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]