1 hr 19 min

Peregrination of a raconteur through maths, computing, and life with Chris Rackauckas (MIT‪)‬ Random Walks

    • Science

In this episode, I converse with Dr. Christopher Rackauckas, the Research Affiliate and Co-PI of the Julia Lab at the Massachusetts Institute of Technology, Director of Modeling and Simulation at Julia Computing and Creator / Lead Developer of JuliaSim, Director of Scientific Research at Pumas-AI and Creator / Lead Developer of Pumas, and Lead Developer of the SciML Open Source Software Organization. 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. He completed his Masters and Ph.D. at the University of California, Irvine where his research doctoral focused on the methods for simulating stochastic biological models and detailing how the randomness inherent in biological organisms can be controlled using stochastic analysis and he was awarded the Mathematical and Computational Biology institutional fellowship, the National Science Foundation's Graduate Research Fellowship, the Ford Predoctoral Fellowship, the NIH T32 Predoctoral Training Grant, and the Data Science Initiative Summer Fellowship. 

Chris' research and software is focused on Scientific Machine Learning (SciML): the integration of domain models with artificial intelligence techniques like machine learning. By utilizing the structured scientific (differential equation) models together with the unstructured data-driven models of machine learning, our simulators can be accelerated, our science can better approximate the true systems, all while enjoying the robustness and explainability of mechanistic dynamical models. We indulge in a fantastic conversation on his wonderful Random Walks through science and life; brilliant research in building numerical methods and software for scientific machine learning, scientific machine learning as next-generation healthcare, and development of high performance solving of differential equations; straddling the industry-academia interface with great elan; mathematics as a progressive form of rock music; the revolutionary rise of computing in the last half a century; dealing with rejections and making progress when stuck; great mentors and prescient insights on mentorship; and many more things!!

In this episode, I converse with Dr. Christopher Rackauckas, the Research Affiliate and Co-PI of the Julia Lab at the Massachusetts Institute of Technology, Director of Modeling and Simulation at Julia Computing and Creator / Lead Developer of JuliaSim, Director of Scientific Research at Pumas-AI and Creator / Lead Developer of Pumas, and Lead Developer of the SciML Open Source Software Organization. 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. He completed his Masters and Ph.D. at the University of California, Irvine where his research doctoral focused on the methods for simulating stochastic biological models and detailing how the randomness inherent in biological organisms can be controlled using stochastic analysis and he was awarded the Mathematical and Computational Biology institutional fellowship, the National Science Foundation's Graduate Research Fellowship, the Ford Predoctoral Fellowship, the NIH T32 Predoctoral Training Grant, and the Data Science Initiative Summer Fellowship. 

Chris' research and software is focused on Scientific Machine Learning (SciML): the integration of domain models with artificial intelligence techniques like machine learning. By utilizing the structured scientific (differential equation) models together with the unstructured data-driven models of machine learning, our simulators can be accelerated, our science can better approximate the true systems, all while enjoying the robustness and explainability of mechanistic dynamical models. We indulge in a fantastic conversation on his wonderful Random Walks through science and life; brilliant research in building numerical methods and software for scientific machine learning, scientific machine learning as next-generation healthcare, and development of high performance solving of differential equations; straddling the industry-academia interface with great elan; mathematics as a progressive form of rock music; the revolutionary rise of computing in the last half a century; dealing with rejections and making progress when stuck; great mentors and prescient insights on mentorship; and many more things!!

1 hr 19 min

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