Mark T. Hamilton

Computer Science PhD Student at MIT
and Research 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 Research Engineer on the Azure Cognitive Services Research team.  My interests include information theory, computer vision, and distributed systems. I value working on projects for social, cultural, and environmental good.

Experiences

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

News

Jan 2021
Website redesigned, please reach out if you encounter bugs or have suggestions!
Dec 2020
Large-Scale Intelligent Microservices presented in IEEE Big Data
Dec 2020
The full MosAIc app launches for NuerIPs 2020 Demos
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

Publications

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

Conditional Image Retrieval

Mark Hamilton, Stephanie Fu,  Mindren Lu, William T Freeman

NeurIPs 2020 Demonstration

It Is Likely That Your Loss Should be a Likelihood

Mark Hamilton, Evan Shelhamer, William T Freeman

In Review, ICLR 2021

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

Discovering Hidden Connections in Art

Snow Leopard Recognition

Drone-Based Poacher Detection

Microbial Risk Assesment

Talks

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

Microsoft ML for Apache Spark (MMLSpark)

Elastically distributed machine learning, microservice orchestration, and model deployment. MMLSpark is Microsoft's open source contributions to the Apache Spark distributed computing framework and is usable from 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.