Metti Paak, Machine Learning Developer in Toronto, ON, Canada
Metti Paak

Machine Learning Developer in Toronto, ON, Canada

Member since April 30, 2020
Metti is a data scientist and machine learning expert who has extensive experience in software development and mathematical/statistical modeling. He has worked in the aerospace, manufacturing, and healthcare industries developing custom, data-driven predictive software tools. He is proficient in translating business goals into data products and architecting the entire pipeline to the point of delivery. His work has led to multiple patents, publications, and successful fundraising.
Metti is now available for hire




Toronto, ON, Canada



Preferred Environment

TFS, Git, PyCharm, Jupyter, Visual Studio, Windows, Linux

The most amazing...

...product I developed is a deep learning based app that predicts cardiac diseases from non-invasively acquired data (e.g., ECG, PPG), leading to two US patents.


  • Senior AI Scientist

    2018 - 2020
    Analytics 4 Life
    • Developed an ML app that predicts cardiac diseases using non-invasively acquired data leading to pending US/international patents.
    • Led a project to develop a protocol for patient data collection and automated quality assessment, reduced retrial efforts, and cost.
    • Developed an ML-based anomaly detection algorithm for ECG and PPG signals: design of experiment, statistical analysis, ML model training and deployment, and automated reports.
    • Improved inference performance of production ML model deployed on AWS by 60%: parallelization and algorithmic improvements.
    • Led projects on feature engineering, model selection, and robustness analysis: data collection, quality assessment, feature engineering, model selection, deployment, and performance monitoring.
    • Developed a deep learning-based app to predict the respiration information and waveform form the cardiac and blood volume data (ECG and PPG): providing a valuable health marker for disease prediction.
    • Established effective communication with other teams (systems, QA, hardware, clinical), development in close liaison with cardiologists, clinical staff, and end-users.
    • Prepared detailed technical reports for FDA submission; prepared marketing presentations; presented results to executives, investors, and partners; and authored the scientific manuscript.
    • Conducted code reviews, involved in the hiring process, and trained junior data scientists and interns.
    Technologies: Amazon Web Services (AWS), Docker, Keras, TensorFlow, AWS, MATLAB, Python, C++
  • Senior Research Scientist, HPC Software Developer

    2015 - 2018
    Hexagon Manufacturing Intelligence
    • Developed ML models to replace heavy simulations for the design and optimization of auto parts, a fast tool for the initial design and prototyping phase.
    • Developed a high performance parallel linear system solver leading to 1,000 times speed-up: sparse direct solvers and iterative solvers (PCG, Algebraic multigrid).
    • Developed a sparse/dense linear algebra library in C++, scientific code design, and optimization.
    • Developed a tool for the probabilistic analysis of uncertainty propagation from input materials to the final product.
    • Created mathematical, statistical, and numerical models for the simulation of sheet metal processes: differential equations, finite element method, and visualization.
    • Authored, published, and presented a paper on probabilistic analysis of spring back phenomenon in the Institute of Physics (IOP) conference series.
    • Utilized supervised and clustering ML techniques, design of experiments, Monte-Carlo simulation, and HPC.
    Technologies: TFS, GPGPU, MPI, OpenMP, UML, Python, C++
  • Research Scientist

    2014 - 2015
    Alstom Power
    • Developed a semi-analytical software tool and pipeline for improving the performance of old hydraulic turbines, thereby avoiding significant replacement cost.
    • Developed a tool for predicting what parameters and loads (fluid dynamic forces and conditions) lead to a dangerous structural response (e.h. catastrophe vibrations).
    • Designed and implemented an experimental setup for validating the results of the simulation tool.
    • Prepared documentation and design guidelines, presented results, and published internal white paper.
    Technologies: Models, Finite Element Method (FEM), Nastran, ANSYS, MATLAB, Python
  • Research Scientist

    2013 - 2014
    Bombardier Aerospace
    • Developed a fast predictive software tool for designing fuselage components with lower weights and yet stable under the loads, reducing the initial design and prototyping cost.
    • Developed design guidelines and design charts for various loads and geometries, resulting in a 10% weight reduction in the initial phase of the design.
    • Developed mathematical and numerical models for the post-instability behavior of the fuselage component.
    • Supervised and trained applications engineer on how to utilize the developed tool and guideline.
    Technologies: Finite Element Analysis (FEA), SOLIDWORKS, ANSYS, Python, MATLAB
  • PhD Researcher

    2008 - 2013
    McGill University
    • Studied and developed mathematical models for predicting the behavior of complex nonlinear systems: fluid-structure interaction, cardiovascular fibrillation, weather forecast, etc.
    • Developed a high-performance numerical software tool for solving the nonlinear differential equations of the complex systems (airplane wing).
    • Developed a pipeline to compile and deploy the program on a super-computing cluster (Mammoth), request shared (OpenMP) and distributed memory (MPI) resources, and collecting and analyzing Terabyte results.
    • Designed and performed experiments on the nonlinear and chaotic response of elastic shell conveying fluid (artery conveying blood), and validated the results of the software tool.
    • Developed machine learning meta models for forecasting the behavior of the complex systems without running heavy simulations.
    • Taught courses on numerical methods, computer programming, mathematical modeling. Published and presented results in various journals and conferences.
    • Utilized C++, Fortran, Python, distributed computing, OpenMP, MPI, statistical data analysis, stochastic signal processing, and experimental design.
    Technologies: Bash, Linux, MPI, OpenMP, Python, Fortran, C++


  • Predicting Respiration Information and Waveform From ECG and PPG Data (Development)

    Respiration is an important biological marker for determining the state of health or for predicting an aberration or disease. It is not easy to measure respiration outside clinical settings as it requires inconvenient equipment and sensor attachments. Therefore, it is important to accurately estimate the respiration information from available data such as the ECG and PPG data. I conducted this project in two phases: (i) developing and testing against available data with ground truth and (ii) improving and modifying the program to act on unlabeled data. I completed the project using a variety of tools (data ingestion, feature engineering, deep/transfer learning, ensemble learning) which achieved the desired business target. In phase 2, I modified the model to use propriety data, I implemented a performance monitoring tool to assess potential model drift. This model can be used interactively and in a production environment (deployed on AWS).
    In this two-phase project, I developed an object-oriented program to predict RR from ECG and PPG (single or multi-channel). The program ingests data from various sources and in different formats after data preprocessing (anomaly detection, outlier removal, imputation, and quality assessment).


  • Languages

    Python, C++, Fortran, SQL, UML, Bash
  • Libraries/APIs

    Pandas, Scikit-learn, TensorFlow Deep Learning Library (TFLearn), TensorFlow, Keras, OpenMP, MPI
  • Tools

    MATLAB, Mathematica, Visual Studio, Jupyter, PyCharm, Git, TFS, SOLIDWORKS
  • Paradigms

    Data Science, Parallel Programming, Agile Software Development, GPGPU
  • Other

    Machine Learning, Deep Learning, Mathematical Models, Software Development, Time Series Analysis, Statistics, Signal Processing, Stochastic Modeling, Forecasting, AWS, ANSYS, Nastran, Finite Element Method (FEM), Models, Finite Element Analysis (FEA)
  • Platforms

    CUDA, Linux, Windows, Docker, Amazon Web Services (AWS)
  • Industry Expertise



  • PhD in Computational Mechanics and Complex Systems
    2008 - 2013
    McGill University - Montreal, Quebec, Canada


  • Professional Engineer (PEng)
    Professional Engineers of Ontario

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