Mehdi Ghasemi, Mathematics Developer in Regina, SK, Canada
Mehdi Ghasemi

Mathematics Developer in Regina, SK, Canada

Member since May 4, 2017
Mehdi is a mathematician and scientific programmer who specializes in applied math and very abstract concepts. His scientific background is rooted in optimization, geometry, and computations, and he has been working as a data scientist for years now, building statistical and machine learning models.
Mehdi is now available for hire



Regina, SK, Canada



Preferred Environment

PyCharm, JupyterLab, Git

The most amazing...

...project I've worked on is a global optimization method based on a series of SDPs that in theory is capable of solving any given optimization problem.


  • Adjunct Professor

    2018 - PRESENT
    University of Saskatchewan
    • Collaborated with other researchers in scientific projects that involve mathematical modeling.
    • Co-supervised graduate students in areas related to optimization and computer science.
    Technologies: Mathematics and Statistics
  • Data Scientist

    2017 - PRESENT
    Government of Saskatchewan
    • Designed and implemented pipelines to extract specialized datasets out of the administrative database.
    • Using government data, analyzed some of the existing practices to find bottlenecks and optimize procedures.
    • Employed machine learning to improve upon decisions made based on standard assessments.
    • Evaluated feasibility of new policies to achieve certain goals by employing timeseries analysis and forecasting.
    • Made local/provincial evaluation of initiatives in correction, justice, and child welfare.
    Technologies: Python, SQL
  • Consultant

    2017 - 2018
    The Centre for Forensic Behavioural Science and Justice Studies
    • Analyzed the risk assessment tool LSI (Level of Service Inventory).
    • Applied machine learning to LSI data in order to obtain personalized preventive interventions for offenders.
    Technologies: Python, SPSS, SQL
  • MITACS Postdoctoral Fellow

    2015 - 2017
    University of Saskatchewan
    • Organized the Saskatoon Police Predictive Analytics Laboratory.
    • Built a mathematical simulation of the missing children phenomenon to identify its deriving factors among youth.
    • Researched the optimization and moment problem.
    Technologies: Mathematical Modeling, Optimization, Python
  • Postdoctoral Research Fellow

    2013 - 2014
    Nanyang Technological University
    • Implemented SDP UI for SAGE/Python.
    • Researched polynomial and convex optimization.
    • Developed the topological moment problem using functional analysis and real algebraic geometry.
    Technologies: Mathematical Modeling, Python/SAGE


  • SKSurrogate (Development)

    SKSurrogate is a suite of tools which implements surrogate optimization for expensive functions based on scikit-learn. The main purpose of SKSurrogate is to facilitate hyperparameter optimization for machine learning models and optimized pipeline design (AutoML).

    The version of the surrogate optimization implemented here heavily relies on regressors. A custom regressor based on Hilbert Space techniques is implemented, but all scikit-learn regressors are accepted for optimization.

    Finding an optimized pipeline—based on a given list of transformers and estimators—is a time-consuming task. A version of evolutionary optimization has been implemented to reduce its time in lieu of global optimality.

  • Irene Project (Development)

    Irene is a Python package that aims to be a toolkit for global optimization problems that can be realized algebraically. It generalizes Lasserre's Relaxation method to handle theoretically any optimization problem with a bounded feasibility set. The method is based on solutions of generalized truncated moment problem over commutative real algebras.

  • InventoryOptim (Development)

    Given inventory data of multiple (interacting) commodities from stock with limited but variable capacity, provide insight on:
    1. Estimating future required capacity for each item based on a certain terminal segment of data,
    2. Future cost estimation for each item,
    3. How the trends of individual items would change, assuming a trend change at given times (in future) for some items?
    4. Given a budget limit, how should the trends change to make sure a non-negative residual?

  • pyProximation (Development)

    This package was originally written to solve systems of integro-differential equations via collocation method in an arbitrary number of variables. The current implementation of the method is based on a finite-dimensional orthogonal system of functions. Therefore additional modules were required to achieve this goal.


  • Languages

    Python, PHP
  • Libraries/APIs

    Scikit-learn, Keras, Sage, OpenCV
  • Tools

    LaTeX, Git
  • Other

    Mathematics, Mathematical Modeling, Optimization, Machine Learning, Web Programming, Visualization, Data Visualization, Time Series Analysis, Bayesian Inference & Modeling
  • Frameworks

  • Paradigms

    Model View Controller (MVC)
  • Platforms

  • Storage

    MySQL, SQLite


  • Ph.D. in Mathematics
    2009 - 2012
    University of Saskatchewan - Saskatoon, Canada
  • Master of Science degree in Mathematical Logic
    2002 - 2004
    Tarbiat Modares University - Tehran, Iran
  • Bachelor of Science degree in Mathematics
    1998 - 2002
    Amirkabir University of Technology - Tehran, Iran

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