Machine Learning and Statistics
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Machine Learning and Statistics

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Advances in machine learning (ML) have had a profound impact on a vast variety of applications across diverse fields. At Microsoft Research (MSR) New England, we are dedicated to advancing the state of the art of ML and actively pursue research across a wide variety of ML disciplines. These include using ML techniques to drive discovery in new domains, pioneering automation methods that allow non-experts to leverage the power of ML, and exploring the ability of ML to not only reveal correlations within data but also identify the causal mechanisms that drive those correlations.

While our lab pursues a broad and diverse research agenda, many of our projects fall into the following categories:

  • Novel applications of ML to challenging and impactful problems: ML has shown itself to be a powerful tool for addressing problems that are challenging using classical techniques. From sub-seasonal climate forecasting to analyzing the efficacy of cancer immunotherapy to program synthesis, the members of our lab are actively exploring how statistical and ML techniques can yield new and impactful results.
  • Automated ML: While ML continues to demonstrate its utility across many domains, successfully applying ML techniques requires significant expertise and development time. The AutoML team works on developing techniques that can automate much of the development of ML pipelines, allowing non-experts to leverage the power of ML techniques, and freeing experts from much of the tedious and time consuming tasks often required to develop and deploy a ML pipeline.
  • Causal Inference: Traditional ML primarily is concerned with recognizing correlations within data, but not attempting to understand the causal mechanisms that drive those correlations. We are exploring new techniques that can identify the causal relationships in the data, and exploring how these techniques can be applied to significant problems in economics.

We are excited about the potential of ML as a powerful tool to drive discovery, and are passionate about contributing new, novel, and meaningful results across wide ML domains and applications.

Blogs & more

What’s in a name? Using Bias to Fight Bias in Occupational Classification

Bias in AI is a big problem. In particular, AI can compound the effects of existing societal biases: in a recruiting tool, if more men than women are software engineers, AI is likely to use that data to identify job applicants and overscreen for men, creating a vicious circle of bias…

Microsoft Research Blog | May 2019

Is drought on the horizon? Researchers turn to AI in a bid to improve forecasts

As winter drags on, some people wonder whether to pack shorts for a late-March escape to Florida, while others eye April temperature trends in anticipation of sowing crops. Water managers in the western U.S. check for the possibility of early-spring storms to top off mountain snowpack that is crucial for irrigation, hydropower and salmon…

The AI Blog | March 2019

What are the biases in my data?

One challenge with AI algorithmic fairness is that one usually has to know the potential group(s) that an algorithm might discriminate against in the first place. However, in joint work with Maria De-Arteaga, Nathaniel Swinger, Tom Heffernan, and Max Leiserson, we automatically enumerate…

Microsoft Research Blog | February 2019

All about automated machine learning with Dr. Nicolo Fusi

Episode 43, September 26, 2018You may have heard the phrase, necessity is the mother of invention, but for Dr. Nicolo Fusi, a researcher at the Microsoft Research lab in Cambridge, Massachusetts, the mother of his invention wasn’t so much necessity as it was boredom: the special machine learning boredom of manually fine-tuning models…

Microsoft Research Podcast | September 2018

Microsoft unveils AI capability that automates AI development

The tedious but necessary process of selecting, testing and tweaking machine learning models that power many of today’s artificial intelligence systems was proving too time-consuming for Nicolo Fusi. The final straw for the Microsoft researcher and machine learning expert came while fussing over model selection…

The AI Blog | September 2018

Announcing automated ML capability in Azure Machine Learning

Intelligent experiences powered by machine learning can seem like magic to users. Developing them, however, can be anything but. Consider this “simple” tutorial chart from the scikit-learn machine learning library…

Microsoft Azure Blog | September 2018

In the news

Microsoft Makes a Push to Simplify Machine Learning

Ahead of its Build conference, Microsoft today released a slew of new machine learning products and tweaks to some of its existing services. These range from no-code tools to hosted notebooks, with a number of new APIs and other services in-between. The core theme, here, though, is that Microsoft is continuing its strategy…

TechCrunch | May 2019

Microsoft Puts More Brain-Power Into Machine Learning For Azure Cloud

The discussion has been open for a while now. We know we want Machine Learning (ML) to be able to drive the Artificial Intelligence (AI) and smart automation that we demand in tomorrow’s software — but how do we build, train and further educate the ML brains we seek to now bring forward?

Forbes | December 2018

Power BI delivers AI power

Microsoft’s self-service BI tool will soon let business analysts build and use machine learning models, with minimal expertise, and no code. Access to Azure Cognitive Services and models hosted in Azure Machine Learning, as well as a new feature that explains KPI outcomes, are also included…

ZDNet | November 2018

Microsoft Expands AI Offerings At Ignite 2018

Artificial intelligence (AI) development and deployment in the cloud is rapidly growing and becoming a critical market for Amazon Web Services (AWS), Google Cloud, and Microsoft Azure. Many enterprises are now developing their own AI applications or adding AI features…

Forbes | October 2018

Azure’s new machine learning features embrace Python

Microsoft has several new additions to its Azure ML offering for machine learning, including better integration with Python and automated self-tuning features for faster model development. Python is a staple language for machine learning, thanks to its low barrier to entry …

InfoWorld | September 2018

Microsoft introduces Azure service to automatically build AI models

Microsoft is today introducing a number of new AI services for the workplace, including new ways to speed the training of AI systems, custom Cortana voice apps for the enterprise, and a new service for the automated creation of machine learning models…

Venture Beat | September 2018

Microsoft brings new brains to Azure AI at Ignite conference

This might sound a bit meta, but Microsoft is applying new digital brains to cut down on the difficulties of using artificial intelligence technology. Artificial intelligence, which these days typically refers to technology called neural networks or machine learning modeled loosely on human brains…

c|net | September 2018

Microsoft’s machine learning tools for developers get smarter

It’s a big day for Microsoft, which announced a slew of updates across virtually all of its product lines at its Ignite conference today. Unsurprisingly, one theme this year is artificial intelligence and machine learning. Microsoft is launching new tools to bring its Cortana assistant to the enterprise…

TechCrunch | September 2018

He's Brilliant, She's Lovely: Teaching Computers To Be Less Sexist

Computer programs often reflect the biases of their very human creators. That’s been well established. The question now is: How can we fix that problem? Adam Kalai thinks we should start with the bits of code that teach computers how to process language…

NPR | August 2016