Caltech and Disney Research have entered into a joint research agreement to pioneer robotic control systems and further explore artificial intelligence technologies. Pietro Perona will work with Disney roboticist Martin Buehler to create navigation and perception software that could allow robotic characters to safely move through dense crowds and interact with people. Aaron Ames will work with Disney Research's Lanny Smoot to further explore robot autonomy and machine learning by creating objects that can self-navigate and perform stunts. Yisong Yue has been working with engineers from Disney Research on the use of machine learning to analyze the behavior of soccer players and to measure audience engagement. [Caltech story]
As she steps down as CEO of the Anita Borg Institute, Telle Whitney (PhD ’85) reflects on her career in tech—and the path ahead for the next generation of women. From Caltech to researcher to entrepreneur to advocate for women in technology, this Caltech alumna’s career has thrived on risk-taking and transition—and she’s inspired and assisted hundreds of thousands of women along the way. [Techer profile]
Marco Bernardi, Assistant Professor of Applied Physics and Materials Science, has been awarded the National Science Foundation's (NSF) Faculty Early Career Development (CAREER) Award for his 5-year project, “First-Principles Electron and Spin Dynamics in Materials with Spin-Orbit Coupling”. The CAREER program is NSF's most prestigious awards for junior faculty members. The level and 5-year duration of the awards are designed to enable awardees to develop careers as outstanding teacher-scholars. Awardees are chosen because they exemplify the role of teacher-scholars through outstanding research, excellent education and the integration of education and research within the context of the mission of their organizations.
Animashree (Anima) Anandkumar, Bren Professor of Computing and Mathematical Sciences, and colleagues have won a Best Poster Award at the Neural Information Processing Systems (NIPS) MLtrain workshop. The submission was called “Tensor Regression Networks with TensorLy and MXNet” and the work showed that tensor contractions and regression layers are an effective replacement for fully connected layers in deep learning architectures. The MLtrain workshop focuses on making research more accessible through ipython notebooks and the submissions are judged based on the technical clarity and ease of understanding of the poster and the code. [View the poster]
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.