About me

            Greetings! My name is Firas Al-Hindawi, a dedicated Data Scientist rooted in Industrial Engineering. Born in Amman, Jordan, I've navigated a diverse academic and professional journey, fueled by a passion for finding solutions to real-world problems through the lens of data. There is a famous quote by the Machine Learning scientist Andrew Ng that says “Artificial Intelligence is the new electricity”. Ever since I have heard this sentence it resonated in my mind and inspired me to explore Machine Learning. If Artificial Intelligence (AI) truly is the new electricity, then what a great time it is to be alive! knowing that we are witnessing the rise of a life changing technology is extremely exciting. I surely do not want to miss out on the opportunity of using this new electricity to enhance the life of human beings for years and years to come. I hope I get to take part in innovating what would be considered as the next light bulb. 

As for hobbies, I'm a huge fan of Mixed Martial Arts (MMA), high level Boxing and combat sports in general. Working out is a major stress reliever for me and a great way to refocus my mind and keep it off of distractions. I also enjoy hiking (at least whenever my knees feel like allowing it). I would like to list skiing as a hobby, but I only tried it once ... I would definitely practice it regularly however if I was living in a snowy environment. 

Education

            I graduated from the University of Jordan with a bachelor's degree in industrial engineering. I was able to complete my five-year program in only four while maintaining a GPA of 3.48/4.00 and ranked 11th in my class of 199 students (top 6%). Afterward, I was awarded the Fulbright Scholarship to pursue an MS degree in Industrial Engineering at Arizona State University, where I graduated with a GPA of 3.96/4.00. I recently defended my PhD in Data Science, Analytics, and Engineering at ASU under the supervision of Professor Teresa Wu with a GPA of 4.00/4.00.

I post most of the PhD Research projects and previous course projects I worked on during my studies in the projects section of this website. Definitely go check them out if you want to learn more about my work during my studies, or simply click on this link: https://www.firashindawi.com/projects

Professional Experience

            My professional experience is a very interesting mix of  technical, managerial, academic research and educational  experience. My career started with an eight-weeks internship at an international audit and management consultancy named PKF. Afterwards, I worked for two years as a Design Engineer at an international HVAC equipment manufacturer named Petra Engineering Industries. I was responsible for the sheet metal design and the final 3D design assembly of one of the company’s main products, which is the Air Handling Unit (AHU) using the CAD software SolidWorks. This job enabled me to understand the flow of products in production lines, and the processes of assembly and manufacturing required to transform a design into a reality. After two years at Petra, I decided to look for a new career challenge. While I was searching for my next career challenge, I wanted to expand my knowledge through online learning. Inspired by how my direct manager in Petra used to automate excel tasks using VBA (visual basic for applications), I started learning Excel VBA to automate excel tasks and started learning VB.Net to create desktop applications. I also explored other topics such as 3D printing, robotics, new CAD software and Arduino micro-controllers programming.

Afterwards, I got the opportunity to Join an international Aviation company called Air Arabia as an Intern Planning Engineer, where I took part in several tasks, such as Defect reporting using Computerized Maintenance Program (CMP), Assist in department forecasting and the Development and control of aircraft documentation and update Tech Logs.

Then, I got the opportunity to join the German Jordanian University (GJU) as a Research and Teaching Assistant. By far this has been the most exciting job I have ever had. The teaching experience has been very fulfilling, and it really is self-rewarding. The back and forth questions with students makes one notice things from a different point of view, thus increasing the depth of knowledge regarding the topic being taught. I took part in the teaching process of several laboratories, such as: Industrial Automation, Manufacturing Processes, Materials Science & Mechanics and The Engineering Workshop. The research part of my job is not of less importance, I have taken part in conducting Research papers and assisting students in their graduation project. Moreover, I keep trying to come up with ways to ease the education process, I have designed a proctoring system based on google sheets query and designed an android app that connects to it so that it is easier for lecturers and proctors to monitor information related to their exams. Furthermore, I have used Excel VBA to Automate the grading procedure of several reports to ease the grading process for the Teaching Assistants.

During my time at GJU, I wanted to dive deeper into Machine Learning and Data Science; hence, I started an online Machine Learning & Data Science internship with a hybrid healthcare company dedicated for women health called Nabta Health. I had the opportunity to work under the supervision of their CTO who's a highly experienced machine learning scientist where I was assigned to a project focusing on building a chat bot for their App.

Currently, I work as a Research Assistant at the ASU-Mayo-Clinic Center For Innovative Imaging (AMCII) lab, under the supervision of Professor Teresa Wu, where we developed a framework to apply transfer learning to solve the boiling crisis detection problem in energy generators (Paper published), developed a cross-domain classification framework using unsupervised image-to-image translation models to solve the boiling crisis detection problem in energy generators (Paper under review), developed a framework to generate Pseudo Supervised Metrics to support the unsupervised cross-domain classification framework (Paper in the works), and we work daily on data science & machine learning projects (predictive modeling, data mining, Image Classification, Image2Image translation, Metrics development, ... etc.)

Research Publications

Journal Articles

1. F. Al-Hindawi, M. M. R. Siddiquee, T. Wu, H. Hu, and Y. Sun, “Domain-knowledge inspired pseudo supervision (dips) for unsupervised image-to-image translation models to support cross-domain classification,” Engineering Applications of Artificial Intelligence, vol. 127, p. 107 255, 2024.

2. F. Al-Hindawi, T. Soori, H. Hu, et al., “A framework for generalizing critical heat flux detection models using unsupervised image-to-image translation,” Expert Systems with Applications, vol. 227, p. 120 265, 2023.

3. T. Li, Y. Xu, T. Wu, J. R. Charlton, K. M. Bennett, and F. Al-Hindawi, “Blobcut: A contrastive learning method to support small blob detection in medical imaging,” Bioengineering, vol. 10, no. 12, p. 1372, 2023.

4. S. M. Rassoulinejad-Mousavi, F. Al-Hindawi, T. Soori, et al., “Deep learning strategies for critical heat flux detection in pool boiling,” Applied Thermal Engineering, vol. 190, p. 116 849, 2021.

5. S. Altarazi, R. Allaf, and F. Alhindawi, “Machine learning models for predicting and classifying the tensile strength of polymeric films fabricated via different production processes,” Materials, vol. 12, no. 9, p. 1475, 2019.

Journal Articles (in Progress)

1. F. Al-Hindawi, M. M. R. Siddiquee, T. Wu, A. Patharkar, and H. Hu, “Bubblesync-gan: Preserving physical characteristics consistency in unsupervised image-to-image translation through intelligent physical features extraction for cross-domain chf detection,” Manuscript in preparation, 2024.

2. F. Al-Hindawi, M. M. R. Siddiquee, T. Wu, A. Patharkar, and H. Hu, “Sequence-sync-gan: Preserving temporal sequential consistency in ui2i translation for cross-domain chf detection,” Manuscript in preparation, 2024.

3. T. Li, T. Wu, J. R. Charlton, K. M. Bennett, and F. Al-Hindawi, “Cu-net: Help small blob detection in medical imaging with a human-in-the-loop approach,” Manuscript in preparation, 2024.

4. A. Patharkar, F. Cai, F. Al-Hindawi, and T. Wu, “Forecasting of biomedical temporal data in healthcare applications: Overview and future directions,” Manuscript in preparation, 2024.

5. A. Patharkar, F. Al-Hindawi, and T. Wu, “Healthy bio-core: An instance selection framework for enhancing biomedical temporal data classification,” Manuscript in preparation, 2024.

Conference Proceedings

1. Y. Sun, M. Rassoulinejad-Mousavi, F. Al-Hindawi, et al., “Deep learning strategies for critical heat flux detection in boiling,” in APS Division of Fluid Dynamics Meeting Abstracts, 2020, R03–007.

2. F. Alhindawi and S. Altarazi, “Predicting the tensile strength of extrusion-blown high density polyethylene film using machine learning algorithms,” in 2018 IEEE International Conference on IEEM.

For more information, please check the Research publication section of this website or simply click on the following link: https://www.firashindawi.com/research-publications

Honors & Awards

• 2nd place in the Hacks4Humanity Hackathon ($3,000) - (ASU - Fall 2022)

• SCAI Doctoral Fellowship Award ($3,078), School of Computing & Augmented Intelligence - (Spring 2022 )

• Fulton Fellowship Award ($5,000), Ira A. Fulton Schools of Engineering - (Fall 2021‐ Spring 2022)

• Engineering Graduate Fellowship Award ($1,000), Ira A. Fulton Schools of Engineering - (Fall 2021)

• Graduate Research Assistantship (≈ $210,000), ASU-Mayo Clinic Center for Innovative Imaging (AMCII) lab - (Fall 2021 - TBA)

  • Covered full tuition &  Stipend of my Ph.D. degree in Data Science, Analytics, and Engineering

• Fulbright Pre-Doctoral Scholarship Award (≈ $100,000), Fulbright - (Aug 2019 – Jul 2021)

  • Ranked 1st amongst 11 selected grantees to pursue an MS degree at ASU

• 2nd place in the Devils Invent High Hackathon, ASU - (Oct 2019 – Oct 2019)

• 1 Million Arab Coders initiative - Data Analyst Track, Dubai Future Foundation - (Aug 2018 – Nov 2018)

• Al Ghurair Open Learning Scholars Program - MM SCM, Al Ghurair Foundation - (Nov 2017 – Feb 2018)

• 1st place in the Design Now Competition, OmniPlan – AutoDesk - (Jan 2017 – Jan 2017)

• Children of Teachers Scholarship, Jordanian Ministry of Education - (Sep 2009 – Jul 2013)

  • Covered full tuition of my B.Sc. degree in Industrial Engineering.

Computational Skills

• Machine Learning & Data Mining: Orange · Python · Matlab

• Statistical Analysis: Minitab · MaxStat · JMP · Excel 

• Statistical & Stochastic Simulation: AnyLogic · ProModel

• Optimization : SAS · Excel Solver · AMPL

• Programming Languages: Python · Matlab · Octave · VB.Net · Excel-VBA · MySQL · C++

• Database Relationship Modeling: Visual Paradigm · Microsoft Access

• CAD/CAM: SolidWorks · Fusion360 · Cura · IdeaMaker · Slic3r · SketchUp · AutoCAD

• Industrial Automation & Simulation: Arduino · Step7 · Automation studio · FluidSim · Fritzing

• Android Apps Visual Programming: ThunkableX · MIT App Inventor

• Diagraming & Mind Mapping: Microsoft Visio · Google Drawings

Courses & Certifications

         I have a sum of  more than 20 courses and certificates that I have gained through the years. The following is a summary of the most important ones by topic: 

• Machine Learning & Data Science (total of 15):

  • • TensorFlow: Advanced Techniques Specialization by deeplearning.ai (a sum of 4 courses):

      • Course 1: Custom Models, Layers, and Loss Functions with TensorFlow 

      • Course 2: Custom and Distributed Training with TensorFlow 

      • Course 3: Advanced Computer Vision with TensorFlow

      • Course 4: Generative Deep Learning with TensorFlow

    • Generative Adversarial Networks (GANs) Specialization by deeplearning.ai (a sum of 3 courses):

      • Course 1: Build Basic Generative Adversarial Networks (GANs) 

      • Course 2: Build Better Generative Adversarial Networks (GANs) 

      • Course 3: Apply Generative Adversarial Networks 

    • Machine Learning Specialization by Stanford & deeplearning.ai (a sum of 3 courses):

      • Course 1: Supervised Machine Learning: Regression and Classification 

      • Course 2: Advanced Learning Algorithms 

      • Course 3: Unsupervised Learning, Recommenders, Reinforcement Learning

    • Deep Learning Specialization by deeplearning.ai (a sum of 5 courses):

      • Course 1: Neural Networks and Deep Learning 

      • Course 2: Improving DNNs: Hyperparameter tuning, Regularization and Optimization 

      • Course 3: Structuring Machine Learning Projects 

      • Course 4: Convolutional Neural Networks

      • Course 5: Sequence Models 

    • Machine Learning course by Stanford

    • Data Analyst Track by Udacity

    • Deep Reinforcement Learning Competition held by MIT called Deep Traffic

• Supply Chain Management (total of 5):

  • • Micro-Masters in Supply Chain Management (a sum of 5 courses and a final capstone exam):

      • SC0x: Supply Chain Analytics 

      • SC1x: Supply Chain Fundamentals 

      • SC2x: Supply Chain Design 

      • SC3x: Supply Chain Dynamics

      • SC4x: Supply Chain Technology and Systems 

      • CFx: Final Capstone Exam 

•  Miscellaneous Courses & Certificates (total of 3):

  • • AutoDesk Certified User: Fusion360

    • Fuel Tank Safety

    • Initial Human Factors

All the certificates for the mentioned courses are available on the following link : https://www.firashindawi.com/courses-certificates