Biological Circuits: A Beginner’s Guide
A team of researchers including Noah Olsman (PhD ’19), John Doyle, Jean-Lou Chameau Professor of Control and Dynamical Systems, Electrical Engineering, and Bioengineering, and Richard Murray, Thomas E. and Doris Everhart Professor of Control and Dynamical Systems and Bioengineering, has developed a set of guidelines for designing biological circuits using tools from mechanical and electrical engineering. Like electric circuits—but made out of cells and living matter—biological circuits show promise in producing pharmaceuticals and biofuels. [Caltech story]
Can AI Be Fair?
Experts from across the country in computer science, philosophy, law, and other fields gathered at a Caltech workshop to examine a question: Can artificial intelligences, or machine-learning algorithms, be fair? Computer scientists talked about addressing various issues using specific types of machine-learning techniques. For example, if you are training an algorithm on data that has preexisting biases, then those biases will be reflected in the algorithm's results. Machine-learning programs typically learn from so-called training data and then, from the data, come up with a model that makes predictions about the future. The goal is to attempt to remove any possible racial or other bias from the models. One of the activities in the workshop involved looking through studies investigating the fairness of machine-learning programs, or algorithms, used for making predictions in college admissions, employment, bank lending, and criminal justice. The participants of the workshop said they thought the cross-disciplinary nature of the workshop was tremendously useful. [Caltech story]
CS + Social Good
Through TechReach, a new student club, Caltech undergrads aim to use tech skills to address social problems. Among people who are homeless, lack of connection to family and friends poses an often-overlooked obstacle to stability and well-being. Nivetha Karthikeyan, Myra Cheng, and Andrew Hess address the problem by developing new technological tools for Miracle Messages, a nonprofit that helps reunite homeless people with friends and relatives. Miracle Messages helps homeless individuals record video or audio messages to loved ones they have lost all contact with, and then volunteers scour social media and other digital platforms to find those loved ones and deliver the message. They hope to expand TechReach to five or six new projects involving larger numbers of computer science volunteers and a broader range of issues.
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]