CMS postdoctoral scholar Qi (Rose) Yu, working with Professor Anandkumar, and graduate student Stephan Zheng, working with Professor Yue, have won the Best Poster Presentation Award at the 2017 Neural Information Processing Systems (NIPS) Time Series Workshop. Dr. Yu works on the challenge of long-term forecasting in environments with nonlinear dynamics such as those involving climate and traffic data. She is tackling this challenge uses Tensor-Train RNN which are a novel family of neural sequence models that learn nonlinear dynamics directly using higher order moments and high-order state transition functions. [View her poster]
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. As part of this two-year renewable research collaboration, 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. [AWS AI Blog]
Professor Venkat Chandrasekaran and graduate student Armeen Taeb have developed an empirical statewide model of the California reservoir network. This work offers reservoir managers insight on how to plan and respond to drought conditions. "The bread and butter of hydrology is using physical laws to describe water phenomena. But the behavior of these reservoirs is not solely determined by physical laws of the water cycle, but also by demands and what these reservoirs are being used for," Taeb explains. [Caltech story]
Lihong Wang, Bren Professor of Medical Engineering and Electrical Engineering, and colleagues have improved a technique for taking three-dimensional (3-D) microscopic images of tissue, allowing them to see inside living creatures with greater precision than before. "This gives us the ability to look through opaque materials and see what's inside," Professor Wang says. "It's like an extension of the human eye, like Superman's X-ray vision." [Caltech story]
Animashree (Anima) Anandkumar, Bren Professor of Computing and Mathematical Sciences, develops efficient techniques to speed up optimization algorithms that underpin machine-learning systems. Speaking about the connections between industry and academia she explains,“bridging the gap between industry and academia is really important. It is a big part of what brought me to Caltech. The sooner we can take theory and deploy it practically, the faster innovation moves and the more impact it can have.” [Interview with Professor Anandkumar]
Take a deep dive into a crucial moment in technological history with Carver Mead, Gordon and Betty Moore Professor of Engineering and Applied Science, Emeritus. In this first of a series of videos being produced by the Caltech Archives, titled 'My First Chip’, Professor Mead tells the story of meeting Gordon Moore, who would soon predict that every year the semiconductor industry would double the number of transistors that could be fabricated on a commercial integrated circuit. Carver Mead and his students worked on the physics of ultra-small transistors, and showed that, in addition to allowing greater density, they ran faster and used less power. This work proved that Moore’s prediction did not violate any laws of physics, and it became known as 'Moore's Law'–the term coined and made famous by Professor Mead.
Azita Emami, Andrew and Peggy Cherng Professor of Electrical Engineering and Medical Engineering; Investigator, Heritage Medical Research Institute; and EAS Division Deputy Chair, along with her colleagues including Professor Mikhail Shapiro have developed microscale devices that relay their location in the body. "We wanted to make this chip very small with low power consumption, and that comes with a lot of engineering challenges," says Professor Emami. "We had to carefully balance the size of the device with how much power it consumes and how well its location can be pinpointed." [Caltech story]
Professor John Doyle and colleagues are among only nineteen groups in the United States to receive National Science Foundation (NSF) funding to conduct innovative research focused on neural and cognitive systems. They aim is to integrate the capabilities of deep learning networks into a biologically inspired architecture for sensorimotor control that can be used to design more robust platforms for complex engineered systems. [NSF release]
Caltech and Cornell teamed up to create the iNaturalist Challenge, a competition to create the best machine-learning algorithm for identifying the world's plant and animal species. The contest was an outgrowth of the institutions' previous work together on Visipedia, a visual encyclopedia created by a network of people and machine-learning computers that harvest image information off the internet. The technology was developed for the encyclopedia by Pietro Perona's Vision Group at Caltech and Serge Belongie's Computer Vision Group at Cornell Tech. [Caltech story]
Xiaoyue Ni, a materials science graduate student working with Professor Julia Greer, has shown that metals undergo permanent deformation even prior to yielding—the threshold at which a material under strain becomes permanently deformed. "What Xiaoyue's data are showing is that from the first moment you start deforming it, the dislocations start being active," Greer says. Now that we know how to do this, we can probe a variety of different classes of materials. [Caltech story]
