The Future of Everything - Stanford Engineering

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Cynthia Lee: How to teach computer science Dec 13, 2021

Show notes

As the field of computer science has evolved over the last half century, so too has the way in which computer science is taught and to whom it is taught.

Stanford lecturer Cynthia Lee says she is encouraged by the diversity she sees as she looks out over her classroom. But that wasn’t always the case, particularly when she, a woman, was in college. Lee has since dedicated her career to changing that mindset from a fixed and rigid outlook to one that is more open and welcoming of diverse backgrounds and skills.

Change, she says, can come from the top in how classes are structured and at the foundation in undoing preconceptions about who can excel in the field. Diverse faces, myriad skills and interests, fewer lectures and more hands-on, peer-to-peer collaboration are in order, Lee tells listeners to this episode of Stanford Engineering’s The Future of Everything podcast with host Russ Altman.

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Chelsea Finn: How to make artificial intelligence more meta Nov 15, 2021

Show notes

In one of computer science’s more meta moments, professor Chelsea Finn created an AI algorithm to evaluate the coding projects of her students. The AI model reads and analyzes code, spot flaws and gives feedback to the students. Computers learning about learning—it’s so meta that Finn calls it “meta learning.”

Finn says the field should forgo training AI for highly specific tasks in favor of training it to look at a diversity of problems to divine the common structure among those problems. The result is AI able to see a problem it has not encountered before and call upon all that previous experience to solve it. This new-look AI can adapt to new courses, often enrolling thousands of students at a time, where individual instructor feedback would be prohibitive.

Emboldened by results in class, Finn is now applying her breadth-over-specificity approach to her other area of focus, robotics. She hopes to develop new-age robots that can adapt to unfamiliar surroundings and can do many things well, instead of a few, as she tells host Russ Altman and listeners to this episode of Stanford Engineering’s The Future of Everything podcast. Listen and subscribe here.

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Kayvon Fatahalian: How the pandemic changed the virtual world Nov 03, 2021

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For experts in digital graphics and visual perception, like computer scientist Kayvon Fatahalian, the recent pandemic has been a call to arms. Fatahalian says he and others in the field felt an urgent responsibility to harness their background in computer graphics and interactive techniques to improve life for people across the globe. He says new, virtual tools have proved better than past, real ones in improving certain aspects of our everyday lives.

His job as a computer scientist is to make those experiences more successful, more of the time. His role as a teacher is a case in point. While the virtual world is not a replacement for face-to-face interaction between students and instructors, Fatahalian notes there are many aspects of the live virtual lecture experience that enable more students to participate, and participate more frequently than in a physical classroom.

Fatahalian is now busier than ever discovering where and how the virtual world excels and creating new tools to meet the evolving need, as he tells listeners to this episode of Stanford Engineering’s The Future of Everything podcast with host Russ Altman. Listen and subscribe here.

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Kuang Xu: How to make (and keep) genetic data private Oct 18, 2021

Show notes

One underappreciated fact about the explosion in genetic databases, like consumer sites that provide information about ancestry and health, is that they unlock valuable insights not only into an individual’s past and future, but also for that individual’s entire family. This raises serious concerns about privacy for people who have never submitted their genetic information for analysis, yet share much the same code as one who did.

Today’s guest, Kuang Xu, is an expert in how genetic information can and should be used. He says that the DNA problem weighs heavily on privacy experts in fields ranging from law and engineering to public health and criminal justice. The fundamental question is: Can we create methods for accessing genetic data while maximizing the privacy of all involved?

The problems will only grow more intense as time and data accumulate, Xu says, unless we resolve them now, as he explains on this episode of Stanford Engineering’s The Future of Everything podcast with host Russ Altman. Listen and subscribe here.

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Eric Appel: Gels are changing the face of engineering ... and medicine Oct 04, 2021

Show notes

Readers of Eric Appel’s academic profile will note appointments in materials science, bioengineering and pediatrics, as well as fellowship appointments in the ChEM-H institute for human health research and the Woods Institute for the Environment. While the breadth of these appointments does not leap to mind as being particularly consistent, the connections quickly emerge for those who hear Appel talk about his research.

Appel is an expert in gels, those wiggly, jiggly materials that aren’t quite solid, but not quite liquid either. Gels’ in-betweenness is precisely what gets engineers like Appel excited about them. Appel has used gels for everything from new-age fire retardants that can proactively prevent forest fires to improved drug and vaccine delivery mechanisms for everything from diabetes to COVID-19. Hence the appointments across engineering and medicine.

Listen in with host and bioengineer Russ Altman as Appel explains to Stanford Engineering’s The Future of Everything podcast why gels could be the future of science. Listen and subscribe here.

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Lianne Kurina: How controlling confounders makes better epidemiology Oct 01, 2021

Show notes

As the world has learned through the recent pandemic, epidemiological studies can be complicated by many unanticipated factors. Lianne Kurina is an expert in the design of epidemiological studies who says that the key to greater confidence is better design.

The gold standard, she says, is the randomized controlled trial—a study that compares groups that are essentially identical by every apparent factor but one— the vaccinated vs. the unvaccinated, for instance. In the case of COVID-19 vaccinations, Kurina stresses that investigators did an exemplary job of this.

In situations where we can't use a randomized controlled trial, achieving a similar balance and specificity is far harder. Kurina says that researchers working with observational data, rather than trial data, must always be attuned to the overlooked factors—“confounders” she calls them—that can muddy the data and render a study moot.

However, Kurina notes, the better one controls the confounders in these observational studies via better design and data collection, the greater confidence we can have in the end results, as she tells listeners to this episode of Stanford Engineering’s The Future of Everything podcast with host Russ Altman. Listen and subscribe here.

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Priyanka Raina: How computer chips get speedier through specialization Sep 18, 2021

Show notes

For decades, the general-purpose central processing unit—the CPU—has been the workhorse of the computer industry. It could handle any task—literally—even if most of those capabilities were unnecessary.

This model was all well and good as chips grew smaller, faster and more efficient by the day, but less so as the pace of progress has slowed, says electrical engineer Priyanka Raina, an expert in chip design. Raina says that, to keep chips on their ever-improving trajectory, chip makers have shifted focus to chips that do specific tasks very well. The graphics processing unit (GPU), which handles the intense mathematics necessary for video and gaming graphics, is a perfect example.

Soon, there’ll be a faster, more efficient chip for every task, but it’ll take industry-wide cooperation to get there, as Raina tells listeners to this episode of Stanford Engineering’s The Future of Everything podcast with host Russ Altman. Listen and subscribe here.

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Biondo Biondi: How to measure an earthquake through the internet Aug 24, 2021

Show notes

Most people know the seismograph, those ultrasensitive instruments that record every small shift in the Earth’s crust.

But did you know that the very latest method for measuring earthquakes involves fiber optic cables that carry internet data around the world?

Stanford geophysicist Biondo Biondi says that the waves of energy sent forth by an earthquake cause fiber optic cables to stretch and contract ever so slightly. Using precise mathematical algorithms, experts like Biondi can measure earthquake intensity, making every meter of fiber optic cable a potential seismograph and dramatically increasing the data experts can gather in a day. Biondi’s sensor arrays are so sensitive they can detect sinkholes, landslides and even the rumblings of failing urban infrastructure.

These new technologies – and the secrets they might reveal – are only starting to emerge, as Biondi tells listeners in this episode of Stanford Engineering’s The Future of Everything podcast with host Russ Altman. Listen and subscribe here.

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Emmanuel Candès: How to increase certainty in predictive modeling Aug 23, 2021

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Anyone who’s ever made weekend plans based on the weather forecast knows that prediction – about anything – is a tough business. But predictive models are increasingly used to make life-changing decisions everywhere from health and finance to justice and national elections. As the consequences have grown, so has the weight of uncertainty, says today’s guest, mathematician and statistician Emmanuel Candès.

Candès knows this paradigm all too well. He is an expert in identifying flaws in today’s highly sophisticated computer models. He says the secret to better prediction rests in building models that don’t try to be right every time, but instead offer a high degree of certainty about things of real consequence.

In that regard, the old scientific maxim holds, he says. Correlation does not equal causation. The statistician’s job, therefore, is helping to sort through the noise to find the nuggets of truth in the things that really matter, as Candès tell listeners to this episode of Stanford Engineering’s The Future of Everything podcast with host Russ Altman. Listen and subscribe here.

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Srabanti Chowdhury: New forms of semiconductors are key to the future Jul 19, 2021

Show notes

Electronics are everywhere these days, so much so that often we don't even register that we are using them. The use of electronics will only grow over time as engineers solve societal challenges through increased connectivity, faster computation, new high-tech gadgets, and energy sustainability. Against that backdrop, electrical engineers like Stanford’s Srabanti Chowdhury have been searching for new semiconductors that can expand the application space beyond the ubiquitous silicon. Among the options she’s exploring is an old familiar friend—diamond—and a few new ones, too, like gallium nitride.

The diamonds Chowdhury works with are a far cry from the sparkly gems a jeweler might prize. These diamonds are “doped” with other elements to achieve optimal electrical performance. Meanwhile, gallium nitride has shown promise in LEDs and lasers, as well as in cutting-edge radar systems—among other applications.

While these new semiconductors have raised hopes of scaling new heights where even silicon cannot reach, much work remains if they are ever to move from lab bench to laptops and myriad other electronic devices. The payoff, however, will be smaller, faster, more powerful, more energy efficient, and more versatile electronics, as Chowdhury tells listeners to this episode of Stanford Engineering’s The Future of Everything podcast with host Russ Altman. Listen and subscribe here.

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