About Me
I am a PhD student in the Camera Culture group at the MIT Media Laboratory. My research focuses on data-centric machine learning. I am supported by the National Science Foundation's Graduate Research Fellowship.
I study how data influences machine learning models through data attribution and incentive alignment frameworks.
Outside the lab, I enjoy activites:
Research Focus
My current research focus
- Data Attribution: Measuring influence and data provenance in model.
- Data Markets:Design of economic and ethical frameworks for data use.
- Responsible AI:Improving alignment, uncertainty quantification, and fairness in ML systems.
Experience
2025
Research Intern Microsoft Research, Redmond
Working on training data attribution for large language models in the Deep learning Group.
2024
Visiting Researcher University of Chicago
Explored data markets and data escrow mechanisms in the Department of Computer Science at the University of Chicago.
2020–2022
Machine Learning Researcher Harvard Medical School
Developed fairness and uncertainty quantification methods for medical imaging at the Martinos Center.
2019–2022
Machine Learning Scientist Massachusetts General Hospital
Built deep learning models for medical applications such as stroke detection and aortic aneurysm quantification.
2018
Research Intern Oak Ridge National Laboratory
Applied machine learning to molecular dynamics simulations on supercomputing infrastructure.
2018
Research Intern NASA
Analyzed hurricane damage using satellite imagery for environmental policy planning.
Selected papers
Data Acquisition via Experimental Design for Decentralized Data Markets
NeurIPS 2024
Developed a framework for optimizing budget-aware data acquisition.
Federated Conformal Predictors for Distributed Uncertainty Quantification
ICML 2023
Introduced method for uncertainty quantification in federated learning scenarios.
Improving Trustworthiness in Automatic Disease Severity Rating with Ordinal Conformal Prediction
MICCAI 2022
Developed ordinal conformal prediction for disease severity assessment in medical imaging.
Fair Conformal Predictors for Applications in Medical Imaging
AAAI 2022
Proposed fairness in uncertainty quantification across diverse patient populations to address bias in medical AI systems.
Radiology: Artificial Intelligence
Designed and validated automated system for aortic aneurysm detection in CT scans.