Tech

Engineering Collisions: How NYU Is Remaking Health Research

Published

3 months ago

April 27, 2026

This sponsored article is brought to you by NYU Tandon School of Engineering.

The traditional approach to academic research goes something like this: Assemble experts from a discipline, put them in a building, and hope something useful emerges. Biology departments do biology. Engineering departments do engineering. Medical schools treat patients.

NYU is turning that model inside out. At its new Institute for Engineering Health, the organizing principle centers around disease states rather than traditional disciplines. Instead of asking “what can electrical engineers contribute to medicine?,” they’re asking “what would it take to cure allergic asthma?,” and then assembling whoever can answer that question, whether they’re immunologists, computational biologists, materials scientists, AI researchers, or wireless communications engineers.

Person in blue suit and patterned shirt standing against a plain indoor background Jeffrey Hubbell, NYU’s vice president for bioengineering strategy and professor of chemical and biomolecular engineering at NYU’s Tandon School of Engineering.New York University

The early results suggest they’re onto something. A chemical engineer and an electrical engineer collaborated to build a device that detects airborne threats — including disease pathogens — that’s now a startup. A visually impaired physician teamed with mechanical engineers to create navigation technology for blind subway riders. And Jeffrey Hubbell, the Institute’s leader, is advancing “inverse vaccines” that could reprogram immune systems to treat conditions from celiac disease to allergies — work that requires equal fluency in immunology, molecular engineering, and materials science.

The underlying problem these collaborations address is conceptual as much as organizational. In his field, Hubbell argues that modern medicine has optimized around a single strategy: developing drugs that block specific molecules or suppress targeted immune responses. Antibody technology has been the workhorse of this approach. “It’s really fit for purpose for blocking one thing at a time,” he says. The pharmaceutical industry has become extraordinarily good at creating these inhibitors, each designed to shut down a particular pathway.

But Hubbell asks a different question: Rather than inhibit one bad thing at a time, what if you could promote one good thing and generate a cascade that contravenes several bad pathways simultaneously? In inflammation, could you bias the system toward immunological tolerance instead of blocking inflammatory molecules one by one? In cancer, could you drive pro-inflammatory pathways in the tumor microenvironment that would overcome multiple immune-suppressive features at once?

This shift from inhibition to activation requires a fundamentally different toolkit — and a different kind of researcher. “We’re using biological molecules like proteins, or material-based structures — soluble polymers, supramolecular structures of nanomaterials — to drive these more fundamental features,” Hubbell explains. You can’t develop those approaches if you only understand biology, or only understand materials science, or only understand immunology. You need an understanding and a mastery of all three.

“There will be people doing AI, data science, computational science theory, people doing immunoengineering and other biological engineering, people doing materials science and quantum engineering, all really in close proximity to each other.” —Jeffrey Hubbell, NYU Tandon

Which logically leads to the question: How do you create researchers with that kind of cross-disciplinary depth?

The answer isn’t what you might expect. “There may have been a time when the objective was to have the bioengineer understand the language of biology,” Hubbell says. “But that time is long, long gone. Now the engineer needs to become a biologist, or become an immunologist, or become a neuroscientist.”

Hubbell isn’t talking about engineers learning enough biology to collaborate with biologists. He’s describing something more radical: training people whose disciplinary identity is genuinely ambiguous. “The neuroengineering students — it’s very difficult to know that they’re an engineer or a neuroscientist,” Hubbell says. “That’s the whole idea.”

His own students exemplify this. They publish in immunology journals, present at immunology conferences. “Nobody knows they’re engineers,” he says. But they bring engineering approaches — computational modeling, materials design, systems thinking — to immunological problems in ways that traditional immunologists wouldn’t.

The mechanism for creating these hybrid researchers is what Hubbell calls a “milieu.” “To learn it all on your own is hopeless,” he acknowledges, “but to learn it in a milieu becomes very, very efficient.”

NYU building at 770 Broadway with Future Home of Science + Tech signs and street traffic NYU is expanding its facilities to include a science and technology hub designed to force encounters between people across various schools and disciplines who wouldn’t naturally cross paths.Tracey Friedman/NYU

NYU is making that milieu physical. The university has acquired a large building in Manhattan that will serve as its science and technology hub — a deliberate co-location strategy designed to force encounters between people across various schools and disciplines who wouldn’t naturally cross paths.

Businessperson in dark suit and purple tie standing in a modern office setting Juan de Pablo is the Anne and Joel Ehrenkranz Executive Vice President for Global Science and Technology and Executive Dean of the NYU Tandon School of Engineering.Steve Myaskovsky, Courtesy of NYU Photo Bureau

The strategy mirrors what Juan de Pablo, NYU’s Anne and Joel Ehrenkranz Executive Vice President for Global Science and Technology and Executive Dean at the NYU Tandon School of Engineering, describes as organizing around “grand challenges” rather than traditional disciplines. “What drives the recruitment and the spaces and the people that we’re bringing in are the problems that we’re trying to solve,” he says. “Great minds want to have a legacy, and we are making that possible here.”

But physical proximity alone isn’t enough. The Institute is also cultivating what Hubbell calls an “explicit” rather than “tacit” approach to translation — thinking about clinical and commercial pathways from day one.

“It’s a terrible thing to solve a problem that nobody cares about,” Hubbell tells his students. To avoid that, the Institute runs “translational exercises” — group sessions where researchers map the entire path from discovery to deployment before launching multi-year research programs. Where could this fail? What experiments would prove the idea wrong quickly? If it’s a drug, how long would the clinical trial take? If it’s a computational method, how would you roll it out safely?

NYU Tandon graphic showing seven research areas with futuristic science imagery. The new cross-institutional initiative represents a major investment in science and technology, and includes adding new faculty, state-of-the-art facilities, and innovative programs.NYU Tandon

The approach contrasts sharply with typical academic practice. “Sometimes academics tend to think about something for 20 minutes and launch a 5-year PhD program,” Hubbell says. “That’s probably not a good way to do it.” Instead, the Institute brings together people who have actually developed drugs, built algorithms, or commercialized devices — importing their hard-won experience into the planning phase before a single experiment is run.

The timing may be fortuitous. De Pablo notes that AI is compressing timelines dramatically. “What we thought was going to take 10 years to complete, we might be able to do in 5,” he says.

But he’s quick to note AI’s limitations. While tools like AlphaFold can predict how a single protein folds — a breakthrough of the last five years — biology operates at much larger scales. “What we really need to do now is design not one protein, but collections of them that work together to solve a specific problem,” de Pablo explains.

Hubbell agrees: “Biology is much bigger — many, many, many systems.” The liver and kidney are in different places but interact. The gut and brain are connected neurologically in ways researchers are just beginning to map. “AI is not there yet, but it will be someday. And that’s our job — to develop the data sets, the computational frameworks, the systems frameworks to drive that to the next steps.”

It’s a moment of unusual ambition. “At a time when we’re seeing some research institutions retrench a little bit and limit their ambitions,” de Pablo says, “we’re doing just the opposite. We’re thinking about what are the grand challenges that we want to, and need to, tackle.”

The bet is that the breakthroughs worth making can’t emerge from any single discipline working alone. They require collisions —sometimes planned, sometimes accidental — between people who speak different technical languages and are willing to develop a shared one. NYU is engineering those collisions at scale.

Tech

AI Shopping Traffic Raises Retail Customer Data Concerns

Published

1 minute ago

August 10, 2026

qasimgillani

Retailers have spent years fighting to appear at the top of search results; now they have another gatekeeper to win over as shoppers increasingly ask AI assistants what to buy.

The shift is forcing retailers to rethink how their products are discovered online, as AI systems increasingly interpret shopping requests, compare products, and recommend purchases.

According to Reuters, many retail companies are responding by optimizing their websites and product information so AI systems can more easily understand and surface their products.

But visibility inside an AI response is only half the battle. Retailers also want shoppers to return from AI recommendations to their own sites. These retailers argue that keeping the transaction in-house gives them greater control over the purchase experience and the customer information generated by it.

E-commerce retailers also argue that they already have what it takes to handle the sales process efficiently. In a statement cited by Reuters, The Knot’s CEO, Raina Moskowitz, said they “have three decades of data” that can be used to deliver the best experience to customers.

That creates a new challenge for e-commerce: retailers must make their products easy for AI systems to discover and recommend without letting those systems become permanent middle layers between the brand and its customers.

Even Amazon Web Services (AWS) has backed that idea up, advising retail companies to ensure their platforms are the best place to buy while still leveraging AI tools for brand discovery and referrals.

AI companies that have tried to handle the entire chain end-to-end have hit customer adoption hurdles. A prominent example was OpenAI’s Instant Checkout. In March, the company discontinued the feature after five months of unsuccessful adoption, saying March, the company discontinued the feature after five months of unsuccessful adoption.
AI shopping is gaining traction
The numbers are making it harder to dismiss AI shopping as trivial.

According to Reuters, Juniper Research expects AI agents to drive $8 billion in transaction value in 2026, while Adobe Analytics found that 41% of US consumers used generative AI for online shopping in June.

The conversion data points in the same direction. Adobe also found that shoppers from AI referrals generated 41% higher revenue per visit, underscoring the high intent these shoppers bring while giving retailers a clear financial reason to compete for AI-driven discovery.
Not just an e-commerce story
What is happening in retail is part of a much bigger change: AI is fundamentally reshaping the path between a person’s question and the website that wants their attention.

ChatGPT has already become a serious alternative to traditional search for discovery, while Google is rebuilding Search around AI rather than simply defending the old list of links.

That shift changes the value of traffic itself. Reddit, for example, is increasingly important to AI search because its discussions are frequently surfaced as sources, even as the platform worries that AI-generated answers may satisfy users without sending them through to the original site.

Beyond retailers, many brands are increasingly adding AI search optimization to their dominant SEO playbook, trying to make their brands, products, and content visible inside AI-generated answers as well as traditional search results.

The winners may not always be the biggest brands, but the ones that can consistently get in front of customers wherever they choose to search — while keeping enough of their moat intact to turn that visibility into a lasting advantage.
Also read: Meta’s new Facebook Marketplace Seller app uses AI to create listings while keeping inventory, buyer messages, and performance data connected to Marketplace.

Tech

AI Shopping Traffic Raises Retail Customer Data Concerns

Published

9 minutes ago

August 10, 2026

qasimgillani

Retailers have spent years fighting to appear at the top of search results; now they have another gatekeeper to win over as shoppers increasingly ask AI assistants what to buy.

The shift is forcing retailers to rethink how their products are discovered online, as AI systems increasingly interpret shopping requests, compare products, and recommend purchases.

According to Reuters, many retail companies are responding by optimizing their websites and product information so AI systems can more easily understand and surface their products.

ChatGPT has already become a serious alternative to traditional search for discovery, while Google is rebuilding Search around AI rather than simply defending the old list of links.

Tech

Discovered Materials is playing AI whack-a-mole to hunt cooler chips

Published

2 hours ago

August 10, 2026

qasimgillani

Chips running AI workloads are too hot: That’s one reason why data centers consume so much electricity and require cooling systems. And, inevitably, entrepreneurs are turning to AI to solve the problem it created.

Discovered Materials is the latest, with plans to use swarms of AI agents to find new materials that can be used to build more efficient integrated circuits. The startup said it recently closed a $9 million seed round from Lightspeed India Partners after emerging from from Y Combinator, with investment from Peak XV Partners and angel investors Paul Graham, Gokul Rajaram, and Thariq Shihipar.

Founders Advaith Sridhar and Akash Ramdas teamed up to launch the company, drawing on Ramdas’ experience earning a doctorate in materials science from Stanford, and Sridhar’s work on agents at Persona AI and Luma Labs.

The two have created a software pipeline that uses Anthropic models in a custom harness to generate material leads, and then turns to foundational physics models they’ve trained to run simulations that verify if the candidate materials are actually of interest.

“[Ramdas] was doing maybe 20 guesses a day during his PhD,” Sridhar told TechCrunch. “We’re able to do thousands of guesses a day now by having these agents run 24/7 on the cloud, exploring research directions that he gives them.”

Discovered Materials released examples of hundreds of new materials today, as well as their “Material Discovery Bench” today, which is designed to track how frontier models take on this challenge.

Companies like MatNex, SandboxAQ, and CuspAI have all launched similar efforts, but Discovered Materials is betting that a laser-focus on the thermal problems of semiconductor materials is the path to success. The startup says it has already discovered several materials that match the properties of existing materials used by major chipmakers, but can’t share more details about them.

One challenge is the engineering trade-space: If they find a material that might reduce heat generation or improve dissipation, it might be too difficult to actually manufacture a chip out of it, or its electrical properties are compromised.

“It’s a bit of playing whack-a-mole with atomic structures,” Hemant Mohapatra, the Lightspeed partner who led this round, told TechCrunch. “A material is only useful in the real world if all of them converge at once, which is what makes this a really interesting search problem.”

Mohapatra expects that the business of predicting novel substances will be commoditized as models continue to improve. The difference with Discovered Materials is Ramdas’ deep experience in the field, and the ability to run a lab that can rapidly experiment and validate the candidates — something he says the two founders have already done with several new materials.

When they find valuable candidates, Sridhar says the company will attempt to patent the use of the materials in GPUs, or the process by which chips can be made out of the substance, licensing them out to chipmakers. He hopes that they will have new materials worth patenting in the next year.

However, for all the excitement, we still haven’t seen any drugs or materials discovered by AI actually make a commercial impact. The closest is perhaps Insilico Medicine’s Renterosib, the first drug discovered with generative AI to make it into a Phase II clinical trial. On the materials side, promising candidates have been found, like MatNex’s rare-earth free permanent magnets or new semiconductor materials worked out by Panasonic and Citrine Informatics. But these haven’t been commercially deployed at scale yet.

These techniques may be coming into their own now as AI continues to improve, but it’s one reason why Mohapatra says that he doesn’t believe finding more candidates is the hold-up for AI materials science; instead, “filtering them correctly and synthesizing them is the bottleneck.”

While Sridhar believes that Discovered Materials’ unique data and expertise will help the startup compete with deep-pocketed frontier labs, he acknowledged that the reality is that “a lot of this will involve actually going into wet labs and like making things as well. And this is the process that cannot be sped up.”

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