Paul Rothemund Places Molecule-Scale Devices in Precise Orientation


Paul Rothemund, Research Professor of Bioengineering, Computing and Mathematical Sciences, and Computation and Neural Systems, has developed a technique that allows him to precisely place microscopic devices formed from folded DNA molecules in not only a specific location but also in a specific orientation. This method for precisely placing and orienting DNA-based molecular devices may make it possible to use these molecular devices to power new kinds of chips that integrate molecular biosensors with optics and electronics for applications such as DNA sequencing or measuring the concentrations of thousands of proteins at once. [Caltech story]

Tags: research highlights CMS Paul Rothemund KNI CNS

Metals that Work Like Magic


Metals that Work Like Magic, a podcast from the Wall Street Journal, features Jamil Tahir-Kheli, research staff member working with Carver Mead, Gordon and Betty Moore Professor of Engineering and Applied Science, Emeritus. The podcast focuses on the history of superconductivity research over the past forty years and potential applications.

Tags: EE research highlights CMS Carver Mead CNS Jamil Tahir-Kheli

AI-Driven COVID-19 Model Outperforms Competitors


While existing models to predict the spread of a disease already exist, few, if any, incorporate artificial intelligence (AI). Yaser Abu-Mostafa, Professor of Electrical Engineering and Computer Science, is using a new model for predicting COVID-19's impact using AI and it dramatically outperforms other models, so much so that it has attracted the interest of public health officials across the country. "AI is a powerful tool, so it only makes sense to apply it to one of the most urgent problems the world faces," says Yaser Abu-Mostafa. [Caltech story]

Tags: EE research highlights CMS Yaser Abu-Mostafa CNS

Robotics Engineers Take on COVID-19


Methods that were originally created to help robots to walk and autonomous cars to drive safely can also help epidemiologists predict the spread of the COVID-19 pandemic. Aaron Ames, Bren Professor of Mechanical and Civil Engineering and Control and Dynamical Systems, and colleagues took these tools and applied them to the development of an epidemiological methodology that accounts for human interventions (like mask mandates and stay-at-home orders). By utilizing the U.S. COVID-19 data from March through May, they were able to predict the infection wave during the summer to high accuracy. "This is the greatest health challenge to face our society in a generation at least. We all need to pitch in and help in any way we can," Ames says. [Caltech story]

Tags: research highlights MCE CMS IST Aaron Ames CDS Andrew Singletary

FUTURE Ignited


Nearly 200 undergraduates from more than 120 colleges and universities across the country joined Caltech for FUTURE Ignited, a virtual event that aimed to encourage students of color to pursue graduate studies in science and engineering. The goal of FUTURE Ignited is to diversify STEM with students of color who will go on to become incredible graduate students and scientific leaders in their respective fields. [Caltech story]


Machine Learning Speeds Up Quantum Chemistry Calculations


A new quantum chemistry tool, called OrbNet, uses machine learning, quantum-chemistry calculations that can be performed 1,000 times faster than previously possible, allowing accurate quantum chemistry research to be performed faster than ever before. OrbNet was developed through a partnership between Tom Miller, Professor of Chemistry, and Anima Anandkumar, Bren Professor of Computing and Mathematical Sciences. [Caltech story]

Tags: research highlights CMS IST CNS Animashree Anandkumar

A Molecular Approach to Quantum Computing


The technology behind the quantum computers of the future is fast developing, with several different approaches in progress. Many of the strategies, or "blueprints," for quantum computers rely on atoms or artificial atom-like electrical circuits. In a new theoretical study in the journal Physical Review X, Caltech demonstrates the benefits of a lesser-studied approach that relies not on atoms but molecules. One concept behind the new research comes from work performed nearly 20 years ago by John Preskill, Richard P. Feynman Professor of Theoretical Physics; Allen V. C. Davis and Lenabelle Davis Leadership Chair, Institute for Quantum Science and Technology, Alexei Kitaev, Ronald and Maxine Linde Professor of Theoretical Physics and Mathematics, and their colleague Daniel Gottesman. [Caltech story]

Tags: research highlights CMS Alexei Kitaev John Preskill

Advancing Future Quantum Science Efforts


Five new Department of Energy centers will apply quantum information science to emerging technologies. The centers will develop cutting-edge quantum technologies for use in a wide range of possible applications including scientific computing; fundamental physics and chemistry research; and the design of solar cells and of new materials and pharmaceuticals. Caltech faculty will participate in four of the new science centers: the Quantum Systems Accelerator, led by the Lawrence Berkeley National Laboratory, also known as Berkeley Lab; the Quantum Science Center, led by Oak Ridge National Laboratory; Q-NEXT, led by Argonne National Laboratory; and the Co-design Center for Quantum Advantage, led by Brookhaven National Laboratory. [Caltech story]

Tags: APhMS EE research highlights MedE CMS Oskar Painter KNI Andrei Faraon

AI for a Better Prediction COVID-19 Model


A team of Caltech students, led by Yaser Abu-Mostafa, Professor of Electrical Engineering and Computer Science, have developed a tool to predict the impact of COVID-19 using artificial intelligence (AI). While many models to predict the spread of a disease already exist, few if any incorporate AI, which makes predications based on observations of what is actually happening as opposed to what the model's designers think should happen. AI has the power to discover patterns hidden in data that the human eye might not recognize. [Caltech story]

Tags: EE research highlights CMS Yaser Abu-Mostafa CNS

Machine Learning Helps Robot Swarms Coordinate


Soon-Jo Chung, Bren Professor of Aerospace, Yisong Yue, Professor of Computing and Mathematical Sciences, postdoctoral scholar Wolfgang Hönig, and graduate students Benjamin Rivière and Guanya Shi, have designed a new data-driven method to control the movement of multiple robots through cluttered, unmapped spaces, so they do not run into one another. "Our work shows some promising results to overcome the safety, robustness, and scalability issues of conventional black-box artificial intelligence (AI) approaches for swarm motion planning with GLAS and close-proximity control for multiple drones using Neural-Swarm," says Chung. [Caltech story]

Tags: research highlights GALCIT CMS Yisong Yue CNS Soon-Jo Chung postdocs Benjamin Rivière Guanya Shi Wolfgang Hönig