AWS and Caltech Partner to Accelerate AI and Machine Learning Through a New Research Collaboration

by Joseph Spisak and Adam Wierman | on | Permalink | Comments |  Share

From autonomous robotics to state of-the-art computer vision, Caltech and Amazon have a lot in common, including the belief that pushing the boundaries of artificial intelligence (AI) and machine learning (ML) will not only disrupt industries, but it will fundamentally change the nature of scientific research. We believe these technologies have the potential to transform fields such as industrial automation, robotics, cancer research, neuroscience, and even help discover the next graviton!

Today we are announcing a research partnership between our two organizations to further research in AI, data science, and machine learning.

As part of the two-year renewable partnership, Amazon will provide both financial support, in the form of funding for graduate fellowships, and computing resources, in the form of AWS Cloud credits, to accelerate the work of faculty and students at Caltech in these areas. The teams will use the AWS Cloud (including cutting-edge Nvidia GPU instances), train deep neural networks using open source projects like Apache MXNet, and collaborate to push the fundamental limits of artificial intelligence. The partnership includes researchers in the Computing and Mathematical Sciences (CMS) and Electrical Engineering (EE) departments at Caltech, as well as researchers doing other, applied AI/ML work that crosses the whole of Caltech, including collaborations with the newly inaugurated Center for Autonomous Systems Technology (CAST), the recently announced Chen Institute for Neuroscience, and the world-famous Jet Propulsion Lab (JPL), to name a few. Caltech is a truly interdisciplinary research environment so we aren’t placing restrictions on the type of AI research—we want the teams to think big and creatively!

This partnership is also a natural extension of collaborative work already happening between Caltech and AWS.  Several members of the AWS ML research team, including Anima Anandkumar and Pietro Perona, split their time between Amazon and Caltech. In addition to these two, the principal investigators that lead the partnership include:

Aaron Ames
Bren Professor of Mechanical and Civil Engineering and Control and Dynamical Systems

Professor Ames’ research centers on robotics, nonlinear control, hybrid systems, autonomy and cyber-physical systems, with special emphasis on foundational theory and experimental realization on robotic systems. His lab designs, builds, and tests novel bipedal robots and prostheses with the goal of achieving human-like bipedal robotic walking and translating these capabilities to robotic assistive devices.

Animashree (Anima) Anandkumar
Principal Scientist at AWS and Bren Professor of Computing and Mathematical Sciences

Professor Anandkumar’s research interests are in the areas of large-scale machine learning, non-convex optimization, and high-dimensional statistics. In particular, she has been spearheading the development and analysis of tensor algorithms for machine learning. Tensor decomposition methods are embarrassingly parallel and scalable to enormous datasets. They are guaranteed to converge to the global optimum and yield consistent estimates for many probabilistic models such as topic models, community models, and hidden Markov models. More generally, Professor Anandkumar has been investigating efficient techniques to speed up non-convex optimization, such as escaping saddle points efficiently.

Pietro Perona
Amazon Fellow and Allen E. Puckett Professor of Electrical Engineering

Professor Perona’s research has been pivotal in understanding how we see and how we can build machines that see. Professor Perona has been primarily active in the area of visual recognition, more specifically visual categorization. He is studying how machines can learn to recognize frogs, cars, faces, and trees with minimal human supervision, and how one could make large image collections and even the web searchable by image content.In collaboration with fellow Professors Anderson and Dickinson, professor Perona is building vision systems and statistical techniques for measuring actions and activities in fruit flies and mice. This enables geneticists and neuroethologists to investigate the relationship between genes, brains, and behavior.Professor Perona is also interested in studying how humans perform visual tasks, such as searching and recognizing image content. One of his recent projects studies how to harness the visual ability of thousands of people on the web for classifying and searching image content.

Joel A. Tropp
Steele Family Professor of Applied and Computational Mathematics

Professor Tropp’s work lies at the interface of applied mathematics, electrical engineering, computer science, and statistics. This research concerns the theoretical and computational aspects of data analysis, sparse modeling, randomized linear algebra, and random matrix theory.

Adam Wierman
Professor of Computing and Mathematical Sciences; Executive Officer for Computing and Mathematical Sciences; Director, Information Science and Technology

Professor Wierman’s research focuses on three seemingly distinct areas: cloud computing, economics, and energy. Though diverse, the areas are each essential to his broader research goal to ease the incorporation of renewable energy into IT and, more generally, into the electricity grid. His work is also diverse in area and technique.  It draws on tools from algorithms, networking, operations research, economics, and control, and it starts from theory and continues through implementation to industrial transfer.

Yisong Yue
Assistant Professor of Computing and Mathematical Sciences

Professor Yue’s research interests lie primarily in the theory and application of statistical machine learning. He is particularly interested in developing novel methods for structured prediction, spatiotemporal reasoning, adaptive learning systems, and learning with humans in the loop. In the past, his research has been applied to information retrieval, content recommendation, text classification, learning from rich user interfaces, analyzing implicit human feedback, data-driven animation, sports analytics, policy learning in robotics, and adaptive routing and allocation problems.

We are thrilled to bring together the talent and wisdom from both teams and will be sharing results over the duration of the partnership.  Follow us on Twitter: #Caltech, #AmazonAI

About the Authors

Joseph Spisak leads AWS’ partner ecosystem focused on Artificial Intelligence and Machine Learning. He has more than 17 years in deep tech working for companies such as Amazon, Intel and Motorola focused mainly on Video, Machine Learning and AI. In his spare time, he plays ice hockey and reads sci-fi.




Adam Wierman is a Professor of Computing and Mathematical Sciences at Caltech. Professor Wierman’s research focuses on three seemingly distinct areas: cloud computing, economics, and energy.