All Publications

  • Deep Learning With Functional Inputs JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS Thind, B., Multani, K., Cao, J. 2022
  • High-bandwidth CMOS-voltage-level electro-optic modulation of 780 nm light in thin-film lithium niobate OPTICS EXPRESS Celik, O., Sarabalis, C. J., Mayor, F. M., Stokowski, H. S., Herrmann, J. F., McKenna, T. P., Lee, N. A., Jiang, W., Multani, K. S., Safavi-Naeini, A. H. 2022; 30 (13): 23177-23186

    View details for DOI 10.1364/OE.460119

    View details for Web of Science ID 000813479600073

  • The impact of methodological choices when developing predictive models using urinary metabolite data. Statistics in medicine Krstic, N., Multani, K., Wishart, D. S., Blydt-Hansen, T., Cohen Freue, G. V. 2022


    The continuous evolution of metabolomics over the past two decades has stimulated the search for metabolic biomarkers of many diseases. Metabolomic data measured from urinary samples can provide rich information of the biological events triggered by organ rejection in pediatric kidney transplant recipients. With additional validation, metabolic markers can be used to build clinically useful diagnostic tools. However, there are many methodological steps ranging from data processing to modeling that can influence the performance of the resulting metabolomic classifiers. In this study we focus on the comparison of various classification methods that can handle the complex structure of metabolomic data, including regularized classifiers, partial least squares discriminant analysis, and nonlinear classification models. We also examine the effectiveness of a physiological normalization technique widely used in the clinical and biochemical literature but not extensively analyzed and compared in urine metabolomic studies. While the main objective of this work is to interrogate metabolomic data of pediatric kidney transplant recipients to improve the diagnosis of T cell-mediated rejection (TCMR), we also analyze three independent datasets from other disease conditions to investigate the generalizability of our findings.

    View details for DOI 10.1002/sim.9431

    View details for PubMedID 35567357

  • Superconducting on-chip tunable mm-wave resonator Das, D., Naji, A., Multani, K. S., Safavi-Naeini, A. H., Nanni, E. A., IEEE IEEE. 2022
  • Development of a Millimeter-Wave Transducer for Quantum Networks Multani, K. S., Stokowski, H., Snively, E., Patel, R., Jiang, W., Lee, N., Welander, P. B., Nanni, E. A., Safavi-Naeini, A. H., IEEE IEEE. 2020