Otniel-Bogdan Mercea

Currently, I am a Machine Learning Researcher at Apple, working on multimodal learning.

I earned my PhD in CS with a specialization in AI in 2025 (thesis submitted in 2024) at the Max Planck Institute for Intelligent Systems and the University of Tubingen, supervised by Prof. Zeynep Akata and Prof. Andreas Geiger within the IMPRS-IS doctoral program. I was also a guest PhD student at Helmholtz Munich and Technical University of Munich, supervised by Prof. Zeynep Akata. I earned my MSc in AI from the University of Edinburgh in 2020, with a thesis supervised by Prof. Amos Storkey, and my BEng in Computers and Information technology from Politehnica University of Timisoara in 2019, with a thesis supervised by Prof. Calin-Adrian Popa.

I applied my research expertise in industry through impactful roles, including internships at Google DeepMind (Autumn 2024), supervised by Stefano Pellegrini, Jasper Uijlings, and Cordelia Schmid, on improving SAM 2 for significant occlusion and movement scenarios; and Google Research (2023–2024), collaborating with Anurag Arnab, Alexey Gritsenko, and Cordelia Schmid on efficient adaptation of large-scale models. I also collaborated with Aleksandra Nowak, Utku Evci, and Yann Dauphin from Google DeepMind on related projects. Previously, in 2020-2021, I was a ML Researcher at Everseen, developing real-time multi-camera tracking systems.

My research focuses on improving the efficiency of deep learning through data-efficient (low-shot) and model-efficient adaptation methods for large-scale models. I also have extensive experience in multimodal learning (audio-visual, multimodal language models) and video semantic segmentation and tracking.