About

Wei Lu is a Professor of Mechanical Engineering at the University of Michigan, serving as the Associate Chair for Facilities and Planning. He holds multiple doctoral degrees, including a Ph.D. in Materials Science and Engineering from Princeton University and a Ph.D. in Solid Mechanics from Tsinghua University, along with a master's and bachelor's degree from Tsinghua University. His research interests encompass energy storage and electrochemistry, simulation of nano/microstructure evolution, mechanics in nano/micro systems, advanced manufacturing, and the mechanical properties and performance of advanced materials in relation to their microstructures. His work has significantly contributed to understanding nanoscale mechanics, interfacial adhesion in 2D materials, and the development of innovative approaches for electric vehicle battery analysis. Wei Lu has been recognized with numerous awards, including the George J. Huebner, Jr. Research Excellence Award, the Creative, Innovative, Daring Award, and the Gustus L Larson Memorial Award from the American Society of Mechanical Engineers. He has also been selected as a fellow for the Public Engagement Faculty Fellowship program and has received prestigious grants such as the Bill Gates Grand Challenges Grants. His research has been featured in prominent publications like Nature Communications and ACS Central Science, highlighting his contributions to nanoscale mechanics and advanced materials.

Research topics

• Computer Science
• Artificial Intelligence
• Engineering
• Electrical engineering
• Physics
• Computer network
• Computer hardware
• Electronic engineering
• Materials science
• Nanotechnology
• Cognitive science
• Telecommunications
• Psychology
• Computer architecture

Selected publications

• Intelligent Modeling of GPU Market Trends for Dependable Edge and Cloud Resource Planning

  Lecture notes on data engineering and communications technologies · 2026-01-01

  book-chapterSenior authorCorresponding
  PublisherDOI

• Ultrafast sensitive broadband imaging photodetection based on Vertically stacked structured GaN/Bi2O2Te/Bi2Te3 p-n heterojunctions

  SSRN Electronic Journal · 2026-01-01

  preprintOpen access
  PublisherDOI

• Probabilistic Graphical Modeling for Biomedical Signal Completion with Non-Random Missingness on Patient Networks

  2026-04-21

  articleSenior author

  Electronic Health Records (EHRs) provide a rich source of high-dimensional biomedical signals for clinical decision support. However, these signals, typically represented as patient-medication interaction matrices, present two fundamental challenges for signal processing: 1) extreme sparsity, and 2) a missing data mechanism that is often Missing Not At Random (MNAR), where the pattern of missingness is correlated with the unobserved true signal values. Furthermore, valuable relational information encapsulated in patient similarity networks is often ignored. To address these challenges, we propose PSMR-MNAR, a novel probabilistic graphical model for biomedical signal completion. Our approach explicitly models the MNAR generative process and integrates inter-patient relationships as a graph-based prior to regularize the problem. By defining a patient-specific "reference level," the model learns to adaptively leverage information from clinically similar patients. We develop an efficient variational inference algorithm for posterior estimation. Experiments on the real-world MIMIC-III dataset demonstrate that our model significantly outperforms state-of-the-art methods in predicting medication usage, validating the efficacy of jointly modeling the graph structure and the MNAR mechanism.

  PublisherDOI

• Stable Neural Signal Recording Processed by Memristor‐Based Reservoir Computing System

  Advanced Intelligent Systems · 2026-04-23

  articleOpen accessSenior authorCorresponding

  As brain–machine interfaces (BMIs) and neural recording technologies evolve, there is an increasing demand for edge computing systems capable of processing large amounts of neural data in real‐time to alleviate the data transmission challenges and improve BMI performance. In this work, we propose a memristor‐based reservoir computing (RC) system that leverages the short‐term memory dynamics of memristive devices to process recorded neural signals. We validate the proposed system on a behavioral state classification task using neural spike recordings from a mouse during free movement. The system achieves robust classification performance over a 2‐week period and demonstrates resilience to device‐to‐device variations and limited training data. The proposed system further enabled ablation analysis to identify the dominate neurons responsible for particular actions. These results demonstrate the effectiveness of memristor‐based RC systems as promising solutions for energy‐efficient, real‐time neural signal processing in future BMI systems.

  PublisherDOI

• Fusion Strategy of DNA-Encoded Libraries Drives Discovery of Allosteric Inhibitors of SARS-CoV-2 RdRp

  JACS Au · 2026-01-23 · 1 citations

  articleOpen access

  Allosteric regulation is a central mechanism for modulating biological functions and offers an attractive strategy in drug discovery, particularly for targets considered challenging or "undruggable." However, the discovery of allosteric inhibitors is hindered by poorly defined binding sites and the lack of effective screening approaches. Here, we present a dual DNA-encoded library (DEL) screening strategy that integrates reversible DEL and covalent DEL (CoDEL) technologies to identify novel allosteric inhibitors of the SARS-CoV-2 RNA-dependent RNA polymerase (RdRp). Using this approach, we discovered the first covalent allosteric inhibitors of RdRp, which engage a previously uncharacterized pocket on the nsp8 subunit and form a covalent bond with Cys114. Subsequent SAR studies and biochemical assays confirmed the allosteric mechanism and elucidated structural determinants of activity. This work highlights the power of integrating reversible DEL screening with CoDEL screening for ligand discovery and establishes a generalizable strategy to identify covalent allosteric modulators for therapeutically important targets for therapy or active probe design.

  PublisherDOI

• Effects of Sr–Ti–B–La/Ce and Graded Cu Microalloying on Microstructure and Strength–Toughening of Al–7Si–0.35Mg Casting Alloy

  Advanced Engineering Materials · 2026-02-16

  articleSenior author

  A systematic study was conducted on commercial Al–7Si–0.35Mg cast alloy modified by a multielement microalloying strategy: fixed additions of Sr and Ti–B, 0.3 wt.% La or Ce, and a graded Cu addition (0–3.5 wt.%). Microstructure and mechanical properties were characterized using optical microscopy, scanning electron microscopy, energy‐dispersive X‐ray spectroscopy, electron backscatter diffraction, X‐ray diffraction, Vickers hardness testing, and room‐temperature tensile testing. The results show that the Sr–Ti–B–La/Ce composite modifier effectively rounds/ spheroidizes corners of eutectic Si and reduces the area fraction of large Si particles (>300 μm 2 ) from 47.8% to 21.0%, while markedly increasing the proportion of finer Si particles in the 1–50 μm 2 range. Secondary dendrite arm spacing and mean grain radius were refined by ≈54% and 38.5%, respectively (average grain radius decreased from 351 to 216 μm). With increasing Cu content the alloy behavior transitions from predominantly solid‐solution strengthening to second‐phase particle strengthening controlled by Al 2 CuMg. At about 1.5 wt.% Cu, a fine dispersion of Al 2 CuMg particles forms and the mechanical properties reach an optimum, with the ultimate tensile strength of 271.9 MPa and elongation (EL) of 6.7%. When Cu exceeds ~2.5 wt.%, Al 2 CuMg phases coarsen and form continuous networks, causing ductility to fall sharply (EL down to 2.94% at 3.5 wt.% Cu) despite a continued rise in hardness with Cu content (maximum ~109 HV). Fractographic analysis indicates a uniform ductile dimple morphology at the optimal composition, whereas coarse second‐phase particles induced by excess Cu act as preferred crack‐initiation sites. Based on multiscale characterization, this work reveals synergistic interactions between Sr–Ti–B–La/Ce and Cu in terms of interfacial adsorption, solute redistribution, and precipitation kinetics. An optimized composition represented by 0.3 wt.% La/Ce + 1.5 wt.% Cu is proposed, providing insights into the microstructure and engineering guidance for the alloy design and industrial application of high strength‐and‐toughness cast Al–Si–Mg alloys.

  PublisherDOI

• Author response for "Association of time in tight range and 1,5-anhydroglucitol in type 2 diabetes"

  2025-05-11

  peer-review
  PublisherDOI

• Semantic Change Detection of Roads and Bridges: A Fine-grained Dataset and Multimodal Frequency-driven Detector

  ArXiv.org · 2025-05-19

  preprintOpen access

  Accurate detection of road and bridge changes is crucial for urban planning and transportation management, yet presents unique challenges for general change detection (CD). Key difficulties arise from maintaining the continuity of roads and bridges as linear structures and disambiguating visually similar land covers (e.g., road construction vs. bare land). Existing spatial-domain models struggle with these issues, further hindered by the lack of specialized, semantically rich datasets. To fill these gaps, we introduce the Road and Bridge Semantic Change Detection (RB-SCD) dataset. As the first benchmark to systematically target semantic change detection of roads and bridges, RB-SCD offers comprehensive fine-grained annotations for 11 semantic change categories. This enables a detailed analysis of traffic infrastructure evolution. Building on this, we propose a novel framework, the Multimodal Frequency-Driven Change Detector (MFDCD). MFDCD integrates multimodal features in the frequency domain through two key components: (1) the Dynamic Frequency Coupler (DFC), which leverages wavelet transform to decompose visual features, enabling it to robustly model the continuity of linear transitions; and (2) the Textual Frequency Filter (TFF), which encodes semantic priors into frequency-domain graphs and applies filter banks to align them with visual features, resolving semantic ambiguities. Experiments demonstrate the state-of-the-art performance of MFDCD on RB-SCD and three public CD datasets. The code will be available at https://github.com/DaGuangDaGuang/RB-SCD.

  PublisherOA PDFDOI

• State Space Models Naturally Produce Time Cell and Oscillatory Behaviors and Scale to Abstract Cognitive Functions

  arXiv (Cornell University) · 2025-07-18

  preprintOpen accessSenior author

  A grand challenge in modern neuroscience is to bridge the gap between the detailed mapping of microscale neural circuits and mechanistic understanding of cognitive functions. While extensive knowledge exists about neuronal connectivity and biophysics, how these low-level phenomena eventually produce abstract behaviors remains largely unresolved. Here, we propose that a model based on State Space Models, an emerging class of deep learning architectures, can be a potential biological model for analysis. We suggest that the differential equations governing elements in a State Space Model are conceptually consistent with the dynamics of biophysical processes, while the model offers a scalable framework to build on the dynamics to produce emergent behaviors observed in experimental neuroscience. We test this model by training a network employing a diagonal state transition matrix on temporal discrimination tasks with reinforcement learning. Our results suggest that neural behaviors such as time cells naturally emerge from two fundamental principles: optimal pre-configuration and rotational dynamics. These features are shown mathematically to optimize history compression, and naturally generate structured temporal dynamics even prior to training, mirroring recent findings in biological circuits. We show that learning acts primarily as a selection mechanism that fine-tunes these pre-configured oscillatory modes, rather than constructing temporal codes de novo. The model can be readily scaled to abstract cognitive functions such as event counting, supporting the use of State Space Models as a computationally tractable framework for understanding neural activities.

  PublisherOA PDFDOI

• LoRA Patching: Exposing the Fragility of Proactive Defenses against Deepfakes

  ArXiv.org · 2025-10-04

  preprintOpen accessSenior author

  Deepfakes pose significant societal risks, motivating the development of proactive defenses that embed adversarial perturbations in facial images to prevent manipulation. However, in this paper, we show that these preemptive defenses often lack robustness and reliability. We propose a novel approach, Low-Rank Adaptation (LoRA) patching, which injects a plug-and-play LoRA patch into Deepfake generators to bypass state-of-the-art defenses. A learnable gating mechanism adaptively controls the effect of the LoRA patch and prevents gradient explosions during fine-tuning. We also introduce a Multi-Modal Feature Alignment (MMFA) loss, encouraging the features of adversarial outputs to align with those of the desired outputs at the semantic level. Beyond bypassing, we present defensive LoRA patching, embedding visible warnings in the outputs as a complementary solution to mitigate this newly identified security vulnerability. With only 1,000 facial examples and a single epoch of fine-tuning, LoRA patching successfully defeats multiple proactive defenses. These results reveal a critical weakness in current paradigms and underscore the need for more robust Deepfake defense strategies. Our code is available at https://github.com/ZOMIN28/LoRA-Patching.

  PublisherOA PDFDOI

Recent grants

• CAREER: Programmable Nanoscale Self-Assembly on Solid Surfaces

  NSF · $406k · 2004–2010

• SHF: Small: Efficient In-Memory Computing Architecture Based on RRAM Crossbar Arrays

  NSF · $400k · 2016–2019

• Spintronics and Quantum Information Processing with 1D Ge/Si Nanowires

  NSF · $240k · 2006–2010

• CAREER: Understanding, Development and Applications of Nanoscale Memristor Devices

  NSF · $400k · 2010–2015

• FET: Medium: Memory Processing Unit (MPU) - An Efficient, Reconfigurable In-memory Computing Fabric

  NSF · $960k · 2019–2024

Frequent coauthors

• Xiaojian Zhu

  30 shared

• Run‐Wei Li

  Chinese Academy of Sciences

  30 shared

• Xiaohong Chen

  Hunan Cancer Hospital

  29 shared

• Jason K. Eshraghian

  University of California, Santa Cruz

  28 shared

• Sungho Kim

  Korea Institute of Industrial Technology

  27 shared

• Chao Du

  Xi'an Jiaotong University

  23 shared

• Yuchao Yang

  Beijing Academy of Artificial Intelligence

  23 shared

• Yuting Wu

  23 shared

Labs

• Wei Lu LaboratoryPI

Education

• Ph.D., Physics and Astronomy

  Rice University

  2003

• B.S, Physics

  Tsinghua University

  1996

Awards & honors

• George J. Huebner, Jr. Research Excellence Award, College of…
• Creative, Innovative, Daring (C/I/D) Award, College of Engin…
• Ted Kennedy Family Faculty Team Excellence Award, College of…
• Gustus L Larson Memorial Award, American Society of Mechanic…
• Distinguished Professor Award, Novelis and College of Engine…