Research News

In the Rockaways, a peninsula neighborhood in Queens, New York City, decisions about where to invest in flood barriers, beach nourishment, or shoreline reinforcement could soon be backed by something that has long been missing from the conversation. A clear accounting of what those investments are actually worth.

A research project from NYU Tandon School of Engineering's Center for Urban Science and Progress (CUSP), developed in partnership with NEC Corporation, used geospatial analysis and economic modeling to do exactly that.

But the more disruptive question the project raises is not just what flood resilience is worth, but who is receiving that value, and why they are not yet part of the funding conversation.

Developed through CUSP's Capstone Program, a two-semester initiative in which students work with external partners to address real-world urban challenges, the project was presented on May 1 at the Urban Data Science Showcase at NYU Tandon’s Brooklyn campus.

NEC and NYU have subsequently signed a Memorandum of Understanding (MOU) committing both organizations to further studies in urban disaster prevention, resilience, and advanced technology.

At the core of the project is a value chain framework that maps how a single flood protection investment generates ripple effects across transportation, housing, public health, and community wellbeing. Rather than treating these benefits as abstract, the team built five quantitative models that convert avoided damages into dollar figures.

"The central insight here is that flood resilience creates enormous economic value for stakeholders who have never been asked to contribute to its cost — transit operators, insurers, healthcare systems, tourism economies,” said the project’s Faculty Mentor Yuki Miura, assistant professor at CUSP and Tandon’s Department of Mechanical and Aerospace Engineering, and a member of the New York City Panel on Climate Change. “These are not passive bystanders to flood risk. They are material beneficiaries. Quantifying that stake is the first step to bringing them into the investment conversation."

The numbers are striking. Avoiding traffic disruption from roadway flooding amounts to an estimated $105 million over ten years. Mental health costs linked to flood-triggered trauma, drawing on post-Sandy PTSD rates three times the national average among Rockaway Peninsula residents, reach $501 million per major flood event.

Emergency shelter costs for the 15,932 public housing residents living in the flood zone approach $16.5 million per event. Protecting beachfront tourism preserves an estimated $170 million in revenue over a decade. A fifth model accounts for the health burden that construction noise places on the roughly 44,000 residents living near active worksites.

Taken together, the analysis suggests losses of up to approximately $800 million could potentially be avoided through flood protection measures currently underway under the Greater Rockaway Resilience Plan.

The models were built using ArcGIS and Excel, integrating spatial flood hazard data with socioeconomic indicators, including land use, tax records, and demographics. Findings were validated through prior research comparisons and direct interviews with Rockaway residents, community organizations, and representatives from the public and private sectors.

"When we can show a health system that a flood barrier reduces its emergency admissions by a measurable margin, or show a transit authority the precise cost of service disruption it avoids, the question of who should fund resilience infrastructure starts to have a different answer," said Miura. "That is not only a Rockaways question. It is a question every city with a coastline is facing."

For NEC, whose expertise lies in value chain modeling and digital technologies, the project reflects a broader strategic priority.

"While strengthening resilience is urgent, public and private sector efforts often remain fragmented, and for individual projects, ROI is often unclear," said Ryutaro Adachi, Executive Professional at NEC's GX Business Development Division. "This research presents a basic framework for evaluating the economic impact of projects with ambiguous effects and suggests the potential for expanding future funding methods."

Eighty-eight percent of public housing units on the peninsula sit within the flood zone, and waitlists stretch beyond five years, meaning displaced residents have almost nowhere to go when flooding strikes.

While the Rockaways served as the test case, the framework is designed to generalize across geographies and infrastructure contexts. Under the MOU, NEC and NYU Tandon will explore opportunities for social implementation of the findings, including engagement with financial institutions on new financing methods made possible by technologies such as satellite image analysis, AI, and remote sensing.

The project was led by CUSP graduate students Christian Humann, Kamili Afra, and Ziming Xiong, with Miura as faculty mentor and Adachi and Takuo Shioda of NEC Corporation as project sponsors.

In Downtown Brooklyn, decisions about where to open a coffee shop, attract a retailer, or fill a vacant storefront could soon be informed by an unlikely source: artificial intelligence trained on how people actually move through the neighborhood.

A new research project from the Resilient Urban Networks Lab, led by Takahiro Yabe — an assistant professor at the Center for Urban Science and Progress (CUSP) and the Department of Technology Management and Innovation at NYU Tandon School of Engineering — is using anonymized mobility data and advanced AI models to better understand how people choose where to spend time and how those choices shape the local economy.

Developed in collaboration with the Downtown Brooklyn Partnership (DBP), the work aims to provide a practical tool for retail planning and economic development.

At the core of the project is a system that models thousands of individuals as “AI agents,” each representing a type of person who lives, works, or visits Downtown Brooklyn. Using mobile phone location data, demographic information, and business attributes, researchers created a “synthetic population” of roughly 20,000 agents that simulate real-world behavior.

“We’re essentially building something like SimCity, but grounded in real human behavior,” said Yabe.

These agents can be used to test “what-if” scenarios, such as how foot traffic might change if a new retailer opens, a grocery store is introduced, or a popular café closes. Earlier research could estimate the economic spillover effects of business closures, but this approach goes further by simulating how entirely new additions, such as parks, retail stores, or entertainment venues, might reshape activity across the neighborhood.

“For retail operators, real estate developers, and property owners, one of the biggest challenges is uncertainty,” said Yabe. “This framework allows us to test scenarios before making costly decisions, whether that’s introducing a new tenant, redesigning a space, or rethinking an entire retail mix. The goal is to turn data into a practical decision-making tool that reduces risk and improves outcomes for neighborhoods.”

For organizations like the DBP, which helped shape the research questions and provided local context, the goal is not just to understand behavior but to support decisions about retail strategy. This includes identifying what types of businesses should fill vacant storefronts to attract visitors and encourage them to stay longer in the area.

“They help us frame the right questions,” Yabe said, noting that the Partnership has been closely involved in identifying real-world use cases and providing information about local business openings, closures, and potential tenants.

"Understanding where people go and why is the foundation of a healthy retail market," said Mark Landolina, Senior Director of Real Estate and Economic Development at DBP. "This research gives us the ability to simulate how foot traffic and consumer demand shift when a new business opens or closes before it happens. That means we can go to landlords and tenants with concrete, data-backed recommendations about what types of businesses belong where, taking the guesswork out of leasing decisions and helping the market respond to real demand. That's how you fill the right storefronts with the right businesses and build a stronger Downtown Brooklyn."

Early findings highlight how interconnected the neighborhood’s economy is. In one simulation, when a popular coffee shop was removed, customer demand did not shift to a single competitor. Instead, it spread across many nearby businesses, with most alternatives located within a short walking distance.

This pattern suggests that Downtown Brooklyn operates as a tightly linked retail ecosystem, where businesses share and redistribute foot traffic rather than compete in isolation. It also points to the importance of proximity and diversity in sustaining neighborhood vitality.

The research also demonstrates that everyday movement patterns, particularly predictable routines like weekday lunch trips, can be modeled with a high degree of accuracy. The model has been tested against real-world business openings and closures, and researchers say the level of predictive accuracy improves significantly when combining behavioral personas with spatial and business data.

“The level of accuracy we’re seeing is really, really high, especially for out-of-sample predictions compared to traditional machine learning approaches,” Yabe said.

While the current analysis focuses on restaurant visits as a starting point, the broader framework can be applied to a wide range of retail and urban planning questions, from tenant mix to long-term neighborhood development.

The project is part of NYU CUSP’s capstone program, a two-semester initiative in which students work with external partners to address real-world urban challenges. It was presented on May 1 at the Urban Data Science Showcase at NYU’s Brooklyn campus. The project, titled Enhancing Downtown Brooklyn’s Retail Market Through Data-Driven Interventions, was led by CUSP graduate students Sizhe (Alex) Xu and Divya Natekar, with Ph.D. students DongHak Lee and Boyang Li (CUSP, TMI) as student mentors, Yabe as the faculty mentor, and the DBP serving as the project sponsor.

About DBP Living Lab:

Downtown Brooklyn is a place where collaboration and innovation come together to solve real problems. Through our Living Lab program, DBP partners with groups to solve quality of life challenges facing cities, using Downtown Brooklyn as a platform to test technologies and generate real-world insights. This initiative is a perfect example of what the Living Lab is all about, bringing NYU Tandon's cutting-edge research and technology to work right in our own backyard.

In the race to make AI models not just reason better but respond faster, latency — the delay before an answer appears — is often treated as a purely technical constraint, something to minimize and move past. But how is this relentless push for speed actually impacting the people using these systems every day?

There is a rich body of work in human-computer interaction linking faster response times to better usability. But AI models are fundamentally different from the deterministic systems that previous research was built on. When you wait for a file to download or a page to load, the outcome is fixed and predictable. AI models are probabilistic — you cannot anticipate the precise response. Their conversational interface means users naturally read human social cues into the interaction. A pause might be read as the AI "thinking," for instance. Users are increasingly asked to choose between faster models and slower, deeper-reasoning ones, without guidance on what that choice actually means for their experience.

A recent study presented at CHI’26 explored how response timing shapes the way people use and evaluate AI systems. Felicia Fang-Yi Tan and Technology Management and Innovation Professor Oded Nov recruited 240 participants and asked them to complete common knowledge work tasks using a chatbot. Some tasks focused on creation, such as brainstorming ideas or drafting text. Others centered on advice, like evaluating decisions or offering recommendations. Crucially, the system was engineered to respond at different speeds. Some participants received answers after just two seconds, while others waited nine or even twenty seconds.

The results challenge a long standing assumption in human-computer interaction that faster is always better.

“People assume faster AI is better, but our findings show that timing actually shapes how intelligence is perceived,” says Tan. “A short pause can signal care and deliberation, making the same response feel more thoughtful and useful, even when nothing about the underlying AI model has changed.”

Surprisingly, how quickly the AI responded did not significantly change how people behaved (e.g., frequency of prompting, copy-pasting). Participants prompted just as much and interacted with the system in broadly similar ways regardless of whether they waited two seconds or twenty. Instead, behavior depended more on the type of task. Participants attempting creation tasks (which involve producing new content such as writing) prompted more back and forth, with users refining and iterating on ideas. Advice tasks (which involve providing guidance, critique, or evaluation) led to fewer, more focused exchanges.

Where timing did matter was in perception. Participants who received two-second responses consistently rated the AI’s answers as less thoughtful and less useful. In contrast, those who experienced longer delays tended to view the same kinds of responses more favorably. Many interpreted the pause as a sign that the system was “thinking,” attributing greater care and deliberation to its output.

This effect highlights a subtle but powerful feature of human psychology. In everyday conversation, pauses carry meaning. A quick reply can feel impulsive, while a measured delay suggests reflection. People appear to apply these same social expectations to machines, even when they know they are interacting with software.

The implications extend beyond user experience. Given that latency is an inherent feature of today's AI models, perhaps the more productive question is not how to eliminate it, but what it can be designed to do. Positive friction refers to intentional slowdowns designed to promote cognitive benefits such as reflection. Rather than treating every millisecond of waiting as waste, designers might ask: what can this pause do?

The study also surfaces important ethical considerations. If people equate longer response times with higher quality, they may place undue trust in slower systems, regardless of whether the output is actually better. This raises ethical questions about whether AI systems should be designed to manage timing in ways that shape user perception. And if so, whether users should be informed when they are.

Hydrogen is often described as the fuel of the future — a clean, energy-dense way to store renewable power and decarbonize industries from steelmaking to shipping. But inside the devices that produce it, a surprisingly small and familiar phenomenon is getting in the way: bubbles.

In water electrolysis, electricity splits water into hydrogen and oxygen gases. Those gases naturally form bubbles on the surfaces of electrodes. For decades, researchers have focused on improving catalysts and materials to make this process more efficient. Yet a new paper by postdoctoral researcher Darjan Podbevšek and Sustainable Engineering Initiative Director and Associate Professor of Chemical and Biomolecular Engineering Miguel A. Modestino published in the journal Joule argues that the real bottleneck may be far more mundane.

“Bubble dynamics represent a largely overlooked bottleneck that can account for significant efficiency losses.” says Podbevšek.

At first glance, bubbles might seem harmless, even expected. But their presence sets off a cascade of problems. As bubbles stick to electrode surfaces, they block the very sites where reactions are supposed to occur. They also disrupt the flow of charged particles in the liquid, increasing electrical resistance. As more bubbles accumulate, they can create uneven conditions across the electrode, further degrading performance.

In some cases, these effects are not trivial. The authors note that bubble-related losses can range from about 5 percent to as much as 25 percent of the total energy input, depending on operating conditions. In a technology where efficiency directly affects cost and scalability, that’s a major obstacle.

What makes the problem especially challenging is that bubbles behave in complex ways across multiple scales. At the smallest level, they begin as tiny nuclei, often forming at microscopic imperfections on the electrode surface. Their growth depends on subtle forces, including surface tension and gradients in temperature or concentration.

As bubbles grow and detach, they interact with one another, merging into larger bubbles or forming tiny bubble layers that blanket the electrode. These microscale “bubble carpets” can fundamentally alter electrode/electrolyte interactions and how reactants and products are transported in the system.

“It’s how and where they evolve, grow, detach, and interact that determines their impact,” the authors emphasize.

This multiscale complexity helps explain why the problem has been so difficult to tackle. Many experiments focus on single bubbles under highly controlled conditions, but real electrolyzers operate in messy, turbulent environments filled with countless interacting bubbles. Bridging that gap, the authors argue, is essential for making meaningful progress.

A typical electrolyzer is a tightly sealed, windowless box, operating at high pressure and temperatures (>30bar, >80°C), with highly alkaline or acidic conditions, making it difficult to observe bubbles directly. The authors suggest that it is at least part of the reason there are comparatively few bubble-related studies in the field.

The emerging view is that bubbles are not just a nuisance but an engineering challenge — one that can be managed, and perhaps even exploited. Researchers are exploring ways to design electrode surfaces that encourage bubbles to detach more quickly, preventing them from blocking reactions. Others are experimenting with flowing the liquid electrolyte more aggressively, using motion to sweep bubbles away.

Some of the most intriguing approaches involve changing how the electricity itself is applied. In “pulsed electrolysis,” the current is switched on and off rapidly. During the brief pauses, bubbles have time to dissipate, reducing their buildup and the associated energy losses. “Dynamic operation introduces additional control parameters,” the authors note, opening new possibilities for optimization.

Artificial intelligence is also beginning to play a role, helping scientists analyze complex bubble patterns and optimize operating conditions in ways that would be difficult by hand.

The implications extend far beyond the lab. Global demand for hydrogen is expected to grow dramatically in the coming decades, driven by efforts to cut carbon emissions. Making electrolysis more efficient — even by a small margin — could have an outsized impact on cost and energy use at scale.

“Connecting microscale bubble phenomena to macroscale electrolyzer performance” is therefore critical, the authors argue. In other words, understanding the physics of tiny bubbles could help unlock the full potential of hydrogen as a clean fuel and help improve other gas-evolving electrochemical reactions (or reactors).

Seen this way, the future of green hydrogen may hinge not just on breakthroughs in chemistry or materials science, but on something more fluid and elusive: the behavior of countless small bubbles rising, colliding, and disappearing inside a reactor. Managing them effectively could be the key to turning a promising technology into a practical one.

When a screenwriter told New York University researchers last year that letting AI do her work would make her "miserable inside," she was onto something.

A follow-up study from NYU’s Tandon School of Engineering and Stern School of Business finds that the instinct to compete with generative AI, rather than simply embrace it, is associated with meaningful long-term benefits for writing professionals.

The catch: rivalry alone isn't enough either.

The 2026 study, led by Rama Adithya Varanasi, a postdoctoral researcher in Tandon's Technology, Management and Innovation Department, alongside Tandon Professor Oded Nov, and Batia Mishan Wiesenfeld, a professor of management at Stern, surveyed 403 professional writers across marketing, publishing, education, and the arts. Findings will be presented at the CHI Conference on Human Factors in Computing Systems this month.

The work extends a 2025 qualitative study by the same team, which interviewed 25 experienced writers and introduced the concept of "AI rivalry" — the idea that some writers proactively compete against AI rather than simply avoid it, targeting what they see as its weaknesses, such as its difficulty producing content rooted in specific communities or geographies.

The new research asked a larger question: what actually happens to writers' careers, skills, and satisfaction depending on how they orient themselves toward AI?

The study finds risks at both extremes. Writers who reported strong collaborative attitudes toward AI also reported higher short-term productivity and job satisfaction, but invested less in maintaining their own skills — the risk of over-reliance.

Writers who perceived AI as a rival reported stronger skill maintenance and greater investment in peer relationships, but that perception showed no significant association with productivity or satisfaction — the risk of under-reliance.

"The concern isn't that workers use AI," said Varanasi. "It's that they stop developing the capabilities that make humans irreplaceable. What this study tells managers is that they can't measure success purely by output. If the workflow removes the need for human judgment, the skill atrophies and that cost doesn't show up until it's too late."

Notably, rivalry attitudes didn't reflect a rejection of the technology. The data showed these writers reported more experience with generative AI than those who held neither orientation strongly. They studied the AI competition, rather than ignoring it.

The most striking result came from writers who scored high on both orientations simultaneously. This group showed the strongest associations with job crafting and skill maintenance across nearly every dimension measured, and posted productivity levels closer to the pure collaboration group — though satisfaction remained higher among pure collaborators — without sacrificing the long-term skill maintenance that pure collaborators showed less of.

"What surprised us is that rivalry and collaboration don't cancel each other out," said Wiesenfeld. "Writers who hold both orientations seem to use AI more deliberately. They get the productivity benefits without outsourcing the judgment."

The study is among the first to measure this tradeoff across a broad set of outcomes — relationships, tasks, cognition, skills, satisfaction, and productivity — drawing on expertise in both human-computer interaction and organizational behavior.

The implications for employers are direct. Organizations that push widespread AI adoption to boost efficiency may be optimizing for the wrong thing, particularly if those workflows come at the cost of workers practicing core human skills.

"Most organizations right now are still developing policies on how employees should relate to AI. " said Nov. "Our findings suggest that the relationship workers have with AI matters as much as whether they use it."

The researchers call for a new design structure that builds productive "friction" into AI tools, calibrating how much assistance is offered based on a user's reliance attitudes rather than defaulting to maximum engagement.

The team's next phase will test that concept directly. They are building prototypes of AI tools designed to promote appropriate reliance, and plan to expand the research beyond writing to other creative professions including game developers, graphic designers, and visual artists.

Funding for this research was provided by the National Science Foundation.