A Day in the Half-Life Berkeley Lab (Produced & hosted by Aliyah Kovner)

Have you ever wondered how electricity is available all the time? That’s the seemingly magical science of energy storage. In this episode, we speak to a policy leader and a researcher about the history of piggy-banking power to spend it later, and how this field is evolving to help us prevent extreme weather-related blackouts, adopt more renewable energy, and build bigger, better, more environmentally responsible batteries.

Featuring:

Noël Bakhtian, director of Berkeley Lab's Energy Storage Center. Noel formerly served as director of the Center for Advanced Energy Studies at Idaho National Laboratory and as a senior policy advisor for the White House Office of Science and Technology Policy. Before her shift into policy and leadership, she was an engineer at NASA Ames Research Center working on Mars landing projects.

Mike Gerhardt, research scientist at SINTEF Industry in Norway helping develop new battery and fuel cell technologies using experimentation and computer modeling. Before moving to SINTEF, he was a postdoc in the Energy Conversion Group at Berkeley Lab.

*Special thanks to The Apples in Stereo for use of their song*

This episode was hosted, produced, and edited by Aliyah Kovner. Art by Jenny Nuss. 

Audio samples from Halleck, Joao_Janz, and philtre.

In 1935, the famous physicist Erwin Schrödinger was debating with his friend Albert Einstein about the nature of a fundamental concept in quantum mechanics – a field that was, at the time, still very new. To illustrate his point, Schrödinger proposed a thought experiment wherein a (rather unfortunate) cat sealed in a box is both alive and dead simultaneously – up until the moment someone opens the box. Decades later, that abstract paradox is still very much alive, and enabling the development of a new generation of computers. 
These quantum computers use bits (called qubits) that, unlike the binary bits in today’s electronics, can simultaneously exist in many states between on and off. And although the word gets overused in science, this emerging technology really is revolutionary. A fully developed quantum computer is predicted to be able to perform calculations that would be impossible for a traditional supercomputer, even with thousands of years of processing time.
In this episode, our experts chat about the current state of quantum computers and explain why the mind-bending theories of quantum make coming to work a lot of fun.

Featuring:

Irfan Siddiqi is a professor at UC Berkeley, where he leads the Quantum Nanoelectronics Laboratory, a collaborative group dedicated to developing new and improved superconducting qubits. He is also a faculty scientist at Berkeley Lab, where he leads the Advanced Quantum Testbed and the Quantum Systems Accelerator – a DOE National Quantum Information Science Research Center.

Zahra Pedramrazi is a project scientist at the Advanced Quantum Testbed. During her physics undergraduate, she took a quantum class with Irfan, and became hooked on the field. She is currently focused on the fabrication of superconducting qubits, working to refine their design in order to overcome the limitations of current qubits.

"Thus, the task is, not so much to see what no one has yet seen; but to think what nobody has yet thought, about that which everybody sees." ― Erwin Schrödinger

“How wonderful that we have met with a paradox. Now we have some hope of making progress.” ― Niels Bohr

What do advanced medicines, renewable fuels, vegan burgers, smart fabrics, petroleum-free plastics, and cruelty-free cosmetics have in common? They're all produced with specially engineered microbes! Yep, microbes. 

In episode three, we explore the fields of science making this 21st century industrial revolution possible: synthetic biology and biomanufacturing.

Our guests discuss how humans first developed the tools and knowledge to harness the natural capabilities of bacteria and yeast, and chat about where this rapidly accelerating industry could go next. (Hello painless vaccines and eco-friendly air travel!)

Featuring:

Jay Keasling, CEO of the Joint BioEnergy Institute (JBEI),  senior scientist at Berkeley Lab, and  professor of both Chemical & Biomolecular Engineering and Bioengineering at UC Berkeley. Jay is also the Philomathia Chair in Alternative Energy at UC Berkeley, and cofounder of the biotech company Amyris.

and

Deepika Awasthi, a project scientist in Berkeley Lab's Biological Systems and Engineering Division and an affiliate at JBEI.

Produced and hosted by Aliyah Kovner

Twenty years ago, scientists were surprised to discover that the universe’s expansion is accelerating. The unknown and invisible force causing this acceleration was named “dark energy,” and in the years since, researchers learned more about what the phenomenon is not — but have yet to crack the puzzle of what it actually is. Physicists say it could be an as-of-yet undetected form of energy permeating the cosmos, or it could be an unmeasured property of the force of gravity. Either way, the answer will reshape our models of the universe.

In this episode, we speak with Nobel Laureate Saul Perlmutter (the co-discoverer of dark energy) and rising astrophysics instrumentation scientist Claire Poppett about what we know so far, and how new technology could finally shed (metaphorical) light on this fundamental mystery.

A Day in the Half Life is a podcast from Lawrence Berkeley National Laboratory (Berkeley Lab) about the incredible and often unexpected ways that science evolves over time, as told by the researchers who led it into its current state and those who are going to bring it into the future.
In our very first episode, we discuss machine learning. First developed about 80 years ago, machine learning (ML) is a type of artificial intelligence centered on programs – called algorithms – that can teach themselves different ways of processing data after they are trained on sample datasets.
In the early days of ML, the technology was used for simple tasks such as voice recognition or identifying a specific type of object in images, and was only found in high-end academic, government, or military devices. But now, advanced ML algorithms are everywhere, powering everything from our cars to our voice assistants to the ads appearing on our news feeds. 
And, in addition to making everyday life easier, ML algorithms are beginning to improve and expedite scientific and medical research in truly dramatic ways. In fact, the range of potential applications is so huge that the question has shifted from “Can we use machine learning to solve this?” to “Do we understand the way these algorithms work well enough to feel comfortable using ML for this?” 
Our two ML expert guests are:
John Dagdelen, a materials science graduate student researcher at Berkeley Lab and UC Berkeley. John is part of several scientific teams using ML to discover new materials and material properties, as well as using ML to make discoveries in COVID-19 research.
Prabhat, the former leader of the Data and Analytics Services group at NERSC, Berkeley Lab’s world-renown supercomputing center. Prabhat has been using and developing ML for decades, including for use in climate research. He is now at Microsoft.