A Unified Theory of Representation Learning:
How Hidden Relationships Power Algorithms that can Learn
without Labels

Mark Hamilton advised by William T. Freeman ‍

MIT PhD Thesis

I-Con: A Unifying Framework for Representation Learning

Shaden Alshammari, John R. Hershey, Axel Feldman, William T. Freeman, Mark Hamilton ‍

International Conference on Learning Representations (ICLR) 2025

Separating the "Chirp" from the "Chat": Self-supervised Visual Grounding of Sound and Language

Mark Hamilton, Andrew Zisserman, John R. Hershey, William T. Freeman

Computer Vision and Pattern Recognition (CVPR) 2024

+  (Keynote) Speech and Audio in the Northeast

+  (Invited Talk) Large Scale Holistic Video Understanding Workshop

+  (Invited Talk) Sight and Sound Workshop

FeatUp: A Model-Agnostic Framework for Features at Any Resolution

Mark Hamilton*,Stephanie Fu*, Laura Brandt, Axel Feldman, Zhoutong Zhang, William T. Freeman

International Conference on Learning Representations (ICLR) 2024

Seeing Faces in Things: A Model and Dataset for Pareidolia

Mark Hamilton, Simon Stent, Vasha DuTell, Anne Harrington, Jennifer Corbett, Ruth Rosenholtz, William T. Freeman ‍

European Conference on Computer Vision (ECCV) 2024

Large-Scale Automatic Audiobook Creation

Mark Hamilton*, Brendan Walsh*, Greg Newby, Xi Wang, Serena Ruan, Sheng Zhao, Lei He, Shaofei Zhang, Eric Dettinger,  William T. Freeman, Markus Weimer

Interspeech 2023 Show and Tell

TIME Top 200 Invention of 2023

Workshop Organizer: Multimodal Learning for Earth and Environment

Miriam Cha, Gregory Angelides, Mark Hamilton, Andy Soszynski, Brandon Swenson, Nathaniel Maidel, Phillip Isola, Taylor Perron, William T. Freeman

MultiEarth '22 and '23 Workshops at CVPR

Unsupervised Semantic Segmentation by Distilling Feature Correspondences

Mark Hamilton, Zhoutong Zhang, Bharath Hariharan, Noah Snavely, William T. Freeman

International Conference on Learning Representations (ICLR) 2022

Axiomatic Explanations for Visual Search, Retrieval, and Similarity Learning

Mark Hamilton, Scott Lundberg, Lei Zhang, Stephanie Fu, William T. Freeman

International Conference on Learning Representations (ICLR) 2022

Large-Scale Intelligent Microservices

Mark Hamilton, Nick Gonsalves, Christina Lee, Anand Raman, Brendan Walsh, Siddhartha Prasad, Dalitso Banda, Lucy Zhang, Lei Zhang, William T. Freeman

IEEE Big Data 2020

MosAIc: Finding Artistic Connections across Culture with Conditional Image Retrieval

Mark Hamilton, Stephanie Fu, Mindren Lu, Johnny Bui, Darius Bopp, Zhenbang Chen, Felix Tran, Margaret Wang, Marina Rogers, Lei Zhang, Chris Hoder, William T. Freeman

NeurIPs 2020 Demonstration

Automatic Detection of Poachers and Wildlife with UAVs

Elizabeth Bondi, Fei Fang, Mark Hamilton, Debarun Kar, Donnabell Dmello, Venil Noronha, Jongmoo Choi, Robert Hannaford, Arvind Iyer, Lucas Joppa, Milind Tambe, Ram Nevatia

Artificial Intelligence and Conservation, Ch. 5, 2019

Risk-Based Critical Concentrations of Legionella pneumophila for Indoor Residential Water Uses

Kerry A Hamilton, Mark T Hamilton, William Johnson, Patrick Jjemba, Zia Bukhari, Mark LeChevallier, Charles N Haas, PL Gurian

Environmental Science and Technology 2019

ACS Editors Choice and Cover Article

Companies and the Rise of Economic Thought: The Institutional Foundations of Early Economics in England, 1550–1720

Emily Erikson, Mark Hamilton

American Journal of Sociology 2018

Honorable Mention: Mark Granovetter Award for Best Article

Lead the SynapseML Product at Microsft

Simple and elastically distributed machine learning, microservice orchestration, and model deployment. SynapseML simplifies the creation of production grade ML pipelines across Python, Scala, Java, and R.

MosAIc

Art is one of the few languages which transcends barriers of country, culture, and time. MosAIc is an algorithm that can help discover the common semantic elements of art even between any culture, media, artist, or collection within the combined artworks of The Metropolitan Museum of Art and The Rijksmusem.