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By Sarah Marquart

As tariffs shift and trade regulations grow more complex, a team of Cornell Tech students is developing a smarter solution to address modern trade compliance challenges. Their company, SAIL, is steering into uncharted waters with purpose — and a deep belief that solving the right problem can unlock a new wave of innovation.

SAIL is one of four winners at Cornell Tech’s annual Startup Awards competition on May 16. The students received a $100,000 investment, alongside mentorship and office space, from Cornell Tech.

The company’s founders are Cornell Tech graduates Chansam Kim, MBA ’25; Olivia Mei, Computer Science ’25; William J. Reid, Health Tech ’25; Salik Tehami, MBA ’25; and Ali Raza, MBA at NYU Stern.

Together, the team set out to build a solution that could streamline a critical piece of the trade compliance process: classifying goods under the Harmonized Tariff Schedule (HTS) — the global coding system that determines the duty rates applied to imported goods — and tracking duty exposure and regulatory changes in real time.

Reid’s background creating FDA-regulated machine learning tools shaped the product ethos. “We approached SAIL the same way I approached FDA-regulated machine learning: treat transparency and reliability as first-class requirements,” he said.

Cornell Tech’s Studio curriculum, a critical component of the master’s degree program, gave the team a proving ground. Over two semesters, the team leveraged its access to compliance professionals, interviewing more than 50 experts. This deep immersion helped them solidify that HTS classification as the right first problem to tackle, both in terms of urgency and scope.

“We started by listening,” said Reid. “Those interviews directly informed our decision.”

As the prototype took shape, Mei contributed her knowledge of full-stack software development. “It’s been a great opportunity to apply my skills to an important problem,” she said. “Despite a steep learning curve, collaborating with teammates who bring such diverse backgrounds and strengths has made it rewarding.”

Many of SAIL’s target users have decades of manual practice and limited exposure to AI. For that reason, SAIL positions itself as a co-pilot, not a replacement.

“We believe AI should collaborate with humans,” Kim says, capturing the startup’s ethos. With that in mind, the system explains its reasoning, cites legal rulings, and allows human reviewers to validate each output before it’s submitted.

Winning the Startup Award marked a major milestone for the team, both symbolically and strategically. “It was a great achievement,” says Kim. It gave SAIL what he calls “some certainty” — proof that the venture is much more than a class project.

The $100,000 prize will help fund in-person customer meetings, system improvements, and further development of SAIL’s AI models. It also catalyzed the team’s move into physical office space at Cornell Tech’s Roosevelt Island campus.

“It’s go time,” said Raza. “We’re soaking it in, but also recognizing that things are accelerating.”

Looking ahead, the team is focused on converting several current pilot customers into long-term contracts. Fundraising is planned for early next year, but the founders say inbound investor interest is already picking up.

“Our vision is to build the AI-native supply chain operating system for heavy industry,” says Tehami. “Most supply chain software was built for consumer goods. Heavy industry has different needs: project-based logistics, specialized equipment, and complex regulations. We’re building specifically for that reality.”

Sarah Marquart is a freelance writer for Cornell Tech.


By Henry C. Smith

The Cornell Institute for Digital Agriculture (CIDA) has announced the recipients of its 2025 Research Innovation Fund (RIF) faculty and student grants supporting new, cross-disciplinary research projects designed to improve global food systems through digital innovation. From AI-based dairy diagnostics to virtual reality for youth in agriculture, this year’s awardees are launching bold ideas that promise real-world impact.

“Solving today’s agri-food challenges demands more than innovation—it requires collaboration across disciplines, technologies, and communities,” said Fengqi You, CIDA codirector and the Roxanne E. and Michael J. Zak Professor in Energy Systems Engineering in Cornell Engineering. “The Research Innovation Fund plays a critical role in this mission by empowering faculty and students to pursue bold, early-stage ideas that can drive meaningful change from the lab to the landscape.”

Based at Cornell University, CIDA is a unique partnership that integrates fields such as engineering, computing, life sciences, veterinary medicine, and social sciences to tackle global food challenges with the help of data and digital tools.

“AI has never been more important and learning how to wield and apply AI to digital agriculture is critical,” said Hakim Weatherspoon, codirector of CIDA and professor in the Department of Computer Science in the Cornell Ann S. Bowers College of Computing and Information Science. “CIDA is a beacon of light enabling Cornell students and faculty scientists via the CIDA Research Innovation Fund to explore and advance the state of the art, improving the environment and society.”

CIDA’s RIF provides one-year seed funding for research teams that are exploring brand-new ideas in digital agriculture. The awards help faculty gather early data, build new collaborations, and prepare for larger national or global grants.

Grants are divided into two categories:

  • Planning Grants (up to $15,000): For developing big, early ideas into large-scale proposals.
  • Seed Grants (up to $30,000): For testing innovative solutions with potential for high impact.

 

CIDA integrates fundamental discoveries across the agricultural and life sciences, engineering, computing and information sciences, and social sciences to advance equitable, sustainable, and efficient agriculture and food systems.

To qualify, each project must involve at least two faculty members from different Cornell colleges and represent new collaborations not previously funded by CIDA. Awardees will present at the October 21, 2025 CIDA workshop (registration links will be found at the CIDA News + Events page), as well as giving a presentation in the newly launched Cornell course AI for Digital Agriculture.

“Supporting new cross-disciplinary collaborations among researchers in diverse disciplines from different colleges is at the core of CIDA’s mission,” said Julio Giordano, codirector of CIDA and professor of animal science in the College of Agriculture and Life Sciences (CALS). “These RIF-funded projects exemplify the potential transformative value of innovation across engineering, data science, and agriculture.”

2025 CIDA RIF Awarded Projects

Faculty Planning/Seed Grant Projects

Frameworks for Ethical Agricultural Data Aggregation – Sara Emery, Renata Ivanek (College of Veterinary Medicine), Steven Wolf (CALS), Katie Gold (CALS)

Biosensor Tattoos for Livestock Health – Taika von Königslöw (CVM), Cindy Hsin-Liu Kao (College of Human Ecology), Jennifer Sun (Ann S. Bowers College of Computing and Information Science)

Using VR to Increase Youth & Minority Participation in Agriculture – Tapan Parikh (Cornell Tech/CIS), Jenny Kao-Kniffin (CALS)

Maintaining Agricultural Machinery – Thijs Roumen (Cornell Tech/CIS), Francois Guimbretiere (CIS), Terry Bates (CALS), Elizabeth Bihn (CALS)

Visualizing Water Movement in Root Zones – Taryn Bauerle (CALS), Amit Lal (College of Engineering)

Reducing Methane and Arsenic in NY Rice Paddies – Matthew Reid (COE), Jenny Kao-Kniffin (CALS), Chuan Liao (CALS)

CIDA’s RIF also funds Student Summer Research Awards (up to $11,310) for innovative research projects in digital agriculture under the mentorship of two faculty members from different Cornell colleges.

“The beauty of the CIDA Student RIF Program lies in the rich diversity of student backgrounds and the wide range of challenges being addressed—where innovations in AI, data science, agriculture, biology, and health converge to advance sustainable food production, from dairy calf diagnostics to grapevine genomics,” said Renata Ivanek, codirector of CIDA and professor of population medicine and diagnostic sciences in the College of Veterinary Medicine.

Student Awards

Automated Pose Estimation in Surplus Dairy Calves – Ryan Ye (CALS Undergraduate), Jennifer Sun (CIS), Francisco Leal-Yepes (CVM)

Smart Ultrasound for Diagnosing Calf Respiratory Illnesses – Marina Madureira Ferreira (CALS Graduate Student), Francisco Leal-Yepes (CVM), Jennifer Sun (CIS)

Modeling H5N1 Transmission in Dairy Cattle – Zijun He (CVM Graduate Student), Renata Ivanek (CVM), Jennifer Sun (CIS)

AI Strategies for Sustainable Greenhouse Farming – Jinsung Kim (COE Graduate Student), Fengqi You (COE), Yu Jiang (CALS)

Standardizing Dairy Farm Treatment Records with LLMs – Xinyu Yang (CIS Graduate Student), Jennifer Sun (CIS), Renata Ivanek (CVM)

SplatOverflow: Machine Training Aid – Amritansh Kwatra (Cornell Tech/CIS Graduate Student), Thijs Roumen (Cornell Tech/CIS), Elizabeth Bihn (CALS)

LLM Agent for Grapevine Disease Resistance Research – Daoyuan Jin (COE Graduate Student), Yu Jiang (CALS), Jennifer Sun (CIS)

Validating GrapeSAM Vision Model for Wine Grapes – Angela Paul (CALS Graduate Student), Justine Vanden Hueuvel (CALS), Jonathan Jaramillo (COE)

Sorghum Trait Prediction Using NIR Spectroscopy – Marjorie Hanneman (CALS Graduate Student), Mike Gore (CALS), Abe Davis (CIS)

Henry C. Smith is the communications specialist for Biological Systems at Cornell Research and Innovation.

 


By Grace Stanley

Cornell Tech has announced the appointment of Fei Wang, professor at Weill Cornell Medicine, as the new Senior Faculty Fellow in Clinical AI. The newly established role will strengthen Cornell Tech’s leadership in digital health and artificial intelligence, while also expanding interdisciplinary collaboration between Cornell Tech and Weill Cornell Medicine.

In his new position, Wang will lead efforts to expand research and development in clinical AI across the two campuses. His responsibilities also include working with Cornell Tech’s Health Tech Hub on clinical AI strategy, mentoring faculty and researchers on AI-driven health initiatives, and teaching and advising master’s students at Cornell Tech.

“I am beyond excited to become an official member of the Cornell Tech family,” Wang said. “I’m committed to pushing the boundaries of research and education at Cornell Tech while building even stronger ties with Weill Cornell Medicine.”

Wang brings a distinguished track record in health informatics and AI. He was recently appointed Chief of the Division of Health Informatics and Artificial Intelligence and Associate Dean for Artificial Intelligence and Data Science at Weill Cornell Medicine. He also serves as senior technical advisor at NewYork–Presbyterian Hospital.

Since joining Weill Cornell Medicine in 2016, Wang has played a pivotal role in advancing AI applications in health care. He is a Cornell University field member in computer science, information science, electrical and computer engineering, statistics and data science, and computational biology. His research has focused on developing machine learning models for biomedical data analysis, such as patient similarity analytics, disease subtyping, and clinical trial emulation.

Wang is also the founder of the Institute of AI for Digital Health at Weill Cornell Medicine, which brings together experts in computational science and clinical medicine to develop AI technologies that support individualized health care. The institute collaborates with Cornell Tech, NewYork-Presbyterian Hospital, the Hospital for Special Surgery, and Rockefeller University.

“Fei has already made a tremendous impact through his collaborations with Cornell Tech faculty and students,” said Tanzeem Choudhury, Roger and Joelle Burnell Professor in Integrated Health and Technology at Cornell Tech and and the Jacobs Technion-Cornell Institute. “His deep expertise in clinical AI and his passion for mentorship have enriched our community. We’re excited to see how his leadership will help us scale our efforts and drive meaningful innovation in health care.”

A graduate of Tsinghua University, where he earned his doctoral degree in machine learning, Wang is an elected fellow of the American College of Medical Informatics, the International Academy of Health Sciences Informatics, and the American Medical Informatics Association. He is also a distinguished member of the Association for Computing Machinery.

Wang’s work has earned numerous accolades, including the NSF CAREER Award, the IEEE International Conference on Health Informatics Research Leadership Award, the Google Faculty Research Award, the Amazon Machine Learning for Research Award, and the Sanofi iDEA Award. He holds more than 30 patents related to AI and machine learning in health care.

Grace Stanley is the staff writer-editor for Cornell Tech.


By Grace Stanley

Cornell Tech Assistant Professor Raaz Dwivedi has co-founded Traversal, a startup emerging from stealth this week with a mission: to transform how modern software systems detect and resolve outages using artificial intelligence.

The company has raised a $48 million seed and Series A round led by Sequoia Capital and Kleiner Perkins, with participation from NFDG and Hanabi. With headquarters in New York City, Traversal embodies the Cornell Tech ethos: bridging research, innovation, and real-world impact, right in the heart of the city.

“The company’s growth was fueled by all of us being in New York at the same time. The velocity you hit when you’re in person — I think it accelerated our development. And New York’s burgeoning startup scene is energizing,” said Dwivedi, who is also affiliated with Cornell Engineering.

Dwivedi credits Cornell Tech’s entrepreneurial ecosystem as a key enabler. “Cornell Tech’s support for external engagement made it the perfect place to take that leap,” he said. “From day one, I felt that culture of innovation and entrepreneurship — and it gave me the confidence to start something ambitious.”

Traversal is building what it calls an “AI SRE,” a 24/7 intelligent companion for site reliability engineering. The platform continuously monitors software systems to identify and troubleshoot complex production incidents. By leveraging telemetry — including logs, metrics, traces, and code changes — it pinpoints what Dwivedi calls the “smoking gun” behind outages. But Traversal goes a step further, identifying potential issues before they escalate.

“We’re quickly moving toward self-healing systems,” Dwivedi explained. “AI can detect what’s broken, identify the root cause, and suggest, or even implement, a fix within minutes.”

In practice, this can significantly reduce downtime and ease engineering stress, particularly at large enterprises with complex infrastructure and fragmented data. Traversal is already being used by Fortune 500 companies, including mission-critical cloud providers and financial institutions.

Dwivedi’s expertise in causal machine learning and reinforcement learning has been foundational to solving this problem.

“Root cause analysis is fundamentally a search problem,” he said. “At the heart of it, it’s about finding a needle in a very large haystack (or dozens of haystacks) with many fake needles everywhere. Given the recent advancements in large language models and causal machine learning, I think this is the perfect time to build a semantics-meets-statistics framework to solve this problem. And Traversal is doing just that.”

He co-founded Traversal with fellow AI researchers Anish Agarwal, Raj Agarwal, and Ahmed Lone. The team’s roots trace back to their time at UC Berkeley, MIT, and Columbia.

“We were all researchers, but this felt like the right moment to translate our work in AI into something impactful for industry,” said Dwivedi.

Grace Stanley is the staff writer-editor for Cornell Tech.


By Sarah Marquart

Medical crash carts — stocked with medications, syringes, gauze, and IV fluids — are essential tools in emergency rooms, where seconds matter, space is tight, and confusion can cost lives. However, despite their importance, crash carts vary widely in layout between hospitals and departments. In high-stress situations, this inconsistency can lead to delays as providers search for supplies or open the wrong drawers.

That’s why a team of Cornell researchers set out to reimagine the traditional crash cart, transforming it into a robot designed to support, not disrupt, emergency care teams during critical, time-sensitive procedures.

Their work, led by Angelique Taylor, assistant professor of information science at Cornell Tech, was presented in February 2025 at the ACM/IEEE International Conference on Human-Robot Interaction (HRI). The team’s paper, “Rapidly Built Medical Crash Cart! Lessons Learned and Impacts on High-Stakes Team Collaboration in the Emergency Room,” shares insights from a multi-year effort to design, prototype, and evaluate a robotic, remotely-operated version of the traditional crash cart.

As Taylor explains, the goal wasn’t to introduce entirely new equipment, but to rethink how existing tools could better support teamwork in high-pressure care settings.

“The medical crash cart robot is a cart-based platform designed to support team collaboration during medical procedures, specifically resuscitation code procedures in Emergency Departments,” explained Taylor, who is also affiliated with the Cornell Ann S. Bowers College of Computing and Information Sciences (Bowers CIS). “We wanted to explore how existing equipment in the room can be used to support team collaboration.”

To understand how this robot could function in real-life scenarios, the team designed and tested three versions of the cart-based platform. Each prototype was designed to address a core question: How can a robot effectively communicate, help reduce stress and workload, and support teams during critical care?

One significant insight came from the team’s first field deployment at Base Camp, a large-scale pediatric simulation event hosted by Weill Cornell Medicine that draws over 130 healthcare professionals across the U.S. and Canada.

“We found that the robot was very helpful in reducing mental, temporal, and physical demands,” Taylor said. “But then in terms of frustration, we did not see a reduction.”

That disconnect led the team to dig deeper into how healthcare workers understood the robot’s behavior, particularly when it opened drawers to suggest supplies. Even though participants had been briefed ahead of time, the stress of the scenario left many unsure of the reasons behind the robot’s decisions.

“The robot needed a very clear and direct way of communicating its intentions to the team,” said Taylor.

In response, the team added new ways for the robot to communicate. In subsequent prototypes, they added LED light strips as a visual cue of which drawer the robot was recommending for supply retrieval, and experimented with speech-based guidance to offer task reminders and supply locations. They also conducted lab-based studies with 84 participants to evaluate object search guidance and verbal task reminders.

A front and back view of a later prototype of the medical crash cart, alongside diagrams of its speech and drawer interfaces.
A prototype of the medical crash cart with drawer lights and speech-based guidance.

One discovery: positioning matters. Visual cues were only effective for people directly in front of the cart. Meanwhile, speech could reach more team members, but not always clearly.

“The environments are often very loud,” Taylor said. “So many times, the participants couldn’t even hear what the robot was saying.”

Long or vague spoken messages added to the challenge. Taylor noted that participants often became frustrated when the robot’s speech cues weren’t direct enough. In response, the team refined the robot’s spoken instructions to make them as concise and straightforward as possible.

Building on what they learned from these early deployments, the team is now exploring how robotic crash carts could support medical training and hospital-wide coordination. A multi-site study is currently underway to inform future iterations of the robot.

This project brought together a cross-institutional team of designers, engineers, and healthcare experts. Taylor’s team — including assistant professor Thijs Roumen of Cornell Tech and Cornell Bowers CIS; Ph.D. students Tauhid Tanjim and Michael Joseph Sack; research assistant Kexin Cheng; and Cornell Bowers CIS Associate Professor Malte F. Jung — collaborated closely with Dr. Kevin Ching and Dr. Jonathan St. George of Weill Cornell Medicine. The project also benefited from the contributions of Maia Hirsch, of the Mechanical Engineering Department at the Israel Institute of Technology, and Hee Rin Lee, of Michigan State University.

As healthcare systems face rising burnout and complexity, the team’s research offers a broader takeaway: thoughtful design can do more than assist in a crisis — it can reshape how teams communicate, coordinate, and care.

Sarah Marquart is a freelance writer for Cornell Tech.


By Sarah Marquart

Wireless health monitoring is rapidly evolving, promising a future where breathing, heart rate, and other key metrics can be tracked passively, continuously, and without the need for wearables, such as smartwatches, fitness trackers, or other bulky medical monitors.

However, with that promise comes a pressing concern: Who has access to the sensitive data collected by these devices, and how much control do users actually have?

That’s the question driving VitalHide, a novel privacy-preserving system developed by researchers at Cornell Tech. Assistant professors Rajalakshmi Nandakumar and Thijs Roumen, along with Cornell Tech Ph.D. students Zekun Chang, Yixuan Gao, and Tanvir Ahmed, unveiled the technology in February 2025 at the HotMobile conference in their paper, VitalHide: Enabling Privacy-Aware Wireless Sensing of Vital Signs.

VitalHide aims to restore agency to users without rejecting the benefits of wireless sensing. Roumen, who specializes in digital fabrication and is also affiliated with the Cornell Ann S. Bowers College of Computing and Information Science (Cornell Bowers CIS), emphasized the growing threat of unauthorized monitoring.

“Your TV might collect your heartbeat to gather information on what series excites you, a router at work might inform your employer of your focus during work, and a fan in the waiting room at your insurance company might collect your breathing pattern,” Roumen explained. “In a hospital, it may be great to wirelessly monitor your vitals, and you’d be happy to share that data, but you may want to exclude others from capturing this without your consent.”

That’s where VitalHide stands out. Unlike traditional privacy tools that scramble, block, or limit access only after data is gathered, VitalHide tackles the problem at the source. It uses tiny vibration motors or shape-changing textiles to generate false motion signals — like a decoy heartbeat — to confuse unauthorized sensors.

“Our solution directly ‘encodes’ the physical phenomenon (movement induced by breathing or heartbeat) and only allows trusted actors to access the raw data by sharing the key for decoding,” Roumen explained.

“Authorized devices, knowing this key, can filter out the fake motion signals and accurately recover the true vital signs,” explained Nandakumar, who is also affiliated with the Jacobs Technion-Cornell Institute and Cornell Bowers CIS. “Unauthorized devices, without knowledge of the distribution parameters, cannot distinguish between the real and fake signals and are thus misled.”

The result is seamless, privacy-preserving protection that doesn’t degrade performance for approved health systems or require identifying every nearby device.

Built on a combination of soft robotics, wireless sensing, and functional embroidery, VitalHide’s components can be embedded into clothing or everyday textiles.

The team is working to shrink the system of tiny motors and moving parts — called the actuation system — that creates the protective “fake” motions on the body, making it easier to wear in garments like jackets or vests. They’re also strengthening the system against emerging threats, such as machine learning-powered attacks that attempt to reconstruct biometric signals.

According to the researchers, future improvements include testing across different sensing systems like Wi-Fi and acoustic, as well as creating theoretical guarantees (clear, math-based rules) to show that the system will reliably protect privacy and deliver accurate results.

“It is important to note that VitalHide is not anti-technology,” said Roumen. “Instead, it provides users with the ability to intentionally consent to those who access the vital data. As a marathon runner, I happily track my heart rate with my Garmin watch, but I would not want my heart rate to be tracked by my insurance company.”

The researchers are also conducting user-centered studies to ensure the technology is not only practical but also empowering in practice.

“As new sensing systems are built, there is increasing awareness of privacy concerns, and efforts like VitalHide represent early steps toward designing technologies that recognize these challenges from the beginning,” said Nandakumar. “Building privacy-aware systems is becoming integral to advancing wireless health technologies, not something separate or conflicting with their benefits.”

Ultimately, VitalHide may mark a significant turning point in how society balances innovation with individual rights.

“Without most people’s knowledge, technology has gotten incredibly powerful at detecting and distilling data about our vitals,” said Roumen. “The latest development of this being done wirelessly raises a red flag for how much we should blindly rely on such technological usage and where we draw the line for our privacy. Now is a good time to give people the ability to consciously decide who accesses their data and who doesn’t.”

Sarah Marquart is a freelance writer for Cornell Tech.


By Grace Stanley

Employers often use workplace tracking apps to monitor frontline home health care workers, such as personal care aides, home health aides and certified nursing assistants. A team of Cornell researchers is exploring how these technologies can be used not to surveil workers, but to help them build solidarity and improve their working conditions.

The study, “Exploring Data-Driven Advocacy in Home Healthcare Work,” received a Best Paper award at the Association for Computing Machinery’s Conference on Human Factors in Computing Systems (CHI ’25), which took place April 26-May 1 in Yokohama, Japan. The project included researchers at Cornell Tech, the Cornell Ann S. Bowers College of Computing and Information Science (Cornell Bowers CIS), Weill Cornell Medicine and the ILR School.

“Home health care workers are a vital part of the U.S. health care system, yet their voices are too often overlooked. With this research, we aim to investigate ways to harness the power of data collected by these workers, not just to document their challenges, but to elevate their stories, build solidarity among workers, and advocate for improved working conditions and meaningful change,” said Nicola Dell, co-author of the paper and associate professor of information science at Cornell Tech.

Dell is also an associate professor with the Joan and Irwin Jacobs Technion-Cornell Institute and Cornell Bowers CIS, as well as the director of technology innovation for the Home Care Initiative.

Read more at the Cornell Chronicle.

Grace Stanley is a staff writer-editor for Cornell Tech.


Thirty-four research papers authored by faculty and students from the Department of Information Science in the Cornell Ann S. Bowers College of Computing and Information Science (Cornell Bowers CIS) were honored or featured at the 2025 ACM CHI Conference on Human Factors in Computing Systems (CHI), held recently in Yokohama, Japan. CHI is the premier international venue for research in human-computer interaction (HCI), drawing scholars and practitioners from around the world.

Representing both the Cornell Tech and Ithaca campuses, the Cornell contributions spanned a wide range of topics – from AI and accessibility to design innovation and digital well-being – underscoring the university’s leadership in advancing the field of HCI.

A highlight of the conference was the induction of Wendy Ju, associate professor of information science at Cornell Tech, the Jacobs Technion-Cornell Institute, and Cornell Bowers CIS, into the ACM SIGCHI Academy Class of 2025. This prestigious honor recognizes her pioneering contributions in the field of human-computer interaction.

The ACM SIGCHI Academy is an honorary group of individuals who have made substantial contributions to the field of HCI. It is part of the Association for Computing Machinery’s Special Interest Group on Computer-Human Interaction, one of the world’s largest communities of HCI professionals. Cornell now has four CHI academy members: Wendy Ju, Phoebe Sengers, Susan Fussell, and Tanzeem Choudhury.

Read more about Professor Ju’s achievement in this news story.

Recognized Papers:

The papers and recognitions of faculty and students from both the Ithaca and Cornell Tech campuses are below:

Editor’s Note: Only Cornell-affiliated authors are listed below. Please refer to the individual papers for the full list of contributors.

Best Paper Award:

“Don’t Forget the Teachers”: Towards an Educator-Centered Understanding of Harms from Large Language Models in Education

  • Rene Kizilcec, associate professor of information science, Cornell Bowers CIS
  • Allison Koenecke, assistant professor of information science, Cornell Bowers CIS
  • Emma Harvey, doctoral student in the field of information science

 

Exploring Data-Driven Advocacy in Home Health Care Work

  • Ariel C. Avgar, professor, ILR School
  • Nicola Dell, associate professor of information science, Cornell Tech, the Joan and Irwin Jacobs Technion-Cornell Institute, and Cornell Bowers CIS
  • Joy Ming, doctoral student in the field of information science
  • Chit Sum Eunice Ngai ’24
  • Madeline Sterling, associate professor of medicine, Weill Cornell Medicine
  • Hawi H Tolera ’25
  • Jiamin Tu, M.S. ’24
  • Ella Yitzhaki ’24
  • Aditya Vashistha, assistant professor of information science, Cornell Bowers CIS

 

Towards Hormone Health: An Autoethnography of Long-Term Holistic Tracking to Manage PCOS

  • Daye Kang, doctoral student in the field of information science
  • Gille Leshed, Senior Lecturer and the Director of the MPS program in the Department of Information Science, Cornell Bowers CIS
  • Jeffrey M Rzeszotarski, assistant professor of information science, Cornell Bowers CIS​

 

Honorable Mention:

Beyond Code Generation: LLM-supported Exploration of the Program Design Space

  • Qian Yang, assistant professor of information science, Cornell Bowers CIS​

 

BrickSmart: Leveraging Generative AI to Support Children’s Spatial Language Learning in Family Block Play

  • Chao Zhang, doctoral student in the field of information science

 

“I Need Your Help!” : Facilitating Psychological Communication Between Left-Behind Children and Their Parents with an AI-Powered Sandbox

  • Chao Zhang, doctoral student in the field of information science

 

Shape-Kit: A Design Toolkit for Crafting On-Body Expressive Haptics

  • Daniel Leithinger, Design Tech Innovation Fellow, Cornell APP

 

SplatOverflow: Asynchronous Hardware Troubleshooting

  • Ritik Batra, doctoral student in the field of information science
  • François Guimbretière, a professor of information science, Cornell Bowers CIS
  • Peter He ’27
  • Amritansh Kwatra, doctoral student in the field of information science
  • Thijs Roumen, assistant professor of information science at Cornell Tech and Cornell Bowers CIS, and Director of Matter of Tech Lab
  • Tobias Weinberg, doctoral student in the field of information science
  • Ilan Mandel, doctoral student in the field of information science

 

Why So Serious? Exploring Timely Humorous Comments in AAC Through AI-Powered Interfaces (also received Jury Best Demo honors)

  • Ricky Gonzalez, doctoral student in the field of information science
  • Kowe Kadoma, doctoral student in the field of information science
  • Thijs Roumen, assistant professor of information science at Cornell Tech and Cornell Bowers CIS
  • Tobias Weinberg, doctoral student in the field of information science

 

Additional Papers:

AI Rules? Characterizing Reddit Community Policies Towards AI-Generated Content

  • Travis Lloyd, doctoral student in the field of information science
  • Jennah Gosciak, doctoral student in the field of information science
  • Tung Nguyen
  • Mor Naaman, the Don and Mibs Follett professor of information science at Cornell Tech, the Jacobs Technion-Cornell Institute, and Cornell Bowers CIS

 

AI Suggestions Homogenize Writing Toward Western Styles and Diminish Cultural Nuances

  • Dhruv Agarwal, doctoral student in the field of information science
  • Mor Naaman, the Don and Mibs Follett professor of information science at Cornell Tech, the Jacobs Technion-Cornell Institute, and Cornell Bowers CIS
  • Aditya Vashistha, assistant professor of information science, Cornell Bowers CIS

 

ARticulate: Interactive Visual Guidance for Demonstrated Rotational Degrees of Freedom in Mobile AR

  • Abe Davis, assistant professor of computer science, Cornell Bowers CIS
  • Nhan Tran, doctoral student in the field of computer science
  • Ethan Yang, doctoral student in the field of computer science

 

ASHABot: An LLM-Powered Chatbot to Support the Informational Needs of Community Health Workers

  • Aditya Vashistha, assistant professor of information science, Cornell Bowers CIS

CharacterCritique: Supporting Children’s Development of Critical Thinking through Multi-Agent Interaction in Story Reading

  • Chao Zhang, doctoral student in the field of information science

 

Decoding Driver Intention Cues: Exploring Non-verbal Communication for Human-Centered Automotive Interfaces

  • Ilan Mandel, doctoral student in the field of information science
  • Wendy Ju, associate professor of information science at Cornell Tech, the Jacobs Technion-Cornell Institute, and Cornell Bowers CIS

 

Designing Technologies for Value-based Mental Healthcare: Centering Clinicians’ Perspectives on Outcomes Data Specification, Collection, and Use

  • Daniel A. Adler, doctoral student in the field of information science
  • Yuewen Yang, M.S. ’25
  • Thalia Viranda, doctoral student in the field of information science
  • Emma Elizabeth McGinty, Livingston Farrand Professor of Population Health Sciences, Weill Cornell Medicine
  • Tanzeem Choudhury, Roger and Joelle Burnell Professor in Integrated Health and Technology, Cornell Tech, the Jacobs Technion-Cornell Institute, and Cornell Bowers CIS

 

Digital Technologies and Human Trafficking: Combating Coercive Control and Navigating Digital Autonomy

  • Thomas Ristenpart, professor of computer science, Cornell Tech and Cornell Bowers CIS
  • Lana Ramjit, director of the Clinic to End Tech Abuse at Cornell Tech
  • Nicola Dell, associate professor of information science at Cornell Tech, the Jacobs Technion-Cornell Institute, and Cornell Bowers CIS

 

The Effects and Non-Effects of Social Sanctions from User Jury-Based Content Moderation Decisions on Weibo

  • Will Hobbs, Lois and Mel Tukman Assistant Professor, College of Human Ecology
  • Andy Zhao, doctoral student in the field of information science

 

Exploring Personalized Health Support through Data-Driven, Theory-Guided LLMs: A Case Study in Sleep Health

  • Xingbo Wang, postdoctoral associate, Weill Cornell Medicine
  • Janessa Griffith, postdoctoral associate, Cornell Tech
  • Daniel A. Adler, doctoral student in the field of information science
  • Joey Castillo, technologist in residence, Cornell Tech
  • Tanzeem Choudhury, Roger and Joelle Burnell Professor in Integrated Health and Technology, Cornell Tech, the Jacobs Technion-Cornell Institute, and Cornell Bowers CIS
  • Fei Wang, assistant professor of healthcare policy and research, Weill Cornell Medicine

 

Friction: Deciphering Writing Feedback into Writing Revisions through LLM-Assisted Reflection

  • Peter Bidoshi ’27
  • Kexin Phyllis Ju, master’s student in the field of information science
  • Jeffrey M Rzeszotarski, assistant professor of information science, Cornell Bowers CIS​
  • Chao Zhang, doctoral student in the field of information science

 

Generative AI and Perceptual Harms: Who’s Suspected of using LLMs?

  • Kowe Kadoma, doctoral student in the field of information science
  • Mor Naaman, the Don and Mibs Follett professor of information science at Cornell Tech, the Jacobs Technion-Cornell Institute, and Cornell Bowers CIS

 

“Ignorance is not Bliss”: Designing Personalized Moderation to Address Ableist Hate on Social Media

  • Shiri Azenkot, associate professor of information science at Cornell Tech, the Jacobs Technion-Cornell Institute, and Cornell Bowers CIS
  • Aditya Vashistha, assistant professor of information science, Cornell Bowers CIS
  • Sharon Heung, doctoral student in the field of information science
  • Lucy Jiang, M.S. ’24

 

Large Language Models in Qualitative Research: Uses, Tensions, and Intentions

  • Marianne Aubin Le Quéré, doctoral student in the field of information science
  • David Mimno, associate professor and chair of the Department of Information Science, Cornell Bowers CIS

 

LivingLoom: Investigating Human-Plant Symbiosis through Integrating Living Plants into (E-)Textiles

  • Samantha Chang ’26
  • Cindy Hsin-Liu Kao, associate professor, College of Human Ecology and field member in the Department of Information Science in Cornell Bowers CIS
  • Jingwen Zhu, doctoral student in the field of human centered design

 

Modeling the Impact of Visual Stimuli on Redirection Noticeability with Gaze Behavior in Virtual Reality

  • Tianqi Liu, doctoral student in the field of information science

 

Oral History and Qualitative Analysis with Youth: A Method for Cultivating Attachments

  • Tapan Parikh, associate professor of information science, Cornell Tech and Cornell Bowers CIS

 

Proxona: Supporting Creators’ Sensemaking and Ideation with LLM-Powered Audience Personas

  • Eun Jeong Kang, doctoral student in the field of information science

 

Shifting the Focus: Exploring Video Accessibility Strategies and Challenges for People with ADHD

  • Shiri Azenkot, associate professor of information science at Cornell Tech, the Jacobs Technion-Cornell Institute, and Cornell Bowers CIS
  • Shirley Yuan, doctoral student in the field of information science
  • Woojin Ko, doctoral student in the field of computer science

 

The People Behind the Robots: How Wizards Wrangle Robots in Public Deployments

  • Frank Bu, doctoral student in the field of computer science
  • Wendy Ju, associate professor of information science at Cornell Tech, the Jacobs Technion-Cornell Institute, and Cornell Bowers CIS

 

The Robotability Score: Enabling Harmonious Robot Navigation on Urban Streets

  • Matt Franchi, a doctoral student in the field of computer science
  • Maria Teresa Parreira, a doctoral student in the field of information science
  • Frank Bu, a doctoral student in the field of computer science
  • Wendy Ju, associate professor of information science at Cornell Tech, the Jacobs Technion-Cornell Institute, and Cornell Bowers CIS

 

SpellRing: Recognizing Continuous Fingerspelling in American Sign Language using a Ring

  • François Guimbretière, professor of information science, Cornell Bowers CIS
  • Cheng Zhang, assistant professor of information science, Cornell Bowers CIS
  • Nam Anh Dang ’27
  • Dylan Lee ’25
  • Tianhong Catherine Yu, doctoral student in the field of information science

 

Should Voice Agents Be Polite in an Emergency? Investigating Effects of Speech Style and Voice Tone in Emergency Simulation

  • Susan R. Fussell, professor, Cornell Bowers CIS
  • Jieun Kim, doctoral student in the field of information science

 

Understanding User Perceptions and the Role of AI Image Generators in Image Creation Workflows

  • Susan R. Fussell, professor, Cornell Bowers CIS
  • Shu-Jung Han, doctoral student in the field of information science

 

“Who is running it?” Towards Equitable AI Deployment in Home Care Work

  • Ariel C. Avgar, professor, ILR School
  • Nicola Dell, associate professor of information science at Cornell Tech, the Jacobs Technion-Cornell Institute, and Cornell Bowers CIS
  • Joy Ming, doctoral student in the field of information science
  • Madeline Sterling, associate professor of medicine, Weill Cornell Medicine
  • Ian René Solano-Kamaiko, doctoral student in the field of information science
  • Melissa Tan, M.S. ’25
  • Aditya Vashistha, assistant professor of information science, Cornell Bowers CIS

By Bridget Kuehn

Weill Cornell Medicine has received a projected $4 million grant from the National Cancer Institute, part of the National Institutes of Health, to conduct a clinical trial testing whether a new imaging approach could reduce the need for biopsies to monitor prostate cancer.

The five-year grant, with a possible two-year extension, will evaluate whether adding an imaging modality called Prostate Specific Membrane Antigen (PSMA) Positron Emission Tomography (PET) Computed Tomography (CT) to active surveillance regimens can rule out the presence of cancer that requires treatment. PSMA-PET CT uses a radioactive diagnostic agent to detect a protein that is found on the surface of prostate cells and at higher levels on prostate cancer cells. The technique is already used to detect the spread of cancer in men with high-risk prostate cancers and cancer recurrence in men who are in remission.

“We hope to use PSMA-PET CT as a less invasive and less costly alternative to biopsy in men undergoing active surveillance for low or moderate-risk prostate cancers,” said the study’s principal investigator Dr. Timothy McClure, an assistant professor of urology and radiology in the Division of Interventional Radiology at Weill Cornell Medicine.

Prostate cancer is the second leading cause of cancer death in men in the United States. Yet, most older men diagnosed with prostate cancer have slow-growing, low-risk cancers that will not harm them during their lifetime. Rather than subject these men to surgery or radiation that may cause harmful side effects, most physicians recommend active surveillance instead.

These men undergo regular blood tests measuring prostate-specific antigen (PSA) levels, magnetic resonance imaging and biopsies to ensure that their cancer has not progressed into a more dangerous form. However, repeated biopsies can lead to infection, urination difficulties and other symptoms, causing many to stop monitoring their cancer.

Dr. McClure and his colleagues in the Molecular Imaging and Therapeutics division of the Department of Radiology and the Department of Pathology at Weill Cornell Medicine will test whether adding PSMA-PET CT can help improve the sensitivity and specificity of prostate cancer monitoring while reducing screening-related harms. The researchers will assign 200 men with low- or intermediate-risk prostate cancer who have opted for active surveillance to receive usual active surveillance protocol plus PSMA-PET CT. The trial will enroll patients at NewYork-Presbyterian/Weill Cornell Medical Center and four other sites. In addition to the NCI grant, the study is also receiving support from Lantheus, the company that produces a diagnostic agent used in PSMA-PET CT.

Dr. McClure is also teaming up with Dr. Mert Sabuncu, vice chair of radiology research and a professor of electrical engineering in radiology at Weill Cornell Medicine, to develop a machine learning algorithm that can predict which patients with prostate cancer will progress to a stage requiring treatment. Dr. Sabuncu is also a professor in the School of Electrical and Computer Engineering at Cornell University’s Ithaca campus and Cornell Tech. Additionally, genomic data from the PSA blood tests will help identify genetic signatures indicating a patient at greater risk who may benefit from earlier therapy rather than active monitoring.

“Our trial leverages cross-sector collaboration to innovate and streamline care for patients with prostate cancer,” said Dr. McClure, who is also a urologist at NewYork-Presbyterian/Weill Cornell Medical Center. “We hope to develop alternatives for prostate cancer surveillance that help us more effectively stratify which patients need treatment.”

Many Weill Cornell Medicine physicians and scientists maintain relationships and collaborate with external organizations to foster scientific innovation and provide expert guidance. The institution makes these disclosures public to ensure transparency. For this information, see profile for Dr. Timothy McClure.

The research described in this study is supported in part by the National Cancer Institute, part of the National Institutes of Health, through grant number 5R37CA282407.

Bridget Kuehn is a freelance writer for Weill Cornell Medicine.


By Grace Stanley

Imprint, an organization founded at Cornell Tech that is dedicated to decoding the body’s immune memory and uncovering the causes of chronic diseases, announced that it has raised over $15 million in funding.

Imprint’s journey began in 2019 when its founder, Beck Brachman, was accepted into Cornell Tech’s Runway Postdoc Program, a startup incubator within the Joan and Irwin Jacobs Technion-Cornell Institute. The Runway program helps postdocs, those who recently received their doctoral degrees, transition from an academic mindset to an entrepreneurial outlook. Participants receive substantial support, including a salary, research budget, housing allowance, mentorship from academic and business experts, and access to selected university resources.

The Runway Program has been instrumental in launching over 120 startups. These startups have collectively raised over $375 million in private funding and created more than 500 jobs in New York City. Notable startups include Nanit, OnsiteIQ, and Biotia.

“Imprint exemplifies the kind of bold, translational science we champion at Cornell Tech’s Runway Program — science that moves beyond the lab to deliver real impact,” said Fernando Gómez-Baquero, director of the Runway Startup Postdoc and Spinout Program at Cornell Tech’s Jacobs Institute, and member of Imprint’s Board of Directors.

Imprint, a nonprofit Focused Research Organization (FRO) supported by Convergent Research, aims to pioneer new experimental and machine learning technologies to uncover the hidden causes of chronic diseases, including autoimmune diseases, long COVID, and neuropsychiatric disorders. The $15 million in funding will be used to establish Imprint’s high-throughput data generation pipeline, build its first computational tool, and pilot disease applications. The investment comes from notable backers, including philanthropists Eric and Wendy Schmidt, Peter Reinhart, and the New York City Economic Development Corporation (NYCEDC).

“We may not know what’s causing chronic disease – but the immune system probably does,” said Brachman, CEO and co-founder of Imprint, last month from the TED stage. “In addition to being an army, the immune system is also an archive. We founded Imprint on the thesis that the immune system’s historical archive can be accessed and translated to identify unknown causes of chronic disease, enabling the development of diagnostics, treatments, and even cures.”

Imprint’s “forensic immunology” approach aims to unlock the secrets held within memory immune cells, which contain an archive of all the immune exposures a person has had throughout their life. This history of diseases and their hidden triggers exists in the human body, but is challenging to locate and decipher. Imprint is working to develop experimental and computational immunology tools to uncover this information.

“By decoding the immune system’s memory, Imprint is unlocking a frontier of discovery that could change how we diagnose, treat, and even prevent chronic disease,” said Gómez-Baquero. “We’re proud to have helped Dr. Brachman catalyze this vision and to support the computational and scientific infrastructure that will drive a deeper understanding of autoimmune and neuropsychiatric disorders.”

As a part of this new funding, the NYCEDC has awarded a grant to Imprint through the LifeSci NYC Expansion Fund. This fund aims to support rapidly growing life sciences companies that want to expand in New York City. The LifeSci NYC Expansion Fund is part of the broader LifeSci NYC initiative, a $1 billion project managed by NYCEDC to establish New York City as a global leader in life sciences and create 40,000 jobs over the next 15 years.

“New York City is cementing itself as a global leader in life sciences and paving the way for cutting-edge innovation — like the work happening at Imprint — to emerge right here in the heart of the city,” said Cecilia Kushner, NYCEDC’s chief strategy officer. “We are proud to support trailblazing companies like Imprint whose groundbreaking work will help accelerate scientific discovery and life science jobs in NYC, while uncovering the cause of chronic diseases and enabling people all around the globe to live longer.”

Grace Stanley is the staff writer-editor at Cornell Tech.