Mahdi Kooshkbaghi
Staff Machine Learning Engineer
The Estée Lauder Companies
Machine learning engineer and computational scientist with 10+ years of experience spanning production user-facing AI applications and scientific computing. Currently building and deploying GenAI-powered products at scale. Prior research in scientific computing, CFD simulation, nonlinear dynamics, and data-driven discovery of governing equations, with a track record of developing ML simulation surrogates for complex physical systems.
GenAI & LLMs
Building & deploying production-scale user-facing AI applications.
Bayesian Modeling
Causal inference and MCMC workflows with JAX and NumPyro.
CFD & Simulation
Physics-informed ML and nonlinear dynamics for complex systems.
You can find my current CV here.
Education
Cold Spring Harbor Laboratory
- Computational Postdoc in Simons Center for Quantitative Biology(2020)
Princeton University
- Postdoc in Applied and Computational Mathematics(2017)
ETH Zürich
- PhD in Mechanical Engineering(2015)
Tehran Polytechnic
- MSc in Mechanical Engineering(2011)
- BSc in Aerospace Engineering(2010)
- BSc in Mechanical Engineering(2008)
Experience
Staff Machine Learning Engineer
- Core developer of the Jo Malone AI Scent Advisor, a user-facing GenAI recommendation system deployed to production. Built question-based recommendation logic and scenario planning using LLM integration on GCP infrastructure.
- Architected and deployed production ML pipelines using Kubeflow on GCP for causal inference modeling and Bayesian estimation with JAX/NumPyro frameworks, including GPU-accelerated MCMC workloads.
- Built containerized ML applications (Docker, Cloud Run, FastAPI) with modular software design, production monitoring, and CI/CD automation via GitHub Actions.
- Mentored interns on interpretable ML projects, leading to a 100% conversion rate to full-time roles, with two now reporting directly to me.
Computational Postdoc
- Developed TensorFlow-based software to analyze Massively Parallel Reporter Assay (MPRA) data.
- Implemented Bayesian inference models for drug interaction analysis using CPU/GPU computing.
- Developed computational frameworks for processing and analyzing raw biological datasets.
Research Fellow in Applied Mathematics
- Led physics-informed machine learning research in collaboration with Johns Hopkins and Brown Universities.
- Developed manifold learning algorithms to analyze time-dependent datasets and extract governing equations.
- Mentored graduate students in developing manifold learning and machine learning software.
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.