Tina Diao is studying decision and risk analysis. Her current research examines the principled ways to apply machine learning and/or artificial intelligence in domains of medicine and environmental sciences. She believes in decision-driven data acquisition and analyses, not the other way round. Her previous research interests include decision analysis in social entrepreneurship and modeling matching & pricing with friction between investors and entrepreneurs. She has work experiences in pension consulting and personal property & casualty insurance lines. Tina holds an M.S. in management science and engineering (technical and engineering management) from Stanford University and B.A.s in statistics and economics from University of California - Berkeley.

All Publications

  • Comparison of machine learning approaches to improve diagnosis of optic neuropathy using photopic negative response measured using a handheld device Frontiers in Medicine Diao, T., Kushzad, F., Patel, M. D., Bindiganavale, M. P., Wasi, M., Kochenderfer, M. J., Moss, H. E. 2021; 8

    View details for DOI 10.3389/fmed.2021.771713

  • Comparison of Machine Learning Approaches to Improve Diagnosis of Optic Neuropathy Using Photopic Negative Response Measured Using a Handheld Device. Frontiers in medicine Diao, T., Kushzad, F., Patel, M. D., Bindiganavale, M. P., Wasi, M., Kochenderfer, M. J., Moss, H. E. 1800; 8: 771713


    The photopic negative response of the full-field electroretinogram (ERG) is reduced in optic neuropathies. However, technical requirements for measurement and poor classification performance have limited widespread clinical application. Recent advances in hardware facilitate efficient clinic-based recording of the full-field ERG. Time series classification, a machine learning approach, may improve classification by using the entire ERG waveform as the input. In this study, full-field ERGs were recorded in 217 eyes (109 optic neuropathy and 108 controls) of 155 subjects. User-defined ERG features including photopic negative response were reduced in optic neuropathy eyes (p < 0.0005, generalized estimating equation models accounting for age). However, classification of optic neuropathy based on user-defined features was only fair with receiver operating characteristic area under the curve ranging between 0.62 and 0.68 and F1 score at the optimal cutoff ranging between 0.30 and 0.33. In comparison, machine learning classifiers using a variety of time series analysis approaches had F1 scores of 0.58-0.76 on a test data set. Time series classifications are promising for improving optic neuropathy diagnosis using ERG waveforms. Larger sample sizes will be important to refine the models.

    View details for DOI 10.3389/fmed.2021.771713

    View details for PubMedID 34926514

  • Experimental Study of Big Raster and Vector Database Systems Singla, S., Eldawy, A., Diao, T., Mukhopadhyay, A., Scudiero, E., IEEE IEEE COMPUTER SOC. 2021: 2243-2248