Novi Sad, Vojvodina, Serbia
♠ Good knowledge of machine learning and data science techniques. ♠ Experience in image processing and computer vision. ♠ Writing modular, clear and readable code. ♠ Math and physics enthusiast. ♠ Created several hobby game projects using Unreal Engine. ♠ BSc in Biomedical Engineering, MSc in Signal Processing. ♠ Super power: can count 32 different numbers using one hand. ♠ ✔ GitHub: https://github.com/AleksandarPav/ ✔ Game development: https://aleksandarpftn.wixsite.com/aleksandarpavlovic ✉ Email: [email protected] Technologies: ★ Python (+ numpy, pandas, pytest, Tensorflow, PyTotch, scikit-learn...) ★ SQL ★ Plotly, Matplotlib ★ Dash ★ Git ★ GCP (Google Cloud Platform) ★ Jira ★ PyCharm ★ Jupyter Notebook ★ C++ ★ Visual Studio ★ Unreal Engine 4 ★ Matlab Student and player of funk - jazz - pop - rock.
Supply chain models and optimization.
⌨ Using Google's Document AI for invoice processing. ⌨ Setting the workflow for document processing on GCP (Google Cloud Platform). ⌨ Automatic extraction of fields of interest from various types of invoices. ⌨ Customizing and uptraining Google's default invoice processor. ⌨ Incorporating Human-In-The-Loop where confidence score of the AI processor is below some predefined threshold.
⌨ General market research. ⌨ Research and analysis of loyalty members' habits. ⌨ ML modeling for sales forecast.
⌨ Management of Horizon CrackSense project on VizLore side. ⌨ Responsible for overseeing the end-to-end execution of the VizLore's solutions in a timely manner. ⌨ Ensuring that all of the internal activities and decisions made with other partners are well documented.
⌨ Writing both production ready and proof-of-concept code in Python. ⌨ Leading and mentoring a team of stipendists. ⌨ Successfully guiding and empowering individuals to excel in their roles, while promoting professional growth and development. ⌨ Leveraging effective communication to align team objectives with organizational goals, ensuring achievement of key milestones. ⌨ Developed a data dashboard solution used in a production environment, with data acquired based on user-selected filter parameters. ⌨ Collaborating with backend by using APIs to get data from the MongoDB database. ⌨ Experience with Federated Learning. ⌨ Experience with Variational Autoencoders. ⌨ Participant in Horizon Dedicat 6G. ⌨ Participant in Horizon CrackSense.
⌨ Computer vision algorithm for depth estimation. ⌨ Algorithm for performance evaluation of the team's camera component.