I'm pursuing a PhD in Materials Science and hold dual Master's degrees in Electrical and Computer Engineering, specializing in Computer Vision.
Additionally, I'm enrolled in a Graduate Certificate program in Computational Neuroscience at the University of Michigan, Ann Arbor.
My research interests lie in neuromorphic computing, probabilistic computing, and quantum computing.
The primary focus of my PhD work involves exploring materials science to develop hardware for neuromorphic computing applications. Specifically, I have conducted research on oxygen transport in memristive devices and the development of high-temperature electrochemical random-access memory.
Thermodynamic Origin of Nonvolatility in Resistive Memory Jingxian Li, Anirudh Appachar, Sabrina L. Peczonczyk, Elisa T. Harrison, Brianna Roest, Anton V. Ievlev, Ryan Hood, Sangmin Yoo, Kai Sun, A. Alec Talin, Wei D. Lu, Suhas Kumar, Wenhao Sun, Yiyang Li*
Under Review, 2022
We reveal that the formation and stability of conductive filaments crucially depend on the stability of the amorphous oxygen-rich and oxygen-poor compounds, which undergo composition phase separation.
Our study employs the electrochemical migration and thermodynamic relaxation of silver nanoclusters within dielectric materials to accurately mimic the dynamic processes of synapses and neurons in biological systems.
We report a systematic study on the analog switching of bilayer oxide based memristive synapses and show that transition metal oxides with rich intermediate phases, are able to provide larger number of conductance states compared with oxides with few intermediate phases.
We synthesized a novel phosphorescent dye, Cz-C8-FIrpic, which effectively inhibits the phase aggregation of FIrpic units. Devices incorporating Cz-C8-FIrpic exhibited approximately a 15% enhancement in performance compared to the control devices reliant on FIrpic.
We develop two learning based computer vision systems, based of the Unet++ and Segment Anything models, to automatically identify and label the phase fractions of common constituents inside steels using semantic segmentation approaches.
We explore the challenge of generating 3D models from single or sparse-view 2D images that use advanced neural architectures and techniques like 3D Gaussian splatting for detailed model generation. We provide a series of evaluations that demonstrate significant advances in generative models that bridge the gap between 2D inputs and 3D outputs.