| CARVIEW |
Select Language
HTTP/2 200
server: GitHub.com
content-type: text/html; charset=utf-8
last-modified: Mon, 08 Sep 2025 22:55:45 GMT
access-control-allow-origin: *
etag: W/"68bf5ef1-138d4"
expires: Mon, 29 Dec 2025 09:30:55 GMT
cache-control: max-age=600
content-encoding: gzip
x-proxy-cache: MISS
x-github-request-id: E914:318CF6:885FFF:994A3C:695247F6
accept-ranges: bytes
age: 0
date: Mon, 29 Dec 2025 09:20:55 GMT
via: 1.1 varnish
x-served-by: cache-bom-vanm7210051-BOM
x-cache: MISS
x-cache-hits: 0
x-timer: S1767000055.892880,VS0,VE214
vary: Accept-Encoding
x-fastly-request-id: ac70fc7761bf7c1ab9d5caa6c13e46f999a738d4
content-length: 15522
Mark Hamilton
A Unified Theory of Representation Learning:
I-Con: A Unifying Framework for Representation Learning
Separating the "Chirp" from the "Chat": Self-supervised Visual Grounding of Sound and Language
FeatUp: A Model-Agnostic Framework for Features at Any Resolution
Seeing Faces in Things: A Model and Dataset for Pareidolia
Large-Scale Automatic Audiobook Creation
Workshop Organizer: Multimodal Learning for Earth and Environment
Unsupervised Semantic Segmentation by Distilling Feature Correspondences
Axiomatic Explanations for Visual Search, Retrieval, and Similarity Learning
Simons Institute Workshop on Decoding Nonhuman Species
SANE 2024 Keynote
The AI Show
ODSC Webinar
NeurIPs 2020
IEEE Big Data 2020
Microsoft Research Webinar
Microsoft Research Podcast
Spark + AI Summit Europe 2019 Keynote
Microsoft AI Lab
Spark + AI Summit 2019 Keynote
Spark + AI Summit 2019
Microsoft Build 2018 Keynote
Spark + AI Summit Europe 2018 Keynote
Microsoft AI Lab
Spark + AI Summit Europe 2018
Connect() 2017 Keynote
Microsoft Build 2019
Mark T. Hamilton
Computer Science PhD Student at MIT
and Senior Engineering Manager at Microsoft
About
I am a Principal Engineering Manager at Microsoft where I lead the SynapseML product. My team of 6 engineers brings new ML techniques and generative AI into Microsoft's largest data platforms and databases. Concurreny, I have worked to earn PhD in computer science from William T Freeman's lab at the MIT Computer Science & Artificial Intelligence Laboratory. My research explores how to create algorithms that are capable of learning without human labels, so we can solve problems in emerging scientific domains where humans dont yet know the answers.
Contact
markth (at) mit (dot) edu
Experiences
2024 - Present
2016 - 2024
2025 - Present
MIT - Visiting Researcher
2019 - 2025
MIT - PhD in Computer Science
2012 - 2016
Yale University - B.Sc Math and Physics
Summer 2015
Summer 2014
Summer 2013
News
July 2025
I spoke at the Simons Institute Workshop on Decoding Nonhuman Species.
May 2025
I successfully defended my PhD! View my dissertation and defense here.
Apr 2021
Awarded the National Science Foundation Graduate Fellowship
Oct 2019
Apr 2019
Jan 2019
Risk-Based Critical Concentrations of Legionella pneumophila for Indoor Residential Water Uses wins ACS editors choice award and appears on the cover of Environmental Science and Technology
Selected Publications
For a complete list of my publications, please see my Google Scholar
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
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
Press Coverage
The Project Gutenberg Open Audiobook Collection
Discovering Hidden Connections in Art
Snow Leopard Recognition
Drone-Based Poacher Detection
Gen Studio
Microbial Risk Assesment
Talks
Simons Institute Workshop on Decoding Nonhuman Species
Towards Decoding Dolphin Communication
SANE 2024 Keynote
Separating the "Chirp" from the "Chat": Self-Supervised Visual Grounding of Sound and Language
The AI Show
Creating and Donating Thousands of AI powered Audiobooks to Project Gutenberg
ODSC Webinar
Working with AI Services at Scale
NeurIPs 2020
MosAIc: Finding Artistic Connections across Culture with Conditional Image Retrieval
IEEE Big Data 2020
Large Scale Intelligent Microservices
Microsoft Research Webinar
Discovering hidden connections in art with deep, interpretable visual analogies
Microsoft Research Podcast
MMLSpark: empowering AI for Good with Mark Hamilton
Spark + AI Summit Europe 2019 Keynote
Scalable AI for Good
Microsoft AI Lab
Snow Leopard Trust Image Recognition
Spark + AI Summit 2019 Keynote
Unsupervised Currency Detection for the Visually Impaired
Spark + AI Summit 2019
Apache Spark Serving: Unifying Batch, Streaming, and RESTFul Serving
Microsoft Build 2018 Keynote
Interactive Deep Learning for Circuit Board Quality Assurance
Spark + AI Summit Europe 2018 Keynote
Automated Gas Station Monitoring with the Cognitive Services on Spark
Microsoft AI Lab
Gen Studio
Spark + AI Summit Europe 2018
Unsupervised Object Detection using the Cognitive Services on Spark
Connect() 2017 Keynote
Distributed and Streaming Deep Learning for Snow Leopard Conservation
Microsoft Build 2019
Anamoly Detection for Realtime NASCAR Analytics on Cosmic Spark
Software
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.
Teaching