Mehdi Ghasemi
Verified Expert in Engineering
Mathematics Developer
Edmonton, AB, Canada
Toptal member since July 4, 2019
Mehdi is a mathematician and scientific programmer specializing in applied math and abstract concepts. His scientific background is rooted in optimization, geometry, and computation. Mehdi has been working as a senior scientist for years, building statistical and machine-learning models.
Portfolio
Experience
- Mathematics - 20 years
- Python - 17 years
- Mathematical Modeling - 12 years
- Optimization - 12 years
- Machine Learning - 10 years
- Time Series Analysis - 10 years
- Scikit-learn - 8 years
- Natural Language Processing (NLP) - 4 years
Preferred Environment
Jupyter, Git, Python, Pandas, Linux, Web Programming, Artificial Intelligence (AI), Agentic AI, RAG Systems, GitHub Copilot
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.
Work Experience
Senior Scientist | Mathematician
Edmonton Police Service
- Analyzed and extracted information from historical documents so that the unstructured data becomes semi-structured and digestible by AI algorithms. This task involved image processing, NLP, LLM, and mathematical optimization.
- Made adjustments to police communication branch staffing to meet and improve upon national minimum standards.
- Forecasted the impact of COVID-19 on the police force and its corresponding risk analysis.
- Analyzed the transition of heavy drug users over time. Also, studied the network of drug users and distributors around the city and effective methods for concentrated disruption of drug-related offenses.
Adjunct Professor
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.
- Provided scientific consultation to identify, solve, and implement solutions to industrial problems.
Data Scientist
Government
- Designed and implemented pipelines to extract specialized datasets out of the administrative database.
- Used government data and analyzed some of the existing practices to find bottlenecks and optimized procedures.
- Employed machine learning to improve decisions made based on standard assessments.
- Evaluated the feasibility of new policies to achieve certain goals by employing time-series analysis and forecasting.
- Made a local and provincial evaluation of initiatives in correction, justice, and child welfare.
Consultant
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 to obtain personalized preventive interventions for offenders.
- Organized and managed workshops to expand the professional and scientific network of the Centre.
MITACS Postdoctoral Fellow
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 in the applied context.
Postdoctoral Research Fellow
Nanyang Technological University
- Implemented semidefinite programming UI for SAGE and Python.
- Research advanced polynomial and convex optimization.
- Developed the topological moment problem using functional analysis and real algebraic geometry.
Experience
SKSurrogate
https://github.com/mghasemi/sksurrogateThe version of the surrogate optimization implemented here heavily relies on regressors. A custom regressor based on Hilbert Space techniques is implemented, but all sci-kit-learn regressors are accepted for optimization.
Finding an optimized pipeline based on a given list of transformers and estimators is time-consuming. Evolutionary optimization has been implemented to reduce its time instead of global optimality.
Irene Project
https://github.com/mghasemi/IreneInventoryOptim
https://github.com/mghasemi/InventoryOptim1. Estimating future required capacity for each item based on a particular 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 the future for some items
4. Given a budget limit, how should trends change to ensure a non-negative residual
pyProximation
https://github.com/mghasemi/pyProximationNonlinear Regression
https://github.com/mghasemi/nonlinear-regressionEducation
Ph.D. in Mathematics
University of Saskatchewan - Saskatoon, Canada
Master of Science Degree in Mathematical Logic
Tarbiat Modares University - Tehran, Iran
Bachelor of Science Degree in Mathematics
Amirkabir University of Technology - Tehran, Iran
Certifications
Stable Diffusion AI Art
Udemy
Project Management Fundamentals
INEXERTUS
Skills
Libraries/APIs
Scikit-learn, Keras, Pandas, XGBoost, Sage, OpenCV
Tools
LaTeX, PyCharm, Jupyter, SPSS, Git, GitHub Copilot
Languages
Python, SQL, PHP
Frameworks
Flask
Paradigms
Model View Controller (MVC), Agile Project Management
Platforms
Linux, Databricks
Storage
MySQL, SQLite, Data Pipelines
Other
Mathematics, Mathematical Modeling, Optimization, Machine Learning, Time Series Analysis, Artificial Intelligence (AI), Data Analysis, Data Analytics, Data Scientist, Data Modeling, Data Science, Logistic Regression, Statistical Modeling, Data Manipulation, Modeling, Linear Regression, Time Series Forecasting, Model Development, Model Evaluation, Communication, Scheduling, Trend Analysis, Forecasting, Web Programming, Visualization, Data Visualization, Bayesian Inference & Modeling, Natural Language Processing (NLP), Computational Geometry, Deep Learning, Consulting, Mentorship & Coaching, Time Series, Applied Research, Fine-tuning, Image Generation, Text to Image, Conda, Web Development, Agentic AI, Webhooks, Retrieval-augmented Generation (RAG), Agentic AI Systems, Model Deployment, Demand Forecasting, Feature Engineering, Data Engineering, Statistics, Stochastic Modeling, Image Processing, Global Project Management, Large Language Models (LLMs), OpenAI, Leadership, Technical Leadership, Generative Pre-trained Transformers (GPT), Generative Artificial Intelligence (GenAI), Computer Vision, RAG Systems
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