The Google Cyber NYC Institutional Research Program has awarded funding to seven new Cornell projects aimed at improving online privacy, safety, and security.

Additionally, as part of this broader program, Cornell Tech has also launched the Security, Trust, and Safety (SETS) Initiative to advance education and research on cybersecurity, privacy, and trust and safety. 

Cornell is one of four New York institutions participating in the Google Cyber NYC program, which is designed to provide solutions to cybersecurity issues in society, while also developing New York City as a worldwide hub for cybersecurity research. 

"The threats to our digital safety are big and complex," said Greg Morrisett, the Jack and Rilla Neafsey Dean and Vice Provost of Cornell Tech and principal investigator on the program. "We need pioneering, cross-disciplinary methods, a pipeline of new talent, and novel technologies to safeguard our digital infrastructure now and for the future. This collaboration will yield new directions to ensure the development of safer, more trustworthy systems."

The seven newly selected research projects from Cornell are:

• Protecting Embeddings, Vitaly Shmatikov, professor of computer science at Cornell Tech. 

Embeddings are numerical representations of inputs, such as words and images, fed into modern machine learning (ML) models. They are a fundamental building block of generative ML and knowledge retrieval systems, such as vector databases. Shmatikov aims to study security and privacy issues in embeddings, including their vulnerability to malicious inputs and unintended leakage of sensitive information, and to develop new solutions to protect embeddings from attacks.

• Improving Account Security for At-Risk Users (renewal), Thomas Ristenpart, professor of computer science at Cornell Tech, with co-PI Nicola Dell, associate professor of information science at the Jacobs Technion-Cornell Institute at Cornell Tech. 

Online services often employ account security interfaces (ASIs) to communicate security information to users, such as recent logins and connected devices. ASIs can be useful for survivors of intimate partner violence, journalists, and others whose accounts are more likely to be attacked, but bad actors can spoof devices on many ASIs. Through this project, the researchers will build new cryptographic protocols for identifying devices securely and privately, to prevent spoofing attacks of ASIs, and investigate how to make ASIs more effective and with improved user interfaces.

• From Blind Faith to Cryptographic Certification in ML, Michael P. Kim, assistant professor of computer science. 

Generative language models, like ChatGPT and Gemini, demonstrate great promise, but also pose new risks to users by producing misinformation and abusive content. In existing AI frameworks, individuals must blindly trust that platforms implement their models responsibly to address such risks. Kim proposes to borrow tools from cryptography to build a new framework for trust in modern prediction systems. He will explore techniques to enable platforms to earn users' trust by proving that their models mitigate serious risks.

• Making Hardware Comprehensively Secure Against Spectre — by Construction (renewal), Andrew Myers, professor of computer science. 

In this renewed project, Myers will continue his work to design secure and efficient hardware systems that are safe from Spectre and other "timing attacks." This type of attack can steal sensitive information, such as passwords, from hardware by analyzing the time required to perform computations. Myers is developing new hardware description languages, which are programming languages that describe the behavior or structure of digital circuits, that will successfully prevent timing attacks.

• Safe and Trustworthy AI in Home Health Care Work, Nicola Dell, with co-PIs, Deborah Estrin, professor of computer science at Cornell Tech, Madeline Sterling, associate professor of medicine at Weill Cornell Medicine, and Ariel Avgar, the David M. Cohen Professor of Labor Relations at the ILR School.

This team will investigate the trust, safety, and privacy challenges related to implementing artificial intelligence (AI) in home health care. AI has the potential to automate many aspects of home health services, such as patient–care worker matching, shift scheduling, and tracking of care worker performance, but the technology carries risks for both patients and care workers. Researchers will identify areas where the use of AI may require new oversight or regulation, and explore how AI systems can be designed, implemented, and regulated to ensure they are safe, trustworthy, and privacy-preserving for patients, care workers, and other stakeholders.

• AI for Online Safety of Disabled People, Aditya Vashistha, assistant professor of information science.

Vashistha will evaluate how AI technologies can be leveraged to protect people with disabilities from receiving ableist hate online. In particular, he will analyze the effectiveness of platform-mediated moderation, which primarily uses toxicity classifiers and language models to filter out hate speech.

• DEFNET: Defending Networks With Reinforcement Learning, Nate Foster, professor of computer science, with co-PI Wen Sun, assistant professor of computer science. 

Traditionally, security has been seen as a cat-and-mouse game, where attackers exploit vulnerabilities in computer networks and defenders respond by shoring up weaknesses. Instead, Foster and Sun propose new, automated approaches that will use reinforcement learning – an ML technique where the model makes decisions to achieve the most optimal results – to continuously defend the network. They will focus their work at the network level, training and deploying defensive agents that can monitor network events and configure devices such as routers and firewalls to protect data and prevent disruptions in essential services.

Under director Alexios Mantzarlis, formerly a principal at Google’s Trust and Safety Intelligence team, the newly formed SETS Initiative at Cornell Tech will focus on threats ranging from ransomware and phishing of government officials to breaches of personal information and digital harassment. 

"There are new vectors of abuse every day," said Mantzarlis. He emphasizes that the same vulnerabilities exploited by state actors that threaten national security can also be used by small-time scammers. “If a system is unsafe and your data is leaky, that same system will be a locus of harassment for users.” 

Additionally, SETS will serve as a physical and virtual hub for academia, government, and industry to tackle emerging online threats.

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

Amid the unpredictability and occasional chaos of emergency rooms, a robot has the potential to assist health care workers and support clinical teamwork, Cornell and Michigan State University researchers found.

The research team’s robotic crash cart prototype highlights the potential for robots to assist health care workers in bedside patient care and offers designers a framework to develop and test robots in other unconventional areas.

“When you're trying to integrate a robot into a new environment, especially a high stakes, time-sensitive environment, you can't go straight to a fully autonomous system,” said Angelique Taylor, assistant professor in information science at Cornell Tech and the Cornell Ann S. Bowers College of Computing and Information Science. “We first need to understand how a robot can help. What are the mechanisms in which the robot embodiment can be useful?”

Taylor is the lead author of “Towards Collaborative Crash Cart Robots that Support Clinical Teamwork,” which received a best paper honorable mention in the design category at the Association of Computing Machinery (ACM)/Institute of Electrical and Electronics Engineers (IEEE) International Conference on Human-Robot Interaction in March. 

The paper builds on Taylor’s ongoing research exploring robotics and team dynamics in unpredictable health care settings, like emergency and operating rooms.

Within the medical field, robotics are used in surgery and other health care operations with clear, standardized procedures. The Cornell-Michigan State team, however, set out to learn how a robot can support health care workers in fluid and sometimes chaotic bedside situations, like resuscitating a patient who has gone into cardiac arrest.

The challenges of deploying robots in such unpredictable environments are immense, said Taylor, who has been researching the use of robotics in bedside care since her days as a doctoral student. For starters, patient rooms are often too small to accommodate a stand-alone robot, and current robotics are not yet robust enough to perceive, let alone assist within, the flurry of activity amid emergency situations. Furthermore, beyond the robot’s technical abilities, there remain critical questions concerning its impact on team dynamics, Taylor said.

But the potential for robotics in medicine is huge, particularly in relieving workloads for health care workers, and the team’s research is a solid step in understanding how robotics can help, Taylor said.

The team developed a robotic version of a crash cart, which is a rolling storage cabinet stocked with medical supplies that health care workers use when making their rounds. The robot is equipped with a camera, automated drawers, and – continuing Cornell Bowers CIS researchers’ practice of “garbatrage” – a repurposed hoverboard for maneuvering around.

Through a collaborative design process, researchers worked with 10 health care workers and learned that a robot could benefit teams during bedside care by providing guidance on medical procedures, offering feedback, and tracking tasks, and by managing medications, equipment, and medical supplies. Participants favored a robot with “shared control,” wherein health care workers maintain their autonomy regarding decision-making, while the robot serves as a kind of safeguard and monitors for any possible mistakes in procedures, researchers found.

“Sometimes, fully autonomous robots aren’t necessary,” said Taylor, who directs the Artificial Intelligence and Robotics Lab (AIRLab) at Cornell Tech. “They can cause more harm than good.”

As with similar human-robot studies she has conducted, Taylor said participants expressed concern over job displacement. But she doesn’t foresee it happening.

“Health care workers are highly skilled,” she said. “These environments can be chaotic, and there are too many technical challenges to consider.”

Paper coauthors are Tauhid Tanjim, a doctoral student in the field of information science at Cornell, and Huajie Cao and Hee Rin Lee, both of Michigan State University. 

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

Speak a little too haltingly and with long pauses, and a speech-to-text transcriber might put harmful, violent words in your mouth, Cornell researchers have discovered.

A new, adaptive statistical model developed by a research team involving Cornell will make clinical trials safer and more effective, and – unlike most models – is precise enough to identify when a subset of a trial population is harmed by the treatment.

Developed by researchers from MIT, Microsoft, and Cornell, the model – Causal Latent Analysis for Stopping Heterogeneously (CLASH) – leverages causal machine learning, which uses artificial intelligence to statistically determine the true cause and effect among variables. It continually crunches incoming participant data and alerts trial practitioners if the treatment is causing harm to only a segment of trial participants.

By comparison, most statistical models used to determine when to stop trials early are broadly applied across trial participants and don’t account for heterogeneous populations, researchers said. This can result in harms going undetected in a clinical trial: If an experimental drug is causing serious side effects in elderly patients who make up 10 percent of the trial population, it’s unlikely a statistical model would detect such harm, and the trial would likely continue, exposing those patients to even more harm, researchers said.

“We can’t just be looking at the averages,” said Allison Koenecke, assistant professor of information science in the Cornell Ann S. Bowers College of Computing and Information Science. She is the senior author of “Should I Stop or Should I Go: Early Stopping with Heterogeneous Populations,” which was presented at the 37th Conference on Neural Information Processing Systems (NeurIPS) last December in New Orleans. “You have to look at these different subgroups of people. Our method quantifies and identifies harms to minority populations and brings them to light to practitioners who can then make decisions on whether to stop trials early.”

CLASH is designed to work with a variety of statistical stopping tests, which are used by researchers in randomized experiments as guideposts for whether to continue or end clinical trials early. Researchers said CLASH can also be used in A/B testing, a user-experience test comparing variations of a particular feature to find out which is best.

“We wanted to design a method that would be easy for practitioners to use and incorporate into their existing pipelines,” said Hammaad Adam, a doctoral student who studies machine learning and healthcare equity at MIT and the paper’s lead author. “You could implement some version of CLASH with 10 or 20 lines of code.”

“We need to make sure that harms in minority populations are not glossed over by statistical methods that simply assume all people in an experiment are the same,” Koenecke said. “Our work gives practitioners the tools they need to appropriately consider heterogeneous populations and ensure that minority groups are not being disproportionately harmed.”

Along with Adam and Koenecke, paper authors are: Fan Yin and Huibin (Mary) Hu of Microsoft Corporation, and Neil Tenenholtz, Lorin Crawford, and Lester Mackey of Microsoft Research.

This research was supported through the Cornell Bowers CIS Strategic Partnership Program with LinkedIn.

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

Using experiments with COVID-19 related queries, Cornell sociology and information science researchers found that in a public health emergency, most people pick out and click on accurate information.

Although higher-ranked results are clicked more often, they are not more trusted, and misinformation does not damage trust in accurate results that appear on the same page. In fact, banners warning about misinformation decrease trust in misinformation somewhat but decrease trust in accurate information even more, according to “Misinformation Does Not Reduce Trust in Accurate Search Results, But Warning Banners May Backfire” published in Scientific Reports on May 14.

Twins Alsa Khan and Muhammad Jee explain how their AI platform, Mr. EzPz, could help to make artificial intelligence more reliable for students as well as educators.

More than 1,200 graduates from the Cornell Ann S. Bowers College of Computing and Information Science – the largest graduating class in the college’s history – were recognized in separate department recognition ceremonies held Friday, May 24 and Saturday, May 25 in Barton Hall. 

“You are architects of the future,” said Kavita Bala, dean of Cornell Bowers CIS, to the new graduates. “You should couple this feeling of limitless opportunity with a sense of responsibility, not just to innovate but to actively shape a future where technology serves as a force for enduring good.” 

On April 26, the Office of Diversity, Equity, and Inclusion in the Cornell Ann S. Bowers College of Computing and Information Science held their annual Diversity, Equity, Inclusion, and Belonging (DEIB) Awards ceremony in the Statler ballroom.  

The DEIB Awards recognize exceptional leadership by Cornell Bowers CIS faculty and students and honor those who have made outstanding contributions toward uplifting the Bowers CIS community. 

Graduation is finally here, and the 2024 Cornell Bowers CIS graduates have so much to be proud of. They have pursued their passions through coursework and impactful research, immersed themselves in new experiences, and created a future full of opportunity.

Hear from undergraduate and graduate students below in their own words, as they look back on their foundational years at Bowers CIS.

From platforms that screen mortgage applications and résumés to predicting the likelihood defendants will re-offend, AI systems and the algorithms behind them are relied on to make quick and efficient decisions in areas with major consequences, including healthcare, hiring, and criminal justice. 

Though the potential for AI is immense, its early adoption has been beset by recurring challenges: a home-loan processing algorithm was far more likely to deny applications from people of color than white applicants; hiring algorithms meant to screen applicants are increasingly being used without a hard look under the hood, and AI-powered software used by the U.S. criminal justice system was twice as likely to falsely predict future criminality in black defendants as white defendants.

Now more than ever – at the dawn of an artificial intelligence (AI)-assisted future, Cornell’s leadership in AI and in areas of ethics and fairness in technology is both guiding the development of better, fairer AI and shaping the minds of future AI innovators.