Yuanyuan(Zoey) Zhou

Portrait

yzhou114@umd.edu



About Me

I am a fifth-year PhD candidate in Mechanical Engineering at the University of Maryland, College Park advised by Professor Jin-Oh Hahn. Previously, I completed my BS in Energy and Environment System Engineering at Zhejiang University and my MS in Mechanical Engineering at Tennessee State University. And I have worked as Mechanical Engineer in manufacturing industry for 4 years before starting my PhD journey.

My research interests span multimodal physiological signal analysis, state-space modeling, closed-loop control, and machine learning, with hands-on experience in 3D modeling and manufacturing.

News

Research Highlights

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PAPER

We investigated (i) if blood volume decompensation status (BVDS) can be trend-tracked by hemodynamic parameters, and (ii) if hemodynamic parameters capable of trend-tracking BVDS can be trend-tracked by the physio-markers derived from the physiological signals measured using wearable sensors. In 9 pigs undergoing controlled hemorrhage and blood transfusion, we derived non-invasive physio-markers. Then, we determined the best hemodynamic parameters to trend-track BVDS by comparing their correlation with BVDS. Finally, we investigated the feasibility of trend-tracking BVDS via non-invasive physio-markers in terms of their correlation with hemodynamic parameters as well as BVDS.

Parham Rezaei*, Sina Masoumi Shahrbabak*, John Vandenberge*, Yuanyuan Zhou*, Demet Tangolar, Nancy Kim, Douglas Tran, Randy Perez, Donghyeon Kim, Nicholas Burch, Zeineb Bouzid, Rayan Bahrami, Jacob P Kimball, Chang-Sei Kim, Zhongjun J Wu, Omer T. Inan, Jin-Oh Hahn


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PAPER

To enable just-in-time mitigation of acute mental stress, we developed a data-driven virtual experiment generator (VEG) that models the dynamic interactions between transcutaneous median nerve stimulation (TMNS), acute stressors, and cardiovascular arousal. Using data from 23 experiments, the VEG accurately replicated and generated realistic cardiovascular responses, demonstrating its potential as a virtual platform for developing closed-loop TMNS control strategies.

Yuanyuan Zhou, Sina Masoumi Shahrbabak, Parham Rezaei, Rayan Bahrami, Farhan N. Rahman, Jesus Antonio Sanchez-Perez, Asim H. Gazi, Omer T. Inan, Jin-Oh Hahn


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PAPER

This paper comparatively investigated Median nerve stimulation (MNS) and auricular vagus nerve stimulation (AVNS) in terms of efficacy and mechanism of action in the context of mitigating acute stress-induced arousal. The findings may support future device development for addressing acute mental stress-induced arousal through MNS or AVNS, and they pave the way toward a better understanding of how to quantify the efficacy of such interventions.

Yuanyuan Zhou, Sina Masoumi Shahrbabak, Rayan Bahrami, Farhan N. Rahman, Jesus Antonio Sanchez-Perez, Asim H. Gazi, Omer T. Inan, Jin-Oh Hahn


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PAPER

We developed a novel synthetic multi-modal variable capable of capturing cardiovascular responses to acute mental stress and the stress-mitigating effect of transcutaneous median nerve stimulation (TMNS), as an initial step toward the overarching goal of enabling closed-loop controlled mitigation of the physiological response to acute mental stress.

Yuanyuan Zhou, Jesse D. Parreira, Sina Masoumi Shahrbabak, Jesus Antonio Sanchez-Perez, Farhan N. Rahman, Asim H. Gazi, Omer T. Inan, Jin-Oh Hahn


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PAPER

Continuously track acute mental stress by integrating multimodal physiological signals (ECG, PPG, SCG, BCG, EDA, respiration) to derive interpretable digital signatures and compute stress probabilities via collective inference, significantly outperforming traditional univariate approaches in accuracy and confidence.

Yuanyuan Zhou, Azin S. Mousavi, Yekanth R. Chalumuri, Jesse D. Parreira, Mihir Modak, Jesus Antonio Sanchez-Perez, Asim H. Gazi, Omer T. Inan, Jin-Oh Hahn


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PAPER

Develop analytical formulas which can serve as quantitative guidelines for the selection of the sampling rate for the electrocardiogram (ECG) required to calculate heart rate (HR) and heart rate variability (HRV) with a desired level of accuracy, for the first time.

Yuanyuan Zhou, Bryndan Lindsey, Samantha Snyder, Elizabeth Bell, Lucy Reider, Michael Vignos, Eyal Bar-Kochba, Azin Mousavi, Jesse D. Parreira, Casey Hanley, Jae Kun Shim, Jin-Oh Hahn


* denotes equal contribution.

Honors and Awards

Patent

Certificate

Activities

P.S.