The Cornell Ann S. Bowers College of Computing and Information Science is pleased to announce the recruitment of 11 new faculty this year.

Three of those individuals joined the college for the Fall 2023 semester: Alex Conway, Sainyam Galhotra, and Daniel Susser. The remaining faculty, Saikat Dutta, Kuan Fang, Tanya Goyal, Michael Kim, Wei-Chiu Ma, Kristina Monakhova, Nick Spooner, and Jennifer Sun, will be arriving in coming semesters.  

"I am delighted to have these accomplished and innovative researchers join our faculty," said Kavita Bala, dean of Cornell Bowers CIS. "They are leaders who are building the technologies that drive modern computing and furthering our understanding of the impact of technology on society. Their addition to our faculty will enrich the academic experience for our students and contribute to the continued growth of our institution."

The newest faculty members have expertise in a broad range of fields. Their research will expand the college's leadership in machine learning, computational imaging, robotics, generative artificial intelligence (AI), natural language processing, computer vision, data storage, analytics, software engineering, post-quantum cryptography, and technology governance.

The new additions will also shape the next generation of tech leaders and innovators, while helping to meet the growing demand from students interested in majors offered within the college. Cornell Bowers CIS is now home to more than 2,000 undergraduate majors within its three departments – computer science, information science, and statistics and data science – and 76% of all undergraduate students at Cornell take at least one course within Cornell Bowers CIS. The college aims to increase its faculty number by 35% in the next few years to accommodate the demand.

The Cornell Bowers CIS faculty members joining for the Fall 2023 semester are:

Alex Conway

Assistant professor of computer science at Cornell Tech

Conway's research primarily focuses on randomized data structures and their applications to memory and storage systems. Conway comes to Cornell from the research arm of VMware, a cloud computing and virtualization technology company, where his work spanned every aspect from theory to systems to product. Conway received his Ph.D. in computer science from Rutgers University.

 

Sainyam Galhotra

Assistant professor of computer science 

Galhotra's research program centers around developing data science tools for effective and responsible analytics. He applies techniques from causal inference, data management, theoretical computer science, crowdsourcing, and human-computer interaction to understand various aspects of designing trustworthy systems that are robust, explainable, and fair. Previously, Galhotra was a Computing Innovation Postdoctoral Fellow at the Data Science Institute at the University of Chicago. He received his Ph.D. at the University of Massachusetts, Amherst.

 

Daniel Susser

Associate professor of information science

Susser’s research brings philosophical tools to bear on problems in technology governance, exploring normative issues raised by new and emerging data-driven technologies, and clarifying conceptual issues that stand in the way of addressing them. He has broad interests in technology ethics and policy, philosophy of technology, and science and technology studies, especially critical questions about data, privacy, and the ethics of automation. Previously, Susser was the Haile Family Early Career Assistant Professor of Information Sciences and Technology and a research associate in the Rock Ethics Institute at Penn State University. He received his Ph.D. in philosophy from Stony Brook University. Susser currently serves as a non-resident fellow at the Center for Democracy and Technology.

Additional faculty joining at a later date include:

Saikat Dutta 

Assistant professor of computer science (Fall 2024)

Dutta's research lies at the intersection of software engineering and machine learning, and, at Cornell, he plans to combine these two areas to improve the reliability of traditional and machine learning-based software. He is especially interested in developing novel testing techniques and tools to improve the reliability of machine learning-based systems and leveraging machine learning to address challenging tasks in software engineering. Currently, he is a postdoctoral researcher at the University of Pennsylvania. Saikat completed his Ph.D. in computer science at the University of Illinois Urbana-Champaign, and previously, spent two years as a software engineer at Microsoft, India.  

 

Kuan Fang

Assistant professor of computer science (Fall 2024)

Fang's work aims to enable robots to solve diverse and complex tasks in unstructured environments. Specifically, he is building scalable data-driven methods for perception and control, with a focus on how to automate data collection from tasks performed in simulation and the real world, and acquire general purpose skills for efficiently solving novel, long-horizon tasks, which involve multiple steps. Fang is a postdoctoral scholar at the Berkeley Artificial Intelligence Research lab. He obtained his Ph.D. from Stanford University, and has spent time at Google Brain, Google [x] Robotics, and Microsoft Research Asia.

 

Tanya Goyal

Assistant Professor of computer science (Fall 2024)

Goyal's research interests are in natural language processing, where she works on measuring and improving the generation capabilities of language models. She is interested in building automatic evaluation tools that can be used to reliably compare large-scale AI models. Goyal is currently a postdoctoral scholar at the Language and Intelligence Initiative at Princeton University. She received her Ph.D. in computer science from the University of Texas at Austin in 2023.  

 

Michael P. Kim 

Assistant professor of computer science (Spring 2024)

Kim's research investigates foundational questions about responsible machine learning. Much of his work aims to identify ways in which machine-learned predictors can exhibit problematic behavior (e.g., unfair discrimination) and develop algorithmic tools that mitigate these behaviors. Kim is currently a Miller Postdoctoral Fellow at the University of California, Berkeley. He completed his Ph.D. in computer science at Stanford University. 

 

 

Wei-Chiu Ma 

Assistant professor of computer science (Fall 2024)

Ma's research interests include computer vision, robotics, and machine learning, especially 3D vision and self-driving vehicles. His work involves robot localization, 3D reconstruction, motion estimation, large-scale 3D generation, and closed-loop sensor simulation, in both controlled and noisy, real-world settings. Ma is finishing his Ph.D. at the Massachusetts Institute of Technology and will spend the next year at the Allen Institute for AI. He worked previously at Uber ATG, and is a part-time senior research scientist at Waabi, a self-driving technology company. 

 

Kristina Monakhova 

Assistant professor of computer science (Fall 2024)

Monakhova's work combines ideas from machine learning, signal processing, optics, computer vision, and physics to build better imaging systems (e.g., cameras, microscopes, and telescopes) through the co-design of optics, algorithms, and high-level tasks. Her aim is to design the next generation of smart, computational imagers for scientific discovery, robotics, and medical diagnostics. Monakhova is a postdoctoral fellow at the Massachusetts Institute of Technology and received her Ph.D. from the University of California, Berkeley.

 

Nick Spooner

Assistant professor of computer science (Fall 2024) 

Currently an assistant professor at the University of Warwick and a visiting assistant professor at New York University, Spooner focuses on quantum and post-quantum proof systems. More broadly, he is interested in quantum and post-quantum cryptography, quantum information, coding theory, and computational complexity. Previously, Spooner was a postdoctoral researcher at Boston University. He obtained his Ph.D. from the University of California, Berkeley.

 

Jennifer Sun

Assistant professor of computer science (Fall 2024)

Sun's research focuses on developing AI systems that work together with scientists to accelerate discovery. She is particularly interested in computer vision and machine learning methods that can be integrated into real-world workflows involving expert-in-the-loop interactions, such as the study of behavioral or medical data. Her goal is to optimize expert attention in these workflows by addressing bottlenecks, including data efficiency, model interpretability, generalization, and structure discovery. Sun is finishing her Ph.D. in computing and mathematical sciences at Caltech and is a research scientist at Google.

Sit across from someone wearing augmented reality (AR)  or “smart” glasses and you don’t know what they’re seeing – they could be Googling your face, turning you into a cat, or recording your conversation – and that disparity creates a major power imbalance, Cornell researchers said.

Most work with AR glasses focuses primarily on the experience of the wearer. Researchers from the Cornell Ann S. Bowers College of Computing and Information Science and Brown University teamed up to explore how this technology affects interactions between the wearer and another person. Their explorations showed that, while the device generally made the wearer less anxious, things weren’t so rosy on the other side of the glasses. 

Jenny Fu, a doctoral student in the field of information science, presented the findings in a new study, “Negotiating Dyadic Interactions through the Lens of Augmented Reality Glasses,” at the 2023 Association for Computing Machinery Designing Interactive Systems Conference in July.

AR glasses superimpose virtual objects and text over the field of view to create a mixed-reality world for the user. Some designs are big and bulky, but as AR technology advances, some smart glasses are becoming indistinguishable from regular glasses, raising concerns that a wearer could be secretly recording someone or even generating deepfakes with their likeness.

For the new study, Fu and co-author Malte Jung, associate professor in information science and the Nancy H. ’62 and Philip M. ’62 Young Sesquicentennial Faculty Fellow,  worked with doctoral student Ji Won Chung and Jeff Huang, associate professor of computer science, both at Brown, and Zachary Deocadiz-Smith, an independent extended reality designer. 

They observed five pairs of individuals – a wearer and a non-wearer – as each pair discussed a desert survival activity. The wearer received Spectacles, an AR glasses prototype on loan from Snap Inc., the company behind Snapchat. Spectacles look like avant-garde sunglasses and, in the study, came equipped with a video camera and five custom filters that transformed the non-wearer into a deer, cat, bear, clown or pig-bunny.

Following the activity, the pairs engaged in a participatory design session where they discussed how AR glasses could be improved, both for the wearer, and the non-wearer. The participants were also interviewed and asked to reflect on their experiences.

According to the wearers, the fun filters reduced their anxiety and put them at ease. The non-wearers, however, felt disempowered because they didn’t know what was happening on the other side of the lenses. They were also upset that the filters robbed them of control over their own appearance. The possibility that the wearer could be secretly recording them without consent – especially when they didn’t know what they looked like – also put the non-wearers at a disadvantage.

The non-wearers weren’t completely powerless, however. A few demanded to know what the wearer was seeing, and moved their faces or bodies to evade the filters – giving them some control in negotiating their presence in the invisible mixed-reality world. “I think that’s the biggest takeaway I have from this study: I’m more powerful than I thought I was,” Fu said.

Another issue is that, like many AR glasses, Spectacles have darkened lenses so the wearer can see the projected virtual images. This lack of transparency also degraded the quality of the social interaction.

“There is no direct eye contact, which makes people very confused, because they don’t know where the person is looking,” Fu said. “That makes their experiences of this conversation less pleasant, because the glasses blocked out all these nonverbal interactions.”

To create more positive experiences for people on both sides of the lenses, the study participants proposed that smart glasses designers add a projection display and a recording indicator light, so people nearby will know what the wearer is seeing and recording.

Fu also suggests designers test out their glasses in a social environment, hold a participatory design process like the one in their study, and consider the resulting video interaction as a data source. 

That way, non-wearers can have a voice in the creation of the impending mixed-reality world.

This work received support from the National Science Foundation.

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

A Cornell-led research team has discovered that the algorithm behind Google Ads charged significantly more to deliver online ads to Spanish-speaking people about the benefits of SNAP, formerly known as food stamps.

Together with a survey of 1,500 Americans that found broad support for a more equitable approach to promoting SNAP, the findings led to changes to directly target more Spanish-speakers in California who seek help paying for food.

“SNAP is a really important resource to get right,” said Allison Koenecke, assistant professor of information science in the Cornell Ann S. Bowers College of Computing and Information Science. “When faced with an algorithm that has disparate impact, our research asks, how do you pick a strategy to interact with the algorithm to equitably recruit SNAP applicants?”

Koenecke is the lead author of “Popular Support for Balancing Equity and Efficiency in Resource Allocation: A Case Study in Online Advertising to Increase Welfare Program Awareness,” which was presented at the AAAI Conference on Web and Social Media in June.

“Our goal was to go beyond just quantifying these ad cost disparities,” she said. “We also wanted to address questions asked by real-life decision-makers, like what do we actually do about this? How do people actually want to allocate these ads?"

Californians can apply for SNAP benefits using a website called GetCalFresh, which is developed and managed by Code for America, a civic tech nonprofit that builds digital tools and services for community leaders and governments. Code for America primarily recruits GetCalFresh applicants through Google Ads – for example, spending roughly $400 daily to reach anyone from San Diego County who punches key words and phrases like “how to apply for food stamps” into Google.

However, despite GetCalFresh being offered in multiple languages, Spanish-speakers were filling out proportionally fewer applications than English-speakers. In San Diego County, 23% of families living below the poverty line speak Spanish as their primary language, and yet just 7% had applied for SNAP via GetCalFresh, researchers said.

Koenecke and her collaborators discovered one possible reason: the default, dollar-stretching algorithm behind Google Ads was working too efficiently and disregarding Spanish-speaking people in the process.

When Google Ads is configured to garner the most SNAP enrollments per dollar, it ends up delivering fewer ads to prospective Spanish-speaking applicants because such ads cost more than those for English speakers, the team found. At the time, for every $1 spent on Google Ads to “convert” an English-speaking applicant into a SNAP benefits holder, it cost $3.80 to convert a Spanish-speaking person – nearly four times more. Another bidding option on the Google Ads platform cost 1.4 times more to reach Spanish-speakers versus English-speakers.

Koenecke and her collaborators can’t definitively explain the difference, since Google Ads is a black box – a proprietary machine-learning tool outside of public review. It could be attributed to any number of factors, like supply and demand or a bug in the system, she said.

For GetCalFresh, the research findings pose an important ethical question regarding how to spend its limited online advertising budget: Should they reach as many Californians as cheaply as possible, even if that means fewer Spanish-speaking applicants, or advertise more to Spanish-speakers, even if that yields fewer total applicants?

Trade-offs such as these are at the heart of Koenecke’s research into fairness and algorithmic systems, which are increasingly being used to help with decision-making in areas with real consequences, like health care, banking and child services. But without additional scrutiny, algorithms – including a seemingly harmless one behind an advertising platform – can exacerbate inequality or produce results that run counter to what people actually want or need, she said.

Koenecke and her collaborators asked the public to weigh in, surveying roughly 1,500 Americans on how they would balance efficiency and equity in advertising SNAP benefits. Across age groups, gender, race, welfare status and even political party affiliation, respondents generally preferred reducing total enrollments to facilitate more enrollments among Spanish speakers, researchers found. When they ran the survey among Code for America staff, they found similar results – a strong desire for equitable access.

“If you compare the fact that the majority of both Republicans and Democrats actually prefer some amount of equity in this particular case, then this opens the door to more bipartisanship in thinking about how fairness can play a role in online applications involving the algorithmic distribution of goods,” Koenecke said.

As a result of the team’s findings, Code for America adjusted its online advertising strategy to directly target more Spanish-speaking prospective applicants.

“It’s important for the field and the public to have productive dialogues about the kinds of metrics we should be using in these algorithmic systems,” she said. “The communities most impacted by the algorithms should be given more power in the decision-making process.”

Along with Koenecke, the paper’s co-authors are Eric Giannella of Code for America, Robb Willer of Stanford University, and Sharad Goel of Harvard University’s Kennedy School.

This research was partly funded by the National Science Foundation and Stanford University.

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

Sarah Riley, a doctoral student in the field of information science, has received a 2023 Horowitz Foundation for Social Policy Award for her research into algorithmic pretrial risk assessments.

Riley, who studies municipal algorithmic systems, race, racism, and inequality, is one of 25 scholars whose 2022 application was selected. The Foundation received more than 550 applications.

Her project, “Pretrial Risk Assessments as Organizational Processes” focuses on the administration of pretrial risk assessments in Virginia. These risk assessments are algorithmic tools that make recommendations as to whether a defendant should be released or detained until trial. She uses a mixed-methods approach to understand how human discretion in the pretrial process –particularly on the part of pretrial officers –affects risk scores, pretrial detention decisions, and life outcomes for accused people.

Riley is expected to receive her doctoral degree from Cornell in August. This fall, she will attend Stanford University as an IDEAL Provostial Fellow. The three-year program is designed to support the work of early-career researchers who will lead the next generation of scholarship in race and ethnicity, according to Stanford.

Riley holds a bachelor’s degree in political science from Amherst College and a master’s of public policy from the University of California, Berkeley. She is advised by Karen Levy, associate professor of information science and associate member of the faculty of Cornell Law School, Solon Barocas, adjunct assistant professor of information science, and Martin Wells, the Charles A. Alexander Professor of Statistical Sciences with additional appointments in the ILR School, the College of Agriculture and Life Sciences, Cornell Law School and Weill Cornell Medicine.

Nine innovative faculty members and doctoral students from the Cornell Ann S. Bowers College of Computing and Information Science were selected to receive the second round of grants from the five-year, multimillion-dollar strategic partnership between the college and LinkedIn.

This year’s awards will help fund high-impact research projects on topics ranging from large language models (LLMs) and recommender systems to dynamic information retrieval and algorithmic fairness.

Now in its second year, the Cornell Bowers CIS-LinkedIn partnership – a first for both entities – continues to make good on its primary goals: provide annual funding to Cornell Bowers CIS to support doctoral students; engage faculty through research support; amplify efforts in diversity, equity, and inclusion; and establish a research connection between scientists and engineers at LinkedIn and faculty and students in Cornell Bowers CIS, one of the top artificial intelligence programs in the U.S.

Faculty award winners:

●      Kilian Weinberger, professor of computer science, and Yoav Artzi, associate professor of computer science at Cornell Tech. In their project, “Real-Time Differential Search Indices,” the duo will explore Differential Search Indices (DSI) for constantly changing dynamic content – an open problem in information retrieval. DSI are considered a paradigm shift in information retrieval but are currently limited to retrieving static content only.

●      Sarah Dean, assistant professor of computer science. User-provided feedback, often in the form of clicks, has powered the last decade of search and recommendation algorithms and provides platforms key insights on user engagement. However, a focus on short-term engagement metrics can lead to long-term issues like unfairness or poor platform health. In “Reliable and Efficient Recommendation & Search with Long-Term Objectives,” Dean intends to develop algorithms that make decisions on the short term while ensuring long-term platform health.

●      Mor Naaman, professor of information science at Cornell Tech. Naaman’s project, “Writing with AI, Responsibly: Understanding the Potential of LLMs to Shift Our Writing, and How to Mitigate It,” will build on research into responsible AI by providing a deeper understanding of how autocomplete functions – powered by new LLMs – can transform how we express ourselves and even change our opinions.

●      Cristian Danescu-Niculescu-Mizil, associate professor of information science. Humans have an intuition of when our conversations are heading south. In “Assessing the Health of Ongoing Dialogs,” Danescu-Niculescu-Mizil will develop methods for endowing artificial systems with this kind of intuition and will explore how this artificial intuition can be used to improve the way we communicate with each other, as well as with dialog systems.

Awards to doctoral students include academic year funding and discretionary funds. Awardees are:

●      Princewill Okoroafor, a doctoral student in the field of computer science and advised by Robert Kleinberg, studies theoretical aspects of machine learning, especially in learning theory and reinforcement learning. His project aims to advance our understanding of limitations and trade-offs in predictive models as well as develop new algorithms that can achieve more accurate and calibrated predictions – all of which can have a significant impact on the job search industry.

●      Jonathan D. Chang, a doctoral student in the field of computer science advised by Wen Sun, explores imitation learning, reinforcement learning, representation learning, and their intersection with generative models. His project, “Chatbot Recommendation Systems,” will investigate algorithms that can help reduce the amount of data needed to train reinforcement learning systems and improve responses in chatbot recommendation systems.

●      Benjamin Laufer, a doctoral student in the field of information science advised by Helen Nissenbaum and Jon Kleinberg, is interested in the values and politics embedded in technology systems used to make high-impact decisions. In “Efficient, Equitable Choices in Dynamic Environments,” Laufer will develop methods for identifying, measuring, and mitigating the harmful dynamics that arise in algorithmic recommendations.

●      Shira Mingelgrin, a doctoral student in the field of statistics and data science advised by Sam Wang, studies causal modeling. Mingelgrin’s project will apply causal discovery methods to investigate potential differences in the causal structure under different settings, for instance, testing whether the factors that drive hiring behavior vary among genders.

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

Researchers from Cornell Tech and the Cornell Ann S. Bowers College of Computing and Information Science are part of the first cohort of participants from four institutions to receive funding from the Google Cyber NYC Institutional Research Program, a Google-funded initiative to improve standards of online privacy, safety and security, and to establish New York City as the epicenter of cybersecurity research.

In partnership with Google, seven projects from Cornell faculty have been selected that combine basic research with computer innovations to expand our understanding of – and provide solutions to – cybersecurity issues in society. Each team will work with a Google sponsor and receive funding and access to Google Cloud Platform credits for up to three years.

“Through support from Google and in collaboration with our partners, New York will emerge as the hub for cutting-edge exploration and innovation in the fields of cybersecurity, trust, and safety,” said Greg Morrisett, the Jack and Rilla Neafsey Dean and Vice Provost of Cornell Tech and principal investigator for Cornell.

Along with Cornell, three other schools will receive funding through the program: City University of New York, Columbia University’s Fu Foundation School of Engineering and Applied Science and New York University’s Tandon School of Engineering. The Google Cyber NYC Institutional Research Program is part of the $10 billion cybersecurity initiative announced in 2021.

“Google is dedicated to forward-thinking and responsible AI development,” said Phil Venables, Google Cloud chief information security officer. “We are excited to partner with these leading institutions in AI through the Google Cyber NYC Institutional Research Program to address the ever-evolving threat landscape in cybersecurity.”

Beyond advancing digital technologies that increase digital trust and safety, the funding will also enable Cornell Bowers CIS to expand its leadership in the field of cybersecurity and increase the number and diversity of qualified cybersecurity professionals entering the workforce.

The inaugural projects from Cornell are:

• Fred Schneider, Samuel B. Eckert Professor of Computer Science: “Towards an Applied Theory of Information Flows.” Traditionally in cybersecurity, researchers have devised ways to suppress information flows, such as data leaks. This project will develop a theory for surfacing information flows in systems so they can be used to establish accountability and to determine effective means to control their content.

•  Noah Stephens-Davidowitz, assistant professor of computer science: “Foundations of post-quantum cryptography.” This work focuses on the theoretical foundations of post-quantum cryptography – cryptographic methods that enable a traditional computer to fend off attacks from a quantum computer (one that uses the quantum states of subatomic particles to store information and that can solve especially complex problems). This work is becoming increasingly important as quantum computing technology advances.

• Andrew Myers, professor of computer science: “Making Hardware Comprehensively Secure Against Spectre – by Construction.” Recently, there have been several prominent timing attacks aimed at hardware in which attackers steal sensitive information, such as passwords, by analyzing the time required to perform computations. This project will develop a new secure hardware description language to build processors that are immune to this class of attacks.

• Nicola Dell, associate professor of information science at the Jacobs Technion-Cornell Institute at Cornell Tech, and co-PI Thomas Ristenpart, professor of computer science at Cornell Tech: “Improving Account Security for At-Risk Users.” Many online services employ user-facing account security interfaces (ASIs) to show security-related information, such as recent logins and connected devices. But ASIs can be compromised, putting users – especially survivors of intimate partner violence, journalists and refugees – at risk. The researchers propose to develop more secure ASIs that preserve privacy and offer better user experience.

• Dell and Ristenpart, in collaboration with researchers at NYU: “Discovering and Locating Unwanted IoT Devices for Survivors of Intimate Partner Violence.” Abusers use Internet of Things (IoT) devices, like location trackers and hidden cameras, to surveil and harass their victims. This research aims to build tools that can discover and locate IoT devices, create a guide to train survivors on how to use the tools, and test protocols for deploying them with survivors in NYC.

• Nate Foster, professor of computer science, in collaboration with researchers at NYU: “ProSecco: Programmable In-Network Security.” As the reach of networks has expanded from servers and desktops to phones, cars, IoT devices, and cameras, attacks perpetrated through networks have increased in scale, sophistication, and frequency. The researchers propose ProSecco, a project that will use programmable network devices to create more proactive, dynamic, and automated defenses for network security.

• Kevin Ellis, assistant professor of computer science, with co-PIs Owolabi Legunsen, assistant professor of computer science, and Alexandra Silva, professor of computer science: “Safe Program Generation and Deployment by Large Language Models.” AI systems have tremendous potential for managing a range of problems and tasks in everyday life – such as dispensing medication in a hospital or handling employee calendars – but there is still the possibility of dangerous mistakes. In this project, researchers propose a key safety property, and propose developing new techniques for eliciting, specifying, validating, and monitoring this safety property for machine learning systems.

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

Steven Jackson believes a university should be, first and foremost, about educating students, and that core belief will inspire him in his new role as vice provost for academic innovation (VPAI).

“My interest in the position really came from my central commitment as a teacher at Cornell,” said Jackson, professor in the Department of Information Science in the Cornell Ann S. Bowers College of Computing and Information Science, with a dual appointment in the Department of Science and Technology Studies in the College of Arts and Sciences (A&S).

“Teaching is at the center of what we do,” Jackson said. “For me, teaching and research go together; they are very tightly intertwined.”

Jackson’s term officially begins July 1, although he’s been conducting meetings and easing into the role for several weeks, he said.

“Steve is passionate about his teaching and helping students realize their full potential through his work, both in the classroom and in the lab,” Provost Michael I. Kotlikoff said. “He is also a conscientious university citizen whose commitment to continually improving Cornell’s teaching and learning environment for students and faculty makes him an ideal fit for this role.”

The position, created in 2017 along with the Center for Teaching Innovation, was initially held by Julia Thom-Levy, professor of physics (A&S) and associate vice provost for physical sciences. John Siliciano ’75, professor of law in Cornell Law School and former deputy provost, has been in the role on an interim basis since October 2022.

“I also want to thank Julia for her dedication in five years as our inaugural VPAI, as well as John for stepping in on an interim basis,” Kotlikoff said. “So much has been accomplished since the center was launched and Julia’s leadership and vision have helped keep Cornell at the forefront of pedagogical innovation.”

While some people might equate innovation with technology, Jackson said, that’s only part of the story.

“Technology is a tool, to be sure, but for me, innovation in teaching is really about inviting and empowering a group of committed teachers to use their imagination, their creativity and the tools around them to think and do teaching in a different way,” said Jackson, whose fields of study include technology ethics, law and policy, and human-computer interaction, including the study of how users and groups collaborate around new computational tools and infrastructures. “And we should invite students to also engage in that process and be part of the ways in which we reinvent and reimagine teaching at Cornell.”

Jackson, formerly the chair of information science – as well as house professor-dean of Keeton House on West Campus from 2015-21 – hopes to engage faculty and students and do “a lot of listening” as he grows into his new role.

“The diversity and range of teaching that goes on at Cornell has become increasingly apparent to me. There is not a one-size-fits-all approach,” he said. “There are deep disciplinary traditions of teaching, and exciting examples of innovation and experiment that have been going on for decades.

“There’s also no one-size-fits-all when it comes to students, who aren’t just blank slates or empty vessels waiting to be filled,” he said. “Our students are coming from all kinds of backgrounds and perspectives, and it’s incumbent on us to bring this in as a resource and opportunity for learning.”

Jackson received his bachelor’s in English and creative writing in 1994 from Concordia University in Montreal; his master’s in political economy in 1999 from Carleton University in Ottawa; and his Ph.D. in communication and science studies in 2005 from the University of California, San Diego.

He spent six years on the faculty at the University of Michigan before joining Cornell in 2011.

By Tom Fleischman, Cornell Chronicle

This story was originally published in the Cornell Chronicle.

Lawrence Blume, the Distinguished Arts and Sciences Professor of Economics and professor of information science, has been named the inaugural associate dean for academic affairs for the Cornell Ann S. Bowers College of Computing and Information Science beginning July 1, 2023.

During the three-year term, Larry will oversee academic affairs in the college related to faculty and academic staff. Primarily, this will include managing tenure and promotion cases, reviews and reappointments, and other academic affairs.

“Larry is an outstanding member of the faculty and brings significant research, teaching, and administrative leadership experience to this new and important role for the college,” said Kavita Bala, dean of Cornell Bowers CIS. “Having worked with him closely this past year, I have full confidence he will contribute immensely in this new position.”

Blume received a B.A. in economics from Washington University and a Ph.D. in economics from the University of California, Berkeley. He was one of the general editors of The New Palgrave Dictionary of Economics, 2nd edition, to which he contributed several articles on economic theory. Blume is also a fellow of the Econometric Society, a visiting research professor at the Institute for Advanced Studies in Vienna (IHS), and has been a member of the external faculty at the Santa Fe Institute, where he served as co-director of the Economics Program and on the institute's steering committee.  He also served as chair of Cornell’s Department of Economics. 

“It is an exciting time in the college, and I’m honored to accept this role and to work closely with faculty,” said Blume. “This role presents an incredible opportunity to shape the academic landscape and ensure Cornell Bowers CIS remains a leading academic institution.”

In addition to his administrative duties as associate dean for academic affairs, he will continue in his leadership role as the Charles F. and Barbara D. Weiss Director of Undergraduate Studies for the Department of Information Science. He will also continue to teach and conduct research in general equilibrium theory and game theory, as well as in income and wealth distribution and network design.

Blume has been serving as interim associate dean for education for the 2022-2023 academic year while Claire Cardie, the Joseph C. Ford Professor of Engineering in the departments of computer science and information science, was on sabbatical.

A research team co-led by Cornell found that for schools without the resources to conduct learning analytics to help students succeed, modeling based on data from other institutions can work as well as local modeling, without sacrificing fairness.

“To use data-driven models, you need data,” said Rene Kizilcec, assistant professor of information science in the Cornell Ann S. Bowers College of Computing and Information Science. “And in many schools, especially lower-resourced schools that would benefit the most from learning analytics applications, data is rarely accessible.”

Kizilcec is a senior author of “Cross-Institutional Transfer Learning for Educational Models: Implications for Model Performance, Fairness, and Equity,” to be presented at the Association for Computing Machinery Conference on Fairness, Accessibility and Transparency (ACM FAccT), June 12-15 in Chicago. The lead author is Josh Gardner, doctoral student in computer science at the University of Washington.

Kizilcec and his team used anonymized data from four U.S. universities, and converted it into a common structure for the purpose of modeling which students are likely to drop out of college. Only the university-specific models – no individual student data, which raises privacy issues -- were shared between members of the research team.

More than 1 million students drop out of college each year in the U.S.; they are 100 times more likely to default on their student loan payments than those who graduate. This has led the federal government to impose regulations that incentivize colleges and universities to reduce dropouts by requiring them to report dropout rates, as well as rankings that account for graduation rates.

Kizilcec said that major institutions have the resources to conduct predictive data analytics. But institutions that could most benefit from that data – smaller colleges or two-year institutions – typically don’t.

“They have to rely on the services of a few companies that offer education analytics products.” he said. “Institutions can either build their own models – a very expensive process – or purchase an analytics ‘solution,’ with modeling that is typically done externally on other institutions’ data. The question is whether these external models can perform as well as local models, and whether they introduce biases.”

The goal of the researchers’ work was an accurate prediction of “retention” – whether each student who enters an institution for the first time in the fall would enroll at that same institution the following fall.

To assess the success of transfer learning – taking information from one institution and using it to predict outcomes at another – the team employed three approaches:

• Direct transfer – a model from one institution is used at another;
• Voting transfer – a form of averaging to combine the results of several models (“voters”) trained at disparate institutions to predict outcomes at another; and
• Stacked transfer – combining the predictions of models trained on all available institutions with the training data of the source institution.

The researchers used the three transfer methods, along with local modeling at each of the four institutions, in order to assess the validity of transfer learning. Predictably, local modeling did a better job of predicting dropout rates, “but not by as much as we would have thought, frankly, given how different the four institutions are in size, graduation rates and student demographics,” Kizilcec said.

And in terms of fairness – the ability to achieve equivalent predictive performance across sex and racial subgroups – the modeling performed well without sacrificing fairness.

Kizilcec said his team’s results point to more equity in dropout prediction, which could help lower-resourced schools with earlier intervention and preventing student departures, which cost the institution and can lead to worse outcomes for the students.

“It may not be necessary after all to allocate resources to create local models at every single school,” he said. “We can use insights from schools that have data infrastructure and expertise to offer valuable analytics to schools without these resources, and without sacrificing fairness. That’s a promising result for school leaders and policymakers.”

Other contributors are Christopher Brooks, assistant professor at the University of Michigan School of Information; Renzhe Yu, assistant professor of learning analytics and educational data mining at Columbia University; and Quan Nguyen, instructor of data science at the University of British Columbia.

Support for this work came from Google and Microsoft.

By Tom Fleischman, Cornell Chronicle

This story was originally published in the Cornell Chronicle.