Software Engineer

• Oct 2022 - Present

Frontend Developer

• Aug 2022 - Sep 2022

Undergraduate Researcher

• Oct 2021 - Jun 2022

• Works at Creative AI Technologies Research Lab • Under supervision of Pinar Yanardag • Research Topics: Deep Learning, Generative Models, Latent Space Manipulation, 3D Face Modelling, Representation Learning • Published a novel paper on 3D face editing by manipulating the latent space of a generative network. The model is implemented by using Python, Tensorflow, and PyTorch. • Works at Creative AI Technologies Research Lab • Under supervision of Pinar Yanardag • Research Topics: Deep Learning, Generative Models, Latent Space Manipulation, 3D Face Modelling, Representation Learning • Published a novel paper on 3D face editing by manipulating the latent space of a generative network. The model is implemented by using Python, Tensorflow, and PyTorch.

Data Scientist - Artificial Intelligence Expert Team

• Oct 2021 - Mar 2022

• Implemented a monitoring system that weekly reports outcomes of the optimization model by using Python, SQL, and Pandas. It is used to ensure that the system is working correctly. • Implemented a monitoring system that weekly reports outcomes of the optimization model by using Python, SQL, and Pandas. It is used to ensure that the system is working correctly.

Deep Learning Researcher Intern

• Jul 2021 - Sep 2021

• Worked at Artificial Intelligence Research Group • Under supervision of Prof. Michele Lombardi • Research Topics: Deep Learning, Semi-supervised Learning, Anomaly Detection • Performed preliminary analysis on a dataset collected from Marconi100 supercomputer. • Proposed a neural network architecture to detect anomalies in a semi-supervised way by using Python and Tensorflow. The proposed architecture increased F2- score by 30%. • Worked at Artificial Intelligence Research Group • Under supervision of Prof. Michele Lombardi • Research Topics: Deep Learning, Semi-supervised Learning, Anomaly Detection • Performed preliminary analysis on a dataset collected from Marconi100 supercomputer. • Proposed a neural network architecture to detect anomalies in a semi-supervised way by using Python and Tensorflow. The proposed architecture increased F2- score by 30%.

Software Engineer Intern

• Sep 2020 - Nov 2020

• Developed a full-stack web application to help customers book photo sessions by using React, Redux, Node.js, and MongoDB. The application is used by the company to attract more customers. • Developed a web application to help photographers upload their pictures to the cloud in an efficient way by using React, Node.js, Docker, Kubernetes, and AWS. • Developed a full-stack web application to help customers book photo sessions by using React, Redux, Node.js, and MongoDB. The application is used by the company to attract more customers. • Developed a web application to help photographers upload their pictures to the cloud in an efficient way by using React, Node.js, Docker, Kubernetes, and AWS.

Artificial Intelligence Researcher Intern

• Jul 2020 - Sep 2020

• Presented novel DL models on Image Enhancement weekly to the AI team. • Developed a product that connects novel deep learning models to enhance the quality of an image by using Python and Tensorflow. The product is used by the company to automate photograph serving procedures. • Developed product with AI team on enhancing the quality of an image and automatic retouching systems • Presented novel DL models on Image Enhancement weekly to the AI team. • Developed a product that connects novel deep learning models to enhance the quality of an image by using Python and Tensorflow. The product is used by the company to automate photograph serving procedures. • Developed product with AI team on enhancing the quality of an image and automatic retouching systems

Software Engineer

• Feb 2020 - Jun 2020

• Implemented front-end pages to visualize the publications of faculty members by using HTML/CSS and JavaScript. The application is used in production. • Implemented front-end pages to visualize the publications of faculty members by using HTML/CSS and JavaScript. The application is used in production.