Sotabase

Career

· Applied Science Intern, Amazon (Current)2024–
· Research Associate, Gladstone Institutes2021–2022
· N/A, Computer Science, Princeton University2020–2022
· Doctor of Philosophy - PhD Computer Science, Stanford University2020–2022
· Intern, Allen Institute for Artificial Intelligence (AI2)2019–2019
· Master of Science - MS, Computer Science, University of Washington2019–2020
· Software Engineering Intern, Microsoft2018–2018
· Software Engineering Intern, Sage Bionetworks2018–2019
· Software Engineering Intern, Microsoft2017–2018
· Explorer Intern, Microsoft2016–2016
· Sofware Developer Apprentice, Expedia2015–2015
· Bachelor’s Degree, Computer Science, Philosophy, University of Washington2015–2019
· Doctor of Philosophy - PhD, Computer Science, Stanford University

Publications (17)

Rapid and robust assembly and decoding of molecular tags with DNA-based nanopore signatures
Nature Communications · 2020
40
cited
COP-E-CAT: cleaning and organization pipeline for EHR computational and analytic tasks
ACM International Conference on Bioinformatics, Computational Biology and Biomedicine · 2021
17
cited
Guiding Efficient, Effective, and Patient-Oriented Electrolyte Replacement in Critical Care: An Artificial Intelligence Reinforcement Learning Approach
Journal of Personalized Medicine · 2022
6
cited
Kernel Density Bayesian Inverse Reinforcement Learning
arXiv.org · 2023
4
cited
Adaptive Interventions with User-Defined Goals for Health Behavior Change
ACM Conference on Health, Inference, and Learning · 2023
3
cited
CANDOR: Counterfactual ANnotated DOubly Robust Off-Policy Evaluation
arXiv.org · 2024
2
cited
PERRY: Policy Evaluation with Confidence Intervals using Auxiliary Data
arXiv.org · 2025
1
cited
APRIL: Annotations for Policy evaluation with Reliable Inference from LLMs
arXiv.org · 2025
Compositional Q-learning for electrolyte repletion with imbalanced patient sub-populations
ML4H@NeurIPS · 2021
Efficient Bayesian Inverse Reinforcement Learning via Conditional Kernel Density Estimation
2022
KD-BIRL: Kernel Density Bayesian Inverse Reinforcement Learning
Trans. Mach. Learn. Res. · 2023
Machine Learning for Health (ML4H) 2024
ML4H@NeurIPS · 2024
Molecular Matchmaker: selecting binding peptide-aptamer pairs using machine learning
Porcupine: Rapid and robust tagging of physical objects using nanopore-orthogonal DNA strands
bioRxiv · 2020
Reflections from Research Roundtables at the Conference on Health, Inference, and Learning (CHIL) 2025
arXiv.org · 2025
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Aishwarya Mandyam | Researcher Profile | Sotabase | Sotabase