Jovan Cicvarić

Optocycle GmbH, Tübingen

February 2024 - present | Machine Learning Engineer

• Developed and deployed a computer vision model for construction waste classification, achieving high accuracy and improving sorting efficiency
• Engineered an object size estimation model using monocular and stereo camera inputs, successfully identifying key objects in a majority of test cases.
• Designed and implemented the full data labelling pipeline for 80K+ images, and communicated with experts and customers to define annotation requirements.
• Led the development of a novel plastics classification model by combining multispectral vision with an LLM-based approach, utilizing a newly created dataset from custom multispectral sensors.
• Conducted comparative studies on classification performance that demonstrated the superior performance of RGB data over multispectral data for this specific task.
• Developed training tracking and model deployment pipeline using MLFlow and Triton Inference Server.
• Tech stack: Python, PyTorch, Triton Inference, MLflow, Kubernetes, Docker, RabbitMQ, Helm Charts, MongoDB, Optuna, NumPy, OpenCV, Poetry.

Autonomous Vision Group, Tübingen

February 2024 – present | Research Engineer (part-time)

June 2023 – February 2024 | Research Engineer

June 2021 – May. 2023 | Research Assistant

• Led full-stack development of the scientific paper recommendation system, Scholar Inbox, scaling the platform to support over 20K new users.
• Engineered the personalized recommendation system, trained models on user votes against a database of 3 million scientific articles.
• Optimized SQL queries and the embedding storage system, achieving a 5x improvement in latency and memory usage.
• Co-authored the creation of a dataset of abstract annotations (2425 sub-sentence labels within 691 abstracts).
• Tech stack: Flask, React, PostgreSQL, Python, PyTorch, Pandas, Celery, NLP, NumPy scikit-learn.

Moscow Institute of Physics and Technology, Moscow

December 2019 - April 2020 | Student Assistant

• Assisted in preparatory university courses in Mathematics and Physics for 10th-grade students.
• Checked homework, gave personal reviews and personalized study guidance.

University of Tübingen

M.Sc. Machine Learning

GPA 1.1 (best: 1.0), Graduated with distinction

November 2020 – May 2023

Courses: Deep Learning, Computer Vision, Reinforcement Learning, Statistical ML

Moscow Institute of Physics and Technology

B.Sc. Applied Mathematics & Physics

GPA 4.66 (best: 5.0)

September 2016 – July 2020

Courses: Calculus, Informatics, Linear Algebra, Differential Equations, Computational Mathematics, Physics.

2018 | 2X Scholarship from the Foundation of Developing Innovational Education

Received a scholarship twice for being in the top 5% of students at MIPT.