Brian Pulfer
Hey there, this is Brian! 👋
I am a Machine Learning practitioner and enthusiast. I am currently a Ph.D. student in Machine Learning, with a focus on anomaly detection and adversarial attacks, at the University of Geneva, Switzerland 🇨🇭. Here's my CV. This is my personal portfolio, where I publish updates on my career, projects, publications and more. Hope you enjoy it!
News 🗞️
July 2024
📃 Our paper Robustness Tokens: Towards Adversarial Robustness for Transformers (deep double blind) has been accepted for publication at the European Conference on Computer Vision (ECCV) 2024..
September 2023
🥇 My team and I won the Migros workshop at the HackZurich 2023 hackathon. Read the blog post.
September 2023
📃 Our work Weather4cast at NeurIPS 2022: Super-Resolution Rain Movie Prediction under Spatio-temporal Shifts has been accepted for publication in Proceedings of Machine Learning Research.
January 2023
📃 Our work Model vs System Level Testing of Autonomous Driving Systems: A Replication and Extension Study has been accepted for publication in Empirical Software Engineering.
December 2022
🥉 Our work Solving the Weather4cast Challenge via Visual Transformers for 3D Images got us the third place in the 2022 NeurIPS Weather4cast competition workshop.
August 2022
📃 Our work Anomaly localization for copy detection patterns through print estimations was accepted for publication in the IEEE International Workshop on Information Forensics & Security.
June 2022
📃 Our work Authentication of Copy Detection Patterns under Machine Learning Attacks: A Supervised Approach was accepted for publication in the 29th IEEE International Conference on Image Processing (ICIP).
May 2022
🥇 I won the best presentation award for the 2022 edition of the SwissEngineering Award Foundation.
December 2021
📃 Our work Mind the Gap! A Study on the Transferability of Virtual vs Physical-world Testing of Autonomous Driving Systems was accepted for publication in the IEEE Transactions of Software Engineering (TSE).
November 2021
👥 I Joined the Stochastic Information Processing (SIP) group of the University of Geneva in quality of Ph.D. Student in Machine Learning.