Events
What is happening next in Cornell AI
Summits, demo days, and workshops across the Ithaca and Cornell Tech campuses. Open to students, alumni, and the wider community.
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On the calendar
Every published event still to come, soonest first. Dates and venues follow the Cornell AI events calendar.
Language Resource Center Speaker Series - Aditya Vashistha - AI Safety for the Global Majority
"AI Safety for the Global Majority" Aditya Vashistha Assistant Professor of Information Science and Director of the Cornell Global AI Initiative, Cornell University AI safety is often framed around alignment failures, existential risk, or misuse by powerful actors. Yet for much of the world, the risks of AI are far more immediate: misrepresentation, exclusion, stereotyping, and the erosion of dignity. These harms disproportionately affect communities that are already marginalized, including people with disabilities and those in non-Western cultural contexts. In this talk, I argue that current approaches to AI safety are limited not only in scope but in standpoint. When safety is defined from dominant social and geographic positions, the harms that matter most to marginalized communities become invisible, anecdotal, or secondary. Drawing on case studies from my work with people with disabilities in the US and India, I show how AI systems both promise expanded access and simultaneously reproduce cultural bias, ableism, and structural inequities. I conclude by outlining pathways toward a globally grounded approach to AI safety, one that treats representation, cultural context, and participatory governance as core safety challenges rather than peripheral concerns. Bio: Dr. Aditya Vashistha is an Assistant Professor of Information Science at Cornell University, where he directs the Cornell Global AI Initiative, a university-wide effort to integrate global perspectives into the design, evaluation, and governance of AI. His research focuses on building and studying AI systems that advance equity for historically marginalized communities, particularly across South Asia and Sub-Saharan Africa. His work has led to widely deployed systems impacting over 300,000 people and has received multiple best paper awards. He is the recipient of the Facebook Access Innovation Prize, Google and Microsoft Faculty Awards, and Cornell's President's and Provost's Faculty Award for Excellence in Research, Teaching, and Service. He received his Ph.D. in Computer Science from the University of Washington. This event will be held in person in G25 Stimson and will also be streamed live over Zoom (registration required). Join us at the LRC or on Zoom. The event is free and open to the public.
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Distinguished Raymond G. Thorpe Lecture: John Murphy (U.S. District Judge)
How a Cornell Chemical Engineer Became a Federal Judge, and the Lessons Learned Along the Way John Murphy ‘99, a United States District Judge for the Eastern District of Pennsylvania, will trace his path from Olin Hall to the halls of justice. He will cover topics like: (i) how the law works (from the perspective of an engineer); (ii) careers in law; (iii) unexpected connections among the fields of law, science, and engineering; (iv) some of the challenges facing our legal system at the intersection of law and science; and, of course, (v) why an education in chemical engineering really does prepare you for anything. He will also share insights from his time in chemical engineering that have carried him over the years, such as the role of mentorship, the bedrock of ethical conduct, and how serendipity favors the prepared mind. After Cornell, but before turning to a legal career, Judge Murphy followed the path of his mentors at Cornell, obtaining a Ph.D. in chemical engineering at Caltech, where he was an NSF fellow. There, he worked in the laboratory of Professor Mark E. Davis on a thesis entitled “Methods for Collection and Processing of Gene Expression Data.” He was also chair of Caltech’s Graduate Review Board, and obtained a patent on his work, two experiences that got him thinking about the law. From there, he attended Harvard Law School, where he was editor-in-chief of the Harvard Journal of Law and Technology, and then served as a clerk for Chief Judge Kimberly A. Moore of the U.S. Court of Appeals for the Federal Circuit. Before being appointed to the court, Judge Murphy was a partner at the law firm of Baker & Hostetler in Philadelphia, where he litigated patent, copyright, trademark, and trade secret disputes. Now, outside of the courtroom, Judge Murphy serves on the Committee on Codes of Conduct of the Judicial Conference of the United States and the Subcommittee on Artificial Intelligence. He also teaches at Villanova Law School and Rutgers Law School and is co-author of the last three editions of the popular law-school casebook Patent Litigation & Strategy.
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REAP Workshop Fall 2026
Yu (Charlie) Sun, Graduate Student in the Yu Lab, attended the Machine Learning for Mass Spectrometry Data Analysis short course, hosted by the American Society for Mass Spectrometry on May 30-31, 2026, in San Diego, CA as a recipient of the Weill Institute REAP award. Objective The post-workshop presentation will demonstrate how core machine learning concepts introduced in the workshop can be applied to a real mass spectrometry data analysis problem. The goal is to reinforce data literacy, highlight best practices, and show how to critically interpret machine learning results rather than to develop advanced models. Target Audience This presentation is intended for mass spectrometry researchers seeking a deeper understanding of fundamental machine learning concepts, commonly used algorithms, and typical pitfalls. Emphasis will be placed on developing the ability to critically evaluate machine learning results in the scientific literature and to apply these approaches responsibly and appropriately in their own mass spectrometry studies. Presentation Content The presentation will focus on a concrete, well-defined mass spectrometry use case (e.g., peptide identification confidence, sample classification, or feature selection in proteomics). It will include: Problem definition, the biological or analytical question and why machine learning is appropriate.Data overview, structure of the mass spectrometry data, preprocessing steps, and common challenges (missing values, batch effects, dimensionality).Machine learning approach, a simple, standard method covered in the workshop (e.g., regression, classification, or clustering), with emphasis on model assumptions and parameter choices.Results and interpretation, performance metrics, visualization of outputs, and biological relevance.Limitations, potential overfitting, data leakage, and when expert consultation would be necessary. Outcome By the end of the presentation, attendees should understand how basic machine learning tools integrate into mass spectrometry data analysis workflows and feel more confident interpreting and applying these methods in practice.
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BME7900 Seminar: Antonio Fernandez-Ruiz (Cornell)
Novel neural interfaces for large-scale, long-term recordings and manipulation in behaving animals A central challenge in neuroscience is to understand how neuronal activity across brain areas generates complex behaviors. Recent advances in implantable bioelectronics have enabled simultaneous recordings of hundreds of neurons in behaving animals. However, existing neural interfaces often remain bulky, constraining animal behavior, and rigid recording electrodes do not allow recording the same neurons over multiple days due to tissue micromovements, limiting the study of learning and memory mechanisms. In this talk, I will present our recently developed implantable platforms for large-scale, long-term neural recording and manipulation in rodents and other animals. We created parylene-C-based, high-density flexible electrodes using a multilayer design and micro-patterned conducting polymers, achieving a higher density-to-volume ratio than existing silicon probes. These devices allow stably tracking hundreds of the same neurons for months, overcoming the limitations of rigid recording electrodes. We further engineered a wireless neural interface enabling untethered recording and closed-loop modulation in freely behaving small animals. Direct under-chip bonding of flexible probes yielded a compact system weighing under 1.2 g. Integrated onboard sensors captured behaviorally relevant variables, including locomotion, vocalization, and pupil dynamics. An AI-driven embedded signal-processing pipeline detected neural and behavioral biomarkers in real time to trigger targeted interventions. Together, these technologies have enabled us to investigate neural mechanisms of behavior under conditions that were previously inaccessible. Bio: Antonio Fernandez-Ruiz is an associate professor at the Department of Neurobiology and Behavior in Cornell University. He studied physics and biology at the Universities of Sevilla and Madrid, in Spain. He completed his Ph.D. at the University of Madrid, where he developed machine learning methods to study the biophysical basis of brain dynamics. He then moved to New York University to work as a postdoctoral fellow in the laboratory of Gyorgy Buzsaki. His research focused on the neural circuit mechanisms of learning and memory in rodents. His work elucidated how the temporal coordination of excitatory and inhibitory inputs mediates communication between brain areas and supports learning. He developed a novel approach to causally probe the role of specific patterns of neural activity in behavior and used it to demonstrate the key function of neuronal sequence in memory formation. The overarching mission of his lab at Cornell is to understand how neuronal dynamics in distributed brain circuits support complex cognitive functions and how small imbalances can lead to pathological states. His group investigates the algorithmic and mechanistic underpinnings of learning, memory and decision making in health and disease at the computational, circuit, and cellular levels. Work from his laboratory elucidated how cellular heterogeneity and circuit dynamics contribute to the computational capabilities of hippocampal and cortical structures, found novel circuit for the associative and predictive functions of memory and discovered how the temporal organization of brain dynamics in sleep contribute to memory consolidation and integration. They also develop novel technology for a more precise interrogation of neural circuit activity in health and disease. Antonio is a Pew, Klingenstein and Sloan Scholar, and the recipient of the Gruber International Research Award in Neuroscience (SfN), the Blavatnik Award for Young Scientists in the Life Sciences, the Freedman Prize for Exceptional Basic Research (BBRF), the New Innovator Award (NIH) and the MIND Prize (PSF).
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2026 Cornell Food Hackathon
The annual Cornell Food Hackathon convenes 150 undergraduate and graduate students from diverse disciplines, including food science, business, nutrition, engineering, design, and data science, to collaboratively explore the future of food innovation. Working in cross-functional teams, students simulate real-world startup environments where they are challenged to integrate product development, consumer insights, go-to-market strategy, and technical feasibility. Their mission at the 2026 event: to reimagine consumer health and wellness and propose bold, next-generation product innovations. Through rapid ideation, prototyping, and pitching, students not only develop functional concepts but also gain an entrepreneurial mindset and systems-thinking skills essential for leading the future of food. This event offers a unique opportunity for students to work side-by-side with peers in food, ag, chemistry, nutrition, health, design, operations, business, engineering, and more. Industry mentors are also present, providing feedback to the competing interdisciplinary teams of students and great career networking opportunities. This year's Cornell Food Hackathon will kick off at 4 pm on Friday, November 6, 2026. The event runs until 8 pm on Nov 6; 9 am, 9 pm on Nov 7, & 9 am, 3 pm on Nov 8.
Event detailsInterpretable Machine Learning
Some predictive machine learning models provide interpretable results, while others are black boxes. In either case, it’s important to know how and why a model made the predictions it did. This workshop will introduce tools for interpreting machine learning models and explaining their predictions. Topics covered include: Inherently interpretable models (linear and logistic regression, decision trees)Feature importanceIndividual conditional expectation (ICE) and partial dependence (PDP) plotsLocal surrogate models (LIME)Shapley additive explanations (SHAP) All methods will be discussed at an approachable, non-technical level and demonstrated using worked examples in R and Python. Register Now
Event detailsHype Springs Eternal: AI, Memory, and War
In 1958, the Office of Naval Research unveiled the Perceptron, the first neural network for artificial intelligence, created by Cornell psychologist Frank Rosenblatt. The Navy expected it to read and write within a year. Nearly seventy years later, the Pentagon is again betting on AI to revolutionize war, while industry leaders promise that recursive self-improvement and superintelligence are just around the corner. Why does the military accept exceptional expectations about AI despite repeated disappointment? Part of the answer is rethinking technology hype. Because we cannot fact-check the future, hype is better understood as performative discourse than as exaggeration. This expectant, dramatic, and imminent discourse can be studied in real time, regardless of whether it proves true. Part of the answer is remembering forgetfulness. Learning supposedly tempers expectations, but forgetting can reset them. I trace AI hype from Dartmouth and DARPA through the Lighthill Report, Strategic Computing, and the Third Offset. I show that hype runs on both hope and fear, and that the exceptional expectations it evokes cycle as technical performance and forgetting interact. About the speaker Frank L. Smith III is the director of the Cyber and Innovation Policy Institute (CIPI), part of the Strategic and Operational Research Department in the Center for Naval Warfare Studies at the U.S. Naval War College. Smith was previously a senior lecturer in the Department of Government and International Relations at the University of Sydney. His interdisciplinary research examines the relationship between emerging technology and national security, particularly in cyberspace. He has a Ph.D. in political science and a B.S. in biological chemistry, both from the University of Chicago. Host Reppy Institute for Peace and Conflict Studies, part of the Einaudi Center for International Studies
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