Michael Salins
665 Commonwealth Ave, Boston, MA 02215 | msalins@bu.edu

Employment
Boston University, Department of Mathematics and Statistics,
Associate Professor, May 2024 – present
Boston University, Department of Mathematics and Statistics,
Assistant Professor, July 2017 – May 2024
Boston University, Department of Mathematics and Statistics,
Instructor and Postdoctoral Faculty Fellow, July 2015 – June 2017
Education
University of Maryland, College Park, Maryland
Doctor of Philosophy in Mathematics, April 2015
University of Maryland, College Park, Maryland
Master of Arts in Mathematics, December 2012
University of Maryland, College Park, Maryland
Bachelor of Science in Mathematics, magna cum laude, May 2010
Research Interests
Asymptotic properties of stochastic partial differential equations and stochastic ordinary
differential equations including finite time explosion, large deviations, exit problems,
metastability, accelerated Monte Carlo simulation, and averaging.
Publications
1. J. Ivanhoe, M. Salins, Preventing finite-time blowup in a constrained potential forreaction-diffusion equations, 18 pgs. arXiv:2406.15672.
2. M. Salins, Solutions to the stochastic heat equation with polynomially growing
multiplicative noise do not explode in the critical regime, to appear in Annals ofProbability (2024), 17 pgs. arXiv:2309.04330.
3. M. Salins, S. Tindel, Regularity of the law of solutions to the stochastic heat equation
with non-Lipschitz reaction term, Stochastic Processes and their Applications 168 (2024),
24 pgs. arXiv:2302.10678.
4. I. Gasteratos, M. Salins, and K. Spiliopoulos, Importance sampling for stochastic
reaction-diffusion equations in the moderate deviation regime, to appear in Stochastic
and Partial Differential Equations: Analysis and Computations (2023), 45 pgs.
arXiv:2206.00646.
5. I. Gasteratos, M. Salins, and K. Spiliopoulos, Moderate deviations for systems of slowfast stochastic reaction-diffusion equations, Stochastics and Partial Differential
Equations: Analysis and Computations 11(2) (2023), pp.503-598. arXiv:2101.00085.
6. M. Salins and L. Setayeshgar, Uniform large deviations for a class of Burgers-type
stochastic partial differential equations in any spatial dimension, Potential Analysis
58(1) (2023). pp. 181-201.

7. M. Salins, Global solutions to the stochastic heat equation with superlinear accretive
reaction term and superlinear multiplicative noise term on a bounded spatial domain,
Transactions of the American Mathematical Society 375(11) (2022), pp. 8083-8099
arXiv: 2110.10130
8. M. Salins, Global solutions for the stochastic reaction-diffusion equation with superlinear multiplicative noise and strong dissipativity, Electronic Journal of Probability 27(2022), 17 pgs. arXiv: 2107.04459.
9. M. Salins, Existence and uniqueness of global solutions to the stochastic heat equation
with super-linear drift on an unbounded spatial domain, Stochastics and Dynamics 22(5)(2022), Article Number 2250014, 30 pgs. arXiv:2106:13221.
10. M. Salins, Systems of small-noise stochastic reaction-diffusion equations satisfy a large
deviations principle that is uniform over all initial data, Stochastic Processes and Their
Applications 142 (2021), pp. 159-194. arXiv: 2008.01140.
11. M. Salins, K. Spiliopoulos, Metastability and exit problems for systems of stochastic
reaction-diffusion equations, The Annals of Probability 49(5) (2021), pp. 2317-2370.
arXiv: 1903.06038.
12. M. Salins, Existence and uniqueness for the mild solution of the stochastic heat equation
with non-Lipschitz drift on an unbounded spatial domain, Stochastics and Partial
Differential Equations: Analysis and Computations 9(3) (2021), pp. 714-745.
arXiv:2002.02016.
13. C. Mueller, E. Neuman, M. Salins, and G. Truong, An improved uniqueness result for asystem of stochastic differential equations related to the stochastic wave equation,
Journal of Stochastic Analysis 1(2) (2020), pp. 1-7. arXiv: 1909.05944
14. W. Hu, M. Salins, and K. Spiliopoulos, Large deviations and averaging for systems ofslow-fast stochastic reaction-diffusion equations, Stochastics and Partial Differential
Equations: Analysis and Computation 7(4) (2019), pp. 808-874. arXiv:1710.02618.
15. M. Salins, A. Budhiraja, and P. Dupuis, Uniform large deviation principles for Banach
space valued stochastic evolution equations, Transactions of the American MathematicalSociety 372(12) (2019), pp. 8363–8421. arXiv:1803.00648.
16. M. Salins, Equivalences and counterexamples between several definitions of the uniform
large deviations principle, Probability Surveys 16(1) (2019), pp. 99-142.
arXiv:1712.07231.
17. M. Salins, Smoluchowski-Kramers approximation for the damped stochastic wave
equation with multiplicative noise in any spatial dimension, Stochastics and Partial
Differential Equations: Analysis and Computation 7(1), (2019), pp. 86-122
arXiv:1801.10538.
18. A. Gomez, J.J. Lee, C. Mueller, E. Neuman, and M. Salins, On Uniqueness and blowup
properties for a class of second order SDEs, Electronic Journal of Probability 22(72)(2017), 17 pgs. arXiv:1702.07419.
19. M. Salins and K. Spiliopoulos, Rare event simulation via importance sampling for linear
SPDE’s, Stochastics and Partial Differential Equations: Analysis and Computation 5(4)(2017), pp. 652-690 arXiv:1609.04365.
20. Z. Pajor-Gyulai and M. Salins, On dynamical systems perturbed by a null-recurrent
motion: The general case, Stochastic Processes and their Applications 127(6) (2017), pp.
1960-1997. arXiv:1508.05346.

21. S. Cerrai, M. Freidlin, and M. Salins, On the Smoluchowski-Kramers approximation for
SPDEs and its interplay with large deviations and long time behavior, Discrete andContinuous Dynamical Systems 37(1) (2017), pp. 33-76. arXiv:1602.04279.
22. S. Cerrai and M. Salins, On the Smoluchowski-Kramers approximation for a system with
infinite degrees of freedom exposed to a magnetic field, Stochastic Processes and their
Applications 127(1) (2017), pp. 273-303. arXiv:1409.0803.
23. M. Salins and K. Spiliopoulos, Markov processes with spatial delay: path spacecharacterization, occupation time and properties, Stochastics and Dynamics 17(06)(2016), 24 pgs. arXiv:1601.03759.
24. S. Cerrai and M. Salins, On the Smoluchowski-Kramers approximation and large
deviations for infinite-dimensional nongradient systems with applications to the exit
problem, the Annals of Probability 44(4) (2016), pp. 2591-2642. arXiv:1403.5745.
25. Z. Pajor-Gyulai and M. Salins, On dynamical systems with perturbation driven by a nullrecurrent fast motion: The continuous coefficient case, Journal of Theoretical Probability
29(3) (2016), pp. 1083-1099. arXiv:1410.4625.
26. S. Cerrai and M. Salins, Smoluchowski-Kramers approximation and large deviations forinfinite dimensional gradient systems, Asymptotic Analysis 88(4) (2014), pp. 201-215.
arXiv:1403.5743.
Submitted Articles
27. L. Chen, M. Foondun, J. Huang, M. Salins, Global solution for superlinear stochastic
heat equation on Rd under Osgood-type conditions, (2023), 22 pgs. arXiv: 2310.02153.

Grants and Awards▪ Simon’s Foundation Collaboration Support for Mathematicians – Stochastic partial
differential equations with superlinear forcing – September 1, 2022 – August 31, 2027.
Gift number: 962543.
▪ IOP Publishing Outstanding Reviewer for the Nonlinearity, 2017
▪ James C. Alexander Prize for Graduate Research, Spring 2015.
▪ Ann G. Wylie Dissertation Fellowship, Fall 2014.
▪ University of Maryland Mathematics Department Summer Fellowship, Summer 2012.
▪ Strauss Undergraduate Teaching Assistantship, Fall 2009-Spring 2010.
▪ Banneker-Key Scholarship, Fall 2006-Spring 2010.
Professional Organizations▪ American Mathematical Society – member.
Teaching and Grading
Boston University, 2015-present
Instructor: Advanced Calculus, Basic Statistics and Probability, Calculus I, Calculus II,
Differential Equations, Introduction to Stochastic Processes, Mathematics of Financial
Derivatives, Multivariate Calculus, Probability, Graduate Probability I, Graduate
Probability II.
Course developer and Instructor: Special Topics in Probability: Stochastic Partial
Differential equations, Special Topics in Probability: Large Deviations

University of Maryland, 2009-2015
Instructor: Calculus III, Precalculus.
Teaching Assistant: Precalculus, Calculus I, Calculus II, Calculus III, Linear Algebra.
Grader: Applied Stochastic Processes, Advanced Calculus I, Complex Analysis I, Real
Analysis II, Applied Probability and Statistics I.
Presentations▪ Superlinear SPDEs that do not explode, AMS Spring Sputheastern Sectional Meeting,Florida State University, Tallahassee, FL, March 23, 2024.
▪ Finite time explosion for stochastic partial differential equations, Iowa State Analysis
and Probability Seminar, Ames, IA, February 28, 2024.
▪ Global solutions to the stochastic reaction-diffusion equation with superlinear forcing
and superlinear multiplicative noise, BU-Keio-Tsinghua Workshop, Boston, MA, June27, 2023.
▪ Stochastic partial differential equations with superlinear forcing, New Trends in
Stochastic Analysis, Casa Matematica Oaxaca, Oaxaca, Mexico, May 23, 2023.
▪ Stochastic heat equation with superlinear forcing defined on an unbounded spatial
domain, AMS Spring Sectional Meeting, Georgia Institute of Technology, Atlanta, GA,
March 18, 2023.
▪ Stochastic heat equation with superlinear forcing, Auburn University Probability
Seminar, Auburn, AL, March 15, 2023.
▪ Global existence of mild solutions to SPDEs with superlinear forcing, Joint Mathematics
Meeting, Special Session on Stochastic Analysis, Boston, MA, January 4, 2023.
▪ Global solutions to the stochastic reaction-diffusion equation with superlinear forcing
and superlinear multiplicative noise, Workshop on Modern Topics in Probability, Brin
Mathematics Research Center, University of Maryland, College Park, MD, October 17,
2022.
▪ Systems of stochastic reaction-diffusion equations satisfy a large deviations principle thatis uniform over all initial data, Conference on Stochastic Processes and their
Applications, online on Zoom (originally scheduled for Wuhan, China), June 27, 2022.
▪ Systems of stochastic reaction-diffusion equations satisfy a large deviations principle thatis uniform over all initial data, AMS Spring Sectional Meeting, online on Zoom
(originally scheduled for West Lafayette, IN), March 26, 2021.
▪ Systems of stochastic reaction-diffusion equations satisfy a large deviations principle thatis uniform over all initial data, Beijing International Center for Mathematical Research
Stochastic Analysis Seminar (online on Zoom), December 7, 2021.
▪ Systems of stochastic reaction-diffusion equations satisfy a large deviations principle thatis uniform over all initial data, Ohio State Probability and Stochastic Analysis Seminar(online on Zoom), November 4, 2021.
▪ Global solutions for the stochastic reaction-diffusion equation with polynomiallydissipative forcing, Canadian Mathematical Society Summer Meeting, online on Zoom
(Ottawa, Canada), June 7, 2021.
▪ Existence and uniqueness for the mild solution of the stochastic heat equation with nonLipschitz drift on an unbounded spatial domain, Stochastic Analysis Under COVID,
online on Zoom, March 23, 2021.

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Existence and uniqueness for the mild solution of the stochastic heat equation with nonLipschitz drift on an unbounded spatial domain, AMS Spring Sectional Meeting, online
on Zoom (formerly scheduled for Providence, RI), March 20, 2021.
Existence and uniqueness for the mild solution of the stochastic heat equation with nonLipschitz drift on an unbounded spatial domain, Drexel University Probability Seminar,
Philadelphia, PA, March 12, 2020.
Existence and uniqueness for the mild solution of the stochastic heat equation with nonLipschitz drift on an unbounded spatial domain, University of Maryland Probability
Seminar, College Park, MD, March 11, 2020.
Existence and uniqueness for the mild solution of the stochastic heat equation with nonLipschitz drift on an unbounded spatial domain, Purdue University Probability Seminar,West Lafayette, IN, February 19, 2020.
Uniform large deviations principles, SIAM Minisymposium on Deterministic andProbabilistic Approaches for Nonlinear PDEs, Joint Mathematics Meetings, Denver, CO,January 18, 2020.
Uniform large deviations principles, University of Pittsburgh Analysis and PDE Seminar,
Pittsburgh, PA, November 4, 2019.
Uniform large deviations principles, Workshop on the Theory and Applications of
Stochastic Partial Differential Equations, Fields Institute, Toronto, ON, June 10, 2019.
Uniform large deviations principles and applications to the exit problem for SPDEs,
University of Maryland Probability Seminar, College Park, MD, May 23, 2019.
Uniform large deviations principles and applications to the exit problem for SPDEs,
Tufts University Probability Seminar, Medford, MA, March 29, 2019.
Uniform large deviations principles and applications to the exit problem for SPDEs,
University of Connecticut Discrete Mathematics and Statistical Mechanics Seminar, Feb
13, 2019.
The Smoluchowski-Kramers approximation in finite and infinite dimensions, Boston
University Dynamics Seminar, Boston, MA, Oct. 30, 2017.
Uniform large deviations for a general class of stochastic partial differential equations,
Brown University Probability Seminar, Providence, RI, May 9, 2017.
Rare event simulation via importance sampling for linear SPDEs, University of
Maryland Probability Seminar, College Park, MD, March 29, 2017.
Uniform large deviations for a general class of stochastic partial differential equations,
Courant Institute Probability Seminar, New York, NY, Feb. 10, 2017.
Uniform large deviations for a general class of stochastic partial differential equations,
Boston University Probability Seminar, Boston, MA, Jan. 23, 2017
Rare event simulation via importance sampling for linear SPDEs, Northeast Probability
Seminar, New York, NY, Nov. 18, 2016.
Rare event simulation via importance sampling for linear SPDEs, AMS Sectional
Meeting, Raleigh, NC, Nov. 13, 2016.
Random perturbations of dynamical systems, Pre-colloquium talk, Boston University,
Sept. 2016.
Local time and null-recurrent averaging, BU/Keio Workshop, Aug. 2016.
Null-recurrent averaging, Analysis and Probability Seminar, University of Connecticut,
Feb. 2016.

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The Smoluchowski-Kramers approximation and large deviations for the stochastic wave
equation, Brown/BU Dynamics and PDE Seminar, Apr. 25, 2016.
Null-recurrent averaging, Seminar on Stochastic Processes, University of Maryland,Mar. 17, 2016.
The Smoluchowski-Kramers approximation and large deviations for the stochastic wave
equation, Workshop on Stochastic PDEs, University of Pittsburgh, Dec. 5, 2015.
The Smoluchowski-Kramers approximation and large deviations for the stochastic wave
equation, Northeast Probability Seminar, Courant Institute, Nov. 29, 2015.
Freidlin-Wentzell exit problems for stochastic equations in Banach spaces, AMS Spring
Eastern Sectional Meeting, Georgetown University, Mar. 7, 2015.
Smoluchowski-Kramers approximation and the exit problem for infinite dimensional
systems, Statistics and Probability Seminar, Boston University, Feb. 2015.
Smoluchowski-Kramers approximation and the exit problem for stochastic partial
differential equations, PDE Research Interaction Team, Nov. 2014.
A dynamical system driven by fast Brownian motion, Probability Seminar, University of
Maryland, Sept. 2014.
Smoluchowski-Kramers approximation and the exit problem for infinite dimensional
systems, École d’été de Probabilités de Saint Flour, France, Jul. 18, 2014.
Using random functions to model deterministic phenomena, one of four winning talks at
the Monroe-Martin Talks, University of Maryland, May 2014.
Exit place and exit time problems for infinite dimensional stochastic equations withsecond-order time derivatives, Seminar on Stochastic Processes, University of California,
San Diego, Mar. 27, 2014.
Introduction to the large deviations principle, why and how to study rare events, MonroeMartin Talks, University of Maryland, May 2013.
Stochastic partial differential equations and control theory, Student Probability Seminar,
several talks, Summer 2012-Spring 2013.
Quasipotentials of the damped semilinear wave equation and stochastic heat equation,
Seminar on Stochastic Processes, Duke University, Mar. 15, 2013.
Semigroups of linear operators, Monroe-Martin Talks, University of Maryland, May
2012.
The axiom of choice and predicting the future, Monroe-Martin Talks, University of
Maryland, May 2011.

Conference Organizing▪ Emerging Synergies between Stochastic Analysis and Statistical Mechanics (25w5367) –Banff International Research Station – Banff, Canada, October 26-31, 2025.
▪ Workshop on stochastic PDEs – Brin Mathematical Research Center, University of
Maryland – co-organizers Samy Tindel and Raluca Balan – College Park, MD, June 2327, 2025
▪ BU-Keio-Tsinghua Workshop 2023 – Co-PI Siu-Cheong Lau – Boston University,
Boston, MA, June 26-30, 2023.
▪ Joint Mathematics Meeting – Special Session on Stochastic Analysis and Applications –co-organized with Parisa Fatheddin – Boston, MA, January 4-5, 2023.
▪ Union College Mathematics Conference – Stochastic Analysis and Applications Session– Co-organized with Phanuel Mariano, Schenectady, NY, June 3-5, 2022.

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SIAM Northern States Sectional Meeting - SIAM Minisymposium on Recent Trends in
SPDEs – Co-organized with Hakima Bessaih, Laramie, WY, September 29, 2019.

Travel Support Awards▪ Seminar on Stochastic Processes, University of Virginia, March 2017.
▪ Northeast Probability Seminar, Baruch College, CUNY, November 2016.
▪ Seminar on Stochastic Processes, University of Maryland, March 2016.
▪ Northeast Probability Seminar, New York, November 2015.
▪ École d’été de Probabilités de Saint-Flour, France, July 2014.
▪ Seminar on Stochastic Processes, University of California, San Diego, March 2014.
▪ Seminar on Stochastic Processes, Duke University, March 2013.
▪ Topics in Probability, Chennai Mathematical Institute, Chennai, India, December 2012.

Academic Service▪ Referee for: Acta Applicandae Mathematicae, Annals of Applied Probability, Annals ofProbability, Annales de l’Institut Henri Poincaré (B), Applied Mathematics andOptimization, Applied Probability Journals, Asymptotic Analysis, Communications inMathematical Physics, Communications on Pure and Applied Analysis, Discrete andContinuous Dynamical Systems, Electronic Communications in Probability, Electronic
Journal of Probability, ESAIM: Probability and Statistics, Journal of Computational andApplied Mathematics, Journal of Differential Equations, Journal of Dynamical andControl Systems, Journal of Evolution Equations, Journal of Functional Analysis, Journalof Mathematical Analysis and Applications, Journal of Nonlinear Science, Journal of
Statistical Mechanics: theory and experiment, Journal of Statistical Physics, Journal of
Theoretical Probability, Management Science, Markov Processes and Related Fields,
Memoirs of the AMS, Nonlinearity, Partial Differential Equations in Applied
Mathematics, Partial Differential Equations and Applications, Probability Theory and
Related Fields, SIAM Journal on Applied Dynamical Systems, SIAM Journal on
Mathematical Analysis, Stochastics: An International Journal of Probability andStochastic Processes, Stochastics and Dynamics, Stochastic Analysis and Applications,
Stochastic Processes and their Applications, Transactions of the AMS, Zeitschrift fürangewandte Mathematik und Physik,
Doctoral Student Mentorship▪ Ioannis Gasteratos (co-advisor Konstantinos Spiliopoulos) – Phd 2022 – Ioannis is now a
postdoctoral research associate at Imperial College in London. Ioannis works onmoderate deviations principles for coupled fast-slow stochastic partial differential
equations.
▪ John Ivanhoe – current student – John investigates the roles of superlinear forcing terms
in stochastic partial differential equations.
Undergraduate Student Mentorship▪ Boston University▪ Advised Zhenxin Lu on an Honors Thesis 2024-2025.

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Advised Seungsoo Lee on an Honors Thesis about finite time explosion forstochastic differential equations 2023-2024.
Advised Tianyi Chen on a project about stochastic differential equations andoptions pricing, Fall 2021
Advised Amarri Harrison on a project about Brownian motion and stochastic
differential equations, Summer 2021 – Fall 2021
Advised Jiawei Wu on a project about stochastic differential equations andoptions pricing, Summer-Fall 2021
Advised Shai Almagor on a project about stochastic population modelling, Spring
2018.

Directed Reading Program, University of Maryland
Pairs undergraduate students with graduate student mentors for semester-longindependent study projects.
▪ Committee member, Fall 2014
▪ Mentor for undergraduate research projects in:▪ Brownian Motion, Spring 2015
▪ Lines of Best Fit, Fall 2013.
▪ First-order Logic and Model Theory, Spring 2013.
▪ Laplace’s Method and Stirling’s Formula, Fall 2012.
▪ Paradoxes in Voting Theory, Fall 2011.

Interdisciplinary Research Programs▪ Focused Research Program on Simulation Modelling in Population Health – Rafik B.
Hariri Center for Computational Science and Engineering – September 2021-June 2022
Departmental Involvement▪ Director of Graduate Admissions – Statistics, Department of Mathematics and Statistics,
Boston University, September 2022 – present.
▪ Undergraduate student advising Spring 2020 – present.
▪ PhD dissertation committees, Boston University▪ Zachary Bezemek 2023
▪ Max Heldman 2023
▪ Ioannis Gasteratos 2022
▪ Thanh Dang 2022
▪ Jingwei Ma 2021
▪ Jia Yang 2020
▪ Eric Cooper 2019
▪ Matthew Morse 2019
▪ Hiring committee member, Boston University, Probability and Statistics Tenure Tracksearch 2024
▪ Hiring committee member, Boston University, Dynamics Postdoc search 2024
▪ Hiring committee member, Boston University, Probability and Statistics Postdoc search2022▪ Co-organizer, Probability and Statistics Seminar, Boston University, Jan 2019-December
2021

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Hiring committee member, Boston University, Probability and Statistics Postdoc search2019
Graduate Committee Member, Boston University, 2017-present – reviews graduate
student applications for the statistics programs and for the mathematics program.
Department Holiday Party Committee, University of Maryland, Fall 2013-Fall 2014.
Daily Student Tea Time Coordinator, University of Maryland, Fall 2012-Fall 2013.

Language Skills: English (native speaker), French (proficient), Turkish (beginner).