In 2020, Yian Yin teamed up with economists at Northwestern University to look at the impact of researchers who had shifted their focus to study the COVID pandemic. He saw that these researchers faced a "pivot penalty" – their COVID-related work received less attention than previous contributions in their old field – and the greater the pivot, the worse the penalty.

As Yin and his colleagues continued their analyses, however, they discovered the pivot penalty wasn't just a side effect of the pandemic. It occurred any time a scientist, inventor, or organization struck out in a new direction instead of staying in their lane.

“This is really a universal pattern that appears very widespread across science and technology – across different fields, research outcomes, career stages, and team sizes,” said Yin, who was then a research fellow at Northwestern, and is now an assistant professor of information science in the Cornell Ann S. Bowers College of Computing and Information Science. 

Sorin Lerner, professor and chair of the Department of Computer Science and Engineering in the Jacobs School of Engineering at the University of California, San Diego, has been named dean of the Cornell Ann S. Bowers College of Computing and Information Science, Provost Kavita Bala announced June 5.

The largest ever graduating class in the history of the Cornell Ann S. Bowers College of Computing and Information Science – more than 1,400 in total – walked the stage in three recognition ceremonies held May 23 and 24 at Barton Hall. 

 

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Introduction

Artificial intelligence (AI) is reshaping economic systems, geopolitics, and society—and its transformative influence is set to deepen in the years ahead. The United States’ leadership in AI follows a similar blueprint to previous technological revolutions—such as semiconductors and the Internet—where federal investments played a catalytic role. Namely, the U.S. federal government complements the private sector by making strategic investments, in partnership with universities, to support research with high potential but no immediate profit incentive. The federal government can maintain U.S. dominance in AI by strengthening its investments in fundamental AI research at universities that foster AI engineering and research talent. We articulate several areas of research below that we believe are particularly high-leverage areas for supporting U.S. innovation, talent, and leadership.

Fundamental advances in AI algorithms and architectures: Algorithmic and architectural innovation are essential to the development of next generation AI systems. Current AI systems are expensive to train and run, difficult to control, and have limited multi-modal capabilities. Innovations in AI architecture design enable the development of models that scale more efficiently with data and computation, paving the way for  more powerful models. Algorithmic innovations will be needed to make AI more controllable, facilitating its deployment across a range of high-stakes applications where its behavior must conform to precise specifications. Future AI systems must also natively accommodate and fuse diverse input types—language, video, audio, time series, etc.—rather than relying on brittle post-hoc solutions that attempt to align these disparate domains. Advances in these fundamental technologies will support the development of stronger, more reliable models with more contextual awareness, enabling the applications of AI for scientific discovery, education, agentic and embodied reasoning, and interactive behaviors that we elaborate on below.

Interdisciplinary research for AI-accelerated fundamental scientific discovery: AI has the potential to dramatically accelerate discovery in science, mathematics, and engineering. However, this potential cannot be realized simply by applying existing AI techniques. Instead, we need to develop new AI techniques tailored for the problem of scientific discovery. In particular, the data hungry nature of modern AI systems is a challenge in scientific domains where annotated data can be limited. Beyond learning from data, scientific discovery also requires AI systems to synthesize existing scientific knowledge, autonomously perform experiments and produce formal mathematical reasoning. Crucially, these challenges must be solved in the context of the scientific domain in question. Thus, interdisciplinary teams of AI researchers and domain scientists are crucial. Such interdisciplinary teams are outside the scope of industrial operations. They must therefore be a focus of federally funded research at universities which host multiple scientific disciplines in close proximity.

Advances in agentic and physically embodied AI: An open challenge for current AI systems is the physical world, especially in cluttered, dynamic, “open world” and potentially adversarial environments. For example, a robot in the kitchen might need to reach into cluttered cabinets with unknown organization while navigating around hot stoves and running kids. Such challenges are also pervasive in medical settings, rescue operations and even on the factory floor. These open research challenges preclude commercialization. They also require fundamental advances that are beyond the near-term focus of industrial research, requiring new multi-sensory perception systems, new algorithms for fine-grained control in robots and novel AI architectures that integrate perception with planning and reasoning.

High-risk, high-reward AI research relevant for domains critical to future U.S. competitiveness, including human-AI interaction: Many transformative applications of AI require systems that can communicate and adapt fluidly in partnership with humans. As AI becomes embedded in decision-making processes across industries, from healthcare to defense to education, interaction with these systems is no longer optional. We need AI that can communicate its goals, adapt to user preferences, respond to uncertainty, and engage in collaborative activities with human partners. Effective interaction underwrites many of the systems discussed elsewhere in this document: from human-robot interaction that ensures safe and effective behaviors in shared physical spaces, to AI education systems that effectively meet the needs of their students.

Educating an AI Research Workforce: For the AI research that will be done in industry, the nation needs a well-educated workforce that is trained to do cutting edge research. US universities are uniquely positioned to supply the talent that is educated in scientific methodology and AI, especially at the PhD level. Any AI research that is done at universities will not only produce new knowledge, but also train the PhD-level workforce that has driven technological leadership of the US tech industry in the past, and that will be required for AI leadership in the future.

Conclusion

Above, we have laid out high-priority areas where federal funding of academic research is vital for the U.S. to maintain leadership in AI. These include directions (such as physically embodied AI as well as fundamental advances in AI algorithms) where technology is not currently viable for commercialization. They also include directions (such as AI for scientific discovery and education) that U.S. universities are particularly well-poised to explore because of their role as multidisciplinary educational institutions. 

However, crucially, this list above is not meant to be exhaustive. Many of today’s AI breakthroughs are rooted in decades of federal funded research. To sustain this history of innovation and leadership, the U.S. must continue to invest robustly and broadly in AI research. After all, who would have predicted in the 1990s that the niche research area of language modeling would be the breakthrough technology of the 2020s. Ongoing federal support is essential to maintain the nation’s global leadership in AI. 

Contributing authors: John Thickstun, Bharath Hariharan, Nate Foster, Thorsten Joachims, Kilian Weinberger

May 20, 2025

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 22 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

Artificial intelligence is increasingly being used in home health care – but home health care workers are generally unaware of that. Nor do they understand how AI works, why it may retain their information and that it could replicate bias and discrimination in their workplace.

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That old laptop collecting dust on your bookshelf may have a new lease on life, or at least get responsibly recycled, thanks to the upcoming Cornell Bowers CIS Earth Day Repair Fair.

This Earth Day, Tuesday, April 22, local repair pros and members of the Cornell and Ithaca reuse community will be on-hand from 4 to 7 p.m. in the Gates Hall lobby to fix – or teach you how to fix – defunct tech hardware, from laptops and computers to keyboards and headphones. For devices beyond repair, unneeded gear, or that mystery adapter, organizers will be collecting donations with the Cornell Computer Reuse Association (CCRA) for reuse or responsible e-waste recycling. “Anything with a cord (including the cord!)” will be accepted, organizers said. The event is open to the general public.

“This year, we have a particularly great team of fixers ready to help,” said Dylan Van Bramer ‘25, one of the fair’s organizers.

Now in its third year, the Repair Fair is a hybrid crash course in do-it-yourself repair and e-waste recycling drive. It’s also an opportunity to learn about the environmental impacts of computing and consumer technology and the growing right-to-repair movement – a push for manufacturers to make products people can freely fix and modify themselves. 

The hope is to begin tackling global e-waste at the local level. In 2022, countries produced a record 62 million tons of e-waste, and only 22 percent was responsibly recycled, according to the United Nations.

At last year’s fair, volunteers repaired 15 laptops, donated about 100 laptops, keyboards, and mice, and collected more than 450 pounds of e-waste to be recycled through Cornell’s R5 operations, Van Bramer said. 

The Cornell Ann S. Bowers College of Computing and Information Science, Cornell’s Campus Sustainability Office, and CCRA will host the fair. New to the slate this year: at 5 p.m., organizers will do a laptop teardown demo to demystify a device found in roughly 80 percent of American households. The fair will also feature project posters from students in Computing on Earth (INFO 4260).

By Louis DiPietro, a writer for the Cornell Ann S. Bowers College of Computing and Information Science.

At Weill Cornell Medical College, students have a new tool for polishing their bedside manner and making a diagnosis: an artificial intelligence-powered virtual patient that simulates the doctor-patient interaction.

The simulator, called MedSimAI, has a text-based chat function and a voice conversation mode that approximates a telehealth visit. It gives students a low-stress setting to practice communicating with empathy and to reason through potential diagnoses. Researchers in the Cornell Ann S. Bowers College of Computing and Information Science are developing the platform in collaboration with medical professionals at Weill Cornell Medicine, Yale University and the University of California, San Francisco.

Traditionally, medical students develop their patient interviewing skills through graded interactions with actors posing as patients in a simulation clinic. These Objective Structured Clinical Examinations (OSCEs) are expensive and time-consuming, however, which limits students’ practice opportunities. Researchers have attempted to simulate this experience digitally, but earlier chatbots did a poor job producing realistic patient responses, and pricey virtual reality-based systems did little to improve accessibility.

“Simulation-based learning is known to be highly effective for training future physicians, nurses, veterinarians and other clinical professionals,” said René Kizilcec, associate professor of information science in Cornell Bowers CIS and lead researcher on MedSimAI. “Building on the latest advances in generative AI, we can offer students unlimited opportunities to practice their clinical communication and reasoning skills with immediate feedback and just the right level of realism.”

The MedSimAI platform uses state-of-the-art large language models to generate a patient’s responses based on a script provided by medical educators. It also has a second AI model that evaluates the student’s performance, using the same rubric that experts use to score OSCEs. The model gives immediate feedback – instead of waiting days or weeks with traditional OSCEs – and even highlights specific comments that showed empathy or questions that lacked key details.

Dr. MacKenzi Nicole Preston, assistant professor of clinical pediatrics at Weill Cornell Medicine and associate director of the Clinical Skills Center, where students practice with robotic mannequins and actors, is working with Kizilcec’s team to test the efficacy of MedSimAI as part of the curriculum. She said feedback from the students has been positive.

“Part of the physician’s job is being able to communicate in a way that helps patients to be comfortable,” she said. But in addition to showing compassion, she said, “it’s essential that physicians learn to ask the right questions and interpret the information they get in a way that brings them closer to the truth.”

Yann Hicke, a doctoral student in the field of computer science, has been building the platform and developing specific cases to help students prepare for their OSCEs. “The platform provides the opportunity for ‘deliberate practice,’ where students can see their strengths and weaknesses and seek out specific cases that let them practice skills they are lacking,” he said.

This year, first-year students took a complete medical history through MedSimAI as part of a full-day simulation treating a patient with rheumatic heart disease, while second-year students used it in their pediatric rotation to practice taking a child’s history from a parent.

“It’s very natural to use,” said Kellen Vu, a first-year student at Weill Cornell Medical College. “It’s voice-based, so that lets the conversation flow smoothly. I think it’s important for practicing your bedside manner, because tone and phrasing matter a lot in real life with patients.”

Vu found it especially helpful that the platform provided a list of possible diagnoses along with key symptoms for each – and noted whether he had asked about those symptoms – which helped him identify gaps in his questioning.

The team is already developing additional cases, as well as a module that simulates speaking with another doctor, so that medical students can practice calling for a consult. They are also working with collaborators at Yale and UCSF to incorporate MedSimAI into their medical school education programs.

Long term, Kizilcec’s team aims to extend the platform to other clinical environments and advance medical education research.

“While nothing replaces practice with human patients,” he said, “tools like MedSimAI are a cost-effective way to augment clinical education and serve as a research platform to find new ways to train future clinicians.” 

Patricia Waldron is a writer for the Cornell Ann S. Bowers College of Computing and Information Science.

A team of researchers from Cornell Tech has developed a new tool designed to revolutionize hardware troubleshooting, with the help of 3D phone scans. 

SplatOverflow – inspired by StackOverflow, a widely used platform for tackling software issues – brings a similar approach to hardware support, enabling users to diagnose and fix hardware issues asynchronously with the help of remote experts. 

A Cornell-led research team has developed an artificial intelligence-powered ring equipped with micro-sonar technology that can continuously and in real time track fingerspelling in American Sign Language (ASL).

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