Pura Travel
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Earned the President Undergraduate Research Award ($1500) to support my work on EEG/SSVEP sensing!
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Earned the President Undergraduate Research Award ($1500) to support my work on EEG/SSVEP sensing!
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Glad to share my work about non-laboratory SSVEP recognition at Georgia Tech’s Undergraduate Research Symposium!
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I’ll be start teaching the HCI session as a teaching assistant for CS 3600/ CS 6001: Artificial Intelligence taught by Dr. Thad Starner.
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I am happy to present a poster session about my research on SSVEP-based Brain-computer Interface at Non-laboratory Environment at GVU Spring Research Showcase 2023.
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I am glad to share that I will start working as a professional research assistant with Dr. Jinxing Li at Michigan State University!
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Joined SNEL! Advised by Dr. Chethan Pandarinath and Dr. Samuel R. Nason-Tomaszewski.
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Joined BrainGate Emory!
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Started my Ph.D. in Machine Learning at Georgia Tech & Emory as a trainee in the Computational Neural-Engineering Training Program (CNTP), continuing my work on intracortical BCIs with SNEL and BrainGate!
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Our paper on contrastive representation learning for neurochemical concentration prediction was accepted to the NeurIPS 2025 Workshop on Learning from Time Series for Health!
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FlexEGG, our skin-conformal electronics for wearable electrogastrography, is out in IEEE Journal on Flexible Electronics.
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AdapSkin, our age-adaptive polymeric skin electronics for better neural data and ML accuracy, is published in Device!
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New preprint: A generalizable speech neuroprosthesis. A decoder trained across six BrainGate participants, which a new user can adapt to with fewer than 200 sentences.

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At Stanford, worke with Dr Jinxing Li on FSCV image generation using MATLAB and LABView. Generated color plot on FSCV for neurotransmitter detection.

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Worked on 3-D modelling on customized fNIRS electrode placement. Proposed and attend Tsinghua SDG Hackathon at Shenzhen and Won technical innovation award

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A headband designed for EEG signal collection, loaded with customized active electrodes and OpenBCI Cyton board for signal transmission

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Preprocessing and analysis of EEG-based SSVEP signal collected from customized electrodes
research Bluetooth, Data Analysis, EEG, Electrophysiological Sensing

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A electrophysiological sensing front-end using TI electriphysiology frontend chip with bluetooth protocol enabled by nRF52 series
Yunnuo Zhang
Published in Undergraduate Thesis, Georgia Institute of Technology, 2025
A high-frequency SSVEP-based brain-computer interface designed for use outside the lab. It combines dry-contact spring-loaded electrodes with active buffering, an actively driven guard, and a driven-right-leg circuit, plus an LED stimulus flickering above the threshold of visual perception for more comfortable everyday use.
Na Dai, Abdallah Daha, Joshua Labbe, Zhengxu Tang, Yunnuo Zhang, Jinxing Li
Published in NeurIPS 2025 Workshop on Learning from Time Series for Health, 2025
A metadata-aware contrastive learning framework for predicting neurochemical concentrations from fast-scan cyclic voltammetry (FSCV). It models batch identity, scan sequence, and scan rate, and uses preference-based ranking losses to learn discriminative voltammetry features, improving accuracy over traditional baselines.
Liuxi Xing, Yulu Cai, Yunnuo Zhang, Vittorio Mottini, Linux Heller, Jinxing Li
Published in IEEE Journal on Flexible Electronics, 2025
FlexEGG is a soft, stretchable abdominal electrode array built from a hybrid conductive-polymer and silver-nanowire conductor, enabling high-fidelity electrogastrography (EGG) monitoring of gastric myoelectrical activity in clinical and real-world settings.
Electrophysiological Sensing, Wearable Electronics IEEE Xplore
Vittorio Mottini, Liuxi Xing, Yulu Cai, Charlie Meilinger, Soham Inamdar, Xiang Calvin Chen, Bahareh Tajvidi Safa, Jiaqi Wang, Yi Xing, Jack Darbonne, Zhengxu Tang, Yunnuo Zhang, Christopher H. Contag, Ruiguo Yang, Mi Zhang, Jinxing Li
Published in Device, 4(1), 2026
AdapSkin is a stretchable, bioadhesive conductive-polymer skin interface that adapts to age-related changes in skin such as dryness and wrinkles. By improving skin-electrode contact, it reduces variation in impedance, signal-to-noise ratio, and motion artifacts, yielding higher-quality surface EMG and better machine learning accuracy for neuromuscular assessment.
Electrophysiological Sensing, Machine Learning, Wearable Electronics Paper Preprint
Zachery M. Fogg, Nicholas S. Card, Maitreyee Wairagkar, Aparna Srinivasan, Tyler Singer-Clark, Xianda Hou, Elizaveta Okorokova, Hamza Peracha, Carrina Iacobacci, Tiffany Brailow, Justin Jude, Hadar Levi-Aharoni, Trung Le, Domenick Mifsud, Pranav I. Deevi, Samuel R. Nason-Tomaszewski, Anna L. Pritchard, Yunnuo Zhang, Brice Richards, Payton Bechefsky, Leigh R. Hochberg, Ziv Williams, Kiarash Shahlaie, Nicholas AuYong, Daniel B. Rubin, Chethan Pandarinath, David M. Brandman, Sergey D. Stavisky
Published in bioRxiv (preprint), 2026
A transformer-based decoder trained jointly across six intracortical speech BCI participants outperforms single-user models for every participant, cutting relative word error rate by over 50% on average. A new user can reach under 7% word error rate after finetuning on fewer than 200 sentences.
Brain-Computer Interface, Machine Learning, Speech Neuroprosthesis bioRxiv
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Undergraduate course, University 1, Department, 2014
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Workshop, University 1, Department, 2015
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