Arash Mehrzadi

Data Scientist
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Arash Mehrzadi

Data Scientist
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State:

Researching fire prediction systems

About Me

Highly accurate and experienced Data Scientist with several years of experience using predictive modeling, Data processing and Data mining algorithms to solve Challenging Medical problems. Currently, most of my work is focused on medical image processing and I have implemented many image processing neural networks such as UNET, etc. (You can also check them in my GitHub). I have also done projects in field of Drug Discovery and implement RNN neural networks to Discover Chemical Structures. In addition, I have done a lot of work in the field of Build and Develop process management software’s And financial resource control. I am very interested in research work in the field of deep learning and intelligent monitoring systems and I have done a lot works in this fields which you can see in my projects section.

Experience
  • Senior Image Processing Expert
    (May 2020 – present)
  • Work in Salamat20 Startup as a Senior Image processing Expert. Implement OCR system to detect test sheet information, analyze and diagnose patient diseases. In general, the main activity of this startup is in the field of implementing analysis of blood test sheets and disease diagnosis, which they want to implement this system online and using OCR to identify test sheet information.

  • Senior C# Developer
    (Dec 2019 – present)
  • Work in the administrative and security department of IAU Karaj as Senior Csharp Developer. Implement Car Identification System, Student Violation Recorder Software and Improve university management systems.

    Skills

    Python

    C#

    C/C++

    R

    Assembly

    Java

    Adobe
    Photoshop
    Adobe
    Illustrator
    Adobe
    Premier
    Adobe
    After Effect
    Projects

    Cadino

    Finantial Mangement Software

    01

    Create Fluctuation Control Panel

    02

    Create portfolio Panel

    03

    Create Price Expectation Panel

    Description

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    Download:

    Drug Discovery

    Covid-19 main Protease Inhibitor

    01

    Implement Model Structure

    02

    Model Training & Sampling

    03

    Fine tuning for Specific Ligand Subsets

    04

    Fragment Growing Procedure

    05

    Technical implementation

    06

    Molecular Structure Generation

    07

    Target specific Fine tuning

    08

    Fragment growing

    09

    Low data Drug Design

    Description

    Generative artificial intelligence models present a fresh approach to chemogenomics and de novo drug design, as they provide researchers with the ability to narrow down their search of the chemical space and focus on regions of interest. We present a method for molecular de novo design that utilizes generative recurrent neural networks (RNN) containing long short?term memory (LSTM) cells.

    Download:

    Brain Tumor Segmentation

    Medical Image Processing

    01

    Implement Network Structures

    02

    Data Augmentation for Training and Testing

    03

    Uncertainty Estimation

    04

    Experiments and Results

    Description

    The process of segmenting tumor from MRI image of a brain is one of the highly focused areas in the community of medical science as MRI is noninvasive imaging. Therefore, I decided to create Brain Tumor Segmentation Launch File that you can easily use the capabilities of this powerful tool. I am currently in the early stages of designing this Launch File and will be constantly updating these files to get to a usable version.

    Download:

    Covid-19 Statistical Analysis

    statistical Data Analysis

    01

    Implement Geographical Charts

    02

    Implement Treemap

    03

    Implement Bubble Geomap

    Description

    Geographical Charts : Statistical analysis of cities' mortality distribution and implementation of results on geographic maps . Treemap : Analysis of statistical data on the dead, recovered and people with the Corona virus . Bubble Geomap : plot coronavirus Death cases spread on Geomap using Basemap .

    Download: