Sameh Awaida, Software Developer in Montreal, QC, Canada
Sameh Awaida

Software Developer in Montreal, QC, Canada

Member since July 7, 2017
Sameh is a successful data scientist and assistant professor experienced in computer and electrical engineering with more than 20 journal publications, conference papers, and patent applications in the fields of pattern recognition, data science, and machine learning. He possesses expert level skills and accomplishments in numerous technologies and formats.
Sameh is now available for hire



Montreal, QC, Canada



Preferred Environment

Linux, Max OS X, PyCharm, RStudio, Matlab, Git

The most amazing...

...classification model I built is a writer identification and verification system on historical manuscripts using pattern recognition and data science techniques.


  • Assistant Professor

    2011 - 2017
    Qassim University
    • Taught courses in Computer Organization/ Assembly Language, Computer Architecture, Microprocessor Systems, Intelligent Systems and Robotics, iOS Programming, and Signals and Systems. Advise Senior Projects. Serve as chair of Study Plan and ABET Accreditation Committees.
    • Incorporated real world research and industry data into classroom instruction allowing students to exceed academic standards and prepare for careers in computer Engineering.
    • Designed and implemented Quality Assurance systems for the CEN department. Developed QA standards and test plans for audits.
    • Obtained ABET accreditation in record time. Designed new curriculum to educate engineering students in modern computer systems and attain programmatic or technical leadership roles in an organization.
    • Redesigned the CEN department study plan to exceed academic standards and provide students with relevant coursework.
    • Published five journal papers and three IEEE conference papers while working on several future journal papers and patent applications in Pattern Recognition and Machine Learning.
    Technologies: Python, Matlab, C, C++, C#, R, Django, Latex, Microsoft Excel, EMC Cloud Infrastructure and Services, Torch
  • Lecturer

    2007 - 2011
    King Fahd University of Petroleum and Minerals
    • Taught courses in Computer Science. Served on various department committees.
    • Developed department standards for approving courses under Open Course Initiative.
    • Led website redesign project for more than 40 CEN faculty and staff members.
    • Published four journal papers, two U.S. patents, and five conference papers related to the field of pattern recognition and machine learning.
    • Wrote grant applications and awarded funding for four research projects in the fields of Pattern Recognition and Document Analysis.
    Technologies: C, C++, C#, Matlab, Weka, FPGA, Verilog, VHDL, Latex
  • Lecturer

    2005 - 2007
    Princess Sumaya University for Technology
    • Taught undergraduate level courses in Computer Organization, Assembly Language, Logic and Computer Lab, Microprocessors, Computer Architecture, and Microprocessors Lab.
    • Founded and served as advisor for Robotics Club.
    • Redesigned and authored new user manual for the microprocessor lab.
    Technologies: Assembly, C, C++, Matlab, VHDL, Verilog


  • Crowdsourcing Image Annotations using Amazon Turks (Development)

    I have developed a project that can use MTurk to locate objects in images. I created a Human Intelligence Task (HIT) that will ask Workers to draw a bounding box around specific objects.

  • Classify Handwritten Digits Using the Famous MNIST Data (Development)

    The goal in this competition is to take an image of a handwritten single digit, and determine what that digit is.

    For more details on this competition, see

  • Arabic Diacritics Based Steganography (Other amazing things)

    New steganography methods are being proposed to embed secret information into text cover media in order to search for new possibilities employing languages other than
    English. This paper utilizes the advantages of diacritics in Arabic to implement text steganography. Diacritics-or Harakat-in Arabic are used to represent vowel sounds and can be found in many formal and religious documents.

  • Writer Identification of Arabic Text using Statistical and Structural Features (Other amazing things)

    This article addresses writer identification of handwritten Arabic text. Several types of structural and statistical features were extracted from Arabic handwriting text. A novel approach was used to extract structural features that build on some of the main characteristics of the Arabic language. Connected component features for Arabic handwritten text as well as gradient distribution features, windowed gradient distribution features, contour chain code distribution features, and windowed contour chain code distribution features were extracted. A nearest neighbor (NN) classifier was used with the Euclidean distance measure. Data reduction algorithms (viz. principal component analysis [PCA], linear discriminant analysis [LDA], multiple discriminant analysis [MDA], multidimensional scaling [MDS], and forward/backward feature selection algorithm) were used. A database of 500 paragraphs handwritten in Arabic by 250 writers was used. The paragraphs used were randomly generated from a large corpus. NN provided the best accuracy in text-independent writer identification with top-1 result of 88.0%, top-5 result of 96.0%, and top-10 result of 98.5% for the first 100 writers. Extending the work to include all 250 writers and with the backward feature selection algorithm (using 54 out of 83 features), the system attained a top-1 result of 75.0%, top-5 result of 91.8%, and top-10 result of 95.4%.

  • ICFHR2014 Competition on Arabic Writer Identification Using AHTID/MW and KHATT Databases (Other amazing things)

    This paper describes the first edition of the Arabic writer identification competition using AHTID/MW and KHATT databases held in the context of the 14th International Conference on Frontiers in Handwriting Recognition (ICFHR2014).

    This competition has used the new freely available Arabic Handwritten Text Images Database written by Multiple Writers (AHTID/MW) and the Arabic handwritten text database called KHATT presented in ICFHR2012.

    We proposed three tasks in this Arabic writer identification competition: the first and second are based respectively on word and text line level using the AHTID/MW database and the third one is paragraph based using the KHATT database.

    We received one system for the second task, three systems for the third task, and none for the first task.

    All systems were tested in a blind manner using a set of images kept internal. A short description of the participating groups, their systems, the experimental setup, and the observed results were presented.


  • Languages

    Assembly, VHDL, R, C++, C, Python 3, Verilog, Python, HTML5, JavaScript
  • Libraries/APIs

    Torch AI
  • Tools

    PyCharm, MATLAB Neural Network Toolbox, MATLAB Statistics & Machine Learning Toolbox, Excel 2016, GitHub, Git, MATLAB, Dell EMC, ZeroBrane Studio
  • Paradigms

    Data Science, Agile Software Development
  • Platforms

    RStudio, MapR
  • Other

    Program Management, Machine Vision, Scientific Data Analysis, Machine Learning, Pattern Recognition, Project Management Professional (PMP), EMC Certified Data Scientist, Natural Language Processing (NLP), Web Development, Torch, UI Programming
  • Frameworks

  • Storage

    SQLite, NoSQL, MySQL


  • Ph.D. in Computer Science and Engineering
    2007 - 2011
    KFUPM - Kingdom of Saudi Arabia
  • Master’s degree in Electrical Engineering
    2003 - 2005
    University of Hartford - Connecticut, USA
  • Bachelor of Science degree in Electrical Engineering (Computer Engineering Concentration)
    1999 - 2003
    University of Hartford - Connecticut, USA
  • MapR Certified Cluster Administrator 5.1 (MCCA)
    MapR Academy
  • Data Science Associate (EMCDSA)
  • Project Management Professional (PMP)
    JUNE 2014 - JUNE 2020
    Project Management Institute

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