Honors & Awards


  • NSF Fellowship, MMLDT-CSET Conference (2021)
  • Henry J. Ramey, Jr. Fellowship Award, Stanford University (2021)
  • Student Travel Award, SIAM Conference on Uncertainty Quantification (2020)
  • Best Project Award (1/245, Spring 2019 CS231N: Convolutional Neural Networks for Visual Recognition), Stanford University (2019)
  • Frank G. Miller Fellowship Award, Stanford University (2019)
  • EDGE Doctoral Fellowship, Stanford University (2018)
  • Student Research Travel Grant, UW-Madison (2017)
  • Elizabeth S. Hirschfelder Scholarship, UW-Madison (2016)
  • Outstanding Graduate, Shanghai Jiao Tong University (2014)
  • Excellent Student Leader, Shanghai Jiao Tong University (2013)
  • Xinye Scholarship, Shanghai Jiao Tong University (2013)

Education & Certifications


  • M.A., University of Wisconsin-Madison, Mathematics (2015)
  • B.S., Shanghai Jiao Tong University, Mathematics (2014)

Stanford Advisors


Service, Volunteer and Community Work


  • Panelist, School of Earth, Energy & Environmental Sciences IDEAL(Inclusion, Diversity, Equity and Access in a Learning Environment) faculty search student panel, Stanford University (2021)

    Location

    Stanford

  • Chairman of SJTU Student Association, UW-Madison, UW-Madison (2013 - 2015)

    Location

    Madison,Wisconsin

  • Chairman of Student Union, Department of Mathematics, Shanghai Jiao Tong University (2012 - 2013)

    Location

    Shanghai

All Publications


  • Global Sensitivity Analysis for U(VI) Transport for Integrating Coupled Thermal-Hydrological-Chemical Processes Models Into Performance Assessment Model JOURNAL OF NUCLEAR ENGINEERING AND RADIATION SCIENCE Ermakova, D., Wainwright, H., Zheng, L., Shirley, I., Lu, H. 2021; 7 (4)

    View details for DOI 10.1115/1.4050297

    View details for Web of Science ID 000694671600006

  • Hybrid models of chemotaxis with application to leukocyte migration. Journal of mathematical biology Lu, H., Um, K., Tartakovsky, D. M. 2021; 82 (4): 23

    Abstract

    Many chemical and biological systems involve reacting species with vastly different numbers of molecules/agents. Hybrid simulations model such phenomena by combining discrete (e.g., agent-based) and continuous (e.g., partial differential equation- or PDE-based) descriptors of the dynamics of reactants with small and large numbers of molecules/agents, respectively. We present a stochastic hybrid algorithm to model a stage of the immune response to inflammation, during which leukocytes reach a pathogen via chemotaxis. While large numbers of chemoattractant molecules justify the use of a PDE-based model to describe the spatiotemporal evolution of its concentration, relatively small numbers of leukocytes and bacteria involved in the process undermine the veracity of their continuum treatment by masking the effects of stochasticity and have to be treated discretely. Motility and interactions between leukocytes and bacteria are modeled via random walk and a stochastic simulation algorithm, respectively. Since the latter assumes the reacting species to be well mixed, the discrete component of our hybrid algorithm deploys stochastic operator splitting, in which the sequence of the diffusion and reaction operations is determined autonomously during each simulation step. We conduct a series of numerical experiments to ascertain the accuracy and computational efficiency of our hybrid simulations and, then, to demonstrate the importance of randomness for predicting leukocyte migration and fate during the immune response to inflammation.

    View details for DOI 10.1007/s00285-021-01581-7

    View details for PubMedID 33646399

  • Are college campuses superspreaders? A data-driven modeling study. Computer methods in biomechanics and biomedical engineering Lu, H., Weintz, C., Pace, J., Indana, D., Linka, K., Kuhl, E. 2021: 1–11

    Abstract

    The COVID-19 pandemic continues to present enormous challenges for colleges and universities and strategies for save reopening remain a topic of ongoing debate. Many institutions that reopened cautiously in the fall experienced a massive wave of infections and colleges were soon declared as the new hotspots of the pandemic. However, the precise effects of college outbreaks on their immediate neighborhood remain largely unknown. Here we show that the first two weeks of instruction present a high-risk period for campus outbreaks and that these outbreaks tend to spread into the neighboring communities. By integrating a classical mathematical epidemiology model and Bayesian learning, we learned the dynamic reproduction number for 30 colleges from their daily case reports. Of these 30 institutions, 14 displayed a spike of infections within the first two weeks of class, with peak seven-day incidences well above 1,000 per 100,000, an order of magnitude larger than the nation-wide peaks of 70 and 150 during the first and second waves of the pandemic. While most colleges were able to rapidly reduce the number of new infections, many failed to control the spread of the virus beyond their own campus: Within only two weeks, 17 campus outbreaks translated directly into peaks of infection within their home counties. These findings suggests that college campuses are at risk to develop an extreme incidence of COVID-19 and become superspreaders for neighboring communities. We anticipate that tight test-trace-quarantine strategies, flexible transition to online instruction, and-most importantly-compliance with local regulations will be critical to ensure a safe campus reopening after the winter break.

    View details for DOI 10.1080/10255842.2020.1869221

    View details for PubMedID 33439055

  • Lagrangian dynamic mode decomposition for construction of reduced-order models of advection-dominated phenomena JOURNAL OF COMPUTATIONAL PHYSICS Lu, H., Tartakovsky, D. M. 2020; 407
  • PREDICTION ACCURACY OF DYNAMIC MODE DECOMPOSITION SIAM JOURNAL ON SCIENTIFIC COMPUTING Lu, H., Tartakovsky, D. M. 2020; 42 (3): A1639–A1662

    View details for DOI 10.1137/19M1259948

    View details for Web of Science ID 000551255700016

  • EFFICIENT STOCHASTIC ASYMPTOTIC-PRESERVING IMPLICIT-EXPLICIT METHODS FOR TRANSPORT EQUATIONS WITH DIFFUSIVE SCALINGS AND RANDOM INPUTS SIAM JOURNAL ON SCIENTIFIC COMPUTING Jin, S., Lu, H., Pareschi, L. 2018; 40 (2): A671–A696

    View details for DOI 10.1137/17M1120518

    View details for Web of Science ID 000431100400003

  • A HIGH ORDER STOCHASTIC ASYMPTOTIC PRESERVING SCHEME FOR CHEMOTAXIS KINETIC MODELS WITH RANDOM INPUTS MULTISCALE MODELING & SIMULATION Jin, S., Lu, H., Pareschi, L. 2018; 16 (4): 1884–1915

    View details for DOI 10.1137/17M1150840

    View details for Web of Science ID 000453753300015

  • An asymptotic-preserving stochastic Galerkin method for the radiative heat transfer equations with random inputs and diffusive scalings JOURNAL OF COMPUTATIONAL PHYSICS Jin, S., Lu, H. 2017; 334: 182–206