Professor Doyle Receives Test of Time Paper Award


John Doyle, Jean-Lou Chameau Professor of Control and Dynamical Systems, Electrical Engineering, and Bioengineering, and colleagues have received the ACM SIGCOMM Test of Time Paper Award for their paper, A first-principles approach to understanding the Internet's router-level topology. The award recognizes papers published 10 to 12 years in the past that is deemed to be an outstanding paper whose contents are still a vibrant and useful contribution today. [List of recipients]

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A Microscopic Glowing Van Gogh


Paul Rothemund, Research Professor of Bioengineering, Computing and Mathematical Sciences, and Computation and Neural Systems, and colleagues have developed a technique that allows manmade DNA shapes to be placed wherever desired; to within a margin of error of just 20 nanometers. This technique removes a major hurdle for the large-scale integration of molecular devices on chips. As a demonstration of the technique’s capabilities the group has created one of the world's smallest reproductions of Vincent van Gogh's The Starry Night. [Caltech story]

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Professor Murray Receives IEEE Control Systems Award


Richard M. Murray, Thomas E. and Doris Everhart Professor of Control and Dynamical Systems and Bioengineering, is the recipient of the 2017 IEEE Control Systems Award, for outstanding contributions to control systems engineering, science, or technology. Professor Murray is receiving the award, “for contributions to the theory and applications of nonlinear and networked control systems." [List of award recipients]

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Best Paper Award at Symposium on Geometry Processing


Professor Mathieu Desbrun and colleagues’ paper, Symmetry and Orbit Detection via Lie-Algebra Voting, has won the best paper award at the Symposium on Geometry Processing. The award is giving by the Geometry Processing community to authors of seminal papers. The aim is to feature the scientific highlights and breakthroughs in the field and to promote the reproducibility of research results. [Read the paper]

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Community Seismic Network Detected Air Pulse From Refinery Explosion


The Community Seismic Network’s (CSN) tight network of low-cost detectors are improving the resolution of seismic data gathering and could offer city inspectors crucial information on building damage after a quake. On February 18, 2015, an explosion rattled the ExxonMobil refinery in Torrance, causing ground shaking equivalent to that of a magnitude-2.0 earthquake and blasting out an air pressure wave similar to a sonic boom. Traveling at 343 meters per second the air pressure wave reached a 52-story high-rise in downtown Los Angeles 66 seconds after the blast. The building's seismometers, which are part of the CSN, noted and recorded the motion of each individual floor. "We want first responders, structural engineers, and facilities engineers to be able to make decisions based on what the data say," explained Monica Kohler, Research Assistant Professor of Mechanical and Civil Engineering, and the lead author of a paper detailing the high-rise's response that recently appeared in the journal Earthquake Spectra. [Caltech story]

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Realtime Camera Planning


Yisong Yue, Assistant Professor of Computing and Mathematical Sciences, is working with colleagues at Disney Research to develop machine-learning algorithms to make automated cameras more human-like.  Professor Yue's research group is generally interested in building AI systems that imitate demonstrated behavior, including laboratory animals, basketball players, humans playing video games, etc.  In this recent work with Disney Research, they are developing an automated camera system that learns how best to film sports matches by watching how human camera operators behave at particular moments. Early testing shows that its shots are far smoother than other automated cameras. [Learn more about the applications] [Learn more about the theory] [techradar story] [Sports Illustrated story]

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Best Paper At Conference on Uncertainty in Artificial Intelligence


Leonard J. Schulman, Professor of Computer Science, and postdoctoral scholar Piyush Srivastava have won the best paper award at the 2016 Conference on Uncertainty in Artificial Intelligence for their paper, Stability of Causal Inference. [Read the paper]

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Best Student Paper at Greenmetrics


Graduate student Navid Azizan Ruhi, working with Professor Wierman, has won the Best Student Paper at the 2016 Greenmetrics Workshop for his paper, Opportunities for Price Manipulation by Aggregators in Electricity Markets. The objective of the workshop is to explore how improvements to or new uses of Information and Communication Technology (ICT) can improve the environmental, economic and/or social sustainability of ICT systems, networks, and applications and of non-ICT processes. [Read the paper]

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Janet Campagna Named 2016 Distinguished Alumna


Janet C. Campagna (MS ’85, Social Science) has been named a 2016 Caltech Distinguished Alumna for her contributions to quantitative investment and for her leadership in the financial industry. She is the founder of QS Investors and a member of the Caltech IST Council. The information science and technology (IST) council helps increase national and global awareness of research in information science and technology as well as garner support for it. [Alumni story]

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Professor Yue Receives Bloomberg Data Science Grant


Yisong Yue, Assistant Professor of Computing and Mathematical Sciences, is a recipient of the Bloomberg Data Science Research Grant Program. The program aims to support cutting-edge research in the broad field of machine learning, including specific areas such as natural language processing, information retrieval, machine-translation and deep neural networks. Professor Yue has proposed to study an alternative notion of interpretability, which he calls “dynamic interpretability”. The goal of dynamically interpretable models is to make predictions that are interpretable, rather than have the model itself be explicitly interpretable. With this alternative goal, one can circumvent much of the inherent tension between accuracy and traditional “static” interpretability, and move one step closer to interpretable production-strength models.[Bloomberg release]

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