About

Shengxi Huang is an associate professor at Rice University with appointments in the Department of Electrical and Computer Engineering, Department of Materials Science and NanoEngineering, and Department of Bioengineering. Prior to joining Rice, she was an assistant professor at The Pennsylvania State University in the Electrical Engineering Department, Biomedical Engineering Department, and Materials Research Institute. She earned her PhD in Electrical Engineering and Computer Science at MIT in 2017 under the supervision of Professors Mildred Dresselhaus and Jing Kong, followed by a postdoctoral position at Stanford University with Professors Tony Heinz and Jonathan Fan. Shengxi also holds an MS degree in EECS from MIT and a bachelor's degree with highest honors in Micro- and Nano-Electronics from Tsinghua University, China. Her research focuses on optical spectroscopy of low-dimensional materials and Weyl semimetals, exploring the applications of these quantum materials in optoelectronics and sensing. Shengxi Huang has received multiple prestigious awards including the NSF CAREER Award, AFOSR Young Investigator Award, Johnson & Johnson WiSTEM2D Award, Kavli Fellowship for Nanoscience, Jin Au Kong Award for Best PhD Thesis at MIT, and the Ginzton Fellowship at Stanford University.

Research topics

• Computer Science
• Physics
• Condensed matter physics
• Materials science
• Artificial Intelligence
• Nanotechnology
• Optoelectronics
• Optics
• Chemistry
• Quantum mechanics
• Chemical physics
• Crystallography
• Engineering physics
• Biology
• Medicine
• Molecular physics
• Computational biology
• Computational chemistry
• Virology

Selected publications

• Formal Analysis of Hopfield Networks through 0-1 Integer Linear Programming and SMT Solving

  2026-01-01

  articleSenior author
  PublisherDOI

• Facile synthesis of hydrophobic and porous colorimetric gel films using emulsion freeze-drying method for visual monitoring of food freshness

  Chemical Engineering Journal · 2025-12-20

  articleCorresponding
  PublisherDOI

• Machine Learning Interpretation of Optical Spectroscopy Using Peak-Sensitive Logistic Regression

  ACS Nano · 2025-04-15 · 15 citations

  articleOpen accessSenior authorCorresponding

  Optical spectroscopy, a noninvasive molecular sensing technique, offers valuable insights into material characterization, molecule identification, and biosample analysis. Despite the informativeness of high-dimensional optical spectra, their interpretation remains a challenge. Machine learning methods have gained prominence in spectral analyses, efficiently unveiling analyte compositions. However, these methods still face challenges in interpretability, particularly in generating clear feature importance maps that highlight the spectral features specific to each class of data. These limitations arise from feature noise, model complexity, and the lack of optimization for spectroscopy. In this work, we introduce a machine learning algorithm─logistic regression with peak-sensitive elastic-net regularization (PSE-LR)─tailored for spectral analysis. PSE-LR enables classification and interpretability by producing a peak-sensitive feature importance map, achieving an F1-score of 0.93 and a feature sensitivity of 1.0. Its performance is compared with other methods, including k-nearest neighbors (KNN), elastic-net logistic regression (E-LR), support vector machine (SVM), principal component analysis followed by linear discriminant analysis (PCA-LDA), XGBoost, and neural network (NN). Applying PSE-LR to Raman and photoluminescence (PL) spectra, we detected the receptor-binding domain (RBD) of SARS-CoV-2 spike protein in ultralow concentrations, identified neuroprotective solution (NPS) in brain samples, recognized WS2 monolayer and WSe2/WS2 heterobilayer, analyzed Alzheimer’s disease (AD) brains, and suggested potential disease biomarkers. Our findings demonstrate PSE-LR’s utility in detecting subtle spectral features and generating interpretable feature importance maps. It is beneficial for the spectral characterization of materials, molecules, and biosamples and applicable to other spectroscopic methods. This work also facilitates the development of nanodevices such as nanosensors and miniaturized spectrometers based on nanomaterials.

  PublisherOA PDFDOI

• Academic buoyancy and academic engagement in English speaking learning among Chinese college students: the mediation of enjoyment and the moderation of anxiety

  Frontiers in Psychology · 2025-09-15 · 2 citations

  articleOpen accessCorresponding

  Background: Learners' academic engagement in English speaking learning is crucial for improving their English speaking abilities through the process of motivation. Although prior research has explored predictors of EFL (English as a Foreign Language) academic engagement, such as academic buoyancy and foreign language learning emotions, the underlying mechanisms among these variables remain largely unexplored, especially in college English speaking learning. Given this, elucidating these mechanisms is essential for advancing the understanding of how to effectively promote English speaking proficiency among college students. Purpose: This study aims to construct a structural equation model (SEM) with a sample of 244 college students from two highly-ranked Chinese universities to examine the mediating role of foreign language enjoyment and the moderating effect of foreign language anxiety in the relationship between academic buoyancy and academic engagement within the context of Chinese college English speaking learning. Methods: Based on a correlation design, data from 244 Chinese EFL college students were collected via an online questionnaire in December 2023 and analyzed through a moderated mediation model with SPSS 26.0, IBM Amos 22, Mplus 8.3, and PROCESS v4.2, applying 2,000 bootstrap iterations. Results: The results reveal that English speaking learning buoyancy (ESLB) positively predicted English speaking learning engagement (ESLEG) both directly and indirectly through the mediating role of English speaking learning enjoyment (ESLE). Additionally, English speaking learning anxiety (ESLA) significantly and negatively moderated the relationships between ESLB and ESLE, as well as between ESLB and ESLEG. Conclusion: These findings highlight the complex interactions of academic buoyancy and foreign language learning emotions on engagement in English speaking learning. The study provides valuable pedagogical implications for enhancing English speaking instruction in Chinese colleges.

  PublisherOA PDFDOI

• Ultrahigh-Purity Single-Photon Emission from 2D WSe₂ via Effective Suppression of Classical Emission

  Nano Letters · 2025-07-05 · 3 citations

  articleSenior authorCorresponding

  Single-photon emitters (SPEs) in two-dimensional WSe2 offer high extraction efficiency and on-chip compatibility, but achieving high purity remains challenging. We present two strategies to suppress classical emission and enhance purity in WSe2-based SPEs. In monolayer WSe2, we exploited the presence and absence of valley–spin locking in free and bound excitons, respectively, to achieve purity of 98.3% via polarization control and 99.0% combined with near-resonant excitation. In bilayer WSe2, we obtained 97.0% purity without polarization filtering, enabled by the indirect band gap and inversion symmetry. These values represent some of the highest as-measured purities reported for 2D TMD SPEs. Our methods do not require complex fabrication or instrumentation and are supported by first-principles calculations of the vacancy state of Se and spin degeneracy. This work offers practical pathways for realizing high-quality single-photon sources for emerging quantum technologies.

  PublisherDOI

• Synthesis and characterization of nanostructured topological materials

  Progress in Quantum Electronics · 2025-09-01

  articleSenior authorCorresponding
  PublisherDOI

• RecGPT-V2 Technical Report

  ArXiv.org · 2025-12-16

  preprintOpen access

  Large language models (LLMs) have demonstrated remarkable potential in transforming recommender systems from implicit behavioral pattern matching to explicit intent reasoning. While RecGPT-V1 successfully pioneered this paradigm by integrating LLM-based reasoning into user interest mining and item tag prediction, it suffers from four fundamental limitations: (1) computational inefficiency and cognitive redundancy across multiple reasoning routes; (2) insufficient explanation diversity in fixed-template generation; (3) limited generalization under supervised learning paradigms; and (4) simplistic outcome-focused evaluation that fails to match human standards. To address these challenges, we present RecGPT-V2 with four key innovations. First, a Hierarchical Multi-Agent System restructures intent reasoning through coordinated collaboration, eliminating cognitive duplication while enabling diverse intent coverage. Combined with Hybrid Representation Inference that compresses user-behavior contexts, our framework reduces GPU consumption by 60% and improves exclusive recall from 9.39% to 10.99%. Second, a Meta-Prompting framework dynamically generates contextually adaptive prompts, improving explanation diversity by +7.3%. Third, constrained reinforcement learning mitigates multi-reward conflicts, achieving +24.1% improvement in tag prediction and +13.0% in explanation acceptance. Fourth, an Agent-as-a-Judge framework decomposes assessment into multi-step reasoning, improving human preference alignment. Online A/B tests on Taobao demonstrate significant improvements: +2.98% CTR, +3.71% IPV, +2.19% TV, and +11.46% NER. RecGPT-V2 establishes both the technical feasibility and commercial viability of deploying LLM-powered intent reasoning at scale, bridging the gap between cognitive exploration and industrial utility.

  PublisherOA PDFDOI

• Spatial Control of Optical Emission and Conductivity in Molybdenum Oxide through Electron-Beam Irradiation

  Nano Letters · 2025-09-01

  article

  Deterministic spatial control of material properties is essential for advanced electronic and optoelectronic device technologies. van der Waals (vdW) materials stand out for their high tunability, yet achieving multifunctional on-chip control remains challenging. Here, we focus on α-MoO3 and site-selectively modulate both its optical emission and conductivity via electron-beam irradiation. In situ cathodoluminescence spectroscopy reveals a cumulative enhancement of blue emission under the beam, exhibiting superlinear increases in intensity with beam current. Irradiated regions show distinct contrasts in Raman and work functions, indicating oxygen defect formation. Moreover, these local structural modifications substantially increase lateral and vertical conductivities, enabling conductive channels with sharp boundaries. Building on this, we demonstrate stable, deterministic optical patterning with subdiffraction spatial resolution, highlighting the potential for submicron devices without chemical processes or traditional lithography. Our results provide a versatile platform for on-chip light sources, conductive interconnects, and customizable optoelectronic elements, expanding the design space of vdW materials.

  PublisherDOI

• Probing the meV QCD Axion with the $\texttt{SQWARE}$ Quantum Semiconductor Haloscope

  ArXiv.org · 2025-09-17

  preprintOpen accessSenior author

  We propose the Semiconductor-Quantum-Well Axion Radiometer Experiment ($\texttt{SQWARE}$) -- a new experimental platform for direct detection of axion dark matter in the meV mass range -- based on resonantly enhanced axion-photon conversion through the inverse Primakoff effect in engineered quantum semiconductor heterostructures. The core of the radiometer is a GaAs/AlGaAs multiple quantum well structure forming a magnetoplasmonic cavity, containing an ultrahigh-mobility two-dimensional electron gas, which realizes a tunable epsilon-near-zero resonance in the terahertz frequency range. By controlling the orientation of the cavity within a strong external magnetic field, both the resonance frequency and the axion-induced current are optimized $\textit{in situ}$, enabling efficient scanning across a broad mass range without complex mechanical adjustment. The axion-induced electromagnetic signal radiatively emitted from the magnetoplasmonic cavity is detected by a state-of-the-art photodetector. We present the theoretical basis for resonant enhancement, detail the experimental design and benchmarks through extensive simulations, and project the sensitivity of $\texttt{SQWARE}$ for several realistic configurations. Our results demonstrate that $\texttt{SQWARE}$ can probe the well-motivated quantum chromodynamics axion parameter space and close a critical gap in direct searches at meV masses.

  PublisherOA PDFDOI

• Tunable phononic quantum interference induced by two-dimensional metals

  Science Advances · 2025-08-06 · 3 citations

  articleOpen accessSenior author

  Harnessing quantum interference among bosons provides opportunities due to their longer coherence time than fermions. Fano resonance, an example of quantum interference between discrete and continuous states, is marked by an asymmetric lineshape. While photon-based Fano resonance has enabled high-sensitivity molecule sensing, phonon-based Fano resonance remains underexplored because of ineffective interference between discrete phonons and electronic continuum. In this work, we report phonon-based Fano resonance in a graphene/2D Ag/SiC heterostructure, arising from frequency and lifetime matching between discrete and continuous phonons of SiC. The observed Fano asymmetry is tunable over two orders of magnitude, surpassing previously reported phonon-based systems. The 2D Ag layer restructures the interfacial SiC and facilitates resonant scattering to enhance Fano asymmetry, which is unattainable in conventional Ag. We further demonstrated that this Fano resonance allows ultrasensitive molecule detection at the single-molecule level. Our work highlights phonon-based Fano resonance, opening avenues for engineering quantum interference with phonons.

  PublisherDOI

Recent grants

• CAREER: Multiplexed and Selective Molecular Sensing Based on Raman Enhancement Through 2D Materials

  NSF · $500k · 2020–2022

• CAREER: Multiplexed and Selective Molecular Sensing Based on Raman Enhancement Through 2D Materials

  NSF · $393k · 2022–2027

• Topologically Enhanced Raman Spectroscopy

  NSF · $330k · 2023–2027

Frequent coauthors

• Zongyu Feng

  Grinm Advanced Materials (China)

  109 shared

• Liangshi Wang

  67 shared

• Long Zhiqi

  General Research Institute for Nonferrous Metals (China)

  62 shared

• Dali Cui

  General Research Institute for Nonferrous Metals (China)

  43 shared

• Kunyan Zhang

  Rice University

  39 shared

• Jing Kong

  Beijing Aerospace Flight Control Center

  38 shared

• Deliang Meng

  Grinm Advanced Materials (China)

  37 shared

• M. S. Dresselhaus

  Massachusetts Institute of Technology

  35 shared

Labs

• SCOPE LabPI

Education

• PhD, Electrical Engineering and Computer Science

  Massachusetts Institute of Technology

  2017

Awards & honors

• NSF CAREER Award (2020)
• AFOSR Young Investigator Award (2022)
• Johnson & Johnson STEM2D Scholar’s Award (2019)
• Dean’s Faculty Research Award, Penn State College of Enginee…
• Multidisciplinary Research Award, Penn State College of Engi…