The process of science as a whole seems an interesting but daunting prospect. To explore the intricacies of science, join me for ~60 minutes each week as I indulge in a freewheeling conversation on science and myriad other things with an eclectic set of professors, graduate students, physician-scientists, journal editors, science communicators, ethicists, and anybody who does science! In our Random Walks, we delve deep into the numerous challenges doing science entails and how everyone charts a unique collaborative path through it. https://linktr.ee/randomwalks
Peregrination of a raconteur through maths, computing, and life with Chris Rackauckas (MIT)
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!!
28 Nov 2021
Developing methods to break new grounds in science and life with James Fraser (UCSF)
In this episode, I converse with Prof. James Fraser at the University of California, San Francisco. James was an undergraduate at McGill University, where he worked in the lab of Dr. Francois Fagotto on Xenopus developmental biology. As a graduate student, with Tom Alber at UC Berkeley, James established room-temperature X-ray data collection techniques and electron density sampling strategies to define protein conformational ensembles essential for catalysis. Prior to starting an independent position at UCSF, he was a visiting EMBO Short Term Fellow in the lab of Dan Tawfik at the Weizmann Institute of Science in Israel and developed expertise in directed evolution and high-throughput assays of enzymatic or binding activity. In January 2011, James started his independent career as a QB3 at UCSF Fellow affiliated with the Department of Cellular and Molecular Pharmacology. In January 2013, he was appointed as an Assistant Professor in the Department of Bioengineering and Therapeutic Sciences and the California Institute for Quantitative Biosciences (QB3) with promotion to Associate Professor in 2016, and Full Professor in 2020. James is also a Faculty Scientist in the Molecular Biophysics and Integrated Bioimaging Division of Lawrence Berkeley National Lab.
The long-term goals of James' research group is to understand how protein conformational ensembles are reshaped by perturbations, such as mutation and ligand binding, and to quantify how these perturbations impact protein function and organismal fitness. To accomplish these goals, they create new computational and biophysical approaches to study how proteins move between different conformational states. Additionally, the group uses two complementary approaches to study the relationship between protein conformational ensembles and function. To dissect consequences of mutations on organismal fitness, they use high-throughput systems biology and biophysical methods to analyze large sets of clinically or biophysically interesting mutations and to improve the ability to engineer new protein functions, they investigate changes to the conformational ensemble as new enzymatic and binding functions emerge from directed evolution studies. We indulge in a fascinating conversation on his enjoyable journey through science and life; foraying into academia from a family of non-academics; the thrill of methods development; the enormous influence of his brilliant mentors, friends, and collaborators; creating a more equitable, open, and just environment in science; and many more things!!
13 Nov 2021
Constructing an edifice of life and science with Rocío Mercado (MIT)
In this episode, I converse with Dr. Rocío Mercado, who's currently a postdoc in the Coley group at MIT. Previously, Rocío was a postdoc in the Molecular AI team at AstraZeneca, where she worked on the development of deep generative models for small molecule drug discovery. Before AstraZeneca, Rocío was a PhD student in Prof. Berend Smit’s molecular simulation group at UC Berkeley and EPFL and received her PhD in Chemistry from UC Berkeley in August 2018, and her BS in Chemistry from Caltech in June 2013.
Rocío's research expertise lies in data-driven molecular design, at the interface of computer science and chemistry and she's passionate about the development of computational tools which can be used to enhance the process of pharmaceutical drug discovery, such as deep molecular generative models and molecular optimization methods. We indulge in a riveting conversation on her phenomenal journey through science and life; thoroughly inspirational mentors; fascinating research; confronting the imposter syndrome; fostering a more equitable and just environment in science; and many more things!!
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