First Day of Classes 2019 Fall Semester
Abstract: Generative models have transformed natural image synthesis, driven by billion-scale datasets of image-caption pairs. Specialized domains, such as digital histopathology and satellite imagery, differ in two ways: their images reach gigapixel resolution, far beyond any feasible training resolution, and they lack paired captions, since captioning the patches of a single whole-slide image (WSI) takes about 40 hours of pathologist time. Overcoming these challenges would enable generative models that create teaching cases of rare diseases, simulate tumor progression, and serve as a foundation for digital twins.
In this thesis, we build generative foundation models for these domains, scaling from small patches to gigapixel images without patch-level annotations. We begin with PathLDM, which conditions histopathology patch generation on
slide-level pathology reports, enabling the first text-to-image generation in histopathology. Since reports do not describe individual patches, we propose LRDM, which uses embeddings from pretrained self-supervised learning (SSL) encoders as patch-level conditioning for both histopathology and satellite images. ZoomLDM extends SSL conditioning across eight magnifications with a single model, capturing structure from individual cells to tissue architecture. To scale across cancer types, we present PixCell, the first generative foundation model for histopathology, trained on 30.8 million patches from 69,184 WSIs across 28 organ types. Synthetic images from PixCell can stand in for real data in SSL training, enable privacy-preserving data sharing, and improve downstream tile-level classifiers. Extending this to gigapixel scale, we introduce BigCell, which generates coherent WSIs of up to 32768x32768 pixels by guiding a 2048x2048 flow-matching transformer with a grid of foundation model embeddings, and speeds up generation by over 3x with an importance-based schedule. BigCell enables text-to-image synthesis at 8192x8192 resolution, with synthetic samples improving slide-level few-shot classification by up to 18%. Beyond pathology, our SSL-conditioned models serve as data generators for self-supervised learning on natural images. Finally, to make gigapixel synthesis practical, we plan to reduce BigCell's generation time from about 5 hours to under 20 minutes per 32768x32768 image through few-step distillation and efficient attention, and to extend text-to-image synthesis from 8192x8192 regions to entire slides with a pretrained language model.
Speaker: Srikar Yellapragada
Location: NCS 220
In this thesis, we build generative foundation models for these domains, scaling from small patches to gigapixel images without patch-level annotations. We begin with PathLDM, which conditions histopathology patch generation on
slide-level pathology reports, enabling the first text-to-image generation in histopathology. Since reports do not describe individual patches, we propose LRDM, which uses embeddings from pretrained self-supervised learning (SSL) encoders as patch-level conditioning for both histopathology and satellite images. ZoomLDM extends SSL conditioning across eight magnifications with a single model, capturing structure from individual cells to tissue architecture. To scale across cancer types, we present PixCell, the first generative foundation model for histopathology, trained on 30.8 million patches from 69,184 WSIs across 28 organ types. Synthetic images from PixCell can stand in for real data in SSL training, enable privacy-preserving data sharing, and improve downstream tile-level classifiers. Extending this to gigapixel scale, we introduce BigCell, which generates coherent WSIs of up to 32768x32768 pixels by guiding a 2048x2048 flow-matching transformer with a grid of foundation model embeddings, and speeds up generation by over 3x with an importance-based schedule. BigCell enables text-to-image synthesis at 8192x8192 resolution, with synthetic samples improving slide-level few-shot classification by up to 18%. Beyond pathology, our SSL-conditioned models serve as data generators for self-supervised learning on natural images. Finally, to make gigapixel synthesis practical, we plan to reduce BigCell's generation time from about 5 hours to under 20 minutes per 32768x32768 image through few-step distillation and efficient attention, and to extend text-to-image synthesis from 8192x8192 regions to entire slides with a pretrained language model.
Speaker: Srikar Yellapragada
Location: NCS 220
Abstract: Formalization of mathematics is the process by which pen-and-paper mathematics is translated into a strict chain of logical deductions down to the axioms of mathematics. The subject has seen renewed interest in the last decades thanks to the development of computer systems called proof assistants, which make this feasible in practice.
There have now been several examples of high-profile mathematical results which have been formalized. In principle, any mathematical domain is accessible. However, existing projects are skewed towards algebra instead of analysis. Notable exceptions are a project which formalized enough of Gromov's convex integration theory to deduce Smale's sphere eversion theorem and the ongoing project to formalize Carleson's convergence theorem for Fourier series.
This workshop will bring together formalization experts and interested mathematicians to give a new impulse to formalization of analysis (in a very broad sense), and to develop abstractions and tools to deduplicate effort.
Application Information: ICERM welcomes applications from faculty, postdocs, graduate students, industry scientists, and other researchers who wish to participate. Some funding may be available for travel and lodging. Graduate students who apply must have their advisor submit a statement of support in order to be considered.
There have now been several examples of high-profile mathematical results which have been formalized. In principle, any mathematical domain is accessible. However, existing projects are skewed towards algebra instead of analysis. Notable exceptions are a project which formalized enough of Gromov's convex integration theory to deduce Smale's sphere eversion theorem and the ongoing project to formalize Carleson's convergence theorem for Fourier series.
This workshop will bring together formalization experts and interested mathematicians to give a new impulse to formalization of analysis (in a very broad sense), and to develop abstractions and tools to deduplicate effort.
Application Information: ICERM welcomes applications from faculty, postdocs, graduate students, industry scientists, and other researchers who wish to participate. Some funding may be available for travel and lodging. Graduate students who apply must have their advisor submit a statement of support in order to be considered.
The deadline to apply for this workshop is January 24, 2026.
Abstract: How do humans learn the sound patterns of their language? Despite a variety of methods and advances in phonotactic learning, there is still a paucity of computational research, methods and data for languages with tones. In this talk, I will explore this question specifically in light of tone languages, where pitch plays a crucial role in distinguishing words' meaning. I provide an implementation of the Bottom-Up Factor Inference Algorithm over Autosegmental Representations (BUFIA-AR), which learns the rules governing possible tone patterns. Using a dataset of Hausa, a West African tone language, the algorithm successfully identifies patterns that are not permitted in the language. These results (i) confirm long-standing linguistic generalizations, (ii) make more specific predictions about exceptional cases, and (iii) reveal previously unnoticed patterns. The results show how mathematical models of sound structure can be brought into dialogue with both linguistic theory and computational learning, highlighting the broader potential of formal approaches to capture human linguistic knowledge.
Bio: Han Li is a fifth-year Ph.D. student in Linguistics department, specializing in computational linguistics under the supervision of Professor Jeff Heinz. Her research focuses on how sound patterns in language can be formally represented and computationally learned, bridging theoretical linguistics and computer science.
Location: Institute for Advanced Computational Science, Seminar Room
Zoom Meeting: https://stonybrook.zoom.us/j/94043459206?pwd=3ra47h8HghOFRfobRBjZaDMyTwialr.1
Meeting ID: 940 4345 9206
Passcode: 332717
Abstract:
Deep neural network (DNN) is a powerful tool for solving image processing and computer vision tasks such as image and video reconstructions, object recognition, and scene understanding, etc. However, DNN have been used for only the digital domain in the imaging pipeline, such as the feature extractor and classifier models after an image is captured and digitized. In this research, we propose a new framework called deep sensing. The proposed framework also models the analog layer to the neural network model and jointly optimizes the parameters in optics and sensor designs of a camera, as well as reconstruction and classification models by the same training strategy.
Bio:
Hajime Nagahara is a professor at D3 Center, Osaka University, since 2024. He received Ph.D. degree in system engineering from Osaka University in 2001. He was a research associate of the Japan Society for the Promotion of Science from 2001 to 2003. He was an assistant professor at the Graduate School of Engineering Science, Osaka University, Japan from 2003 to 2010. He was an associate professor in Faculty of Information Science and Electrical Engineering at Kyushu University from 2010 to 2017. He was a professor at Institute for Datability Science, Osaka University, from 2017 to 2024. He was a visiting associate professor at CREA University of Picardie Jules Verns, France, in 2005. He was a visiting researcher at Columbia University in 2007-2008 and 2016-2017. Computational photography and computer vision are his research areas. He received IPSJ Nagao Special Researcher Award in 2012, ICCP2016 Best Paper Runners-up, and SSII Takagi Award in 2016. He is a program chair for ICCP2019, General Chair for upcoming ACCV2026 Osaka, Associate Editor for IEEE Transactions on Computational Imaging in 2019-2022, Associate Editor for IEEE Transactions of Pattern Recognisiton and Machine Intelligence, and Director of Information Processing Society of Japan in 2022-2024.
Zoom: https://stonybrook.zoom.
The Pittsburgh Supercomputing Center is pleased to present a Machine Learning and Big Data workshop.
This workshop will focus on topics including big data analytics and machine learning with Spark, as well as deep learning.
This will be an IN PERSON event hosted by various satellite sites, there WILL NOT be a direct to desktop option for this event. SBU's Institute for Advanced Computational Science (IACS) is one of those satellite sites!
Location: IACS Conference Room #2
Interested applicants must first have an ACCESS ID. If you don't have the ID, please visit this page to create one: ACCESS USER REGISTRATION.
Once you have an ACCESS ID, please login (see top right here) then register here.
Making sense of Twitter @ Bloomberg presented by Daniel Preotiuc-Pietro
ABSTRACT: The Bloomberg Terminal has provided ways for investors and journalists to sift through and understand the immense volume of tweets and discover financially-relevant content ever since the SEC approved the use of Twitter for company disclosures back in 2013.
In the first part of the talk, I will showcase how tweets impact financial markets and how Bloomberg is using Natural Language Processing methods to identify financially relevant tweets that move the markets. Our processing pipeline feeds directly to clients, journalists in the newsroom and powers several news analytic products offered by the company including trending companies and consumer sentiment for publicly traded equities.
However, understanding user pragmatic intent in individual tweets would allow us to gain deeper insights and enable new applications. I will present several recent research studies focused on understanding intent including identifying complaints and the roles with which vulgarity is used in social media and how these can help improve applications such as sentiment analysis and hate speech detection.
BIO: Daniel Preotiuc-Pietro is a Senior Research Engineer and Team Lead at Bloomberg LP, where he works on analyzing and building models for real-world large scale social media and news mining and information extraction. His research interests are focused on understanding the social and temporal aspects of text, especially from social media, with applications in domains such as Social Psychology, Law, Political Science and Journalism. Several of his research studies were featured in popular press including the Washington Post, BBC, New Scientist, Scientific American or FiveThirtyEight. He is a co-organizer of the Natural Legal Language Processing workshop series. Prior to joining Bloomberg LP, Daniel was a postdoctoral researcher at the University of Pennsylvania with the interdisciplinary World Well Being Project and obtained his PhD in Natural Language Processing and Machine Learning at the University of Sheffield, UK.
ABSTRACT: The Bloomberg Terminal has provided ways for investors and journalists to sift through and understand the immense volume of tweets and discover financially-relevant content ever since the SEC approved the use of Twitter for company disclosures back in 2013.
In the first part of the talk, I will showcase how tweets impact financial markets and how Bloomberg is using Natural Language Processing methods to identify financially relevant tweets that move the markets. Our processing pipeline feeds directly to clients, journalists in the newsroom and powers several news analytic products offered by the company including trending companies and consumer sentiment for publicly traded equities.
However, understanding user pragmatic intent in individual tweets would allow us to gain deeper insights and enable new applications. I will present several recent research studies focused on understanding intent including identifying complaints and the roles with which vulgarity is used in social media and how these can help improve applications such as sentiment analysis and hate speech detection.
BIO: Daniel Preotiuc-Pietro is a Senior Research Engineer and Team Lead at Bloomberg LP, where he works on analyzing and building models for real-world large scale social media and news mining and information extraction. His research interests are focused on understanding the social and temporal aspects of text, especially from social media, with applications in domains such as Social Psychology, Law, Political Science and Journalism. Several of his research studies were featured in popular press including the Washington Post, BBC, New Scientist, Scientific American or FiveThirtyEight. He is a co-organizer of the Natural Legal Language Processing workshop series. Prior to joining Bloomberg LP, Daniel was a postdoctoral researcher at the University of Pennsylvania with the interdisciplinary World Well Being Project and obtained his PhD in Natural Language Processing and Machine Learning at the University of Sheffield, UK.
NLP Reading Group | Fall 2025
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.
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.
Speaker: Gary Kazantsev (Head of Quant Technology Strategy in the Office of the CTO at Bloomberg)
Date/Time: Friday, October 15, 2021 10:00AM-11:00AM EST
Title: Machine Learning in Finance
Abstract: Machine learning is changing our world at an accelerating pace. In this talk we will discuss the recent developments in how machine learning and artificial intelligence are changing finance, from a perspective of a technology company which is a key participant in the financial markets. We will give an overview and discuss the evolution of selected flagship Bloomberg ML and AI projects, such as sentiment analysis, question answering, social media analysis, information extraction and prediction of market impact of news stories. We will discuss practical issues in delivering production machine learning solutions to problems of finance, highlighting issues such as interpretability, privacy and nonstationarity. We will also discuss current research directions in machine learning for finance. We will conclude with a Q&A session.
Bio: (https://www.
Prior to joining Bloomberg in 2007, Gary had earned degrees in physics, mathematics, and computer science from Boston University.
He is engaged in advisory roles with FinTech and Machine Learning startups and has worked at a variety of technology and academic organizations over the last 20 years. In addition to speaking regularly at industry and academic events around the globe, he is a member of the KDD Data Science + Journalism workshop program committee and the advisory board for the AI & Data Science in Trading conference series. He is also a co-organizer of the annual Machine Learning in Finance conference at Columbia University.
Join Zoom Meetinghttps://stonybrook.zoom.us/j/
Abstract: This dissertation addresses the methodological disconnect between Natural Language Processing (NLP) and human-centric analysis by shifting the unit of analysis from document to human behavior in two broad respects: (i) time-ordering: modeling documents as sequential person-indexed behavioral observations, and (ii) person-level semantics: evaluation and explainability of models by their latent structure of psychological constructs rather than just its predictive accuracy against narrow proxy measures. First, we consider the most basic implication of language as a person's behaviors when measuring their psychological constructs: relationship between language sample size and model's predictive performance. We empirically show that the state-of-the-art transformers are often over-parameterized for typical NLP dataset sizes and can be reduced in dimensionality without performance loss. Establishing the author as the unit of analysis naturally allows us to treat their behavior as a time-ordered sequence. Second, we introduce a longitudinal evaluation framework that establishes ecologically valid evaluation settings, namely, cross-sectional and prospective generalization, and separates error measurement of the model into within-person dynamics and between-person differences. We demonstrate that traditional NLP evaluations based on random document splits can yield reversed conclusions under ecologically valid generalization settings. To address this, we develop models that capture the trajectory of mental states (e.g., mood shifts) rather than static traits. Third, moving into person-level semantics, we evaluate the latent structure of large language models using a novel machine behavior analytic framework. We find that while GPT-4 achieves high predictive correlation with self-reports, its latent symptoms structure diverges from clinical understanding. Finally, we propose a method for modeling multidimensional behaviors, embedding concurrent behavioral signals alongside language to predict future states. Taken together, this work suggests that operationalizing language as behavior advances NLP methods into a rigorous instrument for valid psychological inquiry.
Speaker: Adithya Ganesan
Location: Join Zoom Meeting (ID: 99021939129, Passcode: 569493)
Speaker: Adithya Ganesan
Location: Join Zoom Meeting (ID: 99021939129, Passcode: 569493)