Mahdi Kooshkbaghi
Staff Machine Learning Engineer
The Estée Lauder Companies
Biography
You can find my current CV here.
Interests
- Applied Mathematics
- Machine Learning
- Computational Biology
- Combustion
- Fluid Mechanics
Education
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Computational Postdoc in Simons Center for Quantitative Biology, 2020
Cold Spring Harbor Laboratory
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Postdoc in App. and Comput. Math, 2017
Princeton University
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PhD in Mechanical Engineering, 2015
ETH Zürich
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MSc in Mechanical Engineering, 2011
Tehran Polytechnic
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BSc in Aerospace Engineering, 2010
Tehran Polytechnic
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BSc in Mechanical Engineering, 2008
Tehran Polytechnic
Experience
Staff Machine Learning Engineer
Publications
(2024).
Characterization of partial wetting by CMAS droplets using multiphase many-body dissipative particle dynamics and data-driven discovery based on PINNs.
Journal of Fluid Mechanics , Volume 985, A7.
(2024).
Specificity, synergy, and mechanisms of splice-modifying drugs.
Nature Communications 15, 1880 (2024).
(2022).
Machine-learning-based data-driven discovery of nonlinear phase-field dynamics.
Physical Review E 106 (065303) (2022).
(2022).
On the parameter combinations that matter and on those that do not: data-driven studies of parameter (non)identifiability.
PNAS Nexus (2022).
(2022).
Structural and mechanistic basis of σ-dependent transcriptional pausing.
Proceedings of the National Academy of Sciences 119 (23) (2022).
(2022).
MAVE-NN: learning genotype-phenotype maps from multiplex assays of variant effect.
Genome Biology 23, 98 (2022).
(2021).
Novel tool to quantify with single-cell resolution the number of incoming AAV genomes co-expressed in the mouse nervous system.
Gene Therapy (2021): 1-6.
(2021).
Numerical simulation of atomic layer deposition for thin deposit formation in a mesoporous substrate.
AIChE Journal (2021): e17305.
(2021).
Periodicity Scoring of Time Series Encodes Dynamical Behavior of the Tumor Suppressor p53.
IFAC-PapersOnLine 54, no. 9 (2021): 488-495.
(2020).
Emergent spaces for coupled oscillators.
Frontiers in Computational Neuroscience 14 (2020): 36.
(2020).
Manifold learning for organizing unstructured sets of process observations.
Chaos: An Interdisciplinary Journal of Nonlinear Science 30, no. 4 (2020): 043108.
(2020).
Coarse-scale PDEs from fine-scale observations via machine learning.
Chaos: An Interdisciplinary Journal of Nonlinear Science 30, no. 1 (2020): 013141.
(2019).
Generalized entropy production analysis for mechanism reduction.
Combustion Theory and Modelling 23, no. 2 (2019): 197-209.
(2019).
Manifold learning for parameter reduction.
Journal of computational physics 392 (2019): 419-431.
(2016).
Spectral quasi-equilibrium manifold for chemical kinetics.
The Journal of Physical Chemistry A 120, no. 20 (2016): 3406-3413.
(2015).
n-Heptane/air combustion in perfectly stirred reactors: Dynamics, bifurcations and dominant reactions at critical conditions.
Combustion and Flame 162, no. 9 (2015): 3166-3179.
(2015).
On the model reduction for chemical and physical kinetics.
ETH Zurich.
(2014).
The global relaxation redistribution method for reduction of combustion kinetics.
The Journal of chemical physics 141, no. 4 (2014): 044102.
(2014).
Entropy production analysis for mechanism reduction.
Combustion and flame 161, no. 6 (2014): 1507-1515.
(2014).
Non-perturbative hydrodynamic limits: A case study.
Physica A: Statistical Mechanics and its Applications 403 (2014): 189-194.
(2013).
A collocated grid, projection method for time‐accurate calculation of low‐Mach number variable density flows in general curvilinear coordinates.
International Journal for Numerical Methods in Fluids 72, no. 3 (2013): 301-319.
(2010).
A similarity solution in order to solve the governing equations of laminar separated fluids with a flat plate.
Communications in Nonlinear Science and Numerical Simulation 15, no. 12 (2010): 3965-3973.