Abstract: In today's digital era, language functions not only as a medium of information transmission but also as a mechanism of persuasion, framing, and control. The proliferation of online platforms has amplified this dual role: while enabling unprecedented access to knowledge, it has also exacerbated challenges such as misinformation, rhetorical manipulation, and cultural or linguistic disparities in information access. As a result, pragmatic language understanding and information integrity have emerged as central concerns for both computational linguistics and society at large. This research follows how claims are produced, reframed, and contested online through three interconnected threads. First, it models pragmatic deflection in discourse by investigating whataboutism, a rhetorical device that deflects criticism by redirecting discourse, and introduced novel datasets from Twitter (now X) and YouTube. This work underscores how subtle pragmatic maneuvers can erode discourse integrity without relying on outright falsehoods. Second, it advances retrieval and alignment for information integrity in health and news communication. These systems trace claims and narratives across genres (e.g., social posts and news reports) and languages (Chinese and English), linking social posts with journalistic reporting and aligning Chinese news with English biomedical evidence. By accounting for cultural context, assertions can be linked to reliable evidence and organized for systematic comparison. This work surfaces the risks of missing sources, unverifiable claims, and framing disparities in global health discourse, and demonstrates computational solutions that enhance both the credibility and accessibility of information. Third, the methodological centerpiece is Class Distillation (ClaD), a geometry-aware training paradigm for distilling a small, well-defined target class from a large, heterogeneous background. ClaD couples a distribution-aware contrastive loss (instantiated here in a Mahalanobis form when its assumptions fit the data) with an interpretable decision algorithm tuned for class separation. Evaluated on sarcasm, metaphor, and sexism detection, ClaD delivers strong efficiency and robustness, matching or surpassing larger models while using fewer computational resources, making these pipelines practical by learning reliably from small, sharply defined classes. In sum, this research presents an integrated account of language understanding in the digital age. It exposes how integrity falters through pragmatic deflection, cross-genre drift, and cross-lingual misalignment, and translates these insights to move pragmatic language understanding to systems for evidence retrieval, alignment, and verification; and it sheds light on where and how integrity is threatened, and delivers methods that leverage pragmatic language use.

Speaker: Chenlu Wang

Location: (Old) Computer Science Building, Room 2311
The Natural Language Processing Reading Group at Stony Brook University meets weekly to discuss recent research papers in NLP and related fields.
Join the Google Group here.

Title: Don't Just Fix it in Post: A Science of AI Must Study Training Dynamics

Abstract: What would it mean to have a scientific understanding of AI? Models are not static objects: they are snapshots of time-evolving processes shaped by data, objectives, architectures, and optimization dynamics. Yet much of AI research treats models as fixed artifacts, analyzing behaviors after training rather than asking why they emerge. This position paper argues that a science of AI must move beyond post hoc fixes and study the training dynamics that produce model behavior. Such a science should support progressively stronger forms of understanding: predicting outcomes from early training signals, intervening when trajectories go wrong, and ultimately designing training procedures that more reliably produce desired properties. Scaling laws have made prediction routine for loss; the challenge is extending this success to capabilities, biases, robustness, and safety-relevant behaviors. We articulate requirements for such theories grounded in the history and philosophy of science, examine progress in mechanistic interpretability, fairness, memorization, and simplicity bias, and identify concrete open problems.

Location: NCS 220


Zoom Link: https://stonybrook.zoom.us/j/92942833294?pwd=927683
CG Group member (and SBU faculty) Chao Chen will speak on Fri, March 12, about the use of topological data analysis in machine learning for image analysis.
Chao has shared some of his research with the CG Group previously, and this will be a great opportunity to learn more about this exciting research area related to computational geometry/topology!

Time: Friday, March 12, 2pm-3pm
Place: Zoom
https://stonybrook.zoom.us/my/profweizhu?pwd=RjVIVXg3YUhudzZZQ3pheHUydTJBUT09



Title: Learning with Topological Information - Image Analysis and Label Noise
Speaker: Prof. Chao Chen (SBU)

Abstract: Modern machine learning faces new challenges. We are
analyzing highly complex data with unknown noise. Topology provides
novel structural information to model such data and noise. In this
talk, we discuss two directions in which we are using topological
information in the learning context. In image analysis, we propose a
topological loss to segment and to generate images with not only
per-pixel accuracy, but also topological accuracy. This is necessary
in analysis of images of fine-scale biomedical structures such as
neurons, vessels, etc.  Extracting these structures with correct
topology is essential for the success of downstream
analysis. Meanwhile, we discuss how to use topological information to
train classifiers robust to label noise. This is important in practice
especially when we are using deep neural networks which tend to
overfit noise. These results have been published in NeurIPS, ECCV,
ICML and ICLR.
Abstract: In this talk, we will discuss what a CS PhD entails and the traits and habits that are important for success in PhD programs and future careers. While the talk is targeted to first-year PhD students, PhD students at all levels should derive from it.

Bio: Samir Das is a professor in the Department of Computer Science at Stony Brook
University. He is currently serving as the department chair. He is well recognized in the
community for his research in wireless networks and systems.

Location: NCS120
Join Zoom Meeting
https://stonybrook.zoom.us/j/98079526509?pwd=Wkt5eURhVDN5VE56TUloS2h5V1Jodz09

Meeting ID: 980 7952 6509
Passcode: 949941



Abstract Over the last decade, artificial neural networks have undergone a revolution, catalyzed by better
supervised learning algorithms. However, in stark contrast to young animals (including humans), training such
networks requires enormous numbers of labeled examples, leading to the belief that animals must rely instead
mainly on unsupervised learning. The reason is that most animal behavior is not the result of clever learning
algorithms--supervised or unsupervised-- but is encoded in the genome. Specifically, animals are born with
highly structured brain connectivity, which enables them to learn very rapidly. Because the wiring diagram is
far too complex to be specified explicitly in the genome, it must be compressed through a genomic
bottleneck. I will describe results showing how the genomic bottleneck algorithm can lead to dramatic
compression of networks and better generalization, particularly for rapid transfer in supervised and
reinforcement learning.

Anthony Zador is professor of neuroscience at CSHL.