Author: Steve Nadis | MIT CSAIL<\/p>\n
Some people pass the time with hobbies like crossword puzzles or Sudoku. When Chris Rackauckas has a spare moment, he often uses it to answer questions about numerical differential equations that people have posed online. Rackauckas \u2014 previously an MIT applied mathematics instructor, now an MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) research affiliate and the co-principal investigator of the MIT Julia Lab \u2014 has already posted thousands of these answers, and if you have a question, the odds are that he has already addressed it. His research, unsurprisingly, revolves around differential equations and on computational methods \u2014 using AI and other techniques \u2014 to solve them quickly and efficiently. <\/em><\/p>\n During his graduate studies in mathematics at the University of California at Irvine, which earned him a PhD in 2018, Rackauckas focused on medical and pharmacological applications of his work. In fact, he developed the core software and techniques for Pumas-AI \u2014 a Baltimore-based firm that provides software for pharmaceutical modeling and simulation purposes \u2014 when he was still a graduate student. He now serves as the company\u2019s director of scientific research.<\/em><\/p>\n Since coming to MIT in 2019, Rackauckas has found a much wider range of applications for his \u201caccelerated\u201d differential equation solvers, including global climate modeling and building heating, ventilation, and air conditioning (HVAC) systems. He took time from his efforts to find ever-more rapid ways of attacking differential equations to talk about this work, which has earned him numerous honors, including the 2020 United States Air Force Artificial Intelligence Accelerator Scientific Excellence Award.<\/em><\/p>\n Q: <\/strong>How did you get into what you\u2019re doing today?<\/p>\n A: <\/strong>As an undergraduate math major at Oberlin College, I mostly focused on the \u201cmethods courses\u201d in scientific domains \u2014 statistical methods in psychology, time series econometrics, computational modeling in physics, and so forth. I didn\u2019t have a well-thought-out game plan. I just wanted to understand how science is really done and how we know when our scientific approaches are giving us a correct model of a given system. Fortuitously, that path turned out to be a good one for someone in my current line of work.<\/p>\n In graduate school, I went into biology \u2014 specifically combining differential equation solvers with systems biology. The goal there was to make predictive models of how the randomness of a chemical, and its concentration, changes in the body, although at the time I was working with zebra fish. It turns out that systems biology is very close to systems pharmacology. You basically replace fish with humans.<\/p>\n Q: <\/strong>Why are differential equations so important in the world around us?<\/p>\n A: <\/strong>The way I like to describe it is that all scientific experiments are measuring how something changes. How do I go from an understanding of how things change to a prediction of what will happen? That\u2019s what the process of solving a differential equation is all about. Simulations, which are experiments that we carry out on computers, can involve solving thousands upon thousands of differential equations.<\/p>\n Such a simulation might tell you, for instance, not only how a drug concentration changes over time but also how the effects of the drug on the body changes. It\u2019s not the same for every person, so you have to adapt the equations for individuals, depending on their age, weight, etc.<\/p>\n Q: <\/strong>Given your focus on \u201caccelerated\u201d equation solvers, where can you find the best opportunities for speeding things up? <\/strong><\/p>\n A: <\/strong>The clinical trials for a new drug have a set period of time; you can\u2019t just make the human element faster. But in the preclinical <\/em>domain, there\u2019s always a period of analysis. Developing a new drug could cost $10 billion, so before you start something like that, you want to know the probability that a drug will work on its target population, as well as the optimal dose for an individual. That\u2019s the purpose of preclinical analysis and quantitative systems pharmacology. Suppose that you typically spend three months on analysis and six months on clinical trials. If you can shorten that analysis from three months to a day \u2014 roughly a 100-fold acceleration \u2014 you will have cut the time to release a drug by a third.<\/p>\n Then there\u2019s clinical pharmacology, where if you can understand how to get the first dose correct you might be able to save time on repeating elements of the trials. It turns out that my Pumas colleagues and I have already achieved a 175-fold acceleration in preclinical analyses carried out for Pfizer. Moderna also publicly used Pumas and our clinical analysis methods in its clinical analysis of the Covid-19 vaccine and other drugs.<\/p>\n Here\u2019s another opportunity for time and cost savings: Mitsubishi has a facility in Japan for testing HVAC systems. You have to build the entire system and then test it in a building. Each experiment can cost millions of dollars. We\u2019re now working with them to test out, say, 10 different ideas on a computer in order to pick out the one out of those 10 options that they ought to select for a prototype and subsequent experiments.<\/p>\n Q: <\/strong>Can you discuss some other examples of how your work is used?<\/p>\n A: <\/strong>The SciML.ai website keeps a (woefully incomplete) showcase of the amazing ways people have used these methods. CliMA \u2014 an Earth system model developed by scientists at Caltech, MIT, and other institutions \u2014 relies on the differential equation solvers that I wrote. Recently I was at an applied math conference where a group, independent of me, reported that they had used my software tools to make NASA launch simulations run 15,000 times faster.<\/p>\n Q: <\/strong>What are your plans for the future?<\/p>\n A: <\/strong>There are a lot of things in the pipeline. One application I\u2019ve just started to pursue is predicting the flow of wildfires; another is to predict transient cardiac events like heart attacks, strokes, and arrythmias. A third area I\u2019m moving into is in the realm of neuropsychopharmacology \u2014 trying to predict things like the individualized biosignals in bipolar disorder, depression, and schizophrenia in order to design drugs that are better suited for treating these disorders. This is an area where there is a dire need that can lead to much more effective treatments.<\/p>\n In between these projects, I might take a moment to answer the odd question about differential equations. You\u2019ve got to relax sometime.<\/p>\n<\/div>\n Go to Source<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"