Jovan Cicvarić

Machine Learning Engineer

• • Led development and deployment of computer vision systems for construction waste classification, anomaly detection, object detection, and segmentation.
• • Deployed production ML models to edge devices across dozens of industrial sites, processing tens of thousands of images and videos daily for real-time construction waste analysis.
• • Designed end-to-end ML pipelines covering data collection, annotation of 80K+ images, training, validation, deployment, and monitoring with MLflow and Triton Inference Server.
• • Led development of a plastics classification model combining multispectral vision and LLM-based methods, including RGB-vs-multispectral performance analysis.
• • Coordinated cross-functional work across ML engineering, data annotation, embedded development, backend/API, and customer-facing teams.

• • Led full-stack development of the scientific paper recommendation platform used by 30K+ researchers.
• • 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).

Student Assistant

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

M.Sc. Machine Learning

GPA 1.1 (best: 1.0) · Graduated with distinction

Deep Learning · Computer Vision · Reinforcement Learning · Statistical ML

B.Sc. Applied Mathematics & Physics

GPA 4.66 (best: 5.0)

Calculus · Informatics · Linear Algebra · Differential Equations · Computational Mathematics · Physics

Scholar Inbox: Personalized Paper Recommendations for Scientists

ACL Demo, 2025

M. Flicke, G. Angrabeit, M. Iyengar, V. Protsenko, I. Shakun, J. Cicvaric, B. Kargi, H. He, L. Schuler, L. Scholz, K. Agnihotri, Y. Cao, A. Geiger.

Generative Dataset Distillation: A New Hope?

Workshop on the Dataset Distillation Challenge, ECCV 2024

M. Schneider*, J. Cicvaric*, A. Sauer, A. Geiger, K. Chitta.

Generative Dataset Distillation

• • Master Thesis under the supervision of Prof. Andreas Geiger and Kashyap Chitta.
• • Introduced new approaches combining dataset distillation and generative modelling.
• • Worked with ImageNet-1k, CIFAR10/100, StyleGAN2 and StyleGAN-XL.
• • Used generative dataset distillation for imitation learning on CarRacing env — achieved 80+ of original score with just 1 image per class.

Crypto News Telegram Bot

• • Implemented and deployed Telegram Bot for receiving crypto news from Twitter, Discord and CoinMarket.
• • Actively used by 250+ people and providing access to 60+ crypto-related projects.

Laser-hockey RL

• • Implemented DDPG and TD3 agents for laser-hockey environment.
• • Ranked 3rd out of 70+ participants in a tournament by Autonomous Learning Group @ MPI-IS.

CarRacing IL & RL

• • Implemented DQN, Imitation Learning and Modular pipeline with geometric controller for CarRacing environment.
• • Placed 3rd overall with 35+ participants organized by Autonomous Vision Group @ Uni Tübingen.