|
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. Outside of research, I enjoy distance running, dogspotting, and eating black forest cake.
|
|
|
|
|
Are you attending NeurIPS 2024? Do you enjoy running? Join me (and several others) for the unofficial NeurIPS Run Club! We will run at a conversational (10:00-10:30 min/mile) pace. All runs will start from the Vancouver Convention Center and begin at 7:30 AM. These are the planned routes. Join for however long you would like! All runs will be followed by coffee/breakfast at a local shop. If you're interested in joining, send me a note- we'll wait for you! 12/12/2024: Route (6.38 miles) 12/13/2024: Route (6.9 miles) 12/14/2024: Route (5.4 miles) 12/15/2024: Route (7.15 miles) |
|
|
|
11/2024: Kernel Density Bayesian Inverse Reinforcement Learning was accepted to TMLR! 11/2024: I'll be giving a talk at Rajesh Ranganath's group meeting. 10/2024: I'll be giving a talk at the New York Academy of Science's 15th Machine Learning Symposium. 10/2024: I'll be giving a spotlight talk at the Michigan AI symposium in Ann Arbor. 9/2024: I'm grateful to be named a Stanford Data Science Scholar. 8/2024: I started an internship at Amazon in NYC, working on reinforcement learning. 4/2024: Adaptive Interventions with User-Defined Goals for Health Behavior Change was accepted to CHIL 2024! |
|
|
|
Aishwarya Mandyam, Shengpu Tang, Jiayu Yao, Jenna Wiens, Barbara Engelhardt Under review Doubly robust approaches to off-policy evaluation in the prescence of counterfactual annotations. |
|
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. |
|
Aishwarya Mandyam, Didong Li, Diana Cai, Andrew Jones, Barbara Engelhardt Transactions of Machine Learning Research (TMLR) A Bayesian inverse reinforcement learning method that uses conditional kernel density estimation to make computational gains on existing approaches. |
|
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. |
|
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. |
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. |
|
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. |
|
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. |
|
|
|
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. |
|
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. |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Reviewer: AISTATS 2024, ML4H 2024, NeurIPS 2024, RLC 2024 Organizer: Machine Learning for Healthcare Symposium (ML4H) 2024, Stanford-Berkeley Women’s CS/EE Research Meetup 2024 |
|
Made with permission. |