Charles Lu, PhD student at MIT Media Lab

Charles Lu

PhD Researcher at MIT

an email adress

About Me

I am a PhD student in the Camera Culture group at the MIT Media Laboratory, advised by Ramesh Raskar. My research focuses on data-centric machine learning, specifically data markets, incentives, and data attribution. I am honored to be supported by the National Science Foundation's Graduate Research Fellowship.

My research aims to improve machine learning systems by focusing on the data that powers them. I investigate how we can strategically acquire high-quality data and properly attribute contributions from various data sources to ensure the resulting ML systems are reliable, fair, and transparent.

Outside the Lab

In my free time, I enjoy several creative and physical activities:

Woodworking
Synthesizers
Pottery
Jujitsu
Hiking

Research Focus

My current research lies at the intersection of data-centric machine learning and incentives, with particular emphasis on:

Experience

2025 (Upcoming)

Research Intern — Microsoft Research, Redmond

Working on data attribution in large language models to identify the influence of training data on model outputs.

2024

Visiting Researcher — University of Chicago

Explored data markets and data escrow mechanisms with Raul Castro Fernandez in the Department of Computer Science at the University of Chicago.

2022–present

Research Assistant — MIT Media Lab

Working on data-centric machine learning in the Camera Culture group at the Massachusetts Institute of Technology.

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 including 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.

2017

Computer Vision Researcher — University of Pittsburgh Medical Center

Studied rare genetic disorders using computer vision techniques in microscopy.

Selected papers

Data Acquisition via Experimental Design for Decentralized Data Markets

NeurIPS 2024

Developed a framework for optimizing budget-aware data acquisition in decentralized markets with a experimental design informed loss.

Federated Conformal Predictors for Distributed Uncertainty Quantification

ICML 2023

Introduced novel methods for uncertainty quantification in federated learning scenarios, extending conformal prediction theory to distributed data contexts.

Improving Trustworthiness in Automatic Disease Severity Rating with Ordinal Conformal Prediction

MICCAI 2022

Developed ordinal conformal prediction methods for disease severity assessment in medical imaging, providing rigorous uncertainty quantification for clinical decision support.

Fair Conformal Predictors for Applications in Medical Imaging

AAAI 2022

Proposed novel algorithms for ensuring fairness in uncertainty quantification across diverse patient populations, addressing algorithmic bias in medical AI systems.

Quantification of the Thoracic Aorta and Detection of Aneurysm at CT: Validation of a Fully Automatic Methodology

Radiology: Artificial Intelligence

Designed and validated an automated system for detecting aortic aneurysms in CT scans, demonstrating clinical utility through rigorous evaluation with expert radiologists.

Awards & Service

NSF Graduate Research Fellowship Program (GRFP)

National Science Foundation

Social and Ethical Responsibilities of Computing Scholar (SERC)

MIT Schwarzman College of Computing

Academic Service

Reviewer for ICML, ICLR, NeurIPS, CVPR, MICCAI, AISTATS