Christopher V. Rackauckas
MATHEMATICIAN · SOFTWARE ENGINEER · PHARMACOLOGIST

Department of Mathematics, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Building 2-347, Cambridge, MA, 02139-4307
 (949)230-9190|  crackauc@mit.edu|  www.chrisrackauckas.com |  ChrisRackauckas|  Chris Rackauckas|  chrisrackauckas

Research focus: How can the randomness from scientific data be used to uncover the underlying mechanistic structure?

Summary
Applied Mathematician
Pharmacologist
Well-FundedHigh Real-World Impact
Interdisciplinary
Renowned Lecturer
Influential Writer

6 top paper awards earned in areas ranging from stochastic differential equations to automated GPU acceleration
Awardee of the ISoP Emerging Scientist Award 2020, the top award for early career pharmacometrics
Awarded >20 grants in areas ranging from numerical space weather prediction to power grid simulation
Lead developer of the widely used SciML Organization, DifferentialEquations.jl, JuliaSim, and Pumas
Publications in high impact journals and conferences ranging from machine learning to pharmacology
Known for many online talks with over 20k views, created the MIT SciML graduate course with >100 students
Author of many blogs such as StochasticLifestyle.com repeatedly featured on top tech websites

Research Interests
Mathematics
Computation
Biomedical

Stochastic (Partial) Differential Equations, Computational Differential Equations, Stochastic Analysis
High-Performance Parallel Computing, Scientific Machine Learning, “Big Data”, Julia, Package Development
Systems Biology, Clinical Pharmacology, Quantiative Systems Pharmacology (QsP), Developmental Biology

Current Positions and Positions Held
VP of Modeling and Simulation

Cambridge, Massachusetts

JULIAHUB

2018-Present

Director of Scientific Research

Baltimore, Maryland

PUMAS-AI

2020-Present

Research Affiliate (Co-PI of the Julia Lab)

Boston, Massachusetts

MASSACHUSETTS INSTITUTE OF TECHNOLOGY, COMPUTER SCIENCE AND AI LABORATORY (CSAIL)

Applied Mathematics Instructor

2021-Present

Boston, Massachusetts

MASSACHUSETTS INSTITUTE OF TECHNOLOGY, DEPARTMENT OF MATHEMATICS

Senior Research Scientist

2019-2021

Baltimore, Maryland

UNIVERSITY OF MARYLAND, BALTIMORE, SCHOOL OF PHARMACY

Assistant Specialist

2018-2020

Irvine, California

UNIVERSITY OF CALIFORNIA, IRVINE, DEPARTMENT OF MATHEMATICS

2018

Education
University of California, Irvine

Irvine, California

PH.D. IN MATHEMATICS

2018• Thesis: Simulation and Control of Biological Stochasticity• Awarded Kovalevsky Outstanding Ph.D. Thesis AwardJANUARY 20, 2024

CHRISTOPHER V. RACKAUCKAS · CURRICULUM VITAE

1

University of California, Irvine

Irvine, California

M.S. IN MATHEMATICS

2015• Certificate in Mathematical, Computational, and Systems Biology

Oberlin College

Oberlin, Ohio

B.A. WITH HONORS IN MATHEMATICS WITH MINORS IN COMPUTER SCIENCE, PHYSICS, AND ECONOMICS

2013• GPA: 3.8/4.0, GRE: V166 (96%), Q169 (98%), W5.5 (96%)

GrantsRigorous Optimal Uncertainty Quantification
LEAD PI: ADAM GERLACH. CO-PIS: ALEXANDER VON MOLL, JOHN SCHIERMAN, CHRIS RACKAUCKAS

AFOSR
December 2023

$450K

Collaborative Research: Discovery and Collaboration of Stochastic Chemical Reaction
Network Models
LEAD PI: SAMUEL ISAACSON. CO-PI: CHRIS RACKAUCKAS

NSF
September 2023

$375K

ProteusSim: A Composable Integrated Approach for Automatic Simulator Construction
LEAD PIS: KENO FISCHER, CHRIS RACKAUCKASDARPA ASKEM
August 2022

HR001122S0005 $7.2M

Bringing computation to the climate challenge

MIT Climate Grand Challenge

LEAD PIS: RAFFAELE FERRARI AND NOELLE SELIN. SIGNIFICANT CONTRIBUTORS: TAMARA BRODERICK, ALAN EDELMAN, ARLENE
FIORE, GLENN FLIERL, CHRISTOPHER KNITTEL, JOHN MARSHALL, YOUSSEF MARZOUK, ELSA OLIVETTI, BETHANY PATTEN, CHRIS

May 2022

RACKAUCKAS, DANIELA RUS, ADAM SCHLOSSER, ANDRE SOUZA, GREGORY WAGNER

Amount Undisclosed.

Enhancing the open source SciML stack for clinical trial simulations
PIS: SAMUEL ISSACSON AND CHRISTOPHER RACKAUCKAS

Chan Zuckerberg Initiative
August 2021

EOSS4-0000000437 $290K

Accelerating Drug Discovery with Symbolic-Numerics in SciML
PIS: SAMUEL ISSACSON AND CHRISTOPHER RACKAUCKAS

Wellcome Trust
August 2021

Technology Development Grant $140K
NASA Space Technology Research,

Interpretable machine learning for high-speed, high-fidelity GEOS-Chem model simulations
Development, Demonstration, andwith uncertainty quantification

Infusion-2021

PI: CHRISTOPHER TESSUM (UIUC). COLLABORATORS: DAVEN HENZE, CHRISTOPHER RACKAUCKAS, NICOLE RIEMER

June 2021

SpaceTech-REDDI-2021 $600K

Convergence of Bayesian inverse methods and scientific machine learning in Earth systemmodels through universal differentiable programming
LEAD PI: PATRICK HEIMBACH (UT AUSTIN). CO-PIS: KAREN WILCOX (UT AUSTIN), ALAN EDELMAN (MIT), CHRIS RACKAUCKAS (MIT),CHRIS HILL (MIT), LOOSE (CU BOULDER), MORLIGHEM (UC IRVINE), NARAYANAN (U CHICAGO), SCHANEN (U CHICAGO)National Science Foundation
June 2021

NSF CSSI $4M

Neuroblox: a Data-Driven Platform for Computational Psychiatry
PIS: LR MUJICA-PARODI (SUNYSB/HARVARD), A EDELMAN (MIT), C RACKAUCKAS (MIT), E MILLER (MIT) R GRANGER (DARTMOUTH),H STREY (SUNYSB)NeuralSPICE: Accelerated Simulation and Design of Electrical Circuits
LEAD PI: VIRAL SHAH (JULIA COMPUTING). CO-PIS: CHRIS RACKACUKAS (JC), KENO FISHER (JC), ALAN EDELMAN (JC)Private Funding
June 2021

DARPA
November 2020

DARPA AIE DITTO $1M

Optimization Under Uncertainty: A Generalized Koopman Expectation Framework
TASK MANAGER: ADAM GERLACH (AFRL). KEY PERSONNEL: JOHN SCHIERMAN (AFRL), ANDREW LENARD, CHRIS RACKAUCKAS (MIT)

Airforce Research Laboratory(AFRL)
September 2020

AFRL AFOSR Program in Optimization & Discrete Math. $450K
JANUARY 20, 2024

CHRISTOPHER V. RACKAUCKAS · CURRICULUM VITAE

2

Scientific Machine Learning for Simulation and Control in Large Scale Power Systems

National Renewable Energy
Laboratory (NREL)

PIS: DUNCAN CALLAWAY (LBL AND UC BERKELEY), BRI-MATHIAS HODGE (NREL), CHRIS RACKAUCKAS (MIT)September 2020

NREL internal funding. $1.3M

Model Form Epistemic Uncertainty Quantification for Modeling with Differential Equations

Sandia National Laboratory

ERIN ACQUESTA, TERESA PORTONE, AND AHMAD RUSHDI IN COLLABORATION WITH CHRIS RACKAUCKAS (MIT)August 2020

Sandia National Laboratory internal funding. $180K

SWIFT: Adaptive RFI Cancellation for Radio Science Observatories

National Science Foundation

PI: FRANK D. LIND, COPI : J. SWOBODA, COPI : A. EDELMAN, C. RACKAUCKAS

August 2020

NSF 20-537, Spectrum and Wireless Innovation enabled by Future Technologies (SWIFT). $1.3M

Composable Next Generation Software Framework for Space Weather Data Assimilation andUncertainty Quantification

National Science Foundation

PIS: YOUSSEF MARZOUK (MIT), ALAN EDELMAN (MIT), BORIS KARMER (UCSD), AARON RIDLEY (UMICH), CHRIS RACKAUCKAS (MIT),

July 2020

PHILIP ERICKSON (MIT), DAVID GONDELACH (MIT), JAIME PERAIRE (MIT), JEI FREEMAN (MIT) NGOC NGUYEN (MIT)NSF 20-529, Next Generation Software for Data-driven Models of Space Weather with Quantified Uncertainties (SWQU). $3.1M

Building an open platform for pandemic modelingMicrosoft Pandemic Response

MICROSOFT PI: SIMON FROST, ACADEMIC CO-PIS: SEBASTIAN VOLLMER, AND CHRIS RACKAUCKAS, DEIRDRE HOLLINGSWORTH,

June 2020

RONOJOY ADHIKARI, MICHAEL GRAVENOR, BIAGIO LUCIN

Dr. Sebastian Vollmer (The Alan Turing Institute, UK); Dr. Chris Rackauckas (CSAIL, MIT); Prof. Deirdre Hollingsworth (Big Data Institute, Univ. Oxford);
Dr. Ronojoy Adhikari (Mathematics, Univ. Cambridge); Prof. Michael Gravenor (Biomedical Sciences, Swansea University); Prof. Biagio Lucini(Mathematics, Swansea University). $180K, with 80K budgeted to MIT

Neural Component Architecture to Accelerate Modeling & Simulation
PIS: KENO FISCHER, VIRAL SHAH, AND CHRIS RACKAUCKASNational Science Foundation
March 2020

SBIR Phase I. $220K

Neural Surrogates for Augmenting Models of Complex Dynamical Systems
PIS: ALAN EDELMAN AND CHRIS RACKAUCKAS (MIT)MIT-Ford Alliance
March 2020

$300K

Digital Wells for Optimal Production and Drainage (DigiWell)
PROJECT MANAGER: BERNT LIE (USN). RESEARCH PARTNERS: MARTIN GIESE (UIO), STEIN KROGSTAD (SINTEF), ANN MUGGERIDGE(ICL), CHRIS RACKAUCKAS (MIT)Norwegian Research Council
February 2020

Multi-University Grant. Key Partner: Equinor, 32M KOK ∼ $3.7M

Robust Neural Differential Models for Navigation and Beyond
PI: PROF. EDELMAN. CO-PIS: DR. RACKAUCKAS, DR. O’KEEFFE, DR. BELARGE, P. MORALES , N. DALTON

MIT-Air Force AI Innovation
Accelerator
November 2019

Airforce stakeholders: Lieutenant Colonel Bradley Reuter (AMC/A8XC), Captain Alexander Hutcheson (AMC), Captain Kyle Baldwin (USAF Weapons
School/26th Weapons Squadron), Captain Danae Peoples (USAF Weapons School/26th Weapons Squadron), Captain David Pagano (USAF Weapons
School/26th Weapons Squadron) $2.4M

Accelerating Coupled HVAC-Building Simulation with a Neural Component Architecture
PI: VIRAL SHAH. CO-PI: CHRIS RACKAUCKAS

ARPA-E
November 2019

DIFFERENTIATE, DE-FOA-0002107, Assigned Control No: 2107-1656. $1.1M

MIT-Imperial-AIMS Scientific Machine Learning Workshop Series
PIS: ALAN EDELMAN, CHRIS RACKAUCKAS, SHEEHAN OLVER, SIMUKAI UTETEMISTI GSF
November 2019

Funding for an international three workshop series on scientific machine learning with dedicated funding for underrepresented and students ofdiverse backgrounds. $30K

A Fresh Approach to General, Performant, and Scalable Productivity
PI: ALAN EDELMAN. CO-PIS: CHRIS RACKAUCKAS, STEVEN JOHNSON

DARPANovember 2019

PAPPA, DARPA-PA-19-04-02- FP-020, $1M

High-fidelity Accelerated Design of High-performance Electrochemical Systems
PI: VENKAT VISWANATHAN. CO-PIS: ALAN EDELMAN, CHRIS RACKAUCKAS

ARPA-E
November 2019

DIFFERENTIATE, DE-FOA-0002107, Assigned Control No: 2107-1548. $600K

JANUARY 20, 2024

CHRISTOPHER V. RACKAUCKAS · CURRICULUM VITAE

3

Supervised Thesis
On Efficient Training & Inference of Neural Differential Equations
AVIK PAL

Masters
June 1, 2023

https://dspace.mit.edu/handle/1721.1/151379

Higher-Order Automatic Differentiation and Its Applications
SONGCHEN TAN

Masters
June 1, 2023

https://dspace.mit.edu/handle/1721.1/151501

Approximation of Large Stiff Acausal Models
RANJAN ANANTHARAMAN

PhD
February 1, 2023

https://dspace.mit.edu/handle/1721.1/150242

A new way to do epidemic modeling

PhD

RAJ ABHIJIT DANDEKAR

September 1, 2022

https://dspace.mit.edu/handle/1721.1/147364

TCAD-Informed Surrogate Models of Semiconductor Devices
SAMUEL B. CHINNERY

Masters
May 1, 2022

https://dspace.mit.edu/handle/1721.1/144946

SmartPitch: Applied Machine Learning for Professional Baseball Pitching Strategy
STEPHEN EUGENE OTREMBA JR.

Masters
May 1, 2022

https://dspace.mit.edu/handle/1721.1/145144

Quasi-potential analysis of multi-variate stochastic differential equations
BOLA MALEK

Masters
June 1, 2021

https://dspace.mit.edu/handle/1721.1/142704

Current Research Mentees
POSTDOCTURAL SCHOLARS
2021-P Frank SchaeferMIT2022-P Torkel Loman

MIT2022-P Wiktor Phillips

MIT

PHD STUDENTS
2021-P Emily Helen Nieves

MIT

2021-P Avik Pal

MIT

2021-P Flemming Holtorf

MIT2021-P Nicholas Klugman

MIT2021-P Shashi Gowda

MIT

MASTERS STUDENTS
2021-P Vaibhav Dixit

MIT2021-P Yonatan DeleganMIT

UNDERGRADUATE STUDENTS
2021-P Gaurav AryaMITPatents
Methods and systems for accelerating quantitative systems pharmacology (qsp) models
CHRISTOPHER RACKAUCKAS, VIRAL B SHAH, SEBASTIAN MICLUA-CÂMPEANU

Systems and methods of component-based modeling using training surrogates
CHRISTOPHER RACKAUCKAS, VIRAL B SHAH, MA YINGBOJANUARY 20, 2024

US18/136,654
April 19, 2023

PCT/US2021/050916
September 17, 2021

CHRISTOPHER V. RACKAUCKAS · CURRICULUM VITAE

4

Method and apparatus for individualized administration of medicaments for enhanced safedelivery within a therapeutic range

WO2021041469A1 WPO (PCT)CHRIS RACKAUCKAS, VIJAY IVATURIMarch 4th, 2021

Provisional Patents
Transforming a model in a first language to a surrogate in a second language for simulation
CHRIS RACKAUCKAS

1027/4 PROV
September 25, 2021

Improving automated surrogate training performance by incorporating simulator
information
CHRIS RACKAUCKAS, ANAS ABDELREHIM, RANJAN ANANTHARAMAN

Solver for scientific computing that detects and handles discontinuities or irregularities in asimulation

1027/5 PROV
September 25, 2021

1027/6 PROVCHRIS RACKAUCKAS, YINGBO MA

September 25, 2021

Neural-Embedded Nonlinear Mixed Effects (NENLME) Models

63/136,719 PROVCHRIS RACKAUCKAS, VIJAY IVATURI

January 13, 2021

Honors & Awards
CAREER AWARDS
2021

United States Department of the Air Force Artificial Intelligence Accelerator Scientific Excellence Award

USAF

2020

ISoP Emerging Scientist Award, The top early career award in pharmacometrics

ISoP

FELLOWSHIPS AND SCHOLARSHIPS
2016

Data Science Initiative Summer Fellowship, UC Irvine Data Science Initiative

2016

Allocation DMS160004, XSEDE

2014

National Science Foundation Graduate Research Fellowship , National Science Foundation2014

Ford Predoctoral Fellowship, National Academies of Science

2013

T32 Predoctoral Training Grant, National Institute of Biomedical Imaging and Bioengineering

UC Irvine

2013

Graduate Dean’s Recruitment Fellowship, University of California, Irvine

UC Irvine

2013

Mathematical and Computational Biology (MCB) Fellowship, University of California, Irvine

UC Irvine

2010

S-STEM Scholarship, National Science Foundation

Oberlin College

2009John F. Oberlin Scholarship, Oberlin College

Oberlin College

DSI
XSEDE
NSF
Ford Foundation

MONETARY AWARDS
2022

Best Poster Award, NeurIPS Differentiable Programming Workshop

2018

Inaugural Julia Community Award, JuliaCon 2018

2018

PLOS ONE Early Career Travel Award in the Physical Sciences, Computer Science & Engineering Category

2018

Kovalevsky Outstanding Ph.D. Thesis Award, UCI Department of Mathematics

2015

Opportunity Award, Center for Complex Biological Systems

CCBS

2013

Margaret C. Etter Student Lecturer Award, American Crystallographic Association, Service Crystallography SIG

ACA

2012

Best Poster Presentation for Statistics, Shenandoah Undergraduate Mathematics Conference

JMU

NeurIPSUCIPLOS OneUCI

PAPER AND PRESENTATION AWARDSJANUARY 20, 2024

CHRISTOPHER V. RACKAUCKAS · CURRICULUM VITAE

5

2023

Outstanding Paper Award, IEEE-HPEC 2023

IEEE

2021

Mathematical and Computational Sciences Special Interest Group Award, for DeepNLME

ACOP

2020

Outstanding Paper Award, IEEE-HPEC 2020

IEEE

2020

Quality Award, One out of three awarded by the International Society of Pharmacology

ACOP

2019

Mathematical and Computational Sciences Special Interest Group Award, for UQ in Pumas

ACOP

2018

DSWeb 2018 Software Contest Runner Up, SIAM SIG on Dynamical Systems

SIAM

2017

Best Speaker Award, Tsukuba Global Science Week

TGSW

2014

Outstanding Presentation Award, Mathematical Association of America

MAA

STUDENT’S AWARDS
2023

Best Oral Presentation Award for Emily Nieves, ISoP QSP Student Symposium

2023

Outstanding Masters Research Award for Songchen Tan, MIT Center for CS&E MathWorks Prize

2022

MIT Portugal Program Poster Award for Vinicius Santana, MIT Portrugal Program

ISoP
MIT
University of Porto

Peer Reviewed Publications
Automated translation and accelerated solving of differential equations on multiple GPU
platforms
UTKARSH UTKARSH, VALENTIN CHURAVY, YINGBO MA, TIM BESARD, PRAKITR SRISUMA, TIM GYMNICH, ADAM R GERLACH, ALAN
EDELMAN, GEORGE BARBASTATHIS, RICHARD D BRAATZ, CHRISTOPHER RACKAUCKAS

Computer Methods in Applied
Mechanics and Engineering
Februrary 1, 2024

https://www.sciencedirect.com/science/article/abs/pii/S0045782523007156
2023 IEEE International Conference

Performance Bounds for Quantum Control

on Quantum Computing andEngineering (QCE)

FLEMMING HOLTORF, FRANK SCHÄFER, JULIAN ARNOLD, CHRISTOPHER RACKAUCKAS, ALAN EDELMAN

September 9, 2023

https://ieeexplore.ieee.org/abstract/document/10313626

Physics-enhanced deep surrogates for partial differential equations

Nature Machine Intelligence

RAPHAËL PESTOURIE, YOUSSEF MROUEH, CHRIS RACKAUCKAS, PAYEL DAS, STEVEN G JOHNSON

December 4, 2023

https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1011530

Catalyst: fast biochemical modeling with Julia
TORKEL E LOMAN, YINGBO MA, VASILY ILIN, SHASHI GOWDA, NIKLAS KORSBO, NIKHIL YEWALE, CHRIS RACKAUCKAS, SAMUEL AISAACSONPLoS Computational Biology
October 18, 2023

https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1011530

Efficient hybrid modeling and sorption model discovery for non-linear
advection-diffusion-sorption systems: A systematic scientific machine learning approach
VINICIUS V SANTANA, ERBET COSTA, CARINE M REBELLO, ANA MAFALDA RIBEIRO, CHRISTOPHER RACKAUCKAS, IDELFONSO BR
NOGUEIRA

Chemical Engineering Science
December 5th, 2023

https://www.sciencedirect.com/science/article/abs/pii/S0009250923007790

Continuous Deep Equilibrium Models: Training Neural ODEs Faster by Integrating Them toInfinity
AVIK PAL, ALAN EDELMAN, CHRISTOPHER RACKAUCKASHPEC 2023
September 25th, 2023

Best Student Paper Award, https://arxiv.org/abs/2201.12240 (to be updated)

Differentiable modelling to unify machine learning and physical models for geosciences

Nature Reviews Earth &Environment

CHAOPENG SHEN, ALISON P APPLING, PIERRE GENTINE, TOSHIYUKI BANDAI, HOSHIN GUPTA, ALEXANDRE TARTAKOVSKY, MARCOBAITY-JESI, FABRIZIO FENICIA, DANIEL KIFER, LI LI, XIAOFENG LIU, WEI REN, YI ZHENG, CIARAN J HARMAN, MARTYN CLARK,MATTHEW FARTHING, DAPENG FENG, PRAVEEN KUMAR, DOAA ABOELYAZEED, FARSHID RAHMANI, YALAN SONG, HYLKE E BECK, TADD

July 11th, 2023

BINDAS, DIPANKAR DWIVEDI, KUAI FANG, MARVIN HÖGE, CHRIS RACKAUCKAS, BINAYAK MOHANTY, TIRTHANKAR ROY, CHONGGANG
XU, KATHRYN LAWSON

https://www.nature.com/articles/s43017-023-00450-9

JANUARY 20, 2024

CHRISTOPHER V. RACKAUCKAS · CURRICULUM VITAE

6

A differentiable, physics-informed ecosystem modeling and learning framework forlarge-scale inverse problems: demonstration with photosynthesis simulations

Biogeosciences

DOAA ABOELYAZEED, CHONGGANG XU, FORREST M HOFFMAN, JIANGTAO LIU, ALEX W JONES, CHRIS RACKAUCKAS, KATHRYN

July 6th, 2023

LAWSON, CHAOPENG SHEN

https://arxiv.org/abs/2207.08135

Forecasting virus outbreaks with social media data via neural ordinary differential
equations

Nature Scientific Reports

MATíAS NÚÑEZ, NADIA L BARREIRO, RAFAEL A BARRIO, CHRISTOPHER RACKAUCKASJuly 5th, 2023

https://www.nature.com/articles/s41598-023-37118-9

Locally regularized neural differential equations: some black boxes were meant to remainclosed!

ICMLAVIK PAL, ALAN EDELMAN, CHRISTOPHER VINCENT RACKAUCKASJuly 3rd, 2023

https://proceedings.mlr.press/v202/pal23a.html

Differentiating Metropolis-Hastings to Optimize Intractable Densities

ICML Workshop

GAURAV ARYA, RUBEN SEYER, FRANK SCHÄFER, ALEX LEW, MATHIEU HUOT, VIKASH K MANSINGHKA, CHRIS RACKAUCKAS, KARTIK

July 3rd, 2023

CHANDRA, MORITZ SCHAUER

https://arxiv.org/abs/2306.0796, https://differentiable.xyz/

Julia for biologists

Nature Methods

ELISABETH ROESCH, JOE G GREENER, ADAM L MACLEAN, HUDA NASSAR, CHRISTOPHER RACKAUCKAS, TIMOTHY E HOLY, MICHAEL

April 6th, 2023

PH STUMPF

https://www.nature.com/articles/s41592-023-01832-z

AutoMat: Automated materials discovery for electrochemical systems

MRS Bulletin

EMIL ANNEVELINK, RACHEL KURCHIN, ERIC MUCKLEY, LANCE KAVALSKY, VINAY I HEGDE, VALENTIN SULZER, SHANG ZHU, JIANKUN
PU, DAVID FARINA, MATTHEW JOHNSON, DHAIRYA GANDHI, ADARSH DAVE, HONGYI LIN, ALAN EDELMAN, BHARATH RAMSUNDAR,

December 22nd, 2022

JAMES SAAL, CHRISTOPHER RACKAUCKAS, VIRAL SHAH, BRYCE MEREDIG, VENKATASUBRAMANIAN VISWANATHAN

https://link.springer.com/article/10.1557/s43577-022-00424-0

Automatic differentiation of programs with discrete randomness

NeurIPSGAURAV ARYA, MORITZ SCHAUER, FRANK SCHÄFER, CHRISTOPHER RACKAUCKAS

December 6th, 2022

https://proceedings.neurips.cc/paper_files/paper/2022/hash/43d8e5fc816c692f342493331d5e98fc-Abstract-Conference.html

Symbolic-numeric integration of univariate expressions based on sparse regression

ACM Communications in Computer
AlgebraSHAHRIAR IRAVANIAN, CARL JULIUS MARTENSEN, ALESSANDRO CHELI, SHASHI GOWDA, ANAND JAIN, YINGBO MA, CHRIS

November 23rd, 2022

RACKAUCKAS

https://dl.acm.org/doi/abs/10.1145/3572867.3572882

Continuous-time echo state networks for predicting power system dynamics

Electric Power Systems Research

CIARAN ROBERTS, JOSÉ DANIEL LARA, RODRIGO HENRIQUEZ-AUBA, MATTHEW BOSSART, RANJAN ANANTHARAMAN, CHRIS

November 1st, 2022

RACKAUCKAS, BRI-MATHIAS HODGE, DUNCAN S CALLAWAY

https://www.sciencedirect.com/science/article/pii/S0378779622006587

Validation and parameterization of a novel physics-constrained neural dynamics model
applied to turbulent fluid flow

Physics of Fluids

VARUN SHANKAR, GAVIN D PORTWOOD, ARVIND T MOHAN, PEETAK P MITRA, DILIP KRISHNAMURTHY, CHRISTOPHER RACKAUCKAS,

November 1st, 2022

LUCAS A WILSON, DAVID P SCHMIDT, VENKATASUBRAMANIAN VISWANATHAN

https://pubs.aip.org/aip/pof/article-abstract/34/11/115110/2847650/Validation-and-parameterization-of-a-novel-physics

GlobalSensitivity.jl: Performant and Parallel Global Sensitivity Analysis with Julia
VAIBHAV DIXIT, CHRIS RACKAUCKAS

Journal of Open Source Software
August 17th, 2022

https://joss.theoj.org/papers/10.21105/joss.04561.pdf

Parallelizing Explicit and Implicit Extrapolation Methods for Ordinary Differential Equations
UTKARSH, CHRIS ELROD, YINGBO MA, CHRISTOPHER RACKAUCKASHPEC 2022
September 19, 2022

https://arxiv.org/abs/2207.08135

JANUARY 20, 2024

CHRISTOPHER V. RACKAUCKAS · CURRICULUM VITAE

7

Continuous-time echo state networks for predicting power system dynamics
CIARAN ROBERTS, JOSÉ DANIEL LARA, RODRIGO HENRIQUEZ-AUBA, MATTHEW BOSSART, RANJAN ANANTHARAMAN, CHRISRACKAUCKAS, BRI-MATHIAS HODGE, DUNCAN S CALLAWAY

Differential methods for assessing sensitivity in biological models
RACHEL MESTER, ALFONSO LANDEROS, CHRIS RACKAUCKAS, KENNETH LANGE

Constrained Smoothers for State Estimation of Vapor Compression Cycles
VEDANG DESHPANDE, CHRISTOPHER R LAUGHMAN, YINGBO MA, CHRIS RACKAUCKAS

Two heads are better than one: current landscape of integrating QSP and machine learning
TONGLI ZHANG, IOANNIS P ANDROULAKIS, PETER BONATE, LIMEI CHENG, TOMÁŠ HELIKAR, JAIMIT PARIKH, CHRISTOPHER
RACKAUCKAS, KALYANASUNDARAM SUBRAMANIAN, CAROLYN R CHO

Symbolic-Numeric Integration of Univariate Expressions based on Sparse Regression
SHAHRIAR IRAVANIAN, JULIUS MARTENSEN, ALESSANDRO CHELI, SHASHI GOWDA, ANAND JAIN, YINGBO MA, CHRIS RACKAUCKAS

ReservoirComputing.jl: an efficient and modular library for reservoir computing models
FRANCESCO MARTINUZZI, CHRIS RACKAUCKAS, ANAS ABDELREHIM, MIGUEL D MAHECHA, KARIN MORA

Composable and Reusable Neural Surrogates to Predict System Response of Causal Model
Components
RANJAN ANANTHARAMAN, ANAS ABDELREHIM, FRANCESCO MARTINUZZI, SHARAN YALBURGI, ELLIOT SABA, KENO FISCHER, GLENHERTZ, PEPIJN DE VOS, CHRIS LAUGHMAN, YINGBO MA, VIRAL SHAH, ALAN EDELMAN, CHRIS RACKAUCKAS

AbstractDifferentiation.jl: Backend-Agnostic Differentiable Programming in Julia
FRANK SCHÄFER, MOHAMED TAREK, LYNDON WHITE, CHRIS RACKAUCKAS

The implications of delayed reopening in controlling the COVID-19 surge in the Southern andWest-Central USA
DANDEKAR R, WANG E, BARBASTATHIS G, RACKAUCKAS C

November 1, 2022

PLoS Computational Biology
June 13, 2022

2022 American Control Conference
June 8, 2022

Journal of Pharmacokinetics andPharmacodynamics
Februrary 1, 2022

ACM Communications in Computer
AlgebraJanuary 27, 2022

Journal of Machine Learning
Research
January 1, 2022

AAAI 2022 Workshop on AI for
Design and Manufacturing (ADAM)
November 10, 2021

NeurIPS Differentiable
Programming Workshop
December 13, 2021

Health Data Science
August 31, 2021

Stiff Neural Ordinary Differential Equations
S KIM, W JI, S DENG, C RACKAUCKAS

Chaos
August 31, 2021

A Comparison of Automatic Differentiation and Continuous Sensitivity Analysis for
Derivatives of Differential Equation Solutions
YINGBO MA, VAIBHAV DIXIT, XINGJIAN GUO, MIKE INNES, CHRISTOPHER RACKAUCKAS

Composing Modeling and Simulation with Machine Learning in JuliaC RACKAUCKAS, R ANANTHARAMAN, A EDELMAN, S GOWDA, M GWOZDZ, A JAIN, C LAUGHMAN, Y MA, F MARTINUZZI, A PAL, U
RAJPUT, E SABA, V SHAHJANUARY 20, 2024

Electric Power Systems Research

CHRISTOPHER V. RACKAUCKAS · CURRICULUM VITAE

IEEE-HPEC
August 1, 2021

14th International Modelica
Conference
September 2021

8

High-performance symbolic-numerics via multiple dispatch
S GOWDA, Y MA, A CHELI, M GWOZDZ, V SHAH, A EDELMAN, C RACKAUCKASACM Communications in Computer
AlgebraJuly 2021

Opening the Blackbox: Accelerating Neural Differential Equations by Regularizing Internal
Solver HeuristicsICML

A PAL, Y MA, V SHAH, C RACKAUCKAS

2021

Collocation based Training of Neural Ordinary Differential Equations
E ROESCH, C RACKAUCKAS, M STUMPF

Statistical Applications in Genetics
and Molecular Biology
2021

Bayesian Neural Ordinary Differential Equations

POPL

R DANDEKAR, V DIXIT, M TAREK, A GARCIA-VALADEZ, C RACKAUCKAS

December 14, 2020

LAFI 2021

Accelerating Simulation of Stiff Nonlinear Systems using Continuous-Time Echo State
Networks

AAAI-MLPS 2021

ANANTHARAMAN R, MA Y, GOWDA S, LAUGHMAN C, SHAH V, EDELMAN A, RACKAUCKAS C

December 11, 2020

Safe Blues: The case for virtual safe virus spread in the long-term fight against epidemics

Cell Patterns

R DANDEKAR, SG HENDERSON, HM JANSEN, J MCDONALD, S MOKA, Y NAZARATHY, C RACKAUCKAS, PG TAYLOR, A VUORINEN

March 12, 2021

SIMS 2020: 61st International

Hybrid Mechanistic + Neural Model of Laboratory Helicopter

Conference of Scandinavian
Simulation Society

C RACKAUCKAS, R SHARMA, B LIE

March 3, 2021

A Machine Learning-Aided Global Diagnostic and Comparative Tool to Assess Effect of
Quarantine Control in COVID-19 Spread

Cell Patterns

DANDEKAR R, RACKAUCKAS C, BARBASTATHIS G

December 11, 2020

ACED: Accelerated Computational Electrochemical systems Discovery
KURCHIN R, KAVALSKY L, SUN X, VISWANATHAN V, MUCKLEY E, HEGDE V, SAAL J, MEREDIG B, GANDHI D, SHAH V, JOHNSON M,
EDELMAN A, RACKAUCKAS C

Learning non-linear spatio-temporal dynamics with convolutional Neural ODEsSHANKAR V, PORTWOOD G, MOHAN A, MITRA P, RACKAUCKAS C, WILSON L, SCHMIDT D, VISWANATHAN V

Signal Enhancement for Magnetic Navigation Challenge Problem
ALBERT R. GNADT, JOSEPH BELARGE, AARON CANCIANI, LAUREN CONGER, JOSEPH CURRO, ALAN EDELMAN, PETER MORALES,MICHAEL F. O’KEEFFE, JONATHAN TAYLOR, CHRISTOPHER RACKAUCKAS

Beyond Deterministic Models in Drug Discovery and Development
IRURZUN-ARANA I, RACKAUCKAS C. MCDONALD T, TROCÓNIZ I

Stability-Optimized High Order Methods and Stiffness Detection for Pathwise Stiff Stochastic
Differential Equations
RACKAUCKAS C, NIE Q

NeurIPS Climate Change Workshop
December 11, 2020

NeurIPS ML4PS Workshop
December 11, 2020

NeurIPS ML4Eng Workshop
December 11, 2020

Trends in Pharmacological
Sciences
November 1st, 2020

IEEE-HPEC
August 31, 2020

IEEE Outstanding Paper AwardJANUARY 20, 2024

CHRISTOPHER V. RACKAUCKAS · CURRICULUM VITAE

9

Generalized Physics-Informed Learning Through Language-Wide Differentiable
Programming
RACKAUCKAS C, EDELMAN A, FISCHER K, INNES M, SABA E, SHAH V, TEBBUTT W

AAAI-MLPS 2020
March 24, 2020

StochasticDelayDiffEq.jl - An Integrator Interface for Stochastic Delay Differential Equations
in Julia

ENOC 2020

SYKORA H, RACKAUCKAS C, WIDMANN D, BACHRATHY D

July 5, 2020

Sparsity Programming: Automated Sparsity-Aware Optimizations in Differentiable
Programming
GOWDA S, MA Y, CHURAVY V, EDELMAN A, RACKAUCKAS C

Program Transformations for ML
Workshop at NeurIPS 2019
September 16, 2019

Confederated Modular Differential Equation APIs for Accelerated Algorithm Development
and Benchmarking

Recent Advances in Engineering
Software

RACKAUCKAS C, NIE Q

June 2019

Interdisciplinary Case Study:How Mathematicians and Biologists Found Order in Cellular
Noise

iScience

RACKAUCKAS C, NIE Q

October 26th, 2018

Mean-Independent Noise Control of Cell Fates via Intermediate States
RACKAUCKAS C, NIE Q

iScience
April 10th, 2018

Featured as a cover article for iScience.

DifferentialEquations.jl - A Performant and Feature-Rich Ecosystem for Solving Differential
Equations in Julia
RACKAUCKAS C, NIE Q

Journal of Open Research
SoftwareMay 25, 2017

Awarded DSWeb 2018 Runner-Up by SIAM Dynamical Systems Interest Group

Adaptive Methods for Stochastic Differential Equations via Natural Embeddings andRejection Sampling with Memory
RACKAUCKAS C, NIE Q

Discrete and Continuous
Dynamical Systems Series B
September 1, 2017

Noise modulation in retinoic acid signaling sharpens segmental boundaries of gene
expression in the embryonic zebrafish hindbrain
SOSNIK J, ZHENG L, RACKAUCKAS C, DIGMAN M, GRATTON E, NIE Q, SCHILLING T

On The Budyko-Sellers Energy Balance Climate Model with Ice Line Coupling
WALSH J, RACKAUCKAS C

eLife Sciences
April 12, 2016

Discrete and Continuous
Dynamical Systems – Series B
September 2015

Assessment of Statistical Methods for Water Quality Monitoring in Maryland’s Tidal
Waterways
LE R, RACKAUCKAS C, ROSS A, ULLOA N.

SIAM Undergraduate Research
OnlineApril 17, 2013

Preprints and Technical Reports
Leveraging Julia’s automated differentiation and symbolic computation to increase spectral
DCM flexibility and speed
DAVID HOFMANN, ANTHONY G CHESEBRO, CHRIS RACKAUCKAS, LILIANNE RIVKA MUJICA-PARODI, KARL FRISTON, ALAN EDELMAN,
HELMUT H STREY

Bioarxiv
October 27, 2023

https://www.biorxiv.org/content/10.1101/2023.10.27.564407v1.abstract
JANUARY 20, 2024

CHRISTOPHER V. RACKAUCKAS · CURRICULUM VITAE

10Advancing Odor Classification Models Enhanced by Scientific Machine Learning and
Mechanistic Model: Probabilistic Weight Assignment for Odor Intensity Prediction andUncertainty Analysis for Robust Fragrance Classification

ChemRxiv

VINICIUS SANTANA, ERBET COSTA, CARINE REBELLO, ANA MAFALDA RIBEIRO, CHRIS RACKAUCKAS, IDELFONSO NOGUEIRA

November 30, 2023

https://chemrxiv.org/engage/chemrxiv/article-details/656703395bc9fcb5c9a3a476

Uncertainty quantified discovery of chemical reaction systems via Bayesian scientificmachine learning
EMILY NIEVES, RAJ DANDEKAR, CHRIS RACKAUCKAS

Bioarxiv
October 2, 2023

https://www.biorxiv.org/content/10.1101/2023.09.11.557164v2.abstract

Extending JumpProcess.jl for fast point process simulation with time-varying intensities
GUILHERME AUGUSTO ZAGATTI, SAMUEL A ISAACSON, CHRISTOPHER RACKAUCKAS, VASILY ILIN, SEE-KIONG NG, STÉPHANE
BRESSAN

Arxiv
June 12, 2023

https://arxiv.org/abs/2306.06992

A Practitioner’s Guide to Bayesian Inference in Pharmacometrics using Pumas
MOHAMED TAREK, JOSE STOROPOLI, CASEY DAVIS, CHRIS ELROD, JULIUS KRUMBIEGEL, CHRIS RACKAUCKAS, VIJAY IVATURI

Arxiv
March 31, 2023

https://arxiv.org/abs/2304.04752

Robust Parameter Estimation for Rational Ordinary Differential Equations
OREN BASSIK, YOSEF BERMAN, SOO GO, HOON HONG, ILIA ILMER, ALEXEY OVCHINNIKOV, CHRIS RACKAUCKAS, PEDRO SOTO, CHEE
YAP

ArXiv
March 2, 2023

https://arxiv.org/abs/2303.02159

Stochastic Optimal Control via Local Occupation Measures
FLEMMING HOLTORF, CHRISTOPHER RACKAUCKAS

Arxiv
November 28, 2022

https://arxiv.org/abs/2211.15652

Model-Form Epistemic Uncertainty Quantification for Modeling with Differential Equations:Application to Epidemiology.
ERIN ACQUESTA, TERESA PORTONE, RAJ DANDEKAR, CHRIS RACKAUCKAS, RILEIGH BANDY, JOSE HUERTA

OSTI
September 1st, 2022

Technical Report, https://www.osti.gov/biblio/1888443

DelayDiffEq: Generating delay differential equation solvers via recursive embedding of
ordinary differential equation solvers
DAVID WIDMANN, CHRIS RACKAUCKAS

Arxiv
August 26th, 2022

https://arxiv.org/abs/2208.12879

Plots.jl-a user extendable plotting API for the julia programming language
SIMON CHRIST, DANIEL SCHWABENEDER, CHRISTOPHER RACKAUCKAS, MICHAEL KRABBE BORREGAARD, THOMAS BRELOFF

Arxiv
April 19, 2022

https://arxiv.org/abs/2204.08775

ReservoirComputing.jl: An Efficient and Modular Library for Reservoir Computing Models
FRANCESCO MARTINUZZI, CHRIS RACKAUCKAS, ANAS ABDELREHIM, MIGUEL D MAHECHA, KARIN MORA

Arxiv
April 8, 2022

https://arxiv.org/abs/2204.05117

Mixing Implicit and Explicit Deep Learning with Skip DEQs and Infinite Time Neural ODEs
(Continuous DEQs)
AVIK PAL, ALAN EDELMAN, CHRISTOPHER RACKAUCKAS

Arxiv
January 28, 2022

https://arxiv.org/abs/2201.12240

Differentiable State Space Models and Hamiltonian Monte Carlo Estimation
DAVID CHILDERS, JES US FERNÁNDEZ-VILLAVERDE, JESSE PERLA, CAMERON PFIFFER, CHRISTOPHER RACKAUCKAS, PEIFAN WU

Platooning for Improved Safety and Efficiency of Semi-Trucks (PISES – III)
VENKATASUBRAMANIAN VISWANATHAN, VARUN SHANKAR, GAVIN D PORTWOOD, ARVIND T MOHAN, PEETAK P MITRA, DILIPKRISHNAMURTHY, CHRISTOPHER RACKAUCKAS, LUCAS A WILSON, DAVID P SCHMIDTJANUARY 20, 2024

CHRISTOPHER V. RACKAUCKAS · CURRICULUM VITAE

December 9, 2021

TRID
November 15, 2021

11

Validation and parameterization of a novel physics-constrained neural dynamics model
applied to turbulent fluid flow
SHANKAR V, PORTWOOD GD, MOHAN AT, MITRA PP, KRISHNAMURTHY D, RACKAUCKAS C, WILSON LA, SCHMIDT DP, VISWANATHAN V.

Julia for Biologists

Arxiv
October 22, 2021

Arxiv

ROESCH, E., GREENER, J. G., MACLEAN, A. L., NASSAR, H., RACKAUCKAS, C., HOLY, T. E., & STUMPF, M. P.

NeuralPDE: Automating Physics-Informed Neural Networks (PINNs) with Error
Approximations
ZUBOV, K., MCCARTHY, Z., MA, Y., CALISTO, F., PAGLIARINO, V., AZEGLIO, S., BOTTERO, L., LUJÁN, E., SULZER, V., BHARAMBE, A. AND
VINCHHI, N.

ModelingToolkit: A Composable Graph Transformation System For Equation-Based Modeling

September 9, 2021

Arxiv
July 19, 2021

Arxiv

Y MA, S GOWDA, R ANANTHARAMAN, C LAUGHMAN, V SHAH, C RACKAUCKASMarch 9, 2021

Forecasting virus outbreaks with social media data via neural ordinary differential
equations

Submitted

M NÚÑEZ, N BARREIRO, R BARRIO, C RACKAUCKASJanuary 1, 2021

Efficient Precision Dosing Under Estimated Uncertainties via Koopman Expectations ofBayesian Posteriors with Pumas
CV RACKAUCKAS, V DIXIT, AR GERLACH, V IVATURI

January 1, 2021

Accelerated Predictive Healthcare Analytics with Pumas, A High Performance
Pharmaceutical Modeling and Simulation Platform
CHRIS RACKAUCKAS, ANDREAS NOACK, VAIBHAV DIXIT, YINGBO MA, PATRICK KOFOD MOGENSEN, SHUBHAM MADDHASHIYA, SIMONBYRNE, JOGA GOBBURU, JOAKIM NYBERG, VIJAY IVATURI

Capturing missing physics in climate model parameterizations using neural differential
equations
RAMADHAN A, MARSHALL J, SOUZA A, WAGNER G, PONNAPATI M, RACKAUCKAS C

The Koopman Expectation: An Operator Theoretic Method for Efficient Analysis andOptimization of Uncertain Hybrid Dynamical Systems
GERLACH A, LEONARD A, ROGERS J, RACKAUCKAS C

Bioarxiv
November 30, 2020

Arxiv
October 23, 2020

Arxiv
August 19, 2020

Safe Blues: A Method for Estimation and Control in the Fight Against COVID-19
RAJ ABHIJIT DANDEKAR, SHANE G. HENDERSON, MARIJN JANSEN, SARAT MOKA, YONI NAZARATHY, CHRISTOPHER RACKAUCKAS,PETER G. TAYLOR, AAPELI VUORINEN

Universal Differential Equations for Scientific Machine Learning
CHRISTOPHER RACKAUCKAS, YINGBO MA, JULIUS MARTENSEN, COLLIN WARNER, KIRILL ZUBOV, ROHIT SUPEKAR, DOMINICSKINNER, ALI RAMADHAN

A Differentiable Programming System to Bridge Machine Learning and Scientific Computing
MIKE INNES, ALAN EDELMAN, KENO FISCHER, CHRIS RACKAUCKAS, ELLIOT SABA, VIRAL B SHAH, WILL TEBBUTTDiffEqFlux.jl - A Julia Library for Neural Differential Equations
CHRIS RACKAUCKAS, MIKE INNES, YINGBO MA, JESSE BETTENCOURT, LYNDON WHITE, VAIBHAV DIXITJANUARY 20, 2024

BioarxivCHRISTOPHER V. RACKAUCKAS · CURRICULUM VITAE

Arxiv
May 8, 2020

Arxiv
January 13, 2020

Arxiv
Februray 6, 2019

Arxiv
Februray 6, 201912

Doubly Ensemble Movie Prediction with Social Media Data Using TBEEF
RACKAUCKAS C, CAI W, JARVIS C, XU C, CHING AMLOSS Repository
August 10, 2013

OhioLINK Electronic Theses and

The Jormungand Climate Model

Dissertation Center

RACKAUCKAS C

July 11, 2013

Water Quality Monitoring of Maryland’s Tidal Waterways, HPCF-2012-12

UMBC HPCF

LE R, Rackauckas C, ROSS A, ULLOA N. ADVISORS: POPURI S, NEERCHAL N, SMITH B

October 2012

Notable Software
JuliaSim

Julia Computing

CREATOR AND LEAD DEVELOPER

November 2019 - Present• Julia platform for ML-accelerated modeling and simulation• Automates the acceleration of causal and acausal modeling with neural surrogates

Pumas

Pumas-AI

CREATOR AND LEAD DEVELOPER

July 23, 2017 - Present• Julia platform for integrated pharmacometrics• Integrates nonlinear mixed effects modeling and fitting for Pk/Pd, PBPK, QSP that mixes with NCA and more
• High efficiency mixed with automated parallelism and cloud scaling

DifferentialEquations.jl

SciML Organization

CREATOR AND LEAD DEVELOPER

May 11, 2016 - Present• Julia package for solving various forms of differential equations• Supports stochastic (partial) differential equations (S(P)DEs) via recent research algorithms• Utilizes multi-node parallelization and GPU/Xeon Phi acceleration for HPC applications• Implements finite element and finite difference solvers for various important nonlinear (S)PDEs

Invited Presentations
Scientific Machine Learning as a Future for AI in Pharmacometrics
AI IN PHARMACOMETRICS WORKSHOP BY NOVARTIS

VirtualDecember 13th, 2023

Invited Seminar

Generalizing Scientific Machine Learning and Differentiable Simulation Beyond Continuous
ModelsNORTHWESTERN SEMINAR SERIES

VirtualDecember 6th, 2023

Invited Seminar

Bringing Symbolic-Numeric Computing to Industrial Scientists and Engineers with Julia’s
SciML and ModelingToolkit.jl
JOINT CUNY/COURANT/NCSU SEMINAR IN SYMBOLIC-NUMERIC COMPUTING

VirtualDecember 4th, 2023

Invited Seminar, https://sites.google.com/view/symbolic-numeric-seminar/

Generalizing Scientific Machine Learning and Differentiable Simulation Beyond Continuous
Models
UNIVERSITY OF WASHINGTON AEROASTRO SEMINAR

SeattleNovember 28th, 2023

Invited Seminar

Generalizing Scientific Machine Learning and Differentiable Simulation Beyond Continuous
ModelsUIUC MACHINE LEARNING READING GROUP

VirtualNovember 17th, 2023

Invited Seminar

JuliaSim: Scientific Machine Learning and Digital Twins for System-Level Modeling
UNIVERSITY OF DRESDEN SEMINAR

DresdenNovember 14th, 2023

Invited Seminar
JANUARY 20, 2024

CHRISTOPHER V. RACKAUCKAS · CURRICULUM VITAE

13

Automatic Differentiation and SciML: what can go wrong, and what to do about it
JULIA HIGH ENERGY PHYSICS (JULIAHEP) WORKSHOP

ErlangenNovember 13th, 2023

Invited Workshop, https://indico.cern.ch/event/1292759/

Maintaining Large Scale Julia Ecosystems

Erlangen

JULIA HIGH ENERGY PHYSICS (JULIAHEP) WORKSHOP

November 11th, 2023

Invited Workshop, https://indico.cern.ch/event/1292759/

Generalizing Scientific Machine Learning and Differentiable Simulation Beyond Continuous
ModelsALAN TURING INSTITUTE PHI-ML MEETS ENGINEERING SEMINAR SERIES

VirtualOctober 26th, 2023

Invited Seminar, https://www.turing.ac.uk/events/phi-ml-meets-engineering-generalizing-scientific-machine-learning-and-differentiable

Generalizing Scientific Machine Learning and Differentiable Simulation Beyond Continuous
modelsLLNL DATA-DRIVEN PHYSICAL SIMULATIONS (DDPS) SEMINAR SERIES

VirtualOctober 20th, 2023

Invited Seminar, https://www.youtube.com/watch?v=16jtJDiJIuU

Differentiable Simulation, Neural ODEs, and Universal ODEs: The Real BitsCWI AUTUMN SCHOOL - SCIENTIFIC MACHINE LEARNING AND DYNAMICAL SYSTEMS

Amsterdam
October 15th, 2023

Invited Workshop,
https://www.cwi.nl/nl/events/cwi-research-semester-programs/autumn-school-scientific-machine-learning-semester-programme/

Improved Parallelism and Memory Performance Differentiating Stiff Differential Equations
ICIAM 2023

VirtualAugust 29th, 2023

Invited Talk, https://www.youtube.com/watch?v=5jat8moluUM

Improving Model Discovery with Imposed Structure through Scientific Machine Learning
CARL ZEISS STIFTUNG SUMMER SCHOOL 2023

Heidelburg
August 17th, 2023

Invited Talk, https://www.youtube.com/watch?v=5jat8moluUM

Large Language Models (LLMs) for Pharmacology: Where are they Useful?
ISOP VIRTUAL QSP WEEK, QSP+ML PANEL

VirtualAugust 16th, 2023

Invited Talk / Panel Discussion

Generalizing Scientific Machine Learning and Differentiable Simulation Beyond Continuous
Models
ARGONNE ANL AI APPLIED SPEAKER SERIES

VirtualAugust 14th, 2023

Invited Seminar

Julia’s SciML: Scientific Machine Learning through Symbolic Numerics
ECOLOGICAL SOCIETY OF AMERICA (ESA)

VirtualAugust 9th, 2023

Invited Talk

The Details of Making Nonlinear Mixed Effects Modeling Fast
JOINT STATISTICAL MEETINGS 2023

TorontoAugust 9th, 2023

Invited Talk, https://ww2.aievolution.com/JSMAnnual/index.cfm

The Special Math of Translating Theory to Software in Differential Equations

Cambridge

SYNERGISTIC INTERACTIONS BETWEEN THEORY AND COMPUTATION (ASE60)

July 27th, 2023

Keynote, https://math.mit.edu/events/ase60celebration/program/, https://www.youtube.com/watch?v=s_t6dIKjUUc

Scientific Machine Learning through Symbolic Numerics

Cambridge

JULIACON 2023

July 24th, 2023

Keynote, https://www.youtube.com/watch?v=tynmTkpdAME

Automating Mechanistic Modeling with Scientific Machine Learning
CONFERENCE ON DEEP LEARNING FOR COMPUTATIONAL PHYSICS (MATHS4DL)

LondonJuly 6th, 2023

KeynoteAutomating Mechanistic Modeling with Scientific Machine Learning
NORWEGIAN UNIVERSITY OF SCIENCE AND TECHNOLOGY JULIA SPECIAL SEMINAR

Trondheim
June 7th, 2023

KeynoteJANUARY 20, 2024

CHRISTOPHER V. RACKAUCKAS · CURRICULUM VITAE

14

Julia for Differentiable Control

San Diego

AMERICAN CONTROL CONFERENCE

May 30th, 2023

Invited Talk, Workshop on Differentiable Programming for Modeling and Control of Dynamical Systems
https://d-biswa.github.io/Differentiable-SysCon/

Julia for Differentiable Control

San Diego

AMERICAN CONTROL CONFERENCE

May 30th, 2023

Invited Talk, Workshop on Differentiable Programming for Modeling and Control of Dynamical Systems
https://d-biswa.github.io/Differentiable-SysCon/

Differentiable Simulation and SciML: Fast Solving and Automated Model Construction

Las Angeles

USC QUANTITATIVE AND COMPUTATIONAL BIOLOGY SEMINAR SERIESMay 25th, 2023

Invited Seminar

Differentiable Simulation and SciML: Fast Solving and Automated Model Construction
UCI APPLIED MATHEMATICS SEMINAR

IrvineMay 24th, 2023

Invited Seminar

Extending Scientific Machine Learning and Automatic Differentiation to Stochastic Financial
ModelsBLOOMBERG-COLUMBIA ML IN FINANCE 2023

New York
May 19th, 2023

Invited Talk, https://cfe.columbia.edu/events/9th-annual-bloomberg-columbia-machine-learning-finance-workshop-2023

Differentiable Simulation and SciML: Fast Solving and Automated Model Construction
WASHINGTON ST. LOUIS APPLIED ENGINEERING SEMINAR

St. Louis
May 11th, 2023

Invited Seminar

Extending Scientific Machine Learning to Agent-Based Models
ICLR

VirtualMay 5th, 2023

Invited Talk in AI4ABM, https://iclr.cc/virtual/2023/workshop/12840, https://www.youtube.com/watch?v=fm277BM3Y8M

Differentiable Simulation and Scientific Machine Learning: Fast Solving and AutomatedModel Construction
MODELING ALPHABET TALK SERIES

VirtualApril 20th, 2023

Invited Seminar

Julia Scientific Machine Learning for Industrial Modeling and Simulation
SIUOX TECHNOLOGIES SPECIAL EVENT

Eindhoven
April 18th, 2023

KeynoteDifferentiable Simulation and Scientific Machine Learning: Fast Solving and AutomatedModel Construction
ARGONNE NATIONAL LABS LANS SEMINAR

VirtualApril 12th, 2023

Invited Seminar, https://www.anl.gov/event/differentiable-simulation-and-scientific-machine-learning-fast-solving-and-automated-model

Scientific Machine Learning with Julia: ML-Assisted Model Development and Simulation
Analysis
FDA MODELING AND SIMULATION WORKING GROUP SEMINAR

VirtualFebruary 16th, 2023

Invited Seminar

Machine Learning for Pharmacology: Is It Ready?

Montreal, Canada

SAFETY PHARMACOLOGY SOCIETY ANNUAL MEETING

September 12th, 2022

Keynote Debate Session

Scientific Machine Learning: Mixing Physics with Machine Learning for Speed and Accuracy

Virtual

MICROSOFT RESEARCH INTERNAL SEMINAR

August 23rd, 2022

Improving Forecasting by Merging Deep Learning with Mechanistic Modeling

Washington DC

SIGKDD 2022

August 15th, 2022

Keynote and Panelist, https://kdd-milets.github.io/milets2022/

SIGKDD 2022

Washington DC

FRAGILE EARTH 2022: AI FOR CLIMATE MITIGATION, ADAPTATION, AND ENVIRONMENTAL JUSTICE

August 15th, 2022

Panelist, https://ai4good.org/fragile-earth-2022/
JANUARY 20, 2024

CHRISTOPHER V. RACKAUCKAS · CURRICULUM VITAE

15

Integrating equation solvers with probabilistic programming through differentiable
programming
COMPUTATIONAL ABSTRACTIONS FOR PROBABILISTIC AND DIFFERENTIABLE PROGRAMMING WORKSHOP

VirtualJuly 29th, 2022

https://mlg.eng.cam.ac.uk/capp-workshop/

Automating Predictive Modeling with Differentiable Simulation
NASA/ESA GN&C V&V SEMINAR SERIES

VirtualJuly 21st, 2022

https://jarvis.nist.gov/events/aims2022

Accurate and Efficient Physics-Informed Learning Through Differentiable Simulation
AMERICAN STATISTICAL ASSOCIATION (ASA) SCIENTIFIC COMPUTING WEBINAR SERIES

VirtualApril 20th, 2022

https://www.youtube.com/watch?v=Xwh42RhB7O4

Accurate and Efficient Physics-Informed Learning Through Differentiable Simulation
NIST WORKSHOP: ARTIFICIAL INTELLIGENCE FOR MATERIALS SCIENCE (AIMS)

VirtualJuly 13th, 2022

https://jarvis.nist.gov/events/aims2022

Lessons on Scientific Machine Learning

VirtualPSU SUMMER SCHOOL IN ASTROINFORMATICS II

June 15th, 2022

Associated lab: https://github.com/Astroinformatics/ScientificMachineLearning

Ph.D. Course on Scientific Machine Learning
DTU COMPUTE GRADUATE SCHOOL (ITMAN)

VirtualJune 13th, 2022

Week long Ph.D Course http://www2.compute.dtu.dk/ apek/SCIML2022/

Scientific Machine Learning: Where We Are and Where We Need To GoLINKÖPING UNIVERSITY SOFTWARE AND SYSTEMS RESEARCH SEMINAR SERIES

VirtualJune 10th, 2022

ELLIIT Distinguished lectureData-Efficient and Model Discovery with Scientific Machine Learning
PYTORCH INTERNAL SEMINAR

VirtualMay 16th, 2022

Data-Efficient and Model Discovery with Scientific Machine Learning
ELLIT FOCUS PERIOD, DATA-DRIVEN MODELLING AND LEARNING FOR CANCER IMMUNOTHERAPY, LUND

Accurate and Efficient Physics-Informed Learning Through Differentiable Simulation
UT AUSTIN ODEN INSTITUTE

VirtualMay 5th, 2022

VirtualApril 26th, 2022

Accurate and Efficient Physics-Informed Learning Through Differentiable Simulation
AMERICAN STATISTICAL ASSOCIATION (ASA)

VirtualApril 19th, 2022

Accurate and Efficient Physics-Informed Learning Through Differentiable Simulation
RAYTHEON BBN

VirtualJanuary 27th, 2022

Accurate and Efficient Physics-Informed Learning Through Differentiable Simulation
BROWN MACHINE LEARNING + X SEMINAR

VirtualDecember 3rd, 2021

Accurate and Efficient Scientific machine learning (SciML) Through Differentiable Simulation
SINGAPORE-MIT ALLIANCE FOR RESEARCH AND TECHNOLOGY (SMART) CENTRE RAMP SEMINAR

Data-Efficient and Interpretable AI with Scientific Machine Learning
RECENT ADVANCES IN AI FOR NATIONAL SECURITY (RAAINS)

VirtualDecember 2nd, 2021

VirtualNovember 18th, 2021

Invited Vanguard Topics Talk

The Use and Practice of Scientific Machine Learning
FREIBURG YOUNG SCIENTIST AI NETWORKJANUARY 20, 2024

VirtualNovember 12th, 2021

CHRISTOPHER V. RACKAUCKAS · CURRICULUM VITAE

16

Novel methodology, case studies and collaboration opportunities, career opportunities foryoung trainees
CARDIAC PHYSIOME 2021

VirtualOctober 5th, 2021

Invited panelist

The Continuing Advances of Differentiable Simulation
AAAI SCIENCE-GUIDED AI SYMPOSIUM

VirtualOctober 5th, 2021

Keynote Presentation

The Continuing Advances of Differentiable Simulation
OXFORD MATHEMATICAL BRAIN MODELING GROUP

VirtualSeptember 21st, 2021

New Horizons in Modeling and Simulation with Julia
THE 14TH INTERNATIONAL MODELICA CONFERENCE

VirtualSeptember 20th, 2021

Keynote Presentation

Pharmacometrics-Informed Deep Learning with DeepNLME
42ND CONFERENCE OF THE INTERNATIONAL SOCIETY FOR CLINICAL BIOSTATISTICS (ISCB), INVITED SESSION

VirtualJuly 20th, 2021

https://www.youtube.com/watch?v=fU7ueuz6rjQ

Scientific Machine Learning and Stiffness

Virtual

ARGONNE LANS SEMINAR

April 21st, 2021

Julia Webinar: Differential Equations

VirtualLOS ALAMOS NATIONAL LABORATORY

April 7th, 2021

Scientific Machine Learning and Stiffness

VirtualNSF AI INSTITUTE FOR ARTIFICIAL INTELLIGENCE AND FUNDAMENTAL INTERACTIONS (IAIFI) SEMINAR

March 25th, 2021

https://youtu.be/FENK1SDvPiAHigh Efficiency Differential Equation Solving and Scientific Machine Learning
KROME SCHOOL

VirtualFebruary 26th, 2021

Scientific Machine Learning: Introduction

Virtual

KROME SCHOOL

February 26th, 2021

Scientific Machine Learning: More Predictive ML with Less Data
DATA SCIENCE SUMMIT ’21

VirtualFebruary 26th, 2021

https://youtu.be/VKx-Yky8rQQ

Scientific Machine Learning and Stiffness

Virtual

MIT MECHANICAL ENGINEERING SEMINAR

February 9th, 2021

Stiffness in Scientific Machine Learning

VirtualCORNELL SCAN SEMINAR

December 7th, 2020

A Foundation for Automated High Performance Scientific Machine Learning
LANCASTER DATA SCIENCE OF THE NATURAL ENVIRONMENT SEMINARA Foundation for Automated High Performance Scientific Machine Learning
OXFORD COMPUTATIONAL MATHEMATICS AND APPLICATIONS SEMINAR

A Primer on Universal Differential Equations
CMU SCIML WEBINAR COURSEJANUARY 20, 2024

VirtualNovember 26th, 2020

VirtualNovember 19th, 2020

VirtualNovember 19th, 2020

CHRISTOPHER V. RACKAUCKAS · CURRICULUM VITAE

17

A Foundation for Automated High Performance Scientific Machine Learning
JOHNS HOPKINS APPLIED PHYSICS LABORATORY

VirtualNovember 19th, 2020

A Foundation for Automated High Performance Scientific Machine Learning
UNIVERSITY OF PENNSYLVANIA APPLIED MATHEMATICS AND COMPUTATIONAL SCIENCE SEMINAR

Composable Abstractions for Scientific Machine Learning
BUZZCONF

VirtualOctober 30th, 2020

VirtualJuly 31st, 2020

Keynote Presentation, https://youtu.be/3hM7wn7iJ70

Automated Discovery and Acceleration of Physical Equations with Julia’s Scientific Machine
Learning (SciML) Ecosystem
ARPA-E PHYSICS-INFORMED AND SCIENTIFIC MACHINE LEARNING SEMINAR

COVID-19 Epidemic Mitigation via Scientific Machine Learning (SciML)
COVZOOM PANEL

VirtualJuly 22nd, 2020

VirtualJuly 6th, 2020

https://youtu.be/jMhPZFZ0yvE

Cheap But Effective: Instituting Effective Pandemic Policies Without Knowing Who’s Infected
COVID-19 SEMINAR, MIT-AIR FORCE AI ACCELERATOR

VirtualJune 26, 2020

https://youtu.be/-_8MWoX5rvU

Universal Differential Equations for Scientific Machine Learning
MITSUBISHI ELECTRONIC RESEARCH LAB

VirtualMay 7th, 2020

Universal Differential Equations for Scientific Machine Learning
MODELING, COMPUTATION, NONLINERARITY, RANDOMNESS AND WAVES SEMINAR, UNIVERSITY OF ARIZONA

VirtualApril 16th, 2020

https://youtu.be/5zaB1B4hOnQ

Generalized Physics-Informed Learning Through Language-Wide Differentiable
Programming
AAAI 2020 SPRING SYMPOSIUM SERIES

VirtualMarch 24th, 2020

https://youtu.be/SEhMWkgcTOI

Universal Differential Equations for Scientific Machine Learning
NUMERICAL ANALYSIS AND PDES SEMINAR

University of Delaware
February 21th, 2020

Universal Differential Equations for Scientific Machine Learning
SCIENTIFIC COMPUTING SEMINAR

Florida State University
February 19th, 2020

https://youtu.be/bBH8HVEr0-A

Interpretable and Pharmacologically-Informed Machine Learning with Universal Differential
Equations
TAKEDA DIGITALKS SEMINAR

February 12th, 2020

Universal Differential Equations for Scientific Machine Learning
3RD PHYSICS-INFORMED LEARNING WORKSHOP

Santa Fe
January 15th, 2020

Neural Differential Equations as a Basis for Scientific Machine Learning
APPLIED MATHEMATICS SEMINAR

Dartmouth
November 19th, 2019

Neural Differential Equations as a Basis for Scientific Machine Learning
INVITED SEMINAR

UC Irvine
November 8th, 2019

Neural Differential Equations as a Basis for Scientific Machine Learning
BIOMATHEMATICS SEMINARJANUARY 20, 2024

TakedaUCLA
November 7th, 2019

CHRISTOPHER V. RACKAUCKAS · CURRICULUM VITAE

18

Neural Differential Equations as a Basis for Scientific Machine Learning
SEMINAR NUMERISCHE MATHEMATIK

Weierstraß-Institut
November 4th, 2019

Modern Programming Languages for Science and Statistics: R and Julia
LEIBNIZ MMS SUMMER SCHOOL 2019

Oberwolfach Research Institute forMathematics (MFO)
November, 2019

Week long workshop in Julia and R for scientific computing

The Mathematical and Computational Basis of Next Generation Pharmacometrics
INVITED SEMINAR

University of Buffalo
October 10th, 2019

Recent Advancements in Differential Equation Solver Software
FUTURE DIRECTIONS OF SYSTEM MODELING AND SIMULATION INVITED TALK

Lund University
September 30th, 2019https://www.youtube.com/watch?v=76gLISrSEro

Neural Differential Equations as a Basis for Scientific Machine Learning
MIT SIAM DISTINGUISHED SEMINAR SERIES

Massachusetts Institute ofTechnology
September 24th, 2019

The Mathematical and Computational Basis of Next Generation Pharmacometrics
ISOP INVITED IPSP TALKS

Cambridge Innovation Center
August 26th, 2019

Fast Optimization under Uncertainty for Precision Airdrop
CRDINAL TIM

Airforce Research Lab
July 16th, 2019

Model-Free Scientific Computing with Neural Differential Equations
ACMD INVITED SEMINAR

National Institutes of Science andTechnology (NIST)
July 10th, 2019

Merging Differential Equations with Machine Learning through Differentiable Programming
INVITED SEMINAR

National Institutes of Health (NIH)
July 9th, 2019

Merging Differential Equations with Machine Learning through Differentiable Programming
INVITED SEMINAR

Los Alamos National Lab
June 25th, 2019

Handling Multiscale Stochastic Differential Equations in Julia
SIAM DYNAMICAL SYSTEMS

Snowbird, Utah
May 22nd, 2019

Merging Differential Equations with Machine Learning through Differentiable Programming
NEW YORK JULIA MEETUP

Flatiron InstituteMay 17th, 2019

Modern Differential Equations Solver Software: Where We Are and Where We’re Headed
INVITED SEMINAR

Airforce Research Lab
February 14th, 2019

Julia and DifferentialEquations.jl

Alan Turing Institute

EPIRECIPES WORKSHOP

September 30th, 2018https://www.youtube.com/watch?v=_TK_s3uThWA

Efficient Integration of Stochastic Biological Models

UC Riverside

PARTIAL DIFFERENTIAL EQUATIONS & APPLIED MATH SEMINAR, UC RIVERSIDE

April 11th, 2018

Efficient High Order Adaptive Time Stepping for Langevin and Brownian Dynamics
MOLECULAR DYNAMICS SEMINAR, UC IRVINE DEPARTMENT OF CHEMISTRYJANUARY 20, 2024

CHRISTOPHER V. RACKAUCKAS · CURRICULUM VITAE

UC Irvine
April 4th, 201819

Mixing Flexibility with Speed through DifferentialEquations.jl: From Stochastic Differential
Equations to Zebrafish Hindbrain Development
SMART GRID SEMINAR, LOS ALAMOS NATIONAL LAB

Center for Nonlinear Studies
November 7th, 2017

The Hidden Noise in Biological RandomnessTSUKUBA GLOBAL SCIENCE WEEK

University of Tsukuba
September 26th, 2017

Awarded Best Speaker Award

Superspace Refinement of the (3+1) Dimensional Incommensurately Modulated Phase of theHydrated Sodium Salt of a Commodity Dye Intermediate
AMERICAN CRYSTALLOGRAPHY ASSOCIATION ANNUAL MEETING

Sheraton Waikiki Beach Hotel
July 22, 2013

Award Margaret C. Etter Student Lectuer Award

Did a Jormungand state exist? An investigation using the Budyko-Widiasih model
MATHEMATICS OF CLIMATE RESEARCH NETWORK

WebinarMarch 6th and 20th , 2013

Other Notable Presentations
NonlinearSolve.jl: Efficient rootfinding and solving of algebraic equations in Julia
JULIACON LOCAL EINDHOVEN

Eindhoven
December 1st, 2023

https://www.youtube.com/watch?v=O-2F8fBuRRg

Scientific Machine Learning for Industrial Modeling and Simulation
DETROIT / ANN ARBOR JULIA MEETUP

VirtualOctober 16th, 2023

JuliaSim: Scientific Machine Learning and Digital Twins for System-Level Modeling2023 INTERNATIONAL MODELICA CONFERENCE

AachenOctober 9th, 2023

Scientific Computing + Machine Learning = SciML
EINDHOVEN JULIA USER’S MEETUP

Eindhoven
February 29th, 2023

The SciML Common Solver Interface

Amsterdam

2023 SIAM CONFERENCE ON COMPUTATIONAL SCIENCE AND ENGINEERINGThe Continuing Advances of Differentiable Simulation
2022 SIAM CONFERENCE ON MATHEMATICS OF DATA SCIENCE

Stably Accelerating Stiff Quantitative Systems Pharmacology Models: ContinuousTime Echo
State Networks as Implicit Machine Learning
FOSBE 2022

February 29th, 2023

VirtualSeptember 30th, 2022

VirtualAugust 28th, 2022

How to debug Julia simulation codes (ODEs, optimization, etc.!)
JULIACON 2022

VirtualJune 29th, 2022

https://youtu.be/g-iOOhh2U6o

LinearSolve.jl: because A

b is not good enough

VirtualJULIACON 2022

June 28th, 2022

https://youtu.be/JWI34_w-yYw

Stably Accelerating Stiff Quantitative Systems Pharmacology Models: Continuous-Time Echo
State Networks as Implicit Machine Learning
QSPC 2022

VirtualApril 20th, 2022

Chosen for a Flash Talk

JANUARY 20, 2024

CHRISTOPHER V. RACKAUCKAS · CURRICULUM VITAE

20

State of SciML

VirtualSCIMLCON 2022

March 23rd, 2022

https://www.youtube.com/watch?v=eSeY4K4bITI

Peregrination of a raconteur through maths, computing, and life with Chris Rackauckas(MIT)
RANDOM WALKS PODCAST

VirtualDecember 24th, 2021

https://anchor.fm/random-walks/episodes/Peregrination-of-a-raconteur-through-maths–computing–and-life-with-Chris-Rackauckas-MIT-e1c4cla

A Comparison of Automatic Differentiation and Adjoints for Derivatives of Differential
Equations
IEEE HPEC 2021

VirtualSeptember 23rd, 2021

https://www.youtube.com/watch?v=6hhF6Llv4sI

JuliaSim: Machine Learning Accelerated Modeling and Simulation
JULIACON 2021

VirtualJuly 30th, 2021

https://www.youtube.com/watch?v=lNbU5jNp67s

Simulating Big Models in Julia with ModelingToolkit
JULIACON 2021

VirtualJuly 24th, 2021

https://www.youtube.com/watch?v=HEVOgSLBzWA

Opening the Blackbox: Accelerating Neural Differential Equations
THIRTY-EIGHTH INTERNATIONAL CONFERENCE ON MACHINE LEARNING (ICML) 2021

VirtualJuly 21st, 2021

https://www.youtube.com/watch?v=rFzWuXu7wFA

Symbolics.jl - High performance symbolic numerics via multiple dispatch
INTERNATIONAL SYMPOSIUM ON SYMBOLIC AND ALGEBRAIC COMPUTATION (ISSAC) 2021

VirtualJuly 21st, 2021

https://www.youtube.com/watch?v=rFzWuXu7wFA

JuliaSim: Accelerated Simulation of Stiff HVAC Systems with Continuous-Time Echo State
Networks
16TH U.S. NATIONAL CONGRESS ON COMPUTATIONAL MECHANICS

VirtualJuly 28th, 2021

https://www.youtube.com/watch?v=ZaYinbYWkYE

Accelerated Large-Eddy Simulation via Universal Partial Differential Equations
SIAM CONFERENCE ON MATHEMATICAL & COMPUTATIONAL ISSUES IN THE GEOSCIENCES (GS21)

VirtualJune 22nd, 2021

https://www.youtube.com/watch?v=MV9-RIN0uYw

Accelerating Quantitative Systems Pharmacology with Machine Learning
SOCIETY OF MATHEMATICAL BIOLOGY 2021

VirtualJune 15th, 2021

https://www.youtube.com/watch?v=KLjJps268Wc

Accelerating Simulation of Stiff Nonlinear Systems using Continuous-Time Echo State
Networks
AAAI-MLPS 2021

VirtualMarch 22nd, 2021

https://youtu.be/3lM-Stc7z28

The Julia SciML Ecosystem: Scientific Machine Learning as a Software Problem
SIAM COMPUTATIONAL SCIENCE AND ENGINEERING

VirtualMarch 5th, 2021

https://youtu.be/XRJ-rtP2fVE

Chris Rackauckas - Physics-Informed Neural Networks (PINNs), Podcast #42
THE ENGINEERED-MIND PODCAST

VirtualMarch 5th, 2021

https://youtu.be/OmySUTFwh2g

The Stardust Podcast #7 - Christopher Rackauckas
THE STARDUST PODCAST

VirtualDecember 28th, 2020

https://youtu.be/uoz08EqSpmY

DeepNLME in Pumas

VirtualPUMAS-AI

November 9th, 2020

https://youtu.be/Z_-WxpaygB0

JANUARY 20, 2024

CHRISTOPHER V. RACKAUCKAS · CURRICULUM VITAE

21

GPU Acceleration of Quantitative Systems Pharmacology (QSP) Workflows
APPLIED BIOMATH QSP SUMMIT

VirtualNovember 5th, 2020

https://youtu.be/npXtqYAvcvU

Automated Discovery of Mechanistic Models via Universal Differential Equations
APPLIED BIOMATH QSP SUMMIT

VirtualNovember 5th, 2020

https://youtu.be/AKwqJxhKkoA

Differential Equations in 2021

VirtualDIGIWELL TEAM MEETING

October 21st, 2020

https://youtu.be/XI-iHixmGgg

Stability-Optimized High Order Methods for Pathwise Stiffness in Stochastic Differential
Equations
IEEE HPEC 2020

VirtualSeptember 25th, 2020

https://youtu.be/u5vbjmCKvIg, awarded Outstanding Paper Award

Auto-Optimization and Parallelism in DifferentialEquations.jl
JULIACON 2020

VirtualJuly 29th, 2020

https://youtu.be/UNkXNZZ3hSw

Doing Scientific Machine Learning (SciML) with Julia
JULIACON 2020

VirtualJuly 26th, 2020

https://youtu.be/QwVO0Xh2Hbg

Scientific AI: Domain Modeling with Integrated Machine Learning
JULIACON 2019

University of Maryland, Baltimore
July 23rd, 2019https://www.youtube.com/watch?v=FGfx8CQHdQA

PuMaS.jl: Pharmasceutical Modeling and Simulation Engine
JULIACON 2018

University College London
August 9th, 2018https://www.youtube.com/watch?v=KQ4Vtsd9XNw

Solving Partial Differential Equations in Julia
JULIACON 2018

University College London
August 7th, 2018https://www.youtube.com/watch?v=okGybBmihOE

Simulation and Control of Biological Stochasticity
PHD DEFENSEUC Irvine
June 5th, 2018https://www.youtube.com/watch?v=_h5fVDvGp-8

The Unique Features and Performance of DifferentialEquations.jl

UC Berkley

JULIACON 2017

June 20, 2017https://www.youtube.com/watch?v=75SCMIRlNXM

DifferentialEquations.jl: A performant and feature-rich ecosystem for solving differential
equationsAGS SYMPOSIUM 2017

UC Irvine
June 20, 2017https://www.youtube.com/watch?v=Oojdf2oKHh4

Water Quality Monitoring of Maryland’s Tidal Waterways
SHENANDOAH UNDERGRADUATE MATHEMATICS CONFERENCE (SUMS)James Madison University
September 29, 2012

Awarded Best Poster Presentation in Statistics

Teaching Experience
Applied Mathematics Instructor

MIT

MASSACHUSETTS INSTITUTE OF TECHNOLOGY

2019-Present• Created and taught a course “Scientific Machine Learning and Parallel Computing”, a graduate course which received over 100 students from acrossthe Greater Boston area.
• Created a curriculum and taught a course “Linear Partial Differential Equations” on the mathematics and computation of PDEs

JANUARY 20, 2024

CHRISTOPHER V. RACKAUCKAS · CURRICULUM VITAE

22

Google Summer of Code Administrator and Mentor

JuliaLangGOOGLE SUMMER OF CODE

2017-Present• Helped students prepare project plans and guided them through technical aspects.
• Reviewed student code and led them to developing new software packages.

Data Science Initiative Instructor

UC Irvine

UC IRVINE DATA SCIENCE INITIATIVE

2016-2018• Developed and taught 5 workshops on advanced Julia programming.
• Mentored teams of students in machine learning for the Mobile Data Science Hackathon.

Systems Biology and Big Data Short Course Workshop Tutor

UC Irvine

UC IRVINE CENTER FOR COMPLEX BIOLOGICAL SYSTEMS

2014-2017• Taught Mathematica and MATLAB to workshop participants

Teaching AssistantUC Irvine

UC IRVINE DEPARTMENT OF MATHEMATICS

2014-2015• Taught upper division courses: Mathematical Biology, Probability and Statistics II, Numerical Differential Equations, and Mathematical Finance.
• Lectured twice a week. Developed quizzes. Graded quizzes, homeworks, and exams.

Professional Affiliations
International Society of Pharmacometrics, ISoP
Society for Industrial and Applied Mathematics, SIAMJANUARY 20, 2024

CHRISTOPHER V. RACKAUCKAS · CURRICULUM VITAE

23