Mark T. Hamilton

Computer Science PhD Student at MIT
and Senior Software Engineer at Microsoft

About

I am a PhD student in William T Freeman's lab at the MIT Computer Science & Artificial Intelligence Laboratory. I am also a Senior Software Engineer on the Microsoft Synapse Analytics team.  I am interested in discovering "stucture" in complex systems using unsupervised learning and large foundation models. I value working on projects for social, cultural, and environmental good and aim to use ML to empower scientific discovery.

Experiences

2019 - Present
MIT - PhD in Computer Science
2012 - 2016
Yale University - B.Sc Math and Physics

News

Publications

For the most up to date list of publications, please see my Google Scholar

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

+  (Oral) Large Scale Holistic Video Undrstanding Workshop 2024

+  (Invited Talk) Sight and Sound Workshop CVPR 2024

FeatUp: A Model-Agnostic Frameworkfor Features at Any Resolution

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

International Conference on Learning Representations (ICLR) 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

Multimodal Learning for Earth and Environment Workshop and Challenge

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

Computational framework for evaluating risk trade-offs in costs associated with legionnaires' disease risk, energy, and scalding risk for hospital hot water systems

Ashley Heida, Alexis Mraz, Mark T. Hamilton, Mark H. Weir, Kerry A. Hamilton

Environmental Science: Water Research & Technology 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

It Is Likely That Your Loss Should be a Likelihood

Mark Hamilton, Evan Shelhamer, William T. Freeman

ICML 2020 Uncertainty in Deep Learning Workshop

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

Health risks from exposure to Legionella in reclaimed water aerosols: Toilet flushing, spray irrigation, and cooling towers

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

Water Research 2018

Spot poachers in action: Augmenting conservation drones with automatic detection in near real time.

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

AAAI 2018

AGN populations in large-volume X-ray surveys: photometric redshifts and population types found in the stripe 82X survey

Tonima Tasnim Ananna, Mara Salvato, Stephanie LaMassa, C Megan Urry, Nico Cappelluti, Carolin Cardamone, Francesca Civano, Duncan Farrah, Marat Gilfanov, Eilat Glikman, Mark Hamilton, Allison Kirkpatrick, Giorgio Lanzuisi, Stefano Marchesi, Andrea Merloni, Kirpal Nandra, Priyamvada Natarajan, Gordon T Richards, John Timlin

The Astrophysical Journal 2017

Microbial risk from source-separated urine used as liquid fertilizer in sub-tropical Australia

Warish Ahmed, Kerry Hamilton, Alison Vieritz, Daniel Powell, Ashantha Goonetilleke, Mark Hamilton, Ted Gardner

Microbial Risk Analysis 2017

Flexible and Scalable Deep Learning with MMLSpark

Mark Hamilton, Sudarshan Raghunathan, Akshaya Annavajhala, Danil Kirsanov, Eduardo de Leon, Eli Barzilay, Ilya Matiach, Joe Davison, Maureen Busch, Miruna Oprescu, Ratan Sur, Roope Astala, Tong Wen, ChangYoung Park

Proceedings of Machine Learning Research 2017

Heuristic Models Outperform Traditional Discounting Utility Models Across Multiple Discounting and Reward Types

Catherine Holland, Mark Hamilton, Greg Samenez-Larkin

Society for Neuroeconomics 2017

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

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

SynapseML

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.

Gen Studio

Search and Explore the Metropolitan Museum of Art with a Generative Adversarial Network.

Teaching

Modern Deep Learning

200 Students, 2016-2017

14 week course on deep learning theory and applications.