Qi Zhao

Email
Telephone +49 721 608-41331
Room 165
Address Karlsruhe Institute of Technology
Institute of Information Security and Dependability
Am Fasanengarten 5, Geb. 50.34
76131 Karlsruhe, Germany
Qi Zhao
Academic Staff at the "Artificial Intelligence and Security" research group

About me

I am a doctoral student in the research group of "Artificial Intelligence and Security" headed by Prof. Wressnegger at Karlsruhe Institute of Technology (KIT). I received my Bacholer degree in 2016 at China University of Petroleum (East China). And I finished my M.Sc majored in Mechanical Engineering at Karlsruhe Institute of Technology (KIT) in August 2020. I was focusing on the study of Cognitive System, Machine Learning and Adversarial Robustness of Deep Learning.

Research Interests

  1. Adversarial Vulnerability of Deep Learning Models
  2. Defensive Methods and Model Robustness Optimization
  3. Deployment of Efficient and Robust Deep Learning Models

Publications

Two Sides of the Same Coin: Learning the Backdoor to Remove the Backdoor.
Qi Zhao and Christian Wressnegger.
Proc. of 39th Annual AAAI Conference on Artificial Intelligence (AAAI), February 2025.
Oral Presentation

Adversarially Robust Anti-Backdoor Learning.
Qi Zhao and Christian Wressnegger.
Proc. of 17th ACM Workshop on Artificial Intelligence and Security (AISEC), October 2024.

Holistic Adversarially Robust Pruning.
Qi Zhao and Christian Wressnegger.
Proc. of 11th International Conference on Learning Representations (ICLR), May 2023.

Non-Uniform Adversarially Robust Pruning.
Qi Zhao, Tim Königl and Christian Wressnegger.
Proc. of 1st International Conference on Automated Machine Learning (AutoML), July 2022.

BreakingBED: Breaking Binary and Efficient Deep Neural Networks by Adversarial Attacks.
Manoj Vemparala, Alexander Frickenstein, Nael Fasfous, Lukas Frickenstein, Qi Zhao, Sabine Kuhn, Daniel Ehrhardt, Yuankai Wu, Christian Unger, Naveen Nagaraja and Walter Stechele.
Proc. of 7th Intelligent Systems Conference (IntelliSys), September 2021.

Poster: Adversarial Robust Model Compression using In-Train Pruning.
Manoj Vemparala, Nael Fasfous, Alexander Frickenstein, Sreetama Sarkar, Qi Zhao, Sabine Kuhn, Lukas Frickenstein, Anmol Singh, Christian Unger, Naveen Nagaraja, Christian Wressnegger and Walter Stechele.
2nd Women in Machine Learning Un-Workshop (WiML), July 2021.

Adversarial Robust Model Compression using In-Train Pruning.
Manoj Vemparala, Nael Fasfous, Alexander Frickenstein, Sreetama Sarkar, Qi Zhao, Sabine Kuhn, Lukas Frickenstein, Anmol Singh, Christian Unger, Naveen Nagaraja, Christian Wressnegger and Walter Stechele.
Proc. of 3rd CVPR Workshop on Safe Artificial Intelligence for Automated Driving (SAIAD), June 2021.
Best Paper Award Runner-Up

Awards

  • CCS Distinguished Artifact Reviewer Award in 2024

Reviewing for Conferences & Journals

  • ACM Computing Surveys (CSUR) in 2024
  • The International Conference on Learning Representations (ICLR) in 2025

Artifact Evaluation Committee Memberships

  • The ACM Conference on Computer and Communications Security (CCS) in 2024

Teaching

  • Lectures: Artificial Intelligence & Security in Winter 24/25
  • Seminar: Adversarial Machine Learning in Winter from 20/21 until 23/24
  • Lectures: Security of Machine Learning in Winter 21/22 and Summer 2023
  • Lectures: Machine Learning for Security in Winter 21/22
  • Practical Course: Intelligent Data Analysis for Security (Datalab) in Winter 20/21