About

Dr. Jing Li is a health economist with research interests in the economic, social, and behavioral factors affecting individual decision-making in health and healthcare, for both providers and patients. Her work explores the impact of policies that leverage these factors to improve patient outcomes and healthcare market efficiency. Methodologically, she is interested in innovatively applying advanced experimental and econometric methods to address understudied questions in health economics and policy. Dr. Li's current projects include studying experimentally measured social preferences, such as altruism, of physicians and their relationship with patient outcomes. She also investigates healthcare and financial decision-making for older adults with cognitive impairments, including Alzheimer's disease and related dementias, as well as conflicts of interest in physician drug prescribing. Her research aims to better understand decision-making processes in aging populations and the effects of policy interventions in healthcare.

Research topics

• Computer Science
• Organic chemistry
• Chemistry
• Chemical engineering
• Materials science
• Cancer research
• Genetics
• Biology
• Immunology
• Computational biology
• Physical chemistry
• Nanotechnology
• Optoelectronics
• Inorganic chemistry
• Mechanical engineering

Selected publications

• Dose-dependent co-evolution of helium bubbles, dislocations and Cr23C6 precipitates in Alloy 617 under high-temperature He-ion irradiation

  SSRN Electronic Journal · 2026-01-01

  preprintOpen access
  PublisherDOI

• Precision engineering chiral interfaces for efficient spin injection in metal halide heterostructures.

  Apollo (University of Cambridge) · 2026-02-20

  articleOpen access
  PublisherDOI

• Crystallization-Assisted Water Adsorption in Amorphous Molecular Adsorbents

  Figshare · 2026-04-01

  datasetOpen accessSenior author

  Data for figures

  PublisherDOI

• Molecular Identification of Kava-Kava (Piper methysticum G. Forst.) Using the Internal Transcribed Spacer (ITS2) Region

  DNA · 2026-04-28

  articleOpen access

  Background: Piper is one of the largest genera in the family Piperaceae, with approximately 2100 species. Most Piper species are used as spices or as medicinal plants. Piper methysticum G. Forst., popularly known as kava-kava (or kava), is widely used to treat anxiety disorders. Due to similar morphological features, P. auritum Kunth (known as “false kava”) is sometimes mistakenly or intentionally used as an alternative botanical source for “kava” extracts. The false kava extracts do not contain active kavalactones but contain safrole, which is hepatotoxic. It is important to verify the component botanical materials in order to evaluate the quality and safety attributes of a potential botanical drug. Some studies have evaluated genetic variation in Piper sp. using the chloroplast regions matK, rbcL, rpoC1 and trnH-psbA and the nuclear ITS2 markers. However, none has focused on the identification of P. methysticum using DNA barcodes. In the present investigation, the ITS2 DNA barcode region from the nuclear genome was tested to confirm the identification and authentication of kava-kava samples. Methods: Seven P. methysticum samples were collected from three different geographic lo-cations and two P. auritum samples were collected and the ITS2 region from the nuclear genome, was amplified, sequenced and aligned to determine their genetic distances. Results: The ITS2 locus showed high amplification and sequence output with a discriminating barcode gap. A distance-based phylogenetic tree and BLAST confirmation (using blastn) revealed the ITS2 locus as a diagnostic DNA barcode for the accurate identification of kava-kava species. Discussion: In conclusion, the ITS2 region proves to be an effective and reliable DNA barcode for distinguishing P. methysticum from closely related species such as P. auritum. Its application can significantly improve the safety, quality, and traceability of kava-containing products, addressing a critical need in the standardization of botanical drugs.

  PublisherOA PDFDOI

• The CsFT-FD complex controls flowering by regulating <i>CsAP1</i> and <i>CsLFY</i> in saffron

  Figshare · 2026-04-01

  datasetOpen access

  Saffron (<i>Crocus sativus</i> L.) is a widely recognized medicinal and economic crop, valued primarily for the red stigmas that constitute its pharmaceutically active component. Flowering represents a critical agronomic trait, as it directly governs saffron yield. However, the molecular mechanisms underlying flowering regulation in saffron are not well studied. In this study, we identified a CsFT3-like-FD-AP1/LFY module involved in controlling flowering in saffron under indoor cultivation. Through transcriptomic and DAP-seq analysis, key floral identity genes <i>CsAPETALA1</i> (<i>CsAP1</i>) and <i>CsLEAFY</i> (<i>CsLFY</i>) were identified as targets of the CsFT3-like-FD complex. Direct transcriptional regulation of <i>CsAP1</i> and <i>CsLFY</i> by CsFT3-like-FD was further confirmed by dual-luciferase assays. Expression profiling revealed that <i>CsAP1</i> is predominantly expressed in saffron leaves, tepals, and stamens, whereas <i>CsLFY</i> is mainly expressed in leaves and tepals. Both proteins localize to the nucleus. Ectopic expression of <i>CsAP1</i> or <i>CsLFY</i> in <i>Arabidopsis</i> significantly accelerates flowering. Notably, <i>CsAP1</i> overexpression additionally alters floral organ architecture, indicating its dual role in promoting flowering and regulating floral organ formation. Together, these findings underscore the essential role of the CsFT3-like-FD-AP1/LFY regulatory module in saffron and offer novel insights into flowering regulation in non-model monocot plants.

  PublisherDOI

• Tomonaga-Luttinger liquid and charge-density wave in a quasi-one-dimensional material

  arXiv (Cornell University) · 2026-03-30

  preprintOpen access1st authorCorresponding

  In one-dimensional (1D) electron systems, the Fermi liquid state breaks down due either to electron interactions, which results in a Tomonaga-Luttinger liquid (TLL) state, or to Peierls instability, which leads to an insulating charge-density-wave (CDW) phase. In general, these two phenomena are mutually exclusive, and their coexistence remains elusive in real materials. Here, we report the discovery of a new quasi-1D material, Cs$_{1-δ}$Cr$_3$S$_3$, which unexpectedly exhibits coexistence of the antithetical CDW and TLL states. The CDW state is evidenced by the intra-unit-cell dimerization, and the opening of an optical band gap of $\sim$250 meV. Meanwhile, TLL behaviour is unambiguously demonstrated by the measurements of electrical transport and angle-resolved photoemission spectroscopy, which reveal a power-law scaling with temperature, bias voltage and electron energy. Band structure calculations reveal isolated, linearly dispersive, 1D bands around the Fermi level. For the dimerized CDW phase, the 1D Fermi-surface sheets located at the boundary of the Brillouin zone are gapped from intra-unit-cell bond symmetry breaking. Experimentally, subtle Cs vacancies shift the Fermi level into the linearly dispersive valence band, enabling the observation of TLL behaviour without interrupting the CDW order. This work establishes Cs$_{1-δ}$Cr$_3$S$_3$ as a rare material platform in which the antagonistic Fermi-liquid instabilities coexist and intertwine, opening new avenues for studying emergent quantum phenomena in 1D systems.

  PublisherDOI

• A copper(I)-coordinated PEDOT polymer enabling fast iodine conversion and dendrite-free Zn deposition for durable Zn-I2 batteries

  Journal of Energy Chemistry · 2026-04-02

  article
  PublisherDOI

• Enhancing gas sensing recyclability and recovery rate by crystal engineering of CuI motif in MOFs

  Nature Communications · 2026-04-18

  articleOpen access

  Metal-organic frameworks (MOFs) possess high sensitivity as chemiresistive gas sensing materials at room temperature (RT), while their applications are severely hampered by the issues of slow recovery and poor recyclability. To address these challenges, a strategy integrating DFT calculation with experimental synthesis is developed to identify and remove non-essential active units with excessive interactions to gas molecules, thereby enhancing sensing recovery and recyclability. In a case study, DFT calculations reveal that one-dimensional (1D) {Cu4I3}nn+ chains in a K-MOF, K-CuI-K-INA (HINA = isonicotinic acid), exhibit strong affinity to NO2 while 2D {Cu4I5}nn- layers show moderate interactions. Thus, by modulating the structure features of CuI motifs in M-CuI-K-INA (M = Na+, K+, and Cs+, HINA = isonicotinic acid) type of MOFs, their NO2 sensing recovering and recycling performances are consequently optimized. Particularly, Cs-CuI-K-INA with solely 2D {Cu4I5}nn- layers possesses the best sensing recovery and recycle abilities, meanwhile maintains the excellent sensitivity and selectivity among these RT NO2 sensing materials presented in the current study. This approach paves the way for chemiresistive sensing materials with improved recoverability and recyclability while retaining high sensitivity and selectivity. Here authors present a method combining DFT calculations with experimental synthesis to address the challenges of slow recovery and limited recyclability in chemiresistive sensing MOFs.

  PublisherOA PDFDOI

• Source Code and Curated Datasets for "Hypervolume-Contribution Bandits for Multi-Objective Feature Selection with Nested Cross-Validation

  Zenodo (CERN European Organization for Nuclear Research) · 2026-03-31

  otherOpen access1st authorCorresponding

  This repository provides the source code and curated datasets for the paper:“Hypervolume-Contribution Bandits for Multi-Objective Feature Selection with Nested Cross-Validation”. OverviewWe propose RL-HMOFS, a lightweight contextual-bandit framework for multi-objective feature selection (MOFS) in high-dimensional, small-sample settings. RL-HMOFS adaptively schedules a pool of variation operators, using a reward based on marginal Hypervolume Contribution (HVC) computed on the selected Pareto front. Reproducible evaluation (Nested CV)All experiments follow a strict nested cross-validation protocol to avoid selection bias: the MOEA search and model selection are performed only on outer-training folds, while all reported objective values and performance indicators are evaluated exclusively on outer-test folds. Contents Source code: RL-HMOFS, Random-HMOFS (random operator scheduling ablation), and the NSGA-II baseline; including the operator pool (Standard, MI-guided, Reduce) and the nested-CV evaluation pipeline. Datasets & Attribution: 28 pre-processed benchmark datasets used in the paper. The original raw data were sourced from public repositories, including the UCI Machine Learning Repository, OpenML, and the ASU Feature Selection Lab. To ensure 100% exact reproducibility of the experimental results in our paper, we provide our pre-processed CSV versions. Specifically, non-numeric columns were removed, and original multi-class datasets were formatted into binary classification tasks by selecting the top two most frequent classes. Users are encouraged to cite the original data sources when utilizing these datasets for other purposes. Citation Jing Li, et al. "Hypervolume-Contribution Bandits for Multi-Objective Feature Selection with Nested Cross-Validation", Submitted2026.]

  PublisherDOI

• Integrating Near-Infrared Spectroscopy and High-Fidelity Simulation for Paint drying Optimization

  Journal of Thermal Science and Engineering Applications · 2026-04-27

  article

  Abstract To address deficient online sensing and control in paint drying, this study constructed an integrated process model combining online detection and fluid simulation. An online moisture content prediction model (MCP-Model) was established, combining Near-Infrared Spectroscopy (NIRS) with a Particle Swarm Optimized Support Vector Machine (PSO-SVM). Leveraging moisture data from the MCP-Model, the dynamic moisture diffusion coefficient (D(t)) was inversely calculated based on mass transfer theory. The derived D(t) was subsequently employed as a critical boundary condition to build a high-fidelity Computational Fluid Dynamics model (CFD-Model). A stage-based dynamic control strategy (SDCS) was then proposed. The MCP-Model (Rp2=0.9877) showed superior predictive performance, surpassing traditional models. The derived D(t) showed a dynamic pattern (low, then sharp increase), which, with CFD analysis, revealed a rate-limiting step transition from external convection to internal diffusion. The SDCS adopts high air velocity during the initial (constant-rate) phase to reduce external resistance, then switches to high temperature during the later (falling-rate) phase to intensify internal diffusion. By dynamically matching parameters to the rate-limiting step, the drying process was effectively optimized. The proposed integrated framework (NIRS-driven MCP-Model, D(t) inversion, and D(t)-driven CFD-Model) provides a new methodology for optimizing paint drying, offering theoretical and engineering reference for the coatings industry.

  PublisherDOI

Recent grants

• Developing Highly Luminescent Materials for Low-Cost and Energy-Efficient Lighting Applications

  NSF · $450k · 2015–2019

• Crystalline Hybrid Semiconductors: A systematic Approach to Develop Nanostructured Materials with Enhanced Properties and New Functionality

  NSF · $420k · 2012–2015

• Developing Nanostructured Inorganic-Organic Hybrid Semiconductors for Photovoltaic Applications

  NSF · $394k · 2008–2011

• Hybrid Semiconductor Nanastructures as Multifunctional Materials

  NSF · $473k · 2004–2008

Frequent coauthors

• Xuan Zhang

  Zhejiang Chinese Medical University

  14208 shared

• Wen Chen

  Agriculture and Agri-Food Canada

  11544 shared

• Lei Xu

  Weatherford College

  5985 shared

• Wei Chen

  3652 shared

• Yan Luo

  Sun Yat-sen University

  3079 shared

• Ying Zhang

  Viva Biotech (China)

  3034 shared

• Yu Wang

  Peking University People's Hospital

  2480 shared

• Lu Gan

  Huazhong University of Science and Technology

  2363 shared

Labs

• Lee LabPI

Education

• Ph.D., Health Policy (Economics Track)

  University of California at Berkeley

  2016

• M.S., Economics

  University of California at Berkeley

  2015

• M.A., International Comparative Education

  Stanford University

  2009

• B.A., Economics and English

  Peking University, China

  2008