Research Intern, Trustworthy and Adversarial Computing Lab, Aalto University
Helsinki, Finland
I am a research intern at the Trustworthy and Adversarial Computing Lab at Aalto University, working on adversarial examples for cyber-physical vision systems: attacks realized in the physical world — printed patches, modified objects, AR/VR — rather than purely digital perturbations. The work is gradient-based optimization of attacks on deep computer vision models under physical constraints.
Before that I spent a year with the Visual AI System Research team at Nokia Technologies and Aalto University, on super-resolution post-filters for Versatile Video Coding, low-rank adaptation for finetuning, and quantization-aware training. My MSc thesis, written at the Signal Analysis and Machine Intelligence group in Tampere, recast joint compression–segmentation training as constrained optimization over the rate–quality–performance trade-off.
Parts of this work have gone into standardization: two contributions to MPEG and JVET, one of which was adopted into the Common Test Conditions used to develop the Video Coding for Machines standard.
CASR: Content-Adaptive Neural Super-Resolution Post-Filter for Versatile Video Coding via Low-Rank Overfitting
IEEE International Workshop on Multimedia Signal Processing (MMSP), Istanbul, Turkey, 22–24 September 2026.
Evaluating the Emerging MPEG Video Coding for Machines in Semantic Segmentation
IEEE International Symposium on Multimedia (ISM), 2025, pp. 141–148. Candidate for Best Paper Award.
Estimating Intra- and Inter-Subject Oxygen Consumption in Outdoor Human Gait Using Multiple Neural Network Approaches
PLOS ONE 19(9):e0303317, 2024. † equal contribution.
Neural Network Super Resolution as Postfilter with Content Adaptation
Document JVET-AP0178-v1, Joint Video Experts Team, 42nd meeting, April 2026. ITU-T SG21 WP3/21 and ISO/IEC JTC 1/SC 29.
Semantic Segmentation for VCM with PandaSet
Document m67677, ISO/IEC JTC 1/SC 29/WG 4 (MPEG Video Coding), April 2024. Adopted into the Common Test Conditions used to develop the Video Coding for Machines standard.
Trustworthy and Adversarial Computing Lab, Aalto University, Finland
Visual AI System Research team, Nokia Technologies & Aalto University, Finland
Nordic Inertial, Finland (part-time)
Video Coding for Machines project, Nokia Technologies & Tampere University, Finland
GaitMaven project for gait analysis, Tampere University, Finland
Tampere University, Finland
Tampere University, Finland
