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<!DOCTYPE HTML>
<html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
<title>Aishwarya Mandyam</title>
<meta name="author" content="Aishwarya Mandyam">
<meta name="viewport" content="width=device-width, initial-scale=1">
<link rel="stylesheet" type="text/css" href="stylesheet.css">
</head>
<body>
<table style="width:100%;max-width:800px;border:0px;border-spacing:0px;border-collapse:separate;margin-right:auto;margin-left:auto;"><tbody>
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<p style="text-align:center">
<name>Aishwarya Mandyam</name>
</p>
<p>I am a Computer Science PhD candidate at Stanford University working with Barbara Engelhardt and Emma Brunskill.
I'm broadly interested in building tools for decision-making within healthcare settings. I've focused on
reinforcement learning algorithms, and have a particular recent interest in policy evaluation.
</p>
<p>
Outside of research,
I enjoy distance running, dogspotting, and eating black forest cake.
</p>
<p>
</p>
<p style="text-align:center">
<a href="mailto:[email protected]">Email</a>  / 
<a href="data/CV.pdf">CV</a>  / 
<a href="http://www.linkedin.com/in/aishwaryamandyam/"> LinkedIn </a>  / 
<a href="blog.html"> Blog </a>
</p>
</td>
<td style="padding:2.5%;width:40%;max-width:40%">
<a href="images/headshot.png"><img style="width:100%;max-width:100%" alt="profile photo" src="images/headshot.png" class="hoverZoomLink"></a>
</td>
</tr>
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<heading>News</heading>
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<p>11/2024: Kernel Density Bayesian Inverse Reinforcement Learning was accepted to TMLR! </p>
<p>11/2024: I'll be giving a talk at Rajesh Ranganath's group meeting. </p>
<p>10/2024: I'll be giving a talk at the New York Academy of Science's 15th Machine Learning Symposium. </p>
<p>10/2024: I'll be giving a spotlight talk at the Michigan AI symposium in Ann Arbor. </p>
<p>9/2024: I'm grateful to be named a Stanford Data Science Scholar. </p>
<p>8/2024: I started an internship at Amazon in NYC, working on reinforcement learning. </p>
<p>4/2024: Adaptive Interventions with User-Defined Goals for Health Behavior Change was accepted to CHIL 2024!</p>
</td>
</tr>
</td>
<tr>
<td style="padding:0px;width:100%;vertical-align:middle">
<heading>Research</heading>
</td>
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</tbody></table>
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<a href="https://arxiv.org/abs/2412.08052"><papertitle>CANDOR: Counterfactual ANnotated DOubly Robust off-policy evaluation</papertitle></a>
</a>
<br>
<strong>Aishwarya Mandyam</strong>,
<a href="https://shengpu-tang.me/">Shengpu Tang</a>,
<a href="https://yaojiayu0826.github.io/">Jiayu Yao</a>,
<a href="https://websites.umich.edu/~wiensj/">Jenna Wiens</a>,
<a href="https://profiles.stanford.edu/barbara-engelhardt">Barbara Engelhardt</a>
<br>
<em>Under review </em>  
<p>Doubly robust approaches to off-policy evaluation in the prescence of counterfactual annotations. </p>
</td>
</tr>
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<td style="padding:20px;width:75%;vertical-align:middle">
<a href="https://arxiv.org/abs/2311.09483"><papertitle>Adaptive Interventions with User-Defined Goals for Health Behavior Change</papertitle></a>
</a>
<br>
<strong>Aishwarya Mandyam*</strong>,
<a href="https://matthewjoerke.com/">Matthew Joerke*</a>,
<a href="https://profiles.stanford.edu/barbara-engelhardt">Barbara Engelhardt</a>,
<a href="https://cs.stanford.edu/people/ebrun/">Emma Brunskill</a>
<br>
<em>Conference on Health Inference and Learning (CHIL) 2024 </em>  
<p>A modification to Thompson Sampling to enable personalized goal setting using personalized reward functions. </p>
</td>
</tr>
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<td style="padding:20px;width:75%;vertical-align:middle">
<a href="https://openreview.net/forum?id=B80WUNhTAw"><papertitle>Kernel Density Bayesian Inverse Reinforcement Learning</papertitle>
</a>
<br>
<strong>Aishwarya Mandyam</strong>,
<a href="https://sites.google.com/view/didongli/">Didong Li</a>,
<a href="https://www.dianacai.com/">Diana Cai</a>,
<a href="https://andrewcharlesjones.github.io/">Andrew Jones</a>,
<a href="https://profiles.stanford.edu/barbara-engelhardt">Barbara Engelhardt</a>
<br>
<em>Transactions of Machine Learning Research (TMLR)</em>
<p> A Bayesian inverse reinforcement learning method that uses conditional kernel density estimation to make computational gains on existing approaches.</p>
</td>
</tr>
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<td style="padding:20px;width:75%;vertical-align:middle">
<a href="https://proceedings.mlr.press/v225/mandyam23a.html"><papertitle>Compositional Q-learning for electrolyte repletion with imbalanced patient sub-populations</papertitle>
</a>
<br>
<strong>Aishwarya Mandyam</strong>,
<a href="https://andrewcharlesjones.github.io/">Andrew Jones</a>,
<a href="https://yaojiayu0826.github.io/">Jiayu Yao</a>
<a href="https://scholar.google.com/citations?user=lTUucyEAAAAJ&hl=en">Krzysztof Laudanski</a>,
<a href="https://profiles.stanford.edu/barbara-engelhardt">Barbara Engelhardt</a>
<br>
<em>Machine Learning for Healthcare (ML4H), Best paper award honorable mention</em>
<p> A compositional RL framework to find optimal policies in EHR datasets with known heterogeneous treatment effects.
</p>
</td>
</tr>
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<td style="padding:20px;width:75%;vertical-align:middle">
<a href="https://www.mdpi.com/2075-4426/12/5/661"><papertitle>Guiding Efficient, Effective, and Patient-Oriented Electrolyte Replacement in Critical Care: An Artificial Intelligence Reinforcement Learning Approach</papertitle></a>
</a>
<br>
<a href="https://www.microsoft.com/en-us/research/people/niprasa/">Niranjani Prasad*</a>,
<strong>Aishwarya Mandyam*</strong>,
<a href="https://scholar.google.com/citations?user=4wvnYJEAAAAJ">Corey Chivers</a>,
<a href="https://scholar.google.com/citations?user=Pwk-oTAAAAAJ&hl=en">Michael Draugelis</a>,
<a href="https://sciprofiles.com/profile/1122596">C. William Hanson</a>,
<a href="https://profiles.stanford.edu/barbara-engelhardt">Barbara Engelhardt</a>,
<a href="https://scholar.google.com/citations?user=lTUucyEAAAAJ&hl=en">Krzysztof Laudanski</a>
<br>
<em>Journal of Personalized Medicine</em>
<p> A reinforcement learning guided approach to electrolyte repletion, applied on a cohort from the University of Pennsylvania Medical Center.
</p>
</td>
</tr
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<td style="padding:20px;width:75%;vertical-align:middle">
<a href="https://dl.acm.org/doi/10.1145/3459930.3469536"><papertitle>COP-E-CAT: cleaning and organization pipeline for EHR computational and analytic tasks</papertitle></a>
</a>
<br>
<strong>Aishwarya Mandyam</strong>,
<a href="https://www.linkedin.com/in/elizabeth-c-yoo/">Elizabeth C. Yoo</a>,
<a href="https://www.simonsfoundation.org/people/jeff-soules/">Jeff Soules</a>,
<a href="https://scholar.google.com/citations?user=lTUucyEAAAAJ&hl=en">Krzysztof Laudanski</a>,
<a href="https://profiles.stanford.edu/barbara-engelhardt">Barbara Engelhardt</a>
<br>
<em>BCB '21: Proceedings of the 12th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics</em>
<p> An open-source pre-processing and analysis software for MIMIC-IV, a ubiquitous benchmark EHR dataset.
</p>
</td>
</tr>
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<td style="padding:20px;width:75%;vertical-align:middle">
<a href="data/MolecularMatchmaker.pdf"><papertitle>Molecular Matchmaker: selecting peptide-aptamer binding pairs using machine learning</papertitle></a>
</a>
<br>
<strong>Aishwarya Mandyam</strong>,
<a href="https://yuhao-wan.github.io/">Yuhao Wan</a>,
<a href="https://scholar.google.com/citations?user=KzESVKwAAAAJ&hl=en">Luis Ceze</a>,
<a href="https://scholar.google.com/citations?user=G07ueBQAAAAJ&hl=en">Jeff Nivala</a>,
<a href="https://scholar.google.com/citations?user=dq3yXjkAAAAJ&hl=en">Kevin Jamieson</a>,
<br>
<em>Oral Presentation @ Machine Learning for Computational Biology (MLCB) 2020</em>
<br>
<p>Using machine learning to characterize the relationship between binding aptamers and peptides. </p>
</td>
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<td style="padding:20px;width:75%;vertical-align:middle">
<a href="https://www.biorxiv.org/content/10.1101/2020.03.06.981514v1"><papertitle>Porcupine: Rapid and robust tagging of physical objects using nanopore-orthogonal DNA strands</papertitle></a>
<br>
<a href="https://homes.cs.washington.edu/~kdorosch/">Katie Doroschak</a>,
Karen Zhang,
Melissa Queen,
<strong>Aishwarya Mandyam</strong>,
<a href="https://scholar.google.com/citations?user=tKxzUYwAAAAJ&hl=en">Karin Strauss</a>,
<a href="https://scholar.google.com/citations?user=KzESVKwAAAAJ&hl=en">Luis Ceze</a>,
<a href="https://scholar.google.com/citations?user=G07ueBQAAAAJ&hl=en">Jeff Nivala</a>,
<br>
<em>Nature Communications</em>, 2019  
<br>
<p>A robust, low density concept for DNA barcoding and storage. </p>
</td>
</tr>
</tbody></table>
<table style="width:100%;border:0px;border-spacing:0px;border-collapse:separate;margin-right:auto;margin-left:auto;"><tbody>
<tr>
<td style="padding:0px;width:100%;vertical-align:middle">
<heading>Workshop Papers</heading>
</td>
</tr>
</tbody></table>
<table style="width:100%;border:0px;border-spacing:0px;border-collapse:separate;margin-right:auto;margin-left:auto;"><tbody>
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<td style="padding:20px;width:75%;vertical-align:middle">
<a href="data/FFCML.pdf"><papertitle>Estimating Influential Samples in the Fragile Families Challenge</papertitle></a>
</a>
<br>
<strong>Aishwarya Mandyam</strong>,
<a href="https://sienadumasang.com/">Siena Dumas Ang</a>,
<a href="https://profiles.stanford.edu/barbara-engelhardt">Barbara Engelhardt</a>
<br>
<em>Poster @ WiML Workshop at NeurIPS 2020 </em>  
<p> Using influence functions to identify individuals that disproportionately affect the generalization error of the prediction methods used in the Fragile Families Challenge.
</p>
</td>
</tr>
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<td style="padding:20px;width:75%;vertical-align:middle">
<papertitle>Reducing Identification Time in a Molecular Tagging System</papertitle>
<br>
<strong>Aishwarya Mandyam</strong>,
<a href="https://homes.cs.washington.edu/~kdorosch/">Katie Doroschak</a>,
Karen Zhang, Melissa Queen,
<a href="https://scholar.google.com/citations?user=tKxzUYwAAAAJ&hl=en">Karin Strauss</a>,
<a href="https://scholar.google.com/citations?user=KzESVKwAAAAJ&hl=en">Luis Ceze</a>,
<a href="https://scholar.google.com/citations?user=G07ueBQAAAAJ&hl=en">Jeff Nivala</a>,
<br>
<em>Poster and Oral Presentation @ Grace Hopper Conference 2019, 2nd place ACM Student Research Award</em> 
<br>
<p>Evaluating the results of nanopore sequencing on custom DNA barcodes, and designing new DNA barcodes based on the error analysis results. </p>
</td>
</tr
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<table style="width:100%;border:0px;border-spacing:0px;border-collapse:separate;margin-right:auto;margin-left:auto;"><tbody>
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<td style="padding:2px;width:100%;vertical-align:middle">
<heading>Talks</heading>
</td>
</tr>
</tbody></table>
<table style="width:100%;border:0px;border-spacing:0px;border-collapse:separate;margin-right:auto;margin-left:auto;"><tbody>
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<td style="padding:2px;width:75%;vertical-align:middle">
<papertitle_old>New York University, Rajesh Ranganathan Group Meeting</papertitle_old>
<em>November 2024</em>
<br>
</td>
</tr>
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<td style="padding:2px;width:75%;vertical-align:middle">
<papertitle_old>Harvard University, Dtak Group Meeting</papertitle_old>
<em>November 2024</em>
<br>
</td>
</tr>
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<td style="padding:2px;width:75%;vertical-align:middle">
<papertitle_old>New York Academy of Sciences, Machine Learning Symposium</papertitle_old>
<em>October 2024</em>
<br>
</td>
</tr>
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<td style="padding:2px;width:75%;vertical-align:middle">
<papertitle_old>University of Michigan, Michigan AI Symposium</papertitle_old>
<em>October 2024</em>
<br>
</td>
</tr>
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<td style="padding:2px;width:75%;vertical-align:middle">
<papertitle_old>University of Michigan, MLD3 Group Meeting</papertitle_old>
<em>October 2024</em>
<br>
</td>
</tr>
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<td style="padding:2px;width:75%;vertical-align:middle">
<papertitle_old>American Statistical Association, Colorado-Wyoming Chapter</papertitle_old>
<em>April 2021</em>
<br>
</td>
</tr>
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<td style="padding:2px;width:75%;vertical-align:middle">
<papertitle_old>Machine Learning for Computational Biology (MLCB)</papertitle_old>
<em>October 2021</em>
<br>
<p></p>
</td>
</tr>
</tbody></table>
<table style="width:100%;border:0px;border-spacing:0px;border-collapse:separate;margin-right:auto;margin-left:auto;"><tbody>
<tr>
<td style="padding:2px;width:100%;vertical-align:middle">
<heading>Internships</heading>
</td>
</tr>
</tbody></table>
<table style="width:100%;border:0px;border-spacing:0px;border-collapse:separate;margin-right:auto;margin-left:auto;"><tbody>
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<papertitle_old>Amazon</papertitle_old>
<em>August 2024- November 2024</em>
<br>
</td>
</tr>
<tr onmouseout="nightsight_stop()" onmouseover="nightsight_start()">
<td style="padding:2px;width:75%;vertical-align:middle">
<papertitle_old>Allen Institute for Artificial Intelligence</papertitle_old>
<em>March 2019- September 2019</em>
<br>
</td>
</tr>
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<td style="padding:2px;width:75%;vertical-align:middle">
<papertitle_old>Sage Bionetworks</papertitle_old>
<em>September 2018- March 2019</em>
<br>
</td>
</tr>
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<td style="padding:2px;width:75%;vertical-align:middle">
<papertitle_old>Microsoft</papertitle_old>
<em>June 2018-September 2018</em>
<br>
</td>
</tr>
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<td style="padding:2px;width:75%;vertical-align:middle">
<papertitle_old>Microsoft</papertitle_old>
<em>June 2017-September 2017</em>
<br>
</td>
</tr>
<tr onmouseout="nightsight_stop()" onmouseover="nightsight_start()">
<td style="padding:2px;width:75%;vertical-align:middle">
<papertitle_old>Microsoft</papertitle_old>
<em>June 2016-September 2016</em>
<br>
</td>
</tr>
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<td style="padding:2px;width:75%;vertical-align:middle">
<papertitle_old>Expedia</papertitle_old>
<em>June 2015- August 2015</em>
<br>
</td>
</tr>
</tbody></table>
<br>
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<td>
<heading>Leadership + Service </heading>
</td>
</tr>
</tbody></table>
<table width="100%" align="center" border="0" cellpadding="0"><tbody>
<tr>
<td width="75%" valign="center">
<p><b>Reviewer</b>: AISTATS 2024, ML4H 2024, NeurIPS 2024, RLC 2024 </p>
<p><b>Organizer</b>: Machine Learning for Healthcare Symposium (ML4H) 2024, Stanford-Berkeley Women’s CS/EE Research Meetup 2024 </p>
</td>
</tr>
</tbody></table>
<br>
<table style="width:100%;border:0px;border-spacing:0px;border-collapse:separate;margin-right:auto;margin-left:auto;"><tbody>
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<td style="padding:0px">
<br>
<p style="text-align:right;font-size:small;">
Made with <a href="https://jonbarron.info/">permission</a>.
</p>
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</body>
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