Upcoming Talks

Conventional imaging systems face inherent dimensionality and visibility limits, primarily because image sensors are typically two-dimensional, and light tends to diffuse on rough surfaces or scatter within complex media. In this talk, I will reframe imaging systems through the lens of optical encoding and neural decoding, presenting my key contributions aimed at transcending the traditional limits of dimensionality and visibility. The idea is modelling the forward physical process and iteratively optimizing it with deep denoisers as visual priors, where eventually the priors are physics-informed. First, I introduce Privacy Dual Imaging, which reveals the privacy risk that ambient light sensors embedded in most smart devices could capture images of the scene in front of the screen. This idea of seeing the invisible from subtle intensity fluctuations is inspired by George Orwell’s novel 1984, wherein Big Brother is watching you through a two-way telescreen, and it closely relates to incoherent lensless imaging and non-line-of-sight imaging. Second, I present Snapshot Compressive Imaging, which encodes multiple temporal, spectral, or angular frames into a single measurement captured by a standard two-dimensional sensor. By learning high-dimensional visual priors from image or video data, we can efficiently reconstruct the original higher-dimensional data cube at scale. Lastly, I show that large AI models, particularly diffusion models, can serve as generic visual priors for both cases and beyond. I aim to push the boundaries of imaging and sensing within relevant domains of AI for science and healthcare (with an example).

Yang Liu is a final-year PhD student working on Computational Imaging, Generative AI, and AI for Health at MIT EECS and CSAIL with Prof. Fredo Durand. He is excited about seeing, making, and connecting (almost) anything. He emphasizes the co-design of AI algorithms and hardware systems for imaging and sensing beyond the native capability of visual sensors. His work has been published on Nature Materials, Science Advances, IEEE TPAMI, CVPR, and Photonics Research; featured on Forbes, WIRED, Fox News, and Ars Technica; contributed to a W3C working draft. He is an MIT Presidential Fellow and a Takeda Fellow.