Introduction

Hello! My name is Donna, and I am a health scientist statistician at the Arizona Veterans Research and Education Foundation, where I use computational techniques, such as machine learning, to model long term outcomes in individuals with diabetes. I obtained my PhD in computer science from the University of Michigan in 2024 from the MLD3 group. My dissertation specialized in machine learning, where I focused on survival analysis and noisy label learning, two settings that are broadly applicable in real-world problems. My CV can be found here.

Education

• 2013-2018: University of Toronto
  • 2018: Bachelor’s of Science in molecular genetics and computer science
• 2018-2024: University of Michigan
  • 2020: Master’s of Science in computer science
  • 2024: Doctor of Philosophy in computer science

Publications

• Tjandra D, Irwin C, Migrino RQ, Giordani B, Wiens J. Estimated Effects of Comorbidities on Risk of All-cause Dementia in Patients with Mild Cognitive Impairment. Sage Open Aging. 2025 Jun;11:30495334251347053.

• Kamran F, Tjandra D, Valley TS, Prescott HC, Shah NH, Liu VX, Horvitz E, Wiens J. Reformulating patient stratification for targeting interventions by accounting for severity of downstream outcomes resulting from disease onset: a case study in sepsis. Journal of the American Medical Informatics Association. 2025 May;32(5):905-13.

• Tjandra D, Wiens J. Survival Analysis with Multiple Noisy Labels. ICDM, December 2024

• Kamran F, Tjandra D, Heiler A, Virzi J, Singh K, King, JE, Valley TS, Wiens J. Evaluation of Sepsis Prediction Models before Onset of Treatment. NEJM AI. 2024 Feb 7:AIoa2300032.

• Irwin C, Tjandra D, Hu C, Aggarwal V, Lienau A, Giordani B, Wiens J, Migrino RQ. Predicting 5‐year dementia conversion in veterans with mild cognitive impairment. Alzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring. 2024 Jan;16(1):e12572.

• Tjandra D & Wiens J. Leveraging an Alignment Set in Tackling Instance-Dependent Label Noise. In Conference on Health, Inference, and Learning (pp. 477-497). PMLR. 2023

• Tjandra D, Migrino RQ, Giordani B, Wiens J. Use of blood pressure measurements extracted from the electronic health record in predicting Alzheimer’s disease: A retrospective cohort study at two medical centers. Alzheimer’s & Dementia. 18(11): 2368-72. 2022

• Tjandra D, He Y, Wiens J. A Hierarchical Approach to Multi-Event Survival Analysis. In Proceedings of the AAAI Conference on Artificial Intelligence 2021 May 18, Vol. 35, No. 1, pp. 591-599

• Tjandra D, Migrino RQ, Giordani B, Wiens J. Cohort discovery and risk stratification for Alzheimer’s disease: an electronic health record‐based approach. Alzheimer’s & Dementia: Translational Research & Clinical Interventions, 6(1), e12035. 2020

• Belth C, Kamran F, Tjandra D, Koutra D. When to remember where you came from: Node representation learning in higher-order networks. IEEE/ACMInternational Conference on Advances in Social Networks Analysis and Mining (ASONAM). 2019

• Pascoe N, Seetharaman A, Teyra J, Manczyk N, Satori AM, Tjandra D, Makhnevych T, Schwerdtfeger C, Brasher BB, Moffat J, Costanzo M, Boone C, Sicheri F, Sidhu SS. Yeast Two-Hybrid Analysis for Ubiquitin-variant Inhibitors of Human Deubiquitinases. Journal of Molecular Biology. 436(6): 1160-1171. 2019

• Goyal D, Tjandra D, Migrino RQ, Giordani B, Syed Z, Wiens J. Characterizing heterogeneity in the progression of Alzheimer’s disease using longitudinal clinical and neuroimaging biomarkers. Alzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring. 10: 629-637. 2018