Professor Vahala Elected to the National Academy of Engineering
Kerry J. Vahala, Ted and Ginger Jenkins Professor of Information Science and Technology and Applied Physics; Executive Officer for Applied Physics and Materials Science, has been elected to the National Academy of Engineering (NAE). Professor Vahala was elected for “research and application of nonlinear optical microresonators to the miniaturization of precision time and frequency systems." Election to the National Academy of Engineering is among the highest professional distinctions accorded to an engineer. Academy membership honors those who have made outstanding contributions to "engineering research, practice, or education, including, where appropriate, significant contributions to the engineering literature," and to "the pioneering of new and developing fields of technology, making major advancements in traditional fields of engineering, or developing/implementing innovative approaches to engineering education." [NAE release]
National Academy of Engineering
Winners of the 2019 Demetriades - Tsafka - Kokkalis Prizes Announced
The student winners of the 2019 Demetriades - Tsafka - Kokkalis Prizes were announced at the end of this academic year. Anupama Lakshmanan, advised by Professor Mikhail Shapiro has received the prize in Biotechnology. Her research is in engineering of acoustic protein nanostructures for non-invasive molecular imaging using ultrasound. Seyedeh Mahsa Kamali, advised by Professor Andrei Faraon has received the prize in Nanotechnology. She focuses on changing paradigms in optical design through engineering materials at the nanoscale. Linqi (Daniel) Guo, advised by Professor Steven Low has received the prize in Environmentally Benign Renewable Energy Source. His research quantifies the impact of transmission network topology in electrical power system robustness against disturbances and failures. Chris Rollins, advised by Professor Jean-Phillippe Avouac has received the prize in Seismo-Engineering, Prediction, and Protection. Chris studies the way that the Earth deforms gradually over periods of years and decades and uses this to shed light on how earthquakes work, where and how often they might occur in the future, and the hazard they may pose. Nicholas Flytzanis, advised by Professor Viviana Gradinaru has receive the prize in Entrepreneurship. His research is in engineering viruses to serve as next-generation gene therapy delivery vehicles for the treatment of human disease.
Demetriades - Tsafka - Kokkalis Prizes
"Neural Lander" Uses AI to Land Drones Smoothly
Professors Chung, Anandkumar, and Yue have teamed up to develop a system that uses a deep neural network to help autonomous drones "learn" how to land more safely and quickly, while gobbling up less power. The system they have created, dubbed the "Neural Lander," is a learning-based controller that tracks the position and speed of the drone, and modifies its landing trajectory and rotor speed accordingly to achieve the smoothest possible landing. The new system could prove crucial to projects currently under development at CAST, including an autonomous medical transport that could land in difficult-to-reach locations (such as a gridlocked traffic). "The importance of being able to land swiftly and smoothly when transporting an injured individual cannot be overstated," says Professor Gharib who is the director of CAST; and one of the lead researchers of the air ambulance project. [Caltech story]
Undergraduate Students Win International Data Science Competition
Undergraduate students Hongsen Qin, Emma Qian, Thomas Hoffmann, and Alexander Zlokapa (advised by Professors Aaron Ames, Erik Winfree, Jonathan Katz, Maria Spiropulu, and Yaser Abu-Mostafa) have won the Citadel Data Open International Data Science Competition. This winning team chose to investigate the optimal way to spend $1 billion to save lives from malaria and sanitation-related diseases, allocating funds for different prevention methods and optimizing budget breakdowns country by country. To quantify the socioeconomic impacts of their policy proposal, they modeled a variety of aspects from mosquito feeding cycles to climate change using techniques ranging from causal discovery methods to interpretable machine learning. The Caltech team was among 24 teams that were evaluated and questioned by a panel of experts including the former Chief Scientist of AI at Microsoft, a Princeton professor, and the chief of equities at Citadel. The Caltech team was chosen as the first place winner based on the depth, rigor, and comprehensiveness of their analysis.
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]
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