The Future of Everything

Much of what the world knows about genetic diseases is learned by comparing the DNA of people with a shared disease against the DNA of otherwise healthy people to learn where the differences lie. This is all well and good except that, written into all that DNA, is a lot of other information that the subjects would rather keep private. And that’s where Gill Bejerano enters the scene. He’s an expert in cryptogenomics, a discipline that marries the fields of cryptography and genomics to essentially scramble the genetic code to researchers in such a way that they can still glean valuable information from it without revealing the donor’s entire genetic code. Bejerano’s efforts have been so successful he’s now applying a similar process to medical records, as he explains to host Russ Altman and listeners of this episode of Stanford Engineering’s The Future of Everything podcast. Listen and subscribe here. Connect With Us:Episode Transcripts >>> The Future of Everything WebsiteConnect with Russ >>> Threads / Bluesky / MastodonConnect with School of Engineering >>>Twitter/X / Instagram / LinkedIn / Facebook

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. Connect With Us:Episode Transcripts >>> The Future of Everything WebsiteConnect with Russ >>> Threads / Bluesky / MastodonConnect with School of Engineering >>>Twitter/X / Instagram / LinkedIn / Facebook

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. Connect With Us:Episode Transcripts >>> The Future of Everything WebsiteConnect with Russ >>> Threads / Bluesky / MastodonConnect with School of Engineering >>>Twitter/X / Instagram / LinkedIn / Facebook

Computer scientist Kayvon Fatahalian discusses how the pandemic has highlighted the importance of virtual platforms for communication and education. He explores the challenges of teaching online and the positive aspects of virtual technologies in engagement. The podcast also explores the potential of visual computing technologies in bioengineering and raises ethical considerations in computer graphics, including the analysis of news networks and the threat of fake news.

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. Connect With Us:Episode Transcripts >>> The Future of Everything WebsiteConnect with Russ >>> Threads / Bluesky / MastodonConnect with School of Engineering >>>Twitter/X / Instagram / LinkedIn / Facebook

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. Connect With Us:Episode Transcripts >>> The Future of Everything WebsiteConnect with Russ >>> Threads / Bluesky / MastodonConnect with School of Engineering >>>Twitter/X / Instagram / LinkedIn / Facebook

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 researcher​s 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. Connect With Us:Episode Transcripts >>> The Future of Everything WebsiteConnect with Russ >>> Threads / Bluesky / MastodonConnect with School of Engineering >>>Twitter/X / Instagram / LinkedIn / Facebook

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. Connect With Us:Episode Transcripts >>> The Future of Everything WebsiteConnect with Russ >>> Threads / Bluesky / MastodonConnect with School of Engineering >>>Twitter/X / Instagram / LinkedIn / Facebook

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. Connect With Us:Episode Transcripts >>> The Future of Everything WebsiteConnect with Russ >>> Threads / Bluesky / MastodonConnect with School of Engineering >>>Twitter/X / Instagram / LinkedIn / Facebook

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. Connect With Us:Episode Transcripts >>> The Future of Everything WebsiteConnect with Russ >>> Threads / Bluesky / MastodonConnect with School of Engineering >>>Twitter/X / Instagram / LinkedIn / Facebook