C Diagonal 34, Edif B, Piso 2, No. 3, 08290 Cerdanyola del Vales, Barcelona, Spain https://www.linkedin.com/in/rawi707/
Work in computer vision and data science R&D&I projects Education
Shanghai Jiaotong University 2002 Pattern Recognition and Intelligent Systems (PHD) Developed an illumination invariant model to recognize color image textures, fast algorithms to calculate geometric and Zernike image moments, and published a few articles in these areas. Skills
Full-stack professional programmer using PyTorch, Python, C/C++, Java, Matlab, GPU computing, and SQLAlchemy
Deep learning, Multi-voxel and multimodal analysis, scene, text and handwriting recognition, natural language processing, evolutionary algorithms, among others.
Latex, MSOffice, LibreOffice, Eclipse, VisualStudio, Photoshop and InkSpac, Linux, Windows, Github and Gitkraken.
Soft-thinking and Interpersonal skills: analytical, creative thinking, critical thinking, deductive reasoning, and problem solving, time management and orderliness, team builder and teamwork enthusiastic, entrepreneurial, full-stack and multitasking ability. Experience
Research Fellow February 2018 – present; Computer Vision Center, Barcelona, Spain
Develop deep learning methods and software in areas related to text, images and videos
Develop confidence and probabilistic methods not only for computer vision, but also for data science in general
Supervise MSc and PhD students
Assist in organizing workshops and conferences
Present seminars in the area of computer vision, image processing and pattern recognition
Submit project proposals to national and international funding instruments
Attend training workshops of scientific and non-scientific nature
Publish the work as open-source software, e.g., https://github.com/morawi/MLPHOC Previous Experience
University of Aveiro (PT) Sept 2015 –Dec 2017; University of Coimbra(PT) Oct 2013 –Jul 2015; University of Aveiro (PT) Oct 2008 –Oct 2013, University of Jordan (JD) Oct 2002 –Jul 2006 Used Machine Learning, neural networks and statistical approaches to analyze quite a few image types and other data including natural scenes and objects images, 2D and 4D medical images, underwater sonar images, biomedical data, networking data, music data, and genomics data, etc., in addition to grant attraction and team building, taught and supervised students.