Jovan Cicvarić

Machine Learning Engineer

• • Built most of the company’s ML infrastructure from the ground up, covering data collection and preprocessing, annotation workflows, training pipelines, experiment tracking, evaluation, model deployment, and production inference.
• • Developed computer vision systems for classification, anomaly detection, object detection, and segmentation using datasets with 400K+ annotated images.
• • Deployed models across 30+ industrial sites processing 800K+ frames per day, optimizing edge inference with ONNX and TensorRT and introducing NVIDIA Triton Inference Server for shared GPU inference.
• • Led development of a plastics classification system using multispectral vision, including analysis of RGB versus multispectral performance.
• • 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.
• • Developed backend and frontend systems and the personalized recommendation pipeline over a corpus of approximately 3M 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 System Demonstrations, 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.