About me

I'm Mehdi Ghasemi, a mathematician who loves using math to solve real-world problems. Right now, I'm a senior scientist at the Edmonton Police Service, putting my skills in Real Algebraic Geometry, Optimization, and Cryptography to good use. Before that, I was a data scientist for the Saskatchewan government, tackling complex challenges with data analysis. When I'm not geeking out over math, you'll find me creating cool generative and fractal art, or diving into a challenging LEGO build – both use a surprising amount of problem-solving!

Selected Scientific Libraries

Irene

Irene is a Python library designed to solve a broad class of global optimization problems using algebraic methods. It leverages a generalized version of Lasserre's Relaxation technique, making it theoretically applicable to any optimization problem with a well-defined feasible region. The core concept behind Irene lies in solving generalized truncated moment problems within a framework of commutative real algebras.

SKSurrogate

SKSurrogate is a Python toolkit that simplifies hyperparameter tuning and pipeline optimization for machine learning models. It builds upon the popular scikit-learn library, making it familiar and easy to use.

Nonlinear Regression

NonlinearRegression is a Python library inspired by scikit-learn, designed to tackle problems where data exhibits non-linear relationships. It offers a variety of tools for this purpose.

A few other Python libraries in my GitHub:

pyProximation: Solves systems of integrodifferential equations (any variables) using collocation with finite-dimensional orthogonal functions (Measure Theory & Hilbert Spaces).
InventoryOptim: Analyzes interacting inventory data to:
- - Forecast future capacity needs for multiple items with limited, dynamic storage.
  - Estimate future costs for each item.
  - Predict trend changes for individual items based on simulated future disruptions.
  - Optimize inventory trends within a budget to ensure no stockouts.
Optimithon: An experimental implementation of some standard optimization methods.

Publications

Mathematics

R. E. Curto, M. Ghasemi, M. Infusino, S. Kuhlmann, The Truncated Moment Problem for Unital Commutative R-Algebras; Journal of Operator Theory 90 (2) (2023).
M. Ghasemi, M. Infusino, S. Kuhlmann, M. Marshall, Moment problem for symmetric algebras of locally convex spaces; Integr. Equ. Oper. Theory 90, 29 (2018). https://doi.org/10.1007/s00020-018-2453-7.
M. Alaghmandan, M. Ghasemi, Seminormed ⁎-subalgebras of ℓ∞(X); Journal of Mathematical Analysis and Applications 455 (1), 212-220 (2017).
M. Ghasemi, S. Kuhlmann, M. Marshall, Moment problem in infinitely many variables; Israel Journal of Mathematics 212, 1012-1012 (2016).
M. Ghasemi, M. Marshall, S. Wagner, Closure of the Cone of Sums of 2d-powers in Certain Weighted ℓ1-seminorm Topologies; Canadian Mathematical Bulletin 57 (2), 289-302 (2014).
M. Ghasemi, J. B. Lasserre, M. Marshall, Lower bounds on the global minimum of a polynomial; Computational Optimization and Applications 57, 387–402 (2014).
M. Ghasemi, S. Kuhlmann, M. Marshall, Application of Jacobiʼs representation theorem to locally multiplicatively convex topological R-algebras; Journal of Functional Analysis 266 (2), 1041-1049 (2014).
M. Ghasemi, M. Marshall, Lower Bounds for a Polynomial on a basic closed semialgebraic set using geometric programming; arXiv preprint arXiv:1311.3726 (2013).
M. Ghasemi, S. Kuhlmann, Closure of the cone of sums of 2d-powers in commutative real topological algebras; Journal of Functional Analysis 264 (1), 413-427 (2013).
M. Ghasemi, M. Marshall, Lower bounds for polynomials using geometric programming; SIAM Journal on Optimization 22 (2), 460-473 (2012).
M. Ghasemi, S. Kuhlmann, E. Samei, The moment problem for continuous positive semidefinite linear functionals; Arch. Math. 100, 43–53 (2013).
M. Ghasemi, M. Marshall, Lower bounds for a polynomial in terms of its coefficients; Archiv der Mathematik 95 (4), 343-353 (2010).
M. Ghasemi, M. Moniri, Beatty Sequences and the Arithmetical Hierarchy; Logic in Tehran: Proceedings of the Workshop and Conference on Logic, Algebra, and Arithmetic, Held October 18-22, 2003, Lecture Notes in Logic 26 (2006).

Computer Science

M. Ghasemi, D. Anvari, M. Atapour, J. S. Wormith, K. C. Stockdale, R. J. Spiteri The Application of Machine Learning to a General Risk–Need Assessment Instrument in the Prediction of Criminal Recidivism; Criminal Justice and Behavior 48 (4), 518-538 (2021).
E. Hesamifard, H. Takabi, M. Ghasemi, Deep Neural Networks Classification over Encrypted Data; CODASPY ’19: Proceedings of the Ninth ACM Conference on Data and Application Security and Privacy (2019).
E. Hesamifard, H. Takabi, M. Ghasemi, R. N, Wright, Privacy-preserving machine learning as a service; Proceedings on Privacy Enhancing Technologies (2018).
E. Hesamifard, H. Takabi, M. Ghasemi, CryptoDL: Deep Neural Networks over Encrypted Data; arXiv preprint arXiv:1711.05189 (2017).
E. Hesamifard, H. Takabi, M. Ghasemi, C. Jones, Privacy-preserving Machine Learning in Cloud; Proceedings of the 2017 on Cloud Computing Security Workshop, 39-43 (2017).
E. Hesamifard, H. Takabi, M. Ghasemi, Cryptodl: Deep neural networks over encrypted data; CoRR abs/1711.05189, arXiv preprint arXiv:1711.05189 (2017).
H. Takabi, E. Hesamifard, M. Ghasemi, Privacy preserving multi-party machine learning with homomorphic encryption; 29th Annual Conference on Neural Information Processing Systems (NIPS) 1, 4 (2016).
E. Hesamifard, H. Takabi, M. Ghasemi, CryptoDL: Towards Deep Learning over Encrypted Data; Annual Computer Security Applications Conference (ACSAC 2016), Los Angeles, California, USA (11), (2016).

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