Aishwarya Mandyam

I am a Computer Science PhD candidate at Stanford University working with Barbara Engelhardt and Emma Brunskill. I'm interested in building statistical/ML tools to improve clinical decision-making.

Outside of research, I enjoy distance running, dogspotting, and eating black forest cake.

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04/2024: Adaptive Interventions with User-Defined Goals for Health Behavior Change was accepted to CHIL 2024!

03/2024: I will be interning on the Amazon Reinforcement Learning team in Fall 2024.

CANDOR: Counterfactual ANnotated DOubly Robust off-policy evaluation
Aishwarya Mandyam, Shengpu Tang, Jiayu Yao, Emma Brunskill, Jenna Wiens, Barbara Engelhardt
Under review  

Doubly robust approaches to off-policy evaluation in the prescence of counterfactual annotations.

Adaptive Interventions with User-Defined Goals for Health Behavior Change
Aishwarya Mandyam*, Matthew Joerke*, Barbara Engelhardt, Emma Brunskill
Conference on Health Inference and Learning (CHIL) 2024  

A modification to Thompson Sampling to enable personalized goal setting using personalized reward functions.

Kernel Density Bayesian Inverse Reinforcement Learning
Aishwarya Mandyam, Didong Li, Diana Cai, Andrew Jones, Barbara Engelhardt
In submission

A Bayesian inverse reinforcement learning method that uses conditional kernel density estimation to make computational gains on existing approaches.

Compositional Q-learning for electrolyte repletion with imbalanced patient sub-populations
Aishwarya Mandyam, Andrew Jones, Jiayu Yao Krzysztof Laudanski, Barbara Engelhardt
Machine Learning for Healthcare (ML4H), Best paper award honorable mention

A compositional RL framework to find optimal policies in EHR datasets with known heterogeneous treatment effects.

Guiding Efficient, Effective, and Patient-Oriented Electrolyte Replacement in Critical Care: An Artificial Intelligence Reinforcement Learning Approach
Niranjani Prasad*, Aishwarya Mandyam*, Corey Chivers, Michael Draugelis, C. William Hanson, Barbara Engelhardt, Krzysztof Laudanski
Journal of Personalized Medicine

A reinforcement learning guided approach to electrolyte repletion, applied on a cohort from the University of Pennsylvania Medical Center.

COP-E-CAT: cleaning and organization pipeline for EHR computational and analytic tasks
Aishwarya Mandyam, Elizabeth C. Yoo, Jeff Soules, Krzysztof Laudanski, Barbara Engelhardt
BCB '21: Proceedings of the 12th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics

An open-source pre-processing and analysis software for MIMIC-IV, a ubiquitous benchmark EHR dataset.

Molecular Matchmaker: selecting peptide-aptamer binding pairs using machine learning
Aishwarya Mandyam, Yuhao Wan, Luis Ceze, Jeff Nivala, Kevin Jamieson,
Oral Presentation @ Machine Learning for Computational Biology (MLCB) 2020

Using machine learning to characterize the relationship between binding aptamers and peptides.

Porcupine: Rapid and robust tagging of physical objects using nanopore-orthogonal DNA strands
Katie Doroschak, Karen Zhang, Melissa Queen, Aishwarya Mandyam, Karin Strauss, Luis Ceze, Jeff Nivala,
Nature Communications, 2019  

A robust, low density concept for DNA barcoding and storage.

Workshop Papers
Estimating Influential Samples in the Fragile Families Challenge
Aishwarya Mandyam, Siena Dumas Ang, Barbara Engelhardt
Poster @ WiML Workshop at NeurIPS 2020  

Using influence functions to identify individuals that disproportionately affect the generalization error of the prediction methods used in the Fragile Families Challenge.

Reducing Identification Time in a Molecular Tagging System
Aishwarya Mandyam, Katie Doroschak, Karen Zhang, Melissa Queen, Karin Strauss, Luis Ceze, Jeff Nivala,
Poster and Oral Presentation @ Grace Hopper Conference 2019, 2nd place ACM Student Research Award 

Evaluating the results of nanopore sequencing on custom DNA barcodes, and designing new DNA barcodes based on the error analysis results.

American Statistical Association, Colorado-Wyoming Chapter April 2021
Machine Learning for Computational Biology (MLCB) October 2021

Allen Institute for Artificial Intelligence March 2019- September 2019
Sage Bionetworks September 2018- March 2019
Microsoft June 2018-September 2018
Microsoft June 2017-September 2017
Microsoft June 2016-September 2016
Expedia June 2015- August 2015

Leadership + Service
DubHacks Co-Director 2016-2017
UW ACM 2016-2018

Made with permission.