Lifeboat Foundation

Kathryn Tunyasuvunakool grew up surrounded by scientific activities carried out at home by her mother—who went to university a few years after Tunyasuvunakool was born. One day a pendulum hung from a ceiling in her family’s home, Tunyasuvunakool’s mother standing next to it, timing the swings for a science assignment. Another day, fossil samples littered the dining table, her mother scrutinizing their patterns for a report. This early exposure to science imbued Tunyasuvunakool with the idea that science was fun and that having a career in science was an attainable goal. “From early on I was desperate to go to university and be a scientist,” she says.

Tunyasuvunakool fulfilled that ambition, studying math as an undergraduate, and computational biology as a graduate student. During her PhD work she helped create a model that captured various elements of the development of a soil-inhabiting roundworm called Caenorhabditis elegans, a popular organism for both biologists and physicists to study. She also developed a love for programming, which, she says, lent itself naturally to a jump into software engineering. Today Tunyasuvunakool is part of the team behind DeepMind’s AlphaFold—a protein-structure-prediction tool. Physics Magazine spoke to her to find out more about this software, which recently won two of its makers a Breakthrough Prize, and about why she’s excited for the potential discoveries it could enable.

All interviews are edited for brevity and clarity.

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Mathematics and sex are deeply intertwined. From using mathematics to reveal patterns in our sex lives, to using sex to prime our brain for certain types of problems, to understanding them both in terms of the evolutionary roots of our brain, Dr Clio Cresswell shares her insight into it all.

Continue reading “Mathematics and sex | Clio Cresswell | TEDxSydney” »

Sound waves, like an invisible pair of tweezers, can be used to levitate small objects in the air. Although DIY acoustic levitation kits are readily available online, the technology has important applications in both research and industry, including the manipulation of delicate materials like biological cells.

Researchers at the University of Technology Sydney (UTS) and the University of New South Wales (UNSW) have recently demonstrated that in order to precisely control a particle using ultrasonic waves, it is necessary to take into account both the shape of the particle and how this affects the acoustic field. Their findings were recently published in the journal Physical Review Letters.

Sound levitation happens when sound waves interact and form a standing wave with nodes that can ‘trap’ a particle. Gorkov’s core theory of acoustophoresis, the current mathematical foundation for acoustic levitation, makes the assumption that the particle being trapped is a sphere.

The information security landscape is rapidly changing in response to quantum computing technology, which is capable of cracking modern encryption techniques in minutes, but a promising US government encryption algorithm for the post-quantum world was just cracked in less than an hour thanks to a decades-old math theorem.

In July 2022, the US National Institute of Standards and Technology (NIST) chose a set of encryption algorithms that it hoped would stand up to the encryption-cracking power of quantum computers and tasked researchers with probing them for vulnerabilities, offering a $50,000 prize for anyone who was able to break the encryption.

There is a deep root of mathematics within biology. How this came to be, you’ll have to watch the video to find out.

Created by Prompt Suathim (2nd year undergrad, Integrated Science, UBC)

Continue reading “Using math to decode biology’s programming language” »

Inspired by the results of a game-playing neural network, mathematicians have been making unexpected advances on an age-old math problem.

Continuous-time neural networks are one subset of machine learning systems capable of taking on representation learning for spatiotemporal decision-making tasks. Continuous differential equations are frequently used to depict these models (DEs). Numerical DE solvers, however, limit their expressive potential when used on computers. The scaling and understanding of many natural physical processes, like the dynamics of neural systems, have been severely hampered by this restriction.

Inspired by the brains of microscopic creatures, MIT researchers have developed “liquid” neural networks, a fluid, robust ML model that can learn and adapt to changing situations. These methods can be used in safety-critical tasks such as driving and flying.

However, as the number of neurons and synapses in the model grows, the underlying mathematics becomes more difficult to solve, and the processing cost of the model rises.

Gnawing on his left index finger with his chipped old British teeth, temporal veins bulging and brow pensively squinched beneath the day-before-yesterday’s hair, the mathematician John Horton Conway unapologetically whiles away his hours tinkering and thinkering — which is to say he’s ruminating, although he will insist he’s doing nothing, being lazy, playing games.

Based at Princeton University, though he found fame at Cambridge (as a student and professor from 1957 to 1987), Conway, 77, claims never to have worked a day in his life. Instead, he purports to have frittered away reams and reams of time playing. Yet he is Princeton’s John von Neumann Professor in Applied and Computational Mathematics (now emeritus). He’s a fellow of the Royal Society. And he is roundly praised as a genius. “The word ‘genius’ gets misused an awful lot,” said Persi Diaconis, a mathematician at Stanford University. “John Conway is a genius. And the thing about John is he’ll think about anything.… He has a real sense of whimsy. You can’t put him in a mathematical box.”

“It’s a novel contribution that uses different methods compared to what most people have been doing,” said Steffen Gielen, a cosmologist at the University of Sheffield in the United Kingdom.

The provocative conclusion rests on a mathematical trick involving switching to a clock that ticks with imaginary numbers. Using the imaginary clock, as Hawking did in the ’70s, Turok and Boyle could calculate a quantity, known as entropy, that appears to correspond to our universe. But the imaginary time trick is a roundabout way of calculating entropy, and without a more rigorous method, the meaning of the quantity remains hotly debated. While physicists puzzle over the correct interpretation of the entropy calculation, many view it as a new guidepost on the road to the fundamental, quantum nature of space and time.