About

Simiao Niu is an assistant professor in the Department of Biomedical Engineering at Rutgers University. His current work focuses on wearable physiological signal monitoring systems and energy harvesting systems for biomedical applications. Prior to Rutgers, he was a hardware system engineer in the health sensing and health technologies team at Apple Inc. He received his postdoctoral training in the Department of Chemical Engineering at Stanford University under Prof. Zhenan Bao, and earned his Ph.D. in Materials Science and Engineering from the Georgia Institute of Technology under Prof. Zhong Lin Wang. His educational background also includes a master's degree in Electrical & Computer Engineering from Georgia Tech and a bachelor's degree from Tsinghua University. The Niu Lab at Rutgers aims to develop a wearable wireless bioelectronic network for continuous monitoring of human physiological signals to reduce the health burden of chronic diseases.

Research topics

• Computer Science
• Nanotechnology
• Materials science
• Engineering
• Electrical engineering
• Optoelectronics
• Artificial Intelligence
• Composite material
• Chemistry
• Biology
• Business
• Electronic engineering
• Neuroscience
• Risk analysis (engineering)
• Engineering management
• Optics
• Control engineering
• Physics
• Biochemistry
• Data science
• Systems engineering

Selected publications

• Smart Aquaculture Feeding Strategy Optimization Based on Explainable Deep Reinforcement Learning

  2026-01-23

  article1st authorCorresponding

  To address the “black box” nature and low user trust in deep reinforcement learning (DRL)-based smart feeding systems, this study proposes X-DDQN (Explainable Dueling Double Deep Q-Network), an interpretable aquaculture feeding optimization framework. X-DDQN integrates multi-source real-time sensor data into a high-dimensional state space and employs a multi-objective reward function—balancing feed conversion rate, fish growth, welfare, and water quality—to train a DDQN agent toward near-optimal feeding policies. The key innovation lies in incorporating SHAP (SHapley Additive exPlanations) for local decision attribution and CART (Classification and Regression Trees) for global rule extraction, transforming the trained model into an interpretable “IF-THEN” rule set and visual contribution maps. This enhances transparency and allows expert verification. Experimental results show that X-DDQN maintains decision-making performance comparable to standard DDQN while significantly improving interpretability and user trust. The system achieves a Feed Conversion Ratio (FCR) of 1.28 and a Water Quality Compliance (WQC) rate of 94.7 %, outperforming rule-based and fixed strategies. The extracted decision tree attains 92.5 % fidelity to the original DDQN policy. This work provides a trustworthy AI solution for smart aquaculture that harmonizes performance with explainability, supporting human-machine collaborative decision-making in agricultural intelligence.

  PublisherDOI

• Theory of nanogenerators and Maxwell’s equations for a mechano-driven system

  MRS Bulletin · 2025-02-28 · 8 citations

  article
  PublisherDOI

• <i>(Invited) </i>battery-Free Wearable Electronics for Chronic Disease Management

  ECS Meeting Abstracts · 2025-07-11

  article1st authorCorresponding

  60% of Americans live with at least one chronic disease. These diseases and their associated comorbidities are now the leading causes of death in the United States. The effective management of complex chronic diseases requires body-wide, long-term, accurate, and continuous monitoring of multiple physiological signals from wearable and implantable devices to precisely determine the pathological state. Wearable physiological signal monitoring can dramatically reduce the demand for physician visits and increase patients' engagement and treatment adherence rates. Specifically, battery-free wearables and implantable electronics reduce device volume and mechanical stiffness, significantly improving wear comfort, which is highly desirable for next-generation wearable and implantable electronics. However, battery-free wearables and implantable electronics still face many challenges, mainly wireless energy and data transfer. To address these challenges, my research has been involved in the exploration of rational system design concepts, material and device fabrication innovation, and tailored algorithms to enable smart battery-free wearables and implantable electronics targeting next-generation chronic disease management. Here, I would like to discuss two of my developed technology platforms to elaborate on the concept of battery-free wearable and implantable systems. First, I will describe an RFID-based active living bioelectronic technology platform. This technology encompasses capabilities across the biogenic (bacteria), biomechanical (starch-based hydrogels), and bioelectrical properties (battery-free biosensors and stimulators) simultaneously and show promising results in managing skin inflammation. Second, I will describe a battery-free self-powered gait monitoring device. This technology platform uses triboelectric transducers to harvest biomechanical energy from walking and running. From a self-starting and highly efficient power management circuit, we realized a self-powered sustainable gait monitoring device that can evaluate gait steadiness, perform step counting, and analyze fall risks. Overall, the developed technology platforms can assess multiple health outcomes and treatment responses to various chronic diseases. Ultimately, this technology will help reduce the burden of chronic diseases, lower medical costs, and provide a better quality of life for patients. (1) References: “Active Biointegrated Living Electronics for Managing Inflammation". Science, 2024, 384, 1023-1030.

  PublisherDOI

• Correction: Theory of nanogenerators and Maxwell’s equations for a mechano-driven system

  MRS Bulletin · 2025-03-12

  articleOpen access
  PublisherOA PDFDOI

• High-output, thermally resilient Nano-TiO₂ dielectric gel triboelectric nanogenerator for energy harvesting and reliable temperature-independent pressure sensing

  Journal of Materials Chemistry A · 2025-01-01 · 21 citations

  articleOpen access

  By doping TiO 2 nanoparticles into PVC gel, a high-output TENG was fabricated, enabling a temperature-independent pressure sensor. This sensor achieved stable sensitivity of 2.03 V kPa −1 (10–40 kPa) and 0.97 V kPa −1 (40–100 kPa) from 25 °C to 55 °C.

  PublisherOA PDFDOI

• Skin‐Integrated Wearable Electronics: A Dual‐Interface Perspective

  SmartSys · 2025-12-01 · 5 citations

  articleOpen accessSenior authorCorresponding

  ABSTRACT Skin‐integrated wearable electronics enable continuous, medical‐grade monitoring and therapy in daily life, but must balance conflicting needs related to mechanics, power, and communication. This review uses a dual‐interface approach that separates the sensor–receiver interface, which handles wireless data and energy transfer, from the sensor–skin interface, where physiological signals are converted and mechanical and biological integration occur. We first reviewed wireless connections designed for skin electronics, focusing on Bluetooth Low Energy (BLE), Radio Frequency Identification (RFID)/Near‐Field Communication (NFC) systems, and hybrid systems. Next, we examine sensor–skin interfaces ranging from mediated contact layers such as hydrogels for wearable ultrasound and soft conductive electrodes, to skin‐conformal direct‐contact methods based on structural mechanics, and ultrathin epidermal devices. Finally, we discuss cross‐interface coupling, emphasizing how antenna layouts, power budgets, and body‐induced RF effects limit mechanical design, and how skin mechanics influence link reliability. We conclude by exploring opportunities in battery‐free and energy‐autonomous systems, body‐coupled communication, and integration with artificial intelligence (AI)‐enabled digital health, positioning future electronic skins as soft, networked platforms that are comfortable and reliable.

  PublisherOA PDFDOI

• Interpretable Machine Learning for Evaluating Nanogenerators’ Structural Design

  ACS Nano · 2025-04-07 · 20 citations

  articleOpen accessSenior authorCorresponding

  The limited battery life in modern mobile, wearable, and implantable electronics critically constrains their operational longevity and continuous use. Consequently, as a self-powered technology, triboelectric nanogenerators (TENGs) have emerged as a promising solution to this. Traditional approaches for evaluating TENG structural design typically require manual, repetitive, time-consuming, and high-cost finite element modeling or experiments. To overcome this bottleneck, we developed a fully automated platform that leverages machine learning (ML) techniques. Our framework contains an artificial neuron network-based surrogate model that can provide accurate and reliable performance predictions for any structural parameters and a TreeSHAP interpretable ML model that can generate precise global and local insights for TENG structural parameters. Our platform shows broad adaptability to multiple TENG structures. In summary, our platform is an integrated platform that utilizes interpretable ML techniques to solve the complex multidimensional TENG structural evaluation problem, marking a significant advancement in TENG design and supporting sustainable energy solutions in mobile electronics.

  PublisherDOI

• Closed-loop drug delivery based on epicardial electrocardiography

  Device · 2024-09-01

  articleSenior author
  PublisherDOI

• Active biointegrated living electronics for managing inflammation

  Science · 2024-05-30 · 128 citations

  articleCorresponding

  Seamless interfaces between electronic devices and biological tissues stand to revolutionize disease diagnosis and treatment. However, biological and biomechanical disparities between synthetic materials and living tissues present challenges at bioelectrical signal transduction interfaces. We introduce the active biointegrated living electronics (ABLE) platform, encompassing capabilities across the biogenic, biomechanical, and bioelectrical properties simultaneously. The living biointerface, comprising a bioelectronics layout and a Staphylococcus epidermidis –laden hydrogel composite, enables multimodal signal transduction at the microbial-mammalian nexus. The extracellular components of the living hydrogels, prepared through thermal release of naturally occurring amylose polymer chains, are viscoelastic, capable of sustaining the bacteria with high viability. Through electrophysiological recordings and wireless probing of skin electrical impedance, body temperature, and humidity, ABLE monitors microbial-driven intervention in psoriasis.

  PublisherDOI

• (Invited) Battery-Free Wearable and Implantable Electronics for Chronic Disease Management

  ECS Meeting Abstracts · 2024-08-09

  article1st authorCorresponding

  60% of Americans live with at least one chronic disease. These diseases and their associated comorbidities are now the leading causes of death in the United States. The effective management of complex chronic diseases requires body-wide, long-term, accurate, and continuous monitoring of multiple physiological signals from wearable and implantable devices to precisely determine the pathological state. Wearable physiological signal monitoring can dramatically reduce the demand for physician visits and increase patients' engagement and treatment adherence rates. Specifically, battery-free wearables and implantable electronics reduce device volume and mechanical stiffness, significantly improving wear comfort, which is highly desirable for next-generation wearable and implantable electronics. However, battery-free wearables and implantable electronics still face many challenges, mainly wireless energy and data transfer. To address these challenges, my research has been involved in the exploration of rational system design concepts, material and device fabrication innovation, and tailored algorithms to enable smart battery-free wearables and implantable electronics targeting next-generation chronic disease management. Here, I would like to discuss two of my developed technology platforms to elaborate on the concept of battery-free wearable and implantable systems. First, I will describe an RFID-based body area sensor network technology platform. This technology uses electromagnetic waves and RF antennas as energy and data transmission media and has broad applications in sleep tracking, workout monitoring, chronic wound healing, and inflammation management. Second, I will describe a triboelectric transducer-based implantable battery-free device. This technology platform uses ultrasound waves and triboelectric transducers as energy and data transmission media and has broad applications in implantable sensing. Overall, the developed technology platforms can assess multiple health outcomes and treatment responses to various chronic diseases. Ultimately, this technology will help reduce the burden of chronic diseases, lower medical costs, and provide a better quality of life for patients. (1,2,3,4) References: 1. "A Wireless Body Area Sensor Network System Based on Stretchable Passive Tags". Nature Electronics 2019, 2, 361. 2. "High-Frequency and Intrinsically Stretchable Polymer Diodes". Nature 2021, 600, 246-252. 3. "Wireless, closed-loop, smart bandage with integrated sensors and stimulators for advanced wound care and accelerated healing". Nature Biotechnology 2023, 41, 652-662. 4. “An Ultrasound-Driven Implantable Wireless Energy Harvesting System Using Triboelectric Transducer”. Matter 2022, 5, 4315-4331.

  PublisherDOI

Frequent coauthors

• Zhong Lin Wang

  Georgia Institute of Technology

  173 shared

• Long Lin

  State Grid Corporation of China (China)

  44 shared

• Sihong Wang

  University of Chicago

  30 shared

• Yunlong Zi

  University of Hong Kong

  22 shared

• Zhenan Bao

  21 shared

• Yusheng Zhou

  Lanzhou Jiaotong University

  18 shared

• Jun Chen

  18 shared

• Ying Liu

  Wenzhou University

  17 shared

Education

• Ph.D., Biomedical Engineering

  Rutgers University

Awards & honors

• Research.com 2022 Rising Star of Science Award
• Apple Special Recognition Award (Recognize Employee’s Vital…
• Clarivate Web of Science Highly Cited Researcher in the Cros…
• The 36th Japan Telecommunications Advancement Foundation Awa…
• Materials Research Society (MRS) Graduate Student Silver Awa…