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Schedule Interview NowMy journey at Softaims has been defined by curiosity, growth, and collaboration. I’ve always believed that good software is not just built—it’s carefully shaped through understanding, exploration, and iteration. Every project I’ve worked on has taught me something new about how to balance simplicity with depth, and efficiency with creativity. At its core, my work revolves around helping businesses and people achieve more through thoughtful technology. I’ve learned that the most successful projects come from teams that communicate openly and stay adaptable. At Softaims, I’ve had the opportunity to work alongside professionals who challenge assumptions, share knowledge generously, and inspire continuous improvement. I take pride in focusing on the fundamentals—clarity in logic, consistency in design, and empathy in execution. Software is more than a set of features; it’s a reflection of how we think about problems and how we choose to solve them. By maintaining this perspective, I aim to build solutions that are not only effective today but also flexible enough to support the challenges of tomorrow. The culture at Softaims promotes learning as an ongoing process. Every new project feels like a step forward, both personally and professionally. I see each challenge as a chance to refine my skills and contribute to the shared vision of building technology that genuinely improves lives.
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6 years
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In RSNA Bone Age Challenge 16Bit competition, I developed a deep learning model for bone age prediction from X-Ray images of hands.
Mission is to develop a tool for artists by using specific datasets (E.g.Rose, Daisy, Tulip, etc.). The purpose of this tool is to inspire the artists from a computer vision perspective. We trained pix2pix model with Best Artworks of All Time dataset that is a collection of artworks of the 50 most influential artists of all time. The story behind this dataset is a challenge between a man and his girlfriend, they are challenging themselves like who is the best at guessing the artist behind an artwork. Then the man decides to use the power of machine learning to defeat his girlfriend and creates this dataset by scraping the internet.
During my internship, firstly I learned the Julia basics and I really love Julia language in many perspectives then I learned the Knet.jl, then I learned the ONNX then I worked on implementing ONNX operators to Knet.jl. In this way, you will be able to run an onnx file produced in any framework with Knet.jl.
Bachelor of Engineering (BEng) in Computer engineering
2016-01-01-2021-01-01
Bachelor's degree in Computer science
2017-01-01-2021-01-01