Yipeng Zhang

I am a forth year PhD student at the department of Electrical and Computer Engineering at University of California, Los Angeles and my advisor is Prof. Vwani Roychowdhury. Before that, I got my Master's degree at University of California San Diego and bachelor's degree at University of Wisconsin Madison, Go Badger!

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The described approach can offer novel ways to probe the representation of concepts in time-evolving dynamic behavioral tasks.

Intracranially-recorded interictal high-frequency oscillations (HFOs) have been proposed as a promising spatial biomarker of the epileptogenic zone. However, HFOs can also be recorded in the healthy brain regions, which complicates the interpretation of HFOs. The present study aimed to characterize salient features of physiological HFOs using deep learning.

This paper proposes a novel framework, On-Demand MOtion Generation (ODMO), for generating realistic and diverse long-term 3D human motion sequences conditioned only on action types with an additional capability of customization.

We constructed a deep learning-based algorithm using chronic intracranial EEG data via subdural grids from children with medication-resistant neocortical epilepsy to: (i) replicate human expert annotation of artefacts and high-frequency oscillations with or without spikes, and (ii) discover epileptogenic high-frequency oscillations by designing a novel weakly supervised model.

We propose a hybrid framework extended from traditional slate optimization to solve the conditional slate optimization problem to jointly learn to optimize for traditional ranking metrics as well as prescribed distribution criteria of documents within the slate.