Katie Bouman Joins EAS and CMS
Congratulations to the entire Event Horizon Telescope team, and especially to Dr. Katie Bouman who is joining the Engineering and Applied Science (EAS) Division in June as assistant professor of computing and mathematical sciences (CMS). Currently, Caltech and CO Architects are working with her to design and construct a unique laboratory that will facilitate her work in computational imaging. The laboratory is the first of its kind and is designed for her to conduct experimental work in conjunction with her computational approaches – making it possible, for instance, to observe phenomena previously difficult or impossible to measure. The black hole imaging is one spectacular example of how Professor Bouman’s algorithms are advancing our knowledge of the world; she has also developed algorithms that let us “see around corners” and detect material properties (such as stiffness and dampness) via imaging. In her work, Bouman has also developed methods to combine information from both imaging as well as acoustic systems to analyze sub-pixel scale vibrations of otherwise seemingly still objects. As a result, relatively inexpensive cameras, combined with powerful algorithms, are an increasingly attractive alternative to complex and expensive laser-based systems to sense “invisible” attributes of a material. [Caltech story - How to Take a Picture of a Black Hole]
Joel A. Tropp Named 2019 SIAM Fellow
Joel A. Tropp, Steele Family Professor of Applied and Computational Mathematics has been elected to the 2019 class of Society for Industrial and Applied Mathematics (SIAM) fellows. He was nominated for his exemplary research as well as outstanding service to the community. He is being recognized for contributions to signal processing, data analysis and randomized linear algebra.
Computer Scientists Create Reprogrammable Molecular Computing System
Erik Winfree, Professor of Computer Science, Computation and Neural Systems, and Bioengineering, and colleagues have designed DNA molecules that can carry out reprogrammable computations, for the first time creating so-called algorithmic self-assembly in which the same "hardware" can be configured to run different "software." Although DNA computers have the potential to perform more complex computations than the ones featured in the Nature paper, Professor Winfree cautions that one should not expect them to start replacing the standard silicon microchip computers. That is not the point of this research. "These are rudimentary computations, but they have the power to teach us more about how simple molecular processes like self-assembly can encode information and carry out algorithms. Biology is proof that chemistry is inherently information-based and can store information that can direct algorithmic behavior at the molecular level," he says. [Caltech story]
Teaching Coding in Elementary Schools
On Friday afternoons, Caltech computer science students visit public schools in Pasadena to help third-, fourth-, and fifth-graders learn to code. Their work is part of a recently introduced course in which Caltech undergrads study and practice strategies for teaching programming to children. “We start with basic concepts and, by the end, students have coded their own games in Scratch [a visual programming language developed for children],” says Caltech senior Anna Resnick, who helps lead the class as a teaching assistant. “A few have even told us they want to be programmers someday.” [Caltech story]
Meet the 2018 Amazon Fellows
The Amazon Fellows program is the result of a partnership between Caltech and Amazon AWS around Machine Learning and Artificial Intelligence (AI). The 2018 Amazon fellows are Ehsan Abbasi, Gautam Goel, Jonathan Kenny, Palma London, and Xiaobin Xiong. Abbasi is interest in contributing to a deeper understanding of convex and non-convex learning methods in AI and is an Electrical Engineering graduate student working with Professor Babak Hassibi. Goel’s research interest is at the interface of the theory and practice of machine learning and is advised by Professor Adam Wierman. London is also working with Professor Wierman. She is developing efficient algorithms for solving extremely large optimization problems. The methods are applicable to distributed and parallel optimization. For example in a distributed data center setting, the algorithms are robust to unreliable data transfer between data centers and take into account privacy concerns. Kenny is a Computation & Neural Systems graduate student working with Professor Thanos Siapas on deep neural networks to identify and classify brain states. Xiong is a mechanical engineering graduate student who enjoys working on real physical robots, to make them walk, jump, and run in real life. He is advised by Professor Aaron Ames and their research is focused on robotic bipedal locomotion
Creating a "Virtual Seismologist"
Professor Yisong Yue is collaborating with Caltech seismologists to use artificial intelligence (AI) to improve the automated processes that identify earthquake waves and assess the strength, speed, and direction of shaking in real time. Professor Yue explains, “the reasons why AI can be a good tool have to do with scale and complexity coupled with an abundant amount of data. Earthquake monitoring systems generate massive data sets that need to be processed in order to provide useful information to scientists. AI can do that faster and more accurately than humans can, and even find patterns that would otherwise escape the human eye.” [Read the full Q&A]
Professor Anandkumar Receives 2018 Good Tech Award
Professor Animashree (Anima) Anandkumar has been recognized by the New York Times “good tech” awards as a leading Artificial intelligence (A.I.) researchers who uses “ technology to help others in real, tangible ways.” The New York Times article states, “Artificial intelligence will be one of the most important areas of computer science in the coming years. It’s also one of the least diverse. Just 12 percent of A.I. researchers are women, and the number of black and Latino executives in the field is vanishingly small… Anandkumar, Nvidia’s director of machine learning research and a professor at Caltech, saw that the name of the A.I. field’s marquee annual event — the Neural Information Processing Systems conference, or NIPS — had been used as fodder for sexist jokes. So she started a #ProtestNIPS campaign to change the name, and drew up a petition that gathered more than 2,000 signatures. Eventually, the conference’s board relented, and the event is now abbreviated as “NeurIPS.” It was a small gesture of inclusion that could go a long way toward making women feel more welcome in the field for years to come.” [NYTimes article] [Tensorial-Professor Anima on AI]
New Climate Model to Be Built from the Ground Up
"Projections with current climate models—for example, of how features such as rainfall extremes will change—still have large uncertainties, and the uncertainties are poorly quantified," says Professor Tapio Schneider, principal investigator of the Climate Modeling Alliance (CliMA). "For cities planning their stormwater management infrastructure to withstand the next 100 years' worth of floods, this is a serious issue; concrete answers about the likely range of climate outcomes are key for planning." The new climate model will be built by a consortium of researchers led by Caltech, in partnership with MIT; the Naval Postgraduate School (NPS); and JPL, which Caltech manages for NASA. It will use data-assimilation and machine-learning tools to improve itself in real time, harnessing both Earth observations and the nested high-resolution simulations. "The success of computational weather forecasting demonstrates the power of using data to improve the accuracy of computer models; we aim to bring the same successes to climate prediction," says Professor Andrew Stuart. [Caltech story]