About

Suyi Li is an Associate Professor in the Department of Mechanical Engineering at Virginia Tech, holding the John R. Jones III Faculty Fellowship since 2022. His research interests include bio-inspired engineering, materials design, robotics, smart materials, soft robotics, vibration research, and the development of intelligent robots and functional structures. His work explores the interplay between geometry, mechanics, actuation, and computation to pioneer new paradigms in these fields. Prior to his current position, he was an Assistant Professor at Clemson University from 2016 to 2022 and a Postdoctoral Research Fellow at the University of Michigan, Ann Arbor, from 2014 to 2015. Dr. Li has received numerous awards, including the 2024 Dean’s Awards of Excellence – Faculty Fellow at Virginia Tech, the 2022 C.D. Mote Jr Early Career Award from ASME Design Engineering Division, and the 2021 Gary Anderson Early Achievement Award from ASME Aerospace Division. He earned his Ph.D. in Mechanical Engineering from the University of Michigan in 2014, a Master’s degree from Pennsylvania State University in 2008, and a Bachelor’s degree in Mechanical Engineering from the University of Michigan in 2006.

Research topics

• Computer Science
• Artificial Intelligence
• Computer Security
• Machine Learning
• Engineering
• Structural engineering
• Computer network
• Control engineering
• Physics

Selected publications

• Interdisciplinary Workshop on Mechanical Intelligence: Summary Report

  arXiv (Cornell University) · 2026-03-25

  articleOpen access

  This report provides a summary of the outcomes of the Interdisciplinary Workshop on Mechanical Intelligence held in 2024. Mechanical Intelligence (MI) represents the phenomenon that novel structural features of material/biological/robotic systems can encode intelligence through responsiveness, adaptivity, memory, and learning in the mechanical structure itself. This is in contrast to computational intelligence, wherein the intelligence functions occur through electrical signaling and computer code. The two-day workshop was held at NSF headquarters on May 30-31 and included 38 invited academic researcher participants, and 8 program officers from the NSF. The workshop was structured around active small and large group discussions in groups of 4-5 and 9-10 with the goal of addressing topical questions on MI. Working groups entered notes into shared presentation slides for each discussion session and presented their outcomes in a final presentation on the last day. Here we summarize the overall outcomes of the workshop.

  PublisherOA PDF

• FlashPS: Efficient Generative Image Editing with Mask-aware Caching and Scheduling

  2026-04-24

  articleOpen access

  Generative image editing using diffusion models has become a prevalent application in today's AI cloud services. In production environments, image editing typically involves a mask that specifies the regions of an image template to be edited. The use of mask provides direct control over the editing process and introduces sparsity in the model inference. In this paper, we present FlashPS, a system that efficiently serves image editing requests. The key insight behind FlashPS is that image editing only modifies the masked regions of image templates, while preserving the original content in the unmasked areas. Driven by this insight, FlashPS judiciously skips redundant computations associated with the unmask areas by reusing cached intermediate activations from previous inferences. To mitigate the high cache loading overhead, FlashPS employs a bubble-free pipeline scheme that overlaps computation with cache loading. Additionally, to reduce queuing latency in online serving while improving the GPU utilization, FlashPS proposes a novel continuous batching strategy for diffusion model serving, allowing newly arrived requests to join the running batch in just one step of denoising computation, without waiting for the entire batch to complete. As heterogenous masks induce imbalanced load, FlashPS also develops a load balancing strategy that takes into account the loads of both computation and cache loading. Collectively, FlashPS outperforms state-of-the-art diffusion serving systems for image editing, achieving up to 3× higher throughput and reducing average request latency by up to 14.7× while ensuring image quality.

  PublisherDOI

• Interdisciplinary Workshop on Mechanical Intelligence: Summary Report

  arXiv (Cornell University) · 2026-03-25

  preprintOpen access

  This report provides a summary of the outcomes of the Interdisciplinary Workshop on Mechanical Intelligence held in 2024. Mechanical Intelligence (MI) represents the phenomenon that novel structural features of material/biological/robotic systems can encode intelligence through responsiveness, adaptivity, memory, and learning in the mechanical structure itself. This is in contrast to computational intelligence, wherein the intelligence functions occur through electrical signaling and computer code. The two-day workshop was held at NSF headquarters on May 30-31 and included 38 invited academic researcher participants, and 8 program officers from the NSF. The workshop was structured around active small and large group discussions in groups of 4-5 and 9-10 with the goal of addressing topical questions on MI. Working groups entered notes into shared presentation slides for each discussion session and presented their outcomes in a final presentation on the last day. Here we summarize the overall outcomes of the workshop.

  PublisherDOI

• Embodied multi-modal sensing with a soft modular arm powered by physical reservoir computing

  2025-10-19 · 1 citations

  articleSenior author

  Soft robots have become increasingly popular for complex manipulation tasks requiring gentle and safe contact. However, their softness makes accurate control challenging, and high-fidelity sensing is a prerequisite to adequate control performance. To this end, many flexible and embedded sensors have been created over the past decade, but they inevitably increase the robot’s complexity and stiffness. This study demonstrates a novel approach that uses simple bending strain gauges embedded inside a modular arm to extract complex information regarding its deformation and working conditions. The core idea is based on physical reservoir computing (PRC): A soft body’s rich nonlinear dynamic responses, captured by the inter-connected bending sensor network, could be utilized for complex multi-modal sensing with a simple linear regression algorithm. Our results show that the soft modular arm reservoir can accurately predict body posture (bending angle), estimate payload weight, determine payload orientation, and even differentiate two payloads with only minimal difference in weight — all using minimal digital computing power.

  PublisherDOI

• High-precision fluidic Kirigami morphing surface for ultrasonic holographic lensing and haptic interfacing

  Extreme Mechanics Letters · 2025-11-20

  articleSenior authorCorresponding
  PublisherDOI

• Hyper Yoshimura: How a slight tweak on a classical folding pattern unleashes meta-stability for deployable robots

  ArXiv.org · 2025-05-15

  preprintOpen accessSenior author

  Deployable structures inspired by origami have provided lightweight, compact, and reconfigurable solutions for various robotic and architectural applications. However, creating an integrated structural system that can effectively balance the competing requirements of high packing efficiency, simple deployment, and precise morphing into multiple load-bearing configurations remains a significant challenge. This study introduces a new class of hyper-Yoshimura origami, which exhibits a wide range of kinematically admissible and locally metastable states, including newly discovered symmetric "self-packing" and asymmetric "pop-out" states. This metastability is achieved by breaking a design rule of Yoshimura origami that has been in place for many decades. To this end, this study derives a new set of mathematically rigorous design rules and geometric formulations. Based on this, forward and inverse kinematic strategies are developed to stack hyper-Yoshimura modules into deployable booms that can approximate complex 3D shapes. Finally, this study showcases the potential of hyper-Yoshimura with a meter-scale pop-up cellphone charging station deployed at our university's bus transit station, along with a 3D-printed, scaled prototype of a space crane that can function as an object manipulator, solar tracking device, or high-load-bearing structure. These results establish hyper-Yoshimura as a promising platform for deployable and adaptable robotic systems in both terrestrial and space environments.

  PublisherOA PDFDOI

• Numerical analysis of the vibration isolation effects of infilled trench barrier in unsaturated poroviscoelastic ground

  2025-08-20

  book-chapter1st authorCorresponding

  The vibration isolation effects of infilled trench wave barriers in unsaturated ground were studied for the influences of soil saturation degree on the screening efficiency. The 2.5D finite element method (FEM) were adopted for this study. The numerical models for the unsaturated ground with infilled trench barrier were built, and the configurations of the barriers, such as the trench depth and inclined angle, were studied under different soil saturation degrees. This research found that the increased soil saturation degree would reduce the soil-trench interaction and improve the isolation effects. When the soil saturation degree is high, the increased soil saturation degree would increase the vibration energy transmission.

  PublisherDOI

• Numerical analysis of the vibration isolation effects of infilled trench barrier in unsaturated poroviscoelastic ground

  2025-08-07

  book-chapter1st authorCorresponding

  The vibration isolation effects of infilled trench wave barriers in unsaturated ground were studied for the influences of soil saturation degree on the screening efficiency. The 2.5D finite element method (FEM) were adopted for this study. By the Galerkin method and Fourier transform, the weak form governing equations of the unsaturated ground in the framework of 2.5D FEM were obtained. And the perfectly match layer (PML) was utilized to mitigate the reflected waves from the truncated boundaries. The numerical models for the unsaturated ground with infilled trench barrier were built, and the configurations of the barriers, such as the trench depth and inclined angle, were studied under different soil saturation degrees. This research found that the increased soil saturation degree would reduce the soil-trench interaction and improve the isolation effects. When the soil saturation degree is high, the increased soil saturation degree would increase the vibration energy transmission via the pore water pressure, that may reduce the isolation effects. With a large trench depth, the soil-trench interaction controls the isolation effects for the barrier.

  PublisherDOI

• Re‐Purposing a Modular Origami Manipulator Into an Adaptive Physical Computer for Machine Learning and Robotic Perception

  Advanced Science · 2025-09-14 · 2 citations

  articleOpen accessSenior author

  Physical computing has emerged as a powerful tool for performing intelligent tasks directly in the mechanical domain of functional materials and robots, reducing our reliance on the more traditional CMOS computers. However, no systematic study explains how mechanical design can influence physical computing performance. This study sheds insights into this question by repurposing an origami-inspired modular robotic manipulator into an adaptive physical reservoir and systematically evaluating its computing capacity with different physical configurations, input setups, and computing tasks. By challenging this adaptive reservoir computer to complete the classical NARMA benchmark tasks, this study shows that its time series emulation performance directly correlates with the Peak Similarity Index (PSI), which quantifies the frequency spectrum correlation between the target output and reservoir dynamics. The adaptive reservoir also demonstrates perception capabilities, accurately extracting its payload weight and orientation information from the intrinsic dynamics. Importantly, such information extraction capability can be measured by the spatial correlation between nodal dynamics within the reservoir body. Finally, by integrating shape memory alloy (SMA) actuation, this study demonstrates how to exploit such computing power embodied in the physical body for practical, robotic operations. This study provides a strategic framework for harvesting computing power from soft robots and functional materials, demonstrating how design parameters and input selection can be configured based on computing task requirements. Extending this framework to bio-inspired adaptive materials, prosthetics, and self-adaptive soft robotic systems can enable next-generation embodied intelligence, where the physical structure can compute and interact with its digital counterparts.

  PublisherOA PDFDOI

• Rapid Design and Fabrication of Body Conformable Surfaces with Kirigami Cutting and Machine Learning

  ArXiv.org · 2025-11-17

  preprintOpen accessSenior author

  By integrating the principles of kirigami cutting and data-driven modeling, this study aims to develop a personalized, rapid, and low-cost design and fabrication pipeline for creating body-conformable surfaces around the knee joint. The process begins with 3D scanning of the anterior knee surface of human subjects, followed by extracting the corresponding skin deformation between two joint angles in terms of longitudinal strain and Poisson's ratio. In parallel, a machine learning model is constructed using extensive simulation data from experimentally calibrated finite element analysis. This model employs Gaussian Process (GP) regression to relate kirigami cut lengths to the resulting longitudinal strain and Poisson's ratio. With an R2 score of 0.996, GP regression outperforms other models in predicting kirigami's large deformations. Finally, an inverse design approach based on the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) is used to generate kirigami patch designs that replicate the in-plane skin deformation observed from the knee scans. This pipeline was applied to three human subjects, and the resulting kirigami knee patches were fabricated using rapid laser cutting, requiring only a business day from knee scanning to kirigami patch delivery. The low-cost, personalized kirigami patches successfully conformed to over 75 percent of the skin area across all subjects, establishing a foundation for a wide range of wearable devices. The study demonstrates this potential through an impact-resistant kirigami foam patch, which not only conforms to dynamic knee motion but also provides joint protection against impact. Finally, the proposed design and fabrication framework is generalizable and can be extended to other deforming body surfaces, enabling the creation of personalized wearables such as protective gear, breathable adhesives, and body-conformable electronics.

  PublisherOA PDFDOI

Recent grants

• Collaborative Research: Uncovering the Dynamics and Functionality of Origami Structures and Materials

  NSF · $193k · 2016–2019

• Exploiting Multi-Stability to Enable Mechanical Intelligence for Versatile and Efficient Control of Soft Robotic Locomotion and Manipulation

  NSF · $84k · 2022–2023

• Establishing a Design Framework for Multi-functional composites by Leveraging Kirigami Cutting, Multi-stability, and Multi-level Optimization

  NSF · $726k · 2018–2022

• Collaborative Research: Creating System Level Intelligence by Harvesting the Physical Reservoir Computing Power in Origami

  NSF · $338k · 2024–2027

• Exploiting Multi-Stability to Enable Mechanical Intelligence for Versatile and Efficient Control of Soft Robotic Locomotion and Manipulation

  NSF · $276k · 2019–2022

Frequent coauthors

• Hongbin Fang

  Yiwu Science and Technology Research Institute

  28 shared

• Oliver Myers

  Clemson University

  14 shared

• Dragomir Radev

  12 shared

• Caiming Xiong

  11 shared

• Richard Socher

  11 shared

• K. W. Wang

  University of Michigan–Ann Arbor

  11 shared

• Kon‐Well Wang

  11 shared

• Sahand Sadeghi

  10 shared

Education

• Ph.D, Mechanical Engineering

  University of Michigan

  2014

• Master, Mechanical Engineering

  Pennsylvania State University

  2008

• Bachelor, Mechanical Engineering

  University of Michigan

  2006

Awards & honors

• Dean’s Awards of Excellence – Faculty Fellow (2024)
• C.D. Mote Jr Early Career Award , ASME Design Engineering Di…
• Gary Anderson Early Achievement Award , ASME Aerospace Divis…
• Junior Researcher of the Year Award , College of Engineering…
• CECAS Dean’s Faculty Fellow, Clemson University (2018)