MIT-IBM Watson AI Lab

Building our team

The MIT- IBM Watson AI Lab is currently assembling a diverse team to tackle some of AI's greatest challenges. The lab’s scientists and engineers will focus on fundamental scientific breakthroughs, publish their results, and help guide the development of AI. A distinct objective of the lab is also to encourage MIT faculty and students to launch new companies that will focus on commercializing AI inventions and technologies that are developed at the lab. The lab will issue a call for proposals for MIT and IBM researchers soon.

Lisa Amini | IBM Research AI

I am driven by both the long term vision of AI tackling higher order machine learning and reasoning tasks, becoming robust and continuous, engaging naturally with humans through introspection and explanation, and learning from big data and small, and by the urgency of AI needed to address meaningful challenges in health, life sciences, cybersecurity, and many other industries.
Antonio Torralba| MIT EECS

I am interested in building systems that can perceive the world as humans do. I focus on computer vision, but I am also interested in modalities such as audition and touch. A system able to perceive the world through multiple senses might be able to learn without requiring massive curated datasets.
Mark Ritter | IBM Research AI

Today, scientists are researching methods of optimally controlling quantum devices. Machine learning may be able to help optimize control to improve quantum calculations, and quantum computers may help improve aspects of machine learning. It's through the richness of this enterprise with MIT that we can study the intersection of quantum and AI.
Tommi Jaakkola | MIT EECS

There is tremendous potential and need to co-develop algorithms and systems so as to realize robust, easy to maintain systems that continue to learn from experience.
Francesca Rossi | IBM Research AI

At IBM, and now in partnership with MIT, our focus is understanding how to make sure that AI behaves according to values aligned to those of humans and that each system is accountable for its actions. As a result, AI will be built with responsibility at the core and everyone -- across economies, societies and nations -- will benefit greatly.
Regina Barzilay | MIT EECS

1.7 million people are diagnosed with cancer in the U.S. every year, but only about 3 percent enroll in clinical trials. Current research practice relies exclusively on data drawn from this tiny fraction of patients. We need treatment insights from the other 97 percent receiving cancer care.
Jianying Hu | IBM Research AI

Deep learning has enabled strides in recognizing and analyzing one type of data, such as radiology images for cancer diagnoses. One of the next frontiers is combining multiple forms of data in a way that can be leveraged by AI systems to provide more holistic observations and comprehensive models.
Josh Tenenbaum | MIT BCS

We want to understand everyday inductive leaps in computational terms. What is the underlying logic that supports reliable generalization from so little data? What are its cognitive and neural mechanisms, and how can we build more powerful learning machines based on the same principles?
Michael Witbrock | IBM Research AI

We’ll be addressing really difficult problems in AI that will yield new capabilities. What’s so beneficial about the Lab and its embedded focus on healthcare and cybersecurity is that we’ll be able quickly apply these new capabilities to significant industry challenges that are particularly well-suited to receive the benefits.
David Autor | MIT Economics

MIT aims to prepare students to think at the nexus of economics and computer science, so they can understand and design the kinds of systems that are coming to define modern life. Today's information tools combine complex human decisions with intensive computation, all operating within an engineered economic environment. Economists can help design those environments and interpret the data that they produce.
Wilfried Haensch | IBM Research AI

The ability to co-develop both the materials and the algorithms – drawing on the expertise of researchers from IBM and MIT – is a key advantage of the new Lab. It’s a strong symbiosis. IBM has deep base knowledge of materials in the analog space and their incorporation in commercial technology, which we will bring to the collaboration with MIT.

Four pillars of our research

Research area

Description

AI algorithms

Developing advanced algorithms to expand capabilities in machine learning and reasoning. This involves creating AI systems that move beyond specialized tasks to tackling more complex problems and benefiting from robust, continuous learning. New algorithms that not only leverage big data when available, but also learn from limited data to augment human intelligence are also being explored and invented.

Physics of AI

Investigating new AI hardware materials, devices, and architectures that will support future analog computational approaches to AI model training and deployment, as well as the intersection of quantum computing and machine learning. The latter involves using AI to help characterize and improve quantum devices, and also researching the use of quantum computing to optimize and speed up machine-learning algorithms and other AI applications.

Application of AI to industries

Developing new applications of AI for professional use, including fields such as health care and cybersecurity. We are exploring the use of AI in areas such as the security and privacy of medical data, personalization of healthcare, image analysis, and the optimum treatment paths for specific patients.

Advancing shared prosperity through AI

Exploring how AI can deliver economic and societal benefits to a broader range of people, nations, and enterprises. We are studying the economic implications of AI and investigate how AI can improve prosperity and help individuals achieve more in their lives.

Join us

The MIT-IBM Watson AI Lab is one of the largest long-term university-industry AI collaborations to date. Our Lab is a place where scientists, professors and students collaborate to drive the frontiers of AI. Browse our current openings at IBM Research below. More job opportunities will be added soon.

Current job openings @ IBM Research

Healthcare - Research Staff Member, Artificial Intelligence

We’re looking for research scientists who are passionate about developing next generation AI methodologies that will have profound impact on health and healthcare, and improve people’s lives. Our ambitious AI for Healthcare research agenda include deep learning and deep phenotyping integrating multiple aspects of healthcare data, probabilistic temporal modeling of disease and disease progression, causal inference from observational data, structured prediction, federated learning to leverage distributed data sets, integration of medical and health observational data with systems and chemical biology models, knowledge representation and probabilistic reasoning, and affective computing.

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Research Staff Member, Artificial Intelligence

We’re looking for research scientists who are passionate about AI, advancing science, and inventing the next generation of intelligent machines. Our ambitious AI research agenda is both broad and deep, with leading edge projects in machine learning, deep reinforcement learning, brain-inspired algorithms, learning with memories, knowledge representation and reasoning, symbolic and trainable logic, causal inference, knowledge induction, computer vision, NLP, question answering, and quantum and neuromorphic architectures for next generation AI.

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Research Staff Member, AI Design and Instantiation Lab

We’re looking for research scientists who are interested in developing new methods to facilitate the creation of intelligent systems. We are specifically interested in scientists with interest and expertise in creating intelligent systems leveraging algorithms from deep learning, reinforcement learning, graphical models for knowledge and representation and reasoning, automated knowledge base creation, probabilistic programming, program induction, natural language processing, and NLU, NLG, and QA systems.

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