TRAILBLAZERS Understanding the properties of novel quantum materials is crucial for advancing fundamental condensed matter physics and developing practical devices in fields such as quantum sensing, ultra-fast communications, and green computing. However, this pursuit remains a significant challenge.
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Asst Prof Denis Bandurin NUS Presidential Young Professor (2022) NUS Materials Science & Engineering
For his contributions to advancing science and technology, Asst Prof Bandurin was included in the 2023 edition of the MIT Technology Review Innovators Under 35 (Asia Pacific).
The commercialisation of text-to-image diffusion models (DMs) such as those used in AI-driven image generation raises significant copyright concerns. Despite efforts to protect DMs from copyright issues, effective solutions have yet to be implemented.
02 Asst Prof Kenji Kawaguchi NUS Presidential Young Professor (2021) NUS Computer Science 33
Asst Prof Denis Bandurin's lab explores novel materials with properties governed by strong interactions among charge carriers and non-trivial band topology. His group has developed a multi-messenger measurement system to investigate the electronic, thermal, and magnetic properties of these materials using terahertz and infrared excitation across a wide range of temperatures and under strong magnetic fields. By applying this approach and using innovative nanofabrication methods, they discovered an anomalous change in the conductivity of interacting electron fluids when exposed to technologically challenging terahertz radiation. This phenomenon could be used to design ultrafast detectors, with potential applications in the navigation of self-driving vehicles and beyond 5G communications.
Looking to address this issue is Asst Prof Kenji Kawaguchi. He developed SilentBadDiffusion to show how easily these models could be manipulated to produce copyrighted images without access to the training process. By inserting copyrighted information and text references into the training data, the AI can be tricked into generating these images when prompted with specific text. Experiments show that DMs trained with just a 0.20% poisoning ratio can produce copyrighted images when prompted, with more sophisticated DMs being more vulnerable. This exposes critical flaws in current copyright protections and highlight the need for increased vigilance to prevent the misuse of DMs. Asst Prof Kawaguchi hopes his work can bridge the gap between theoretical research and real-world applications, ensuring that AI models remain robust and efficient.