Professor Michael Bronstein, the chair in Machine Learning and Pattern Recognition in the Department of Computing at Imperial College London
Professor Michael Bronstein, the chair in Machine Learning and Pattern Recognition in the Department of Computing at Imperial College London, is using pioneering machine learning to push the boundaries of drug design, among other things.
Credit Dino Dimopoulos

Michael Bronstein aims to unite the deep learning community

The ARA recipient is pioneering geometric deep learning, an approach that not only promises breakthroughs, but also a way to unify the machine learning “zoo”.

It is an era of explosive growth for machine learning (ML). The technology is driving breakthroughs in biochemistry, physics, materials science, medicine, and more. Professor Michael Bronstein is at the center of this growth. The chair in Machine Learning and Pattern Recognition in the Department of Computing at Imperial College London, Bronstein is using pioneering machine learning to push the boundaries of drug design, reveal the cancer-fighting properties of food, and even decode whale-speak.

In the field of deep learning we have this ‘zoo’ of different neural network architectures. It is often hard to see the relationship between different methods because so many things are being reinvented and rebranded.
Michael Bronstein

Deep learning, a subset of ML, has emerged in the past decade, revolutionizing both the academic and industrial worlds. Bronstein says the enormous popularity of the field has led to a proliferation of different deep learning architectures for different types of data.

“In the field of deep learning we have this ‘zoo’ of different neural network architectures: convolutional nets are used for images, Transformers for text sequences — the list goes on. It is often hard to see the relationship between different methods because so many things are being reinvented and rebranded.”

So as one of just eight invited speakers at this year’s International Conference on Learning Representations (ICLR) conference, which commenced May 3, Bronstein will encourage the wider ML community to see their field from a broader perspective that is simultaneously old and new, and inspired by geometry.

Bronstein says the various deep-learning architectures can all be derived using the fundamental principles of symmetry and invariance — the cornerstones of modern geometry and physics. “This geometric approach offers a strong set of unifying principles, of which many popular deep learning architectures are particular examples. We can unify this zoo, using this common lens. We show this in a recent text co-authored with Joan Bruna, Taco Cohen, and Petar Veličković.”

Improving explainable AI’s explanations

Concept-based explanatory models are a popular approach to explainable AI but can suffer from confounders in training data. In a paper at ICLR, Amazon scientists use causal analysis to debias such models, improving performance and concept relevance.

Much of Bronstein’s recent deep-learning work involves graph neural networks. “A graph is essentially a mathematical representation of a network and it is a powerful abstraction of practically anything, from molecules in your body and how they interact, to social-network users and their relationships to each other,” he says.

Graph neural nets are therefore very versatile. In 2018, with support from an Amazon ML Research Award, Bronstein harnessed graphs to detect misinformation on social media networks.

Novel protein design

But one of the “coolest and most promising applications” of graph-based deep learning is in biological sciences and drug discovery and development, says Bronstein. A key part of his work, done in collaboration with Bruno Correia’s Laboratory of Protein Design & Immunoengineering at EPFL, involves ML-based research into the design of novel proteins with the potential to act as new drugs.

Traditional drugs bind to pocket-like sites on the surface of target proteins within the body. This binding may then activate a beneficial chemical response to combat an illness or block a detrimental disease process.

Many promising therapy targets involve interactions between proteins, such as “immune checkpoints”. These checkpoints exist to prevent our own immune system from destroying healthy cells in the body, but in some situations the system goes awry, allowing cancer cells to evade detection.

Immune checkpoint inhibitors — a new therapeutic approach against cancer recognized by a Nobel Prize in 2018 — work by blocking checkpoint proteins from binding with their partner proteins, allowing the immune system to kill cancer cells. One of the difficulties in designing such inhibitors is the interfaces of the respective proteins are flat, without the typical pocket-like structures, making them “undruggable” by traditional small molecules.

In the last few decades, however, a new breed of drugs called “biologics” has been designed to address this challenge in cancer treatment. Biologics are larger, more complex molecules — in the form of proteins, peptides, and antibodies — and so are more challenging to produce than conventional drugs. However, they have completely transformed the prognosis for some groups of patients with advanced cancer.

Bronstein and his collaborators are using graph neural networks to work out the shape of novel proteins that could then be synthesised to bind to these flat surfaces.

“We use geometric neural networks, modelling proteins as molecular surfaces, that can predict if and where two proteins will bind,” he said. “This allows us to build a new protein that is very likely to bind to the target.”

This is more than theoretical. Bronstein’s collaborators have already synthesized these novel proteins and confirmed that their designs make real-world sense.

“I'm not saying that we’ll have these drugs in the clinic tomorrow, but we now have a computational pipeline that is data-driven, that allows us to both predict properties of proteins, and to build proteins with the desired functionality,” he said.

I'm not saying that we’ll have these drugs in the clinic tomorrow, but we now have a computational pipeline that is data-driven.
Michael Bronstein

They call the technological concept Molecular Surface Interaction Fingerprinting (MaSIF), and Bronstein takes pride in having the first paper with “Geometric Deep Learning” in the title to appear on the cover of a major biological journal (the February 2020 issue of Nature Methods).

Computational pipelines like this require significant computing power and extensive experimentation, which is why Bronstein’s new Amazon Research Award funding is foundational to his research.  

“Amazon Web Service’s cloud computing is essential because when we run these experiments, we need many virtual instances of GPUs, and it allows us to rapidly scale our experiments,” he says. “For example, when we need to tune the parameters of our neural networks or run multiple deep-learning architectures simultaneously, the number of virtual machines we can create is unlimited. Without this capability, which we are heavily exploiting, my work would be impossible. This is how modern machine learning must be done.”

While Bronstein is primarily using his ARA award for access to high-performance computing, he has also been attracted to other AI/ML tools in the AWS suite of services. “We’ve started to experiment with the open-source Deep Graph Library in Amazon SageMaker for some of our projects on graph deep learning,” he notes.

Dark matter of nutrition

Bronstein has also been applying his techniques, and high-performance computing, in the search for what he calls the “dark matter of nutrition” — the variety of chemical types that occur naturally in foods that may help prevent and fight diseases, but which are poorly understood.

Using a training dataset comprised of drug molecules with a known anti-cancer effect, Bronstein and his collaborator, Kirill Veselkov of Imperial College London Faculty of Medicine, trained a graph-based classifier that ultimately predicted the anti-cancer profiles of 250 common food ingredients. A product of the work was a “food map” that revealed anti-cancer potential of each foodstuff, rated by the level of anti-cancer drug-like molecules it contained.

“We found prominent champions that we call ‘hyperfoods’,” says Bronstein.  

The results were good news for tea drinkers and fans of foods including celery, sage, and citrus fruits. Such work also heralds machine learning’s nascent role in the development of AI-powered nutritional programs that could play a part in the prevention and treatment of a variety of health conditions.

But perhaps the most intriguing of Bronstein’s current passions is Project CETI, an ambitious international collaboration and winner of the 2020 Audacious Project prize that will apply ML technology in an effort to decipher sperm whale communication.

The project will use arrays of passive sensors and non-invasive robots to observe and listen in on populations of Caribbean sperm whales. Once an enormous amount of acoustic and behavioural data has been collected, advanced ML will be employed in a bid to understand the majestic creatures’ interactions, vocalizations, and behaviour patterns.

“CETI is a once-in-a-lifetime opportunity,” said Bronstein in a presentation about the project. “And I would say without exaggeration that it is probably the craziest moonshot that I have ever participated in.”

Research areas

Related content

US, WA, Seattle
Amazon internships are full-time (40 hours/week) for 12 consecutive weeks with start dates in May - July 2023. Our internship program provides hands-on learning and building experiences for students who are interested in a career in hardware engineering. This role will be based in Seattle, and candidates must be willing to work in-person. Corporate Projects (CPT) is a team that sits within the broader Corporate Development organization at Amazon. We seek to bring net-new, strategic projects to life by working together with customers and evolving projects from ZERO-to-ONE. To do so, we deploy our resources towards proofs-of-concept (POCs) and pilot programs and develop them from high-level ideas (the ZERO) to tangible short-term results that provide validating signal and a path to scale (the ONE). We work with our customers to develop and create net-new opportunities by relentlessly scouring all of Amazon and finding new and innovative ways to strengthen and/or accelerate the Amazon Flywheel. CPT seeks an Applied Science intern to work with a diverse, cross-functional team to build new, innovative customer experiences. Within CPT, you will apply both traditional and novel scientific approaches to solve and scale problems and solutions. We are a team where science meets application. A successful candidate will be a self-starter comfortable with ambiguity, strong attention to detail, and the ability to work in a fast-paced, ever-changing environment. As an Applied Science Intern, you will own the design and development of end-to-end systems. You’ll have the opportunity to create technical roadmaps, and drive production level projects that will support Amazon Science. You will work closely with Amazon scientists, and other science interns to develop solutions and deploy them into production. The ideal scientist must have the ability to work with diverse groups of people and cross-functional teams to solve complex business problems.
US, WA, Seattle
Amazon internships are full-time (40 hours/week) for 12 consecutive weeks with start dates in May - July 2023. Our internship program provides hands-on learning and building experiences for students who are interested in a career in hardware engineering. This role will be based in Seattle, and candidates must be willing to work in-person. Corporate Projects (CPT) is a team that sits within the broader Corporate Development organization at Amazon. We seek to bring net-new, strategic projects to life by working together with customers and evolving projects from ZERO-to-ONE. To do so, we deploy our resources towards proofs-of-concept (POCs) and pilot programs and develop them from high-level ideas (the ZERO) to tangible short-term results that provide validating signal and a path to scale (the ONE). We work with our customers to develop and create net-new opportunities by relentlessly scouring all of Amazon and finding new and innovative ways to strengthen and/or accelerate the Amazon Flywheel. CPT seeks an Applied Science intern to work with a diverse, cross-functional team to build new, innovative customer experiences. Within CPT, you will apply both traditional and novel scientific approaches to solve and scale problems and solutions. We are a team where science meets application. A successful candidate will be a self-starter comfortable with ambiguity, strong attention to detail, and the ability to work in a fast-paced, ever-changing environment. As an Applied Science Intern, you will own the design and development of end-to-end systems. You’ll have the opportunity to create technical roadmaps, and drive production level projects that will support Amazon Science. You will work closely with Amazon scientists, and other science interns to develop solutions and deploy them into production. The ideal scientist must have the ability to work with diverse groups of people and cross-functional teams to solve complex business problems.
US, MA, Westborough
Are you inspired by invention? Is problem solving through teamwork in your DNA? Do you like the idea of seeing how your work impacts the bigger picture? Answer yes to any of these and you’ll fit right in here at Amazon Robotics. We are a smart team of doers that work passionately to apply cutting edge advances in robotics and software to solve real-world challenges that will transform our customers’ experiences in ways we can’t even imagine yet. We invent new improvements every day. We are Amazon Robotics and we will give you the tools and support you need to invent with us in ways that are rewarding, fulfilling and fun. Amazon Robotics is seeking interns and co-ops with a passion for robotic research to work on cutting edge algorithms for robotics. Our team works on challenging and high-impact projects, including allocating resources to complete a million orders a day, coordinating the motion of thousands of robots, autonomous navigation in warehouses, identifying objects and damage, and learning how to grasp all the products Amazon sells. We are seeking internship candidates with backgrounds in computer vision, machine learning, resource allocation, discrete optimization, search, and planning/scheduling. You will be challenged intellectually and have a good time while you are at it! Key job responsibilities • Identifying creative solutions for challenging research problems in robotics and computer vision • Developing software solutions to test hypotheses and demonstrate new functionality • Prototyping concepts to collect data and measure performance • Writing code and unit tests and integrating code with other software and hardware components • Utilizing Amazon Robotics and Amazon engineering tools, processes and technologies • Delivering a final presentation to managers and engineers on the successes and challenges of their internship and the business value they have contributed
US, MA, Westborough
Are you inspired by invention? Is problem solving through teamwork in your DNA? Do you like the idea of seeing how your work impacts the bigger picture? Answer yes to any of these and you’ll fit right in here at Amazon Robotics. We are a smart team of doers that work passionately to apply cutting edge advances in robotics and software to solve real-world challenges that will transform our customers’ experiences in ways we can’t even imagine yet. We invent new improvements every day. We are Amazon Robotics and we will give you the tools and support you need to invent with us in ways that are rewarding, fulfilling and fun. Amazon Robotics is seeking interns and co-ops with a passion for robotic research to work on cutting edge algorithms for robotics. Our team works on challenging and high-impact projects, including allocating resources to complete a million orders a day, coordinating the motion of thousands of robots, autonomous navigation in warehouses, identifying objects and damage, and learning how to grasp all the products Amazon sells. We are seeking internship candidates with backgrounds in computer vision, machine learning, resource allocation, discrete optimization, search, and planning/scheduling. You will be challenged intellectually and have a good time while you are at it! Please note that by applying to this role you would be considered for Applied Scientist summer intern, spring co-op, and fall co-op roles on various Amazon Robotics teams. These teams work on robotics research within areas such as computer vision, machine learning, robotic manipulation, navigation, path planning, perception, artificial intelligence, human-robot interaction, optimization and more.
US, CA, Palo Alto
The Amazon Search team creates powerful, customer-focused search solutions and technologies. Whenever a customer visits an Amazon site worldwide and types in a query or browses through product categories, Amazon Search services go to work. We design, develop, and deploy high performance, fault-tolerant distributed search systems used by millions of Amazon customers every day. We’re seeking a Principal Scientist with a deep expertise in Search Science. Your responsibilities will include everything from developing and prototyping innovative machine learning, and deep learning algorithms to implementing, testing, and supporting full solutions in a production environment. We are looking for innovators who can contribute to advancing search technology on what’s scientifically possible while remaining committed to creating world-class products. Joining this team, you’ll experience the benefits of working in a dynamic, entrepreneurial environment, while leveraging the resources of Amazon.com (AMZN), Earth's most customer-centric company one of the world's leading internet companies. We provide a highly customer-centric, team-oriented environment in our offices located in Palo Alto, California. Key job responsibilities As a hands-on leader of this team, you’ll be responsible for defining key research questions, identifying relevant data, adopting or proposing innovative machine learning solutions conducting rigorous experiments, publishing results and working with the engineering team to deploy these solutions. As a strategic leader, you will identify investment opportunities, develop long term strategies, and propose, prioritize and deliver on goals. You’ll also participate in organizational planning, hiring, mentorship and leadership development. You will be technically fearless and with a passion for building scalable science and engineering solutions. You will serve as a key scientific resource in full-cycle development (conception, design, implementation, testing to documentation, delivery, and maintenance). About the team Starting in 2009, the Visual Search & Augmented Reality team has thus far launched many visual search solutions on the Amazon App that use computer vision and machine learning/deep learning to help customers complete their shopping missions more easily; multiple internal teams at Amazon (devices, Kindle, Seller services, etc.) also use our libraries and APIs to deliver solutions to their own customers. We are a full stack shop, and our team capabilities cover the whole solution spectrum, ranging across applied science, large scale engineering services, product management, UX design, and mobile app development for iOS and Android.
US, MN, Minneapolis
AWS Central Economics is an interdisciplinary team on the cutting edge of economics, statistical analysis, and machine learning whose mission is to solve problems that have high risk with abnormally high returns. Our team leverages the strengths of our scientists to build solutions for some of the toughest business problems here at Amazon AWS. We are looking for an exceptionally talented, seasoned, and motivated Economist to manage a team of economists and data scientists to drive the science for AWS. Key job responsibilities Manage a team of economists and data scientists to deliver actionable economic analyses to business leaders, provide leadership on the economics and science used in the analyses, and engage with business leaders to identify challenges AWS faces that call for in-depth economic analyses and to ensure the analyses have their intended impact.
LU, Luxembourg
&ltHire Relocation Requisition - not for posting> Provides insights to leadership on improving Supply Chain cost and Speed by using Data Science and Analytics techniques. Build Dashboards and models to industrialize these findings at scale.
US, VA, Arlington
The People eXperience and Technology Central Science Team (PXTCS) uses economics, behavioral science, statistics, and machine learning to proactively identify mechanisms and process improvements which simultaneously improve Amazon and the lives, wellbeing, and the value of work to Amazonians. We are an interdisciplinary team that combines the talents of science and engineering to develop and deliver solutions that measurably achieve this goal. We are looking for economists who are able to work with business partners to hone complex problems into specific, scientific questions, and test those questions to generate insights. The ideal candidate will work with engineers and computer scientists to estimate models and algorithms on large scale data, design pilots and measure their impact, and transform successful prototypes into improved policies and programs at scale. We are looking for creative thinkers who can combine a strong technical economic toolbox with a desire to learn from other disciplines, and who know how to execute and deliver on big ideas as part of an interdisciplinary technical team. Ideal candidates will work closely with business partners to develop science that solves the most important business challenges. They will work in a team setting with individuals from diverse disciplines and backgrounds. They will serve as an ambassador for science and a scientific resource for business teams, so that scientific processes permeate throughout the HR organization to the benefit of Amazonians and Amazon. Ideal candidates will own the data analysis, modeling, and experimentation that is necessary for estimating and validating models. They will work closely with engineering teams to develop scalable data resources to support rapid insights, and take successful models and findings into production as new products and services. They will be customer-centric and will communicate scientific approaches and findings to business leaders, listening to and incorporate their feedback, and delivering successful scientific solutions. Key job responsibilities Use causal inference methods to evaluate the impact of policies on employee outcomes. Examine how external labor market and economic conditions impact Amazon's ability to hire and retain talent. Use scientifically rigorous methods to develop and recommend career paths for employees. A day in the life Work with teammates to apply economic methods to business problems. This might include identifying the appropriate research questions, writing code to implement a DID analysis or estimate a structural model, or writing and presenting a document with findings to business leaders. Our economists also collaborate with partner teams throughout the process, from understanding their challenges, to developing a research agenda that will address those challenges, to help them implement solutions. About the team We are a multidisciplinary team that combines the talents of science and engineering to develop innovative solutions to make Amazon Earth's Best Employer.
US, WA, Seattle
The People eXperience and Technology Central Science Team (PXTCS) uses economics, behavioral science, statistics, and machine learning to proactively identify mechanisms and process improvements which simultaneously improve Amazon and the lives, wellbeing, and the value of work to Amazonians. We are an interdisciplinary team that combines the talents of science and engineering to develop and deliver solutions that measurably achieve this goal. We are looking for economists who are able to apply economic methods to address business problems. The ideal candidate will work with engineers and computer scientists to estimate models and algorithms on large scale data, design pilots and measure their impact, and transform successful prototypes into improved policies and programs at scale. We are looking for creative thinkers who can combine a strong technical economic toolbox with a desire to learn from other disciplines, and who know how to execute and deliver on big ideas as part of an interdisciplinary technical team. Ideal candidates will work in a team setting with individuals from diverse disciplines and backgrounds. They will work with teammates to develop scientific models and conduct the data analysis, modeling, and experimentation that is necessary for estimating and validating models. They will work closely with engineering teams to develop scalable data resources to support rapid insights, and take successful models and findings into production as new products and services. They will be customer-centric and will communicate scientific approaches and findings to business leaders, listening to and incorporate their feedback, and delivering successful scientific solutions. Key job responsibilities Use causal inference methods to evaluate the impact of policies on employee outcomes. Examine how external labor market and economic conditions impact Amazon's ability to hire and retain talent. Use scientifically rigorous methods to develop and recommend career paths for employees. A day in the life Work with teammates to apply economic methods to business problems. This might include identifying the appropriate research questions, writing code to implement a DID analysis or estimate a structural model, or writing and presenting a document with findings to business leaders. Our economists also collaborate with partner teams throughout the process, from understanding their challenges, to developing a research agenda that will address those challenges, to help them implement solutions. About the team We are a multidisciplinary team that combines the talents of science and engineering to develop innovative solutions to make Amazon Earth's Best Employer.
US, WA, Seattle
Amazon is looking for talented Postdoctoral Scientists to join our global Science teams for a one-year, full-time research position. Postdoctoral Scientists will innovate as members of Amazon’s key global Science teams, including: AWS, Alexa AI, Alexa Shopping, Amazon Style, CoreAI, Last Mile, and Supply Chain Optimization Technologies. Postdoctoral Scientists will join one of may central, global science teams focused on solving research-intense business problems by leveraging Machine Learning, Econometrics, Statistics, and Data Science. Postdoctoral Scientists will work at the intersection of ML and systems to solve practical data driven optimization problems at Amazon scale. Postdocs will raise the scientific bar across Amazon by diving deep into exploratory areas of research to enhance the customer experience and improve efficiencies. Please note: This posting is one of several Amazon Postdoctoral Scientist postings. Please only apply to a maximum of 2 Amazon Postdoctoral Scientist postings that are relevant to your technical field and subject matter expertise. Key job responsibilities * Work closely with a senior science advisor, collaborate with other scientists and engineers, and be part of Amazon’s vibrant and diverse global science community. * Publish your innovation in top-tier academic venues and hone your presentation skills. * Be inspired by challenges and opportunities to invent cutting-edge techniques in your area(s) of expertise.