), incorporating symbolic tooling such as ModelingToolkit.jl, and the commonalities of analysis tooling (such as parameter estimation, neural network integration, uncertainty propagation). We invite developers within the community to express their feedback and help guide our next moves within the package space.
Chris' research and software combines AI with differential equation models of human organs to give patients accurate and personalized drug doses: reducing pain and complications for patients while reducing treatment costs for hospitals.
Chris Rackauckas is an applied mathematics instructor at the Massachusetts Institute of Technology and a senior research analyst at the University of Maryland, School of Pharmacy in the Center for Translational Medicine. Chris's recent work is focused on bringing personalized medicine to standard medical practice through the proliferation of mathematical software. His work on developing the DifferentialEquations.jl solver suite along with over a hundred other Julia packages, not only earned him the inaugural Julia Community Prize and front page features in tech community sites, it is also the foundation of the PuMaS.jl package for Pharmaceutical Modeling and Simulation, set to release in March 2019. Chris’ work with PuMaS makes it possible to predict the optimal medication dosage for individuals, reducing the costs and potential complications associated with treatments. The software is currently being tested in the administration of treatment for neonatal abstinence syndrome (NAS), an opioid withdrawal disorder in newborn babies. NAS requires medically administered morphine doses every four hours to prevent the infants from experiencing withdrawal symptoms. PuMaS is being used to predict personalized safe dosage regimens by incorporating realistic biological models (quantitative systems pharmacology) and deep learning into the traditional nonlinear mixed effects (NLME) modeling framework. This software and its methodology are also being tested in clinical trials at Johns Hopkins University for its ability to predict an individual's drug response to vancomycin and automatically prescribe optimal doses directly from a patient's health records.
Chris started this work while completing his Masters and Ph.D. at the University of California, Irvine where he was awarded the Mathematical and Computational Biology institutional fellowship, the Graduate Dean's Fellowship, the National Science Foundation's Graduate Research Fellowship, the Ford Predoctural Fellowship, the NIH T32 Predoctural Training Grant, and the Data Science Initiative Summer Fellowship. His research with his advisor, Dr. Qing Nie, focused on the methods for simulating stochastic biological models and detailing how the randomness inherent in biological organisms can be controlled using stochastic analysis. Chris bridged the gap between theory and practice by having a "wet lab bench" in Dr. Thomas Schilling’s lab, where these methodologies were tested on zebrafish. Fluorescence Light Microscopy (FLIM) measurements of retinoic acid in the zebrafish hindbrain showed that the predicted control proteins could attenuate inherent biological randomness. The result was a verified mathematical theory for controlling the randomness in biological signaling. Chris received the Kovalevsky Outstanding Ph.D. Thesis Award from the Department of Mathematics upon graduation and was showcased in an interview "Interdisciplinary Case Study: How Mathematicians and Biologists Found Order in Cellular Noise" in iScience.
As an undergraduate at Oberlin College, Chris was awarded the NSF S-STEM scholarship and the Margaret C. Etter Student Lecturer Award by the American Crystallographic Association, an award usually given for PhD dissertations, for his work on 3+1 dimensional incommensurate crystal structure identification of H-acid. This award was given for Service Crystallography for its potential impact on industrial dye manufacturing.