Undergraduate students and future innovators who recently chose the Cornell Ann S. Bowers College of Computing and Information Science as their academic home were recognized and celebrated during the college’s New Majors Welcome event.

More than 200 newly declared majors to Cornell Bowers CIS enjoyed dinner and conversation – and received red and black college scarves and beanies – at the event, which was held Wednesday, Feb. 15, in the Statler Hotel Ballroom. Roughly 30 faculty members and several support staff from the college’s three departments – computer science, information science, and statistics and data science – ate with students and shared knowledge about the departments and the many opportunities available to Cornell Bowers CIS undergraduates, whether in academics, research, or clubs.

Attendees heard from student leaders in campus groups like Women in Computing at Cornell, Association of Computer Science Undergraduates, Underrepresented Minorities in Computing, and the Information Science Student Association, as well as staffers from the college’s Student Service Office and Office of Diversity, Equity, and Inclusion.

“You are joining our community at one of the most exciting times in the fields of computer science, information science and statistics and data science,” said Kavita Bala, dean of Cornell Bowers CIS, in her opening remarks. “New computing and information technologies are poised to upend centuries-old institutions like transportation, healthcare, commerce, urban infrastructure, finance, and more.” 

Undergraduates are admitted to Cornell through one of three admitting colleges: Cornell Engineering, the College of Agriculture and Life Sciences, or the College of Arts and Sciences. They then find their way to Cornell Bowers CIS by declaring majors such as biometry and statistics, information science, computer science, statistical science, or information science, systems, and technology.

More students than ever are flocking to Cornell Bowers CIS to gain fundamental knowledge and skills needed to make an impact on our increasingly connected world. Today, the college’s undergraduate majors total more than 2,000, reflecting the relevance of the college’s leading, interdisciplinary education.

“There are so many fields you can get involved with,” Bala said, “and when you get that degree and go out there, you will have an incredible and lasting real-world impact.”

Emily Tseng is a doctoral student in information science (IS) from Singapore and Nashville, Tennessee. She earned a bachelor’s degree from Princeton University, with a focus on global health and mathematical modeling for infectious disease epidemics, and now studies human-computer interaction, machine learning, and data privacy at Cornell.

What is your area of research and why is it important?

I work broadly in human-computer interaction (HCI) and social computing, borrowing approaches from machine learning, computer security and privacy, and global health. I’m interested in how we build computational tools for new systems of caregiving. In fields like medicine, health, and social work, our efforts to improve people’s lives involve gathering large amounts of data on their trauma or pain, analyzing it using machine learning methods increasingly less and less subject to human oversight, and then using that analysis to make design and policy decisions. I want us to be able to do this rigorously and with attention to core human values like privacy, agency, and equity. To me, this is a combination of evolution in our data analysis techniques (e.g., privacy-preserving machine learning), our approaches to gathering and curating datasets (e.g., informed consent), and our technology design frameworks (e.g., participatory design). So far, I’ve worked specifically with text-based psychotherapy platforms, mobile data collection systems in home health care, and computer security and privacy for survivors of intimate partner violence.

What are the larger implications of this research?

Care is core to society, and care systems are increasingly being remade as systems for data collection and analysis—consider, for example, how much time your doctor spends checking boxes on your electronic health record. This means we can do amazing things with that data, like developing new therapeutics, forecasting epidemics, and improving care for underrepresented people—just look at the explosion in machine learning for health. But this also means we’re collecting more data about more people than ever before, and the history of research shows us that can often be extractive and harmful. My goal is to ensure we build the data-driven future of care systems responsibly and ensure this future is uplifting for data subjects and for broader society.

What does it mean to you to have been awarded a Microsoft Research Ph.D. Fellowship?

I’m thrilled to have been selected—it’s an honor and a massive statement of confidence for my work. More importantly, these awards always reflect a community’s worth of effort, and in my Ph.D. I have had the privilege of extraordinary support from my advisors, my peers, and the field of IS. I’m immensely grateful to them. I also take seriously my responsibility to pay it forward—I wrote up a blog post on my approach to fellowship applications that I hope can help other junior scholars. 

You received a best paper award at CHI 2022. Can you tell us about the paper?

It was a tremendous honor to be recognized by the CHI community! “Care Infrastructures for Digital Security and Privacy in Intimate Partner Violence” was a project I led out of the IPV Tech Research Group, which is spearheaded here at Cornell Tech by co-PIs Nicki Dell and Tom Ristenpart. Our research group is interested in characterizing the many ways digital technology exacerbates intimate partner violence (IPV)–so domestic violence, harassment, stalking, etc. by a current or former spouse or significant partner. We pull that knowledge through to interventions that help victims at the Clinic to End Tech Abuse (CETA), a volunteer organization where survivors get 1:1 computer security and privacy support from consultants trained in the specific threat model of IPV. We call this approach “clinical computer security.”

Services like CETA can be a huge benefit to survivors facing targeted and persistent digital threats, but they’re often delivered as one-off tech support. In this paper, we presented an eight-month study of an approach to computer security and privacy inspired by the feminist ethic of care: think less Geek Squad and more primary care physician. We built various technical and social systems to make this work and used it in CETA to support 72 survivors over eight months (from December 2020 to August 2021). Then, through a reflexive qualitative study, we examined how well the model worked in practice. Our paper shows we were able to help more survivors and meet increasing demand for this type of care—but to grow the service, we need to reckon with tensions like ensuring safe connections to survivors, adapting to their changing needs, establishing boundaries on consultants’ time, and assessing risks in the face of uncertainty. 

Since this paper was published, we’ve been using the protocol in CETA, and as of February 2023 we’ve helped over 300 survivors. In ongoing work, we’re building on this infrastructure to continue research with survivors on the many ways IPV affects their lives and to explore how the research process can be made both data-driven and participatory—stay tuned for that.

What are your hobbies or interests outside of your research or scholarship?

In 2020 I started playing soccer in various pickup groups NYC as a pandemic coping mechanism. Now it’s a full-blown hobby: I play several times a week and follow all sorts of leagues. Talk to me about the women’s game especially!

Why did you choose Cornell to pursue your degree?

Phoebe Sengers, professor of information science in the Cornell Ann S. Bowers College of Computing and Information Science and science & technology studies in the College of Arts and Sciences, has been elected to the CHI Academy, an honorary group of leading scholars in the field of human-computer interaction.

Led by the Association for Computing Machinery Special Interest Group on Computer–Human Interaction (SIGCHI), the CHI Academy annually elects a cohort of scholars whose contributions have helped shape the discipline and/or industry, as well as spurred further research and innovation. Sengers is one of eight selected for this year’s cohort. She joins fellow Cornellians Sue Fussell, professor of information science in Cornell Bowers CIS and communication in College of Agriculture and Life Sciences, and Tanzeem Choudhury, the Roger and Joelle Burnell Professor in Integrated Health and Technology at the Jacobs Technion-Cornell Institute at Cornell Tech in Information Sciences, who were elected to the CHI Academy in 2016 and 2022, respectively.

Sengers’ work integrates ethnographic and historical analysis of the social implications of technology with design methods to suggest alternative future possibilities. Her approach brings critical, qualitative scholarship into close conversation with technology design practice to ask what ways of being and values are left out of the imagination of technology design, and what new alternatives for design may appear when we take those ways of being and values into account. Her current research focuses on technology on the rural and remote periphery, identifying how urban assumptions in the design of infrastructure tend to sideline rural communities; she is using this work to develop an alternative design imaginary that centers small-scale places.

At Cornell, Sengers directs the Culturally Embedded Computing research group. She is also affiliated with the departments of Computer Science, Visual Studies, and Art, a member of the Cornell Institute for Digital Agriculture, and a faculty fellow of the Atkinson Center for Sustainability.

Her many awards and honors include a Cornell Public Voices Fellowship (2017), a Fellow in the Cornell Society for the Humanities (2007), a National Science Foundation (NSF) CAREER award (2003), and a Fulbright Fellowship (1998). 

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

Nicola Dell, associate professor at the Jacobs Technion-Cornell Institute at Cornell Tech and in the Cornell Ann S. Bowers College of Computing and Information Science, has received the 2023 Association for Computing Machinery Special Interest Group on Computer–Human Interaction (SIGCHI) Societal Impact Award.

One of three awardees, Dell was recognized for leveraging research in human-computer interaction (HCI) for the greater good: her work improves computer security and privacy for victims of intimate partner violence, strengthens digital privacy in non-Western contexts, and guides the development of technology that supports home health care workers. 

Together with colleague Thomas Ristenpart, professor of computer science at Cornell Tech and Cornell Bowers CIS, Dell founded the Clinic to End Tech Abuse (CETA), which provides free tech support for survivors of intimate partner violence. CETA is embedded within the Family Justice Center social support system run by the New York City Mayor’s Office to End Domestic and Gender-Based Violence. 

“I'm truly honored to receive the SIGCHI Societal Impact Award and am grateful for the amazing collaborators, students, postdocs, and research partners who make my work possible,” Dell said. “This award is a wonderful recognition of our research team's hard work and accomplishments over the last few years.” 

Dell’s research spans HCI and Information and Communication Technologies for Development (ICTD), which explores how technology can assist underserved communities. Specifically, her work focuses on designing, building, and evaluating computer systems for underserved populations in low-income areas around the world. 

Her research has been published widely in journals including the Journal of Clinical and Translational Science, JAMA Internal Medicine, and Proceedings of the ACM on Human-Computer Interaction, and has received several awards at premier conferences including ACM Conference on Human Factors in Computing Systems (CHI), ACM Conference on Computer-Supported Cooperative Work and Social Computing (CSCW), and the USENIX Security Symposium, among others.

Among her many honors and recognitions, Dell received a Google Award for Inclusion Research in 2022, a National Science Foundation (NSF) CAREER Award in 2018, and two Google Faculty Research Awards in 2020 and 2018. 

Dell earned master’s and doctoral degrees in Computer Science and Engineering from the University of Washington in 2011 and 2015, respectively. At Cornell, she is a member of the Center for Health Equity, the Digital Life Initiative at Cornell Tech, and the Atkinson Center for a Sustainable Future.

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

 

To help identify when tense online debates are inching toward irredeemable meltdown, Cornell researchers have developed an artificial intelligence tool that can track these conversations in real-time, detect when tensions are escalating and nudge users away from using incendiary language.

Detailed in two recently published papers that examine AI’s effectiveness in moderating online discussions, the research shows promising signs that conversational forecasting methods within the field of natural language processing could prove useful in helping both moderators and users proactively lessen vitriol and maintain healthy, productive debate forums.

“Well-intentioned debaters are just human. In the middle of a heated debate, in a topic you care about a lot, it can be easy to react emotionally and only realize it after the fact,” said Jonathan Chang, a doctoral student in the field of computer science, and lead author of “Thread With Caution: Proactively Helping Users Assess and Deescalate Tension in Their Online Discussions,” which was presented virtually at the ACM Conference on Computer-Supported Cooperative Work and Social Computing (CSCW) on Nov. 10. The idea is not to tell users what to say, Chang said, but to encourage users to communicate as they would in-person.

The tool, named ConvoWizard, is a browser extension powered by a deep neural network. That network was trained on mountains of language-based data pulled from the subreddit Change My View, a forum that prioritizes good faith debates on potentially heated subjects related to politics, economics and culture.

When participating Change My View users enable ConvoWizard, the tool can inform them when their conversation is starting to get tense. It can also inform users, in real-time as they are writing their replies, whether their comment is likely to escalate tension. The study suggests that AI-powered feedback can be effective in guiding the user toward language that elevates constructive debate, researchers said.

“ConvoWizard is basically asking, ‘If this comment is posted, would this increase or decrease estimated tension in the conversation?’ If the comment increases tension, ConvoWizard would give a warning,” Chang said. The textbox would turn red, for example. “The tool toes this line of giving feedback without veering into the dangerous territory of telling them to do this or that.”

To test ConvoWizard, Cornell researchers collaborated with the Change My View subreddit, where roughly 50 participating forum moderators and members put the tool to use. Findings were positive: 68% felt the tool’s estimates of risk were as good as or better than their own intuition, and more than half of participants reported that ConvoWizard warnings stopped them from posting a comment they would have later regretted.

Chang also noted that, prior to using ConvoWizard, participants were asked if they ever posted something they regretted. More than half said yes.

“These findings confirm that, yes, even well-intentioned users can fall into this type of behavior and feel bad about it,” he said.

“It’s exciting to think about how AI-powered tools like ConvoWizard could enable a completely new paradigm for encouraging high-quality online discussions, by directly empowering the participants in these discussions to use their own intuitions, rather than censoring or constraining them,” said Cristian Danescu-Niculescu-Mizil, associate professor of information science in the Cornell Ann S. Bowers College of Computing and Information Science and research co-author.

In a separate Cornell paper also presented at CSCW, “Proactive Moderation of Online Discussions: Existing Practices and the Potential for Algorithmic Support,” researchers – including Chang – explore how an AI tool powered by similar conversational forecasting technology might be integrated and used among moderators. The research aims to find healthier ways to both address vitriol on forums in real-time and lessen the workload on volunteer moderators. Paper authors are Charlotte Schluger ’22, Chang, Danescu-Niculescu-Mizil and Karen Levy, associate professor of information science, and associate member of the faculty of Cornell Law School.

“There’s been very little work on how to help moderators on the proactive side of their work,” Chang said. “We found that there is potential for algorithmic tools to help ease the burden felt by moderators and help them identify areas to review within conversations and where to intervene.”

Looking ahead, Chang said the research team will explore how well a model like ConvoWizard generalizes to other online communities.

How conversation-forecasting algorithms scale is another important question, researchers said. Chang pointed to a finding from the ConvoWizard research that showed 64% of Change My View participants felt the tool, if broadly adopted, would improve overall discussion quality. “We’re interested in finding out what would happen if a larger slice of an online community used this technology,” he said. “What would be the long-term effects?”

Funding for this research was supported in part by the National Science Foundation and the Cornell Center for Social Sciences.

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

By James Dean, Cornell Chronicle

White guests favor Airbnb properties with white hosts, but are more inclined to rent from Black or Asian hosts if they see featured reviews from previous white guests, new research co-authored by Cornell scholars finds.

The research suggests that sharing-economy platforms may be able to algorithmically harness racial bias in responses to recommendations to reduce racial inequality in access to ride-hailing, lodging, dating and other services.

On Airbnb, guests’ preference for hosts of the same race unexpectedly is offset or overcome by the influence of same-race endorsements, the researchers determined.

“Bias in the recommender system can actually attenuate bias in host selection, rather than reinforcing it,” said Michael Macy, the Distinguished Professor of Arts and Sciences in Sociology, director of the Social Dynamics Laboratory, and faculty member of the Department of Information Science in the Cornell Ann S. Bowers College of Computing and Information Science. “It goes against the assumption that different dimensions of bias would be mutually reinforcing.”

The findings are reported in “Fighting Bias with Bias: How Same-race Endorsements Reduce Racial Discrimination on Airbnb,” published Feb. 8 in Science Advances. In addition to Macy, the co-authors are Chao Yu, M.S. ’17, Ph.D. ’21, visiting lecturer in the Department of Communication, in the College of Agriculture and Life Sciences; and Minsu Park, M.S. Information Science ’18, Ph.D. Information Science ’19, assistant professor of social research and public policy at New York University Abu Dhabi.

Recent studies have shown that Black users of ride-hailing services such as Uber and Lyft wait longer for rides and drivers are more likely to cancel their rides. On Airbnb, Black hosts earn 12% less than white hosts of similar properties, and Black guests are 16% less likely to be accepted.

The researchers asked if racial bias in peer recommendations and responses to them exacerbate or lessen such discrimination.

To answer that question, the team analyzed nearly 8,000 Airbnb listings involving more than 7,100 hosts and more than 150,000 guest reviews between 2009 and 2018. Obtained from Inside Airbnb, an independent third party that collects Airbnb data, the listings were all “instant bookable,” meaning reservations could be made without a host’s review and approval. The team targeted those listings to isolate guests’ racial bias in the selection of a host from hosts’ bias in approving guests.

New York City, the researchers said, provided the density and racial diversity needed to analyze many bookings and control for different neighborhood characteristics that might influence the effects of bias. They used facial recognition software to classify the race of a user as perceived by others based on profile photos.

The team compared the chance that white, Black and Asian guests would book stays at properties with hosts of a different race to the expected probability if race were assigned randomly.

Guests of all three racial groups are more likely to choose hosts of the same race, they found, consistent with prior research.

The team focused on choices by white guests, however, because white users are overrepresented on Airbnb, while other groups are underrepresented. White users accounted for nearly 60% of hosts and guests in the data, compared to about 41% of the city’s population. As a result, same-race bias by white users would reinforce inequality, while among Black and Asian users it would increase opportunity for those groups.

The researchers next investigated how guests write and respond to listing reviews, a potential influence on host selection that they said has received comparatively little attention. They found no evidence that reviews – which were overwhelmingly positive – differed significantly in their language or enthusiasm based on a host’s race.

But who wrote the reviews did matter – even more than the host’s race.

The team analyzed recent reviews (up to six) featured on listings’ front pages. White guests’ reluctance to book with hosts of different races decreases, they found, as the number of endorsements by previous white guests increases. That effect was less pronounced among Black and Asian guests.

“White guests largely overcame their racial bias in host selection when hosts were endorsed by previous white guests,” the authors wrote. “The net effect of same-race endorsement is to reduce racial discrimination on Airbnb.”

The findings suggest a new strategy to encourage more equal access to the sharing economy, the researchers said.

Rather than removing profile photos, they said, platforms should ensure that front-page endorsements feature similar racial compositions. That would better tap the potential for same-race reviews to increase white guests’ willingness to choose nonwhite hosts.

“Increasing the exposure of white guests to white-authored endorsements of Black hosts may lead to more white bookings,” they wrote, “thereby making the algorithmic correction less necessary over time.”

Macy acknowledged funding support from the National Science Foundation and Defense Advanced Research Projects Agency while the research was conducted.

This story was originally published in the Cornell Chronicle.

By Patricia Waldron

Native speakers often dominate the discussion in multilingual online meetings, but adding an automated participant that periodically interrupts the conversation can help nonnative speakers get a word in edgewise, according to new research at Cornell.

Xiaoyan Li, a doctoral student in the field of information science, used multilingual groups to test out the helpful bot – called a conversational agent – which was programmed to intervene after native speakers took six consecutive turns. The agent enabled nonnative speakers to break into the conversation, increasing their participation from 12% to 17% of all words spoken. 

While people who did not have English as a first language generally found the agent to be helpful, native speakers thought the intrusions were distracting and unnecessary.

“Nonnative speakers appreciated having a gap to reflect on the conversation and the opportunity to ask questions,” said Li. “Also, being invited to speak, they felt like their communication partners were valuing their perspectives.”

Li presented the study, “Improving Nonnative Speakers’ Participation with an Automatic Agent in Multilingual Groups,” Jan. 9 at the Association for Computing Machinery (ACM) International Conference on Supporting Group Work. The paper is published in Proceedings of the ACM on Human-Computer Interaction.

The inspiration for the study struck Li when she was a new student at Cornell, trying to contribute to group discussions in her communications seminar. Despite being fluent in English, Li struggled to identify natural gaps in the discussion and to beat native speakers to the openings.

“When the nonnative speakers don’t speak up in class, people assume that it’s just because they had nothing to say,” said co-author Susan Fussell, professor of information science in the Cornell Ann S. Bowers College of Computing and Information Science, and of communication in the College of Agriculture and Life Sciences. “Nobody ever thinks it is because they have problems getting the floor.”

For the study, Li recruited 48 volunteers and placed them into groups of three, with two native English speakers and a native Japanese speaker meeting in a videoconference. The groups completed three survival exercises, which involved discussing imaginary disaster scenarios and ranking which items salvaged from a boat, plane or spaceship (e.g., ax, compass, newspaper, etc.), would be useful for survival.

One exercise included the automated agent and for another, the groups were on their own. In a third exercise, nonnative speakers could secretly activate the agent when they wanted to speak, instead of waiting for it to intervene. The Japanese speakers rarely used this option, however, for fear of interrupting the conversation at the wrong time.

The agent used IBM Watson automatic speech recognition software to track who was speaking, and would blink and wave to allow another participant a chance to talk. Co-author Naomi Yamashita, a distinguished researcher at the Nippon Telegraph and Telephone Corporation (NTT), built the agent.

Previous efforts to overcome language barriers – such as providing meeting transcripts, automatic language translation and graphics showing everyone’s participation level – have failed. In contrast, the agent proved remarkably successful, increasing participation from nonnative speakers by 40%.

In interviews after the survival exercises, nonnative speakers said the agent didn’t always interrupt at a good time, but being put on the spot forced them to be less worried about their grammar, so they could focus on getting their ideas across.

Native speakers, however, had a less positive view of the agent. “Nonnative speakers spoke a lot less, but the native speakers were not aware of that,” Li said. “So they blamed the agent for interrupting when they thought the conversation was equal."

Fussell’s group has recently developed its own agent and has several proposed improvements to test out.

“It’d be nice if the agent only intervened when the nonnative speaker had something they wanted to say, as opposed to just putting them on the spot,” Fussell said.

They may employ more subtle signals that it’s time to yield the floor, such as private messages to the native speakers, or they could use artificial intelligence or biosensors to determine when a nonnative speaker is ready for a gap.

Wen Duan, Ph.D. ’22, now a postdoctoral fellow at Clemson University, and Yoshinari Shirai of NTT, are co-authors on the paper.

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

 

By Julie Greco, School of Industrial and Labor Relations

Rural health care workers face challenges tied primarily to travel, which exacerbates poor working conditions already prevalent in the home care industry, according to a new Cornell study.

The main challenge for rural health care workers is tied to commuting to and from clients, especially in upstate New York, where winter weather can make transportation between clients difficult, and sometimes impossible, according to the paper, “Making a Bad Situation Worse: Examining the Challenges Facing Rural Home Care Workers,” published online in the Journal of Applied Gerontology.

As the U.S. population ages, the demand rises for home care workers – direct caregivers who provide personal, medical and emotional care to clients with a range of health conditions. Despite their importance, these workers experience low compensation and inadequate training. They provide emotional labor that often goes unrecognized and uncompensated, and turnover is high.

“The main finding is really that distance and transportation is a problem in and of itself, but it also really exacerbates the poor working conditions prevalent in the home care sector more broadly,” said doctoral student Johnnie Kallas, who is the paper’s first author. “Policymakers need to understand and account for some of these unique challenges that rural caregivers face.”

According to Kallas, the low wages earned throughout the health care industry put those who work in rural locations in especially precarious positions because they need reliable personal transportation to travel long distances. When the transportation doesn’t work, neither can they.

The paper is part of Cornell multidisciplinary research aimed at elevating the value of home care work and was co-authored by ILR Professor Ariel Avgar, Ph.D. ‘08, Dr. Madeline Sterling ’08, assistant professor of medicine at Weill Cornell Medicine, and Nicola Dell, associate professor at the Jacobs Technion-Cornell Institute at Cornell Tech and in the Cornell Ann S. Bowers College of Computing and Information Science.

The researchers interviewed 23 participants who have experience providing home care in rural areas of upstate New York. Twenty-two of the 23 participants identified as women and 19 identified as white. In conversations, they expressed issues related to working in a rural community.

While the main issue proved to be transportation-related, many also reported difficulty in convincing clients to complete a required task on the care plan because of social norms in rural communities.

“For example, one aide spoke about clients having a ‘farming mentality,’” said Kallas, “meaning the clients were used to living on their own and providing for themselves. They’ve been doing it for decades, and so it can be difficult for the home care workers to get their clients to follow the care plan.”

Short visits are also a common problem in rural areas due to large distances and long travel times between clients. Many health care workers said they see the same client for no more than two hours per day multiple times each week, commuting long distances between several clients daily. These short visits impact the type of care received, as the worker is focused on completing all care plan tasks, leaving little time for some of the companionship and emotional support that are important, though invisible, components of home care.

Client resources also affect rural health care workers. Many clients rely on well water in rural areas, so poor water quality and limited availability can be challenges.

Finally, rural clients are particularly vulnerable to social isolation and loneliness, which leads some health care to provide companionship as an essential part of their work.

“One aide told us about how she had developed a really strong relationship with a client, and she knew that there was an incoming storm and that she might not be able to get to her house the next day. So she actually went to service her client for no pay the night before and then stayed over,” Kallas said. “That’s hours of unpaid labor that this person is doing just to make sure her client receives care because she knew that there was nobody else to do it.”

Additional authors include Olay Ajayi ’23 and Ethan Mulroy ’24, both in the ILR School, as well as Elizabeth Kuo, a research assistant at Cornell Tech, and doctoral student Joy Ming.

Julie Greco is a senior communication specialist for the ILR School.

By Louis DiPietro

The Cornell Ann S. Bowers College of Computing and Information Science welcomes three new faculty members this spring.

With expertise in areas like high-performance computing for genomics data processing, computer networking, and computational social science, the incoming trio arrives amid tremendous growth for the college, which is now home to 2,000 undergraduate majors within its three departments – computer science, information science, and statistics and data science. 

To meet this growing demand, Cornell Bowers CIS is investing heavily in increasing its faculty by 50% in the next few years. The scale of this growth will enable the college to foster research excellence, develop core and emerging fields, and bring about new opportunities for cross-disciplinary and universitywide collaboration.

Arriving this spring semester are:

Giulia Guidi, assistant professor of computer science

Guidi works in the field of high-performance computing for large-scale computational sciences, especially computational biology. Her research involves the development of algorithms and software infrastructures for parallel machines to accelerate data processing without sacrificing programming productivity, and to make high-performance computing more accessible. Guidi received her Ph.D. in computer science from the University of California, Berkeley. Before joining Cornell Bowers CIS, she worked as a project scientist in the Performance and Algorithms Research Group in the Applied Math and Computational Sciences Division at Lawrence Berkeley National Laboratory in Berkeley, California, where she is currently an affiliate researcher.

Rachee Singh, assistant professor of computer science

Singh’s research interests are in computer networking, with a focus on optical interconnects and traffic engineering. Her work has been published widely at conferences like USENIX Symposium on Networked Systems Design and Implementation (NSDI), ACM Special Interest Group on Data Communication (SIGCOMM), and the Symposium on Operating Systems Principles (SOSR). Singh received her masters degree and Ph.D. in computer science from the University of Massachusetts. Most recently, she served as a senior researcher at Microsoft Azure.

Yian Yin, assistant professor information science

Yin’s research applies and develops novel mathematical and computational tools to understand how individual, social, and environmental processes independently and jointly promote (or inhibit) scientific progress and innovation achievements. As a computational social scientist, he has also used science and innovation as a powerful lens to examine broader processes and outcomes in a wide range of complex social systems, from artistic and cultural productions to public policy and media attention to market competition and human conflict. He received his Ph.D. in Industrial Engineering and Management Science at Northwestern University, with research affiliations at the Northwestern Institute on Complex Systems and the Kellogg Center for Science of Science and Innovation. He holds bachelor's degrees in statistics and economics from Peking University.

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

Every semester, students in Cornell Information Science’s master of professional studies (MPS) program draw on ingenuity, problem-solving, and teamwork to develop solutions for client companies.

In Fall 2022, eight student teams designed solutions for organizations like Assurant, the National Center for Biotechnology Information, PepsiCo, and TISTA, among others.

"While many of the skills were new, such as natural language processing (NLP) and Blockchain, student teams did what was necessary to arrive at a feasible solution," said Sharlane Cleare, lecturer in information science and course instructor for INFO 5900, the MPS Project Practicum.

Here’s a rundown of some of the projects:

Assurant – This student team used the human-centered iterative design process to redesign Assurant's core mobile application. Students used processes of clustering and integrating to provide end users with an efficient and smooth user experience. Market and user experience (UX) design research informed the final prototype.

Equitable – Utilizing an open-source NLP model, the team trained call transcripts specifically to extract the intent of the caller. The target audiences are the Equitable Financial Service customers who previously called the company’s call center. 

National Center for Biotechnology (NCBI) – This student team created a machine learning model that optimizes sequence searching of three of NCBI's large databases housing bioinformatics data. The model defined thresholds for the taxonomy trees. The team also refined the UX and user interface of NBCI's Pebblescout website.

Pepsi Data Analytics – MPS students optimized the Pepsi Data Analytics dashboard to satisfy the client’s needs identified in the interviews. They also improved the end-to-end waste management tool to make it easier for clients to navigate and find the requested information in less time, with fewer clicks, and with minimal data manipulation. 

TISTA – Cornell MPS students have worked with TISTA – a company specializing in providing IT services to federal, state, and local governments – for the last three semesters. This past fall, a student team picked up on an ongoing TISTA-Cornell collaboration and refined a web-based cryptocurrency fundraising platform by streamlining navigation and design consistency and designing a short online tutorial to walk first-time donors through the donation process.  

Check out select MPS projects from past semesters: Fall 2021; Fall 2019, and Spring 2019.