Johannes Hachmann, University of Buffalo Assistant Professor of Chemical Engineering presents Making Machine Learning Work in Chemistry

The use of modern machine learning, informatics and data mining approaches is a relatively new development in the chemical and materials domain. These techniques have been exceedingly successful in other application fields, and since there is no fundamental reason why they should not have a similarly transformative impact on chemical and materials research, there is now a concerted effort by the community to introduce data science in this new context. However, adapting techniques from other application domains for the study of chemical and materials systems requires a substantial rethinking and redevelopment of the existing methods.

In this presentation, we will discuss our work on designing advanced, physics-infused neural network architectures, the fusion of unsupervised clustering with supervised regression for local ensemble models, active and transfer learning techniques, bootstrapping approaches to minimize our training data footprint, methods to increase the applicability domain of data-derived models and automated hyperparameter optimization.

Biosketch: Johannes Hachmann is an Assistant Professor of Chemical Engineering at the University at Buffalo (UB), the Director of the Engineering Science in Data Science graduate program, a Core Member of the UB Computational and Data-Enabled Science and Engineering graduate program, and a Faculty Member of the New York State Center of Excellence in Materials Informatics. He earned a Dipl.-Chem. degree (2004) after undergraduate studies at the universities of Jena and Cambridge, M.Sc. (2007) and Ph.D. (2010) degrees in Chemistry from Cornell University, and he conducted postdoctoral research at Harvard University before joining the UB faculty in 2014. The research of the Hachmann Group fuses (first-principles) molecular and materials modeling with virtual high-throughput screening and modern data science (i.e., the use of database technology, machine learning and informatics) to advance a data-driven discovery and rational design paradigm in the chemical and materials disciplines. One of the centerpieces of the group's efforts is the creation of an open, general-purpose software ecosystem for the data-driven design of chemical systems and the exploration of chemical space. This work was recognized with a 2018 NSF CAREER Award.

You are cordially invited to attend the biweekly Brookhaven AI Mixer (BAM). BAM includes one short talk on AI research happening at BNL, followed by an open mixer over coffee and snacks for everyone to network and discuss all things AI. The first half hour will consist of presentations that will be available via ZOOM, and the second half hour will be for in person only networking.

Join us every other Tuesday at noon in CDSD's Training Room (building 725, 2nd floor) to learn about interesting AI methods and applications, engage with potential collaborators, prepare for pending FASST funding calls, and build a community of AI for Science at BNL.

Experiencing Machine Learning in Collider-Accelerator Control System

Abstract: The Relativistic Heavy Ion Collider (RHIC) at Collider-Accelerator Department (C-AD) of BNL provides the world's only high-energy polarized proton beam. It is in the unique position to study where nuclei obtain their spin. During 25 years of operation at RHIC, the C-AD controls group has developed its own control system to tune the accelerator performance, which contains millions of control points. The successful operation of this system will highly affect the machine performance. RHIC's successor, the Electron-Ion Collider (EIC), will be one of the most complex scientific instruments ever built, with the capability of colliding polarized proton and electron beams. The increasing complexity of instruments will require new, sophisticated control methods/tools to tune and optimize the accelerator performance. In this talk, I will summarize some projects developed in recent years that utilize machine learning in the C-AD controls group.

Biography: Dr. Yuan Gao is an assistant scientist at the Collider-Accelerator Department (C-AD) at Brookhaven, primarily working on developing new machine learning schemes in the control group to enhance system performance. His research interests include game theory, algorithm design, anomaly detection, and simulation modeling.

Location: CDS, Bldg. 725, Training Room

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Towards Saving Lives with Natural Language Processing Andrew Schwartz Dept. of Computer Science Stony Brook Analyzing language use patterns is proving to be a valuable and unique approach to understanding the psychological, social, and health factors of people. On the individual level, Facebook and Twitter have been found predictive of mental health, personality, demographics, and occupational class (among others). At the community or county-level, Twitter has been found predictive of flu and allergy outbreaks, life satisfaction, atherosclerotic heart disease mortality, health behavioral risk factors, excessive drinking, and HIV prevalence. While these techniques have shown robust links over a plethora of important aspects of human life, it is not clear whether any lives have been saved, at least directly, by such work. At their core, some barriers to improving health care and saving lives are likely not NLP or even AI problems, but others are perhaps technical in nature and suggest changing the way we model data. This seminar will have two parts: a presentation and a discussion. I will start by going over recent and on-going work toward predicting mental health outcomes --- depression, addiction relapse, future psychological distress --- from human language use patterns. Then, I will present an imperfect vision of a future where NLP helps to save lives and open the floor for discussion of technical barriers and whether such a vision is practical. Biography: Andrew Schwartz received his PhD in Computer Science from the University of Central Florida in 2011 with research on acquiring lexical semantic knowledge from the Web. He then joined the University of Pennsylvania where he was a Postdoctoral Research Fellow and later Visiting Assistant Professor in Computer & Information Science. He is Lead Research Scientist for the World Well-Being Project, a multidisciplinary group of Computer Scientists and Psychologists studying physical and psychological well-being based on language in social media.
Zoom Like a Pro! Unlock Whiteboard, Polls, AI Companion, and more to supercharge student participation. This hands-on workshop explores innovative ways to use Zoom's built-in tools to enhance active learning activities in your classes. Learn how to utilize the Whiteboard feature to make collaborative work more engaging, use Polling and Quizzes for instant feedback, AI Companion for summary, and Breakout Sessions for group activities. Register here: https://stonybrook.zoom.us/meeting/register/tJckf--rpj4pGdRV0ItgTW8Lk7gn_RuykByO#/registration

The Future Histories Studio at Stony Brook University and Guggenheim New York are collaborating to present a day-long symposium on October 24 at the Simons Center for Geometry and Physics. This conference will explore urgent questions at the intersection of artificial intelligence, machine learning, and the human, and is co-organized by Noam Segal, LG Electronics Associate Curator at Guggenheim New York. In this role, Noam plays an important part in researching these topics, promoting a deeper understanding of the ways in which contemporary artists use new technologies, and developing and supporting the Guggenheim's engagement with technology-based art under the LG Guggenheim Art and Technology Initiative.

The event examines the profound transformations brought by automation--how AI compels us to rethink cognition, agency, and the conditions of reason itself. As these systems become ever more embedded in daily life--largely invisible yet deeply consequential--they challenge the very foundations of subjectivity and governance. We are surrounded by logics we cannot fully access, yet which shape our realities, while new forms of alterity arise--distinct modes of reasoning that propose collective unknowns beyond established frameworks of knowledge.

This emerging terrain invites us to consider cognitive plurality, where biological and technological intelligences generate new categories, concepts, and understandings. Once unique to humans--art, authorship, judgment, invention--are now co-articulated with systems of computation and planetary-scale infrastructure. The symposium brings together artists, scholars, and technologists to probe the cultural, philosophical, and ecological implications of this entanglement.

The concept of neurodiversity has shown that neurological differences such as autism, ADHD, and dyslexia are not deficits but variations that enrich collective life. Extending this to machines can be provocative: just as neurodivergence unsettles fixed definitions of intelligence, so too AI challenges anthropocentric assumptions about cognition. Yet the analogy is limited. Neurodiversity is rooted in the lived struggles of human communities, while machines neither think nor struggle. Human cognition involves perception, learning, memory, and reasoning through embodied experience. Machine cognition, by contrast, is computational pattern recognition and statistical modeling, without consciousness or lived context, and with only narrow forms of sensing.

For this reason, the symposium advances a broader framework of cognitive diversity or technodiversity--a recognition of proliferating intelligences, human, machinic, and hybrid, as part of a shared ecology. This shift calls for new models of creativity, responsibility, and collaboration that honor the irreducibility of human thought while engaging the radical alterity of machine logics.

Location: Stony Brook Simons Center for Geometry and Physics, Della Pietra Family Auditorium

This event is co organized by the Guggenheim New York

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
Abstract: Visual generation is a fundamental problem in computer vision and graphics, with applications ranging from 3D capture to image and video synthesis. Despite rapid progress in neural rendering and generative models, efficiency remains a key bottleneck: high-quality 3D reconstruction often relies on dense multi-view supervision; high-fidelity 3D synthesis requires costly optimization, training, and rendering; and modern image and video generators require substantial computation as the number of tokens grows rapidly for high-resolution generation. This dissertation focuses on efficient visual generation by improving sample efficiency in 3D reconstruction, representation efficiency in 3D generation, and computational efficiency in image and video synthesis. First, we improve the sample efficiency of neural implicit surface reconstruction. We integrate multi-view stereo probability volumes as a geometric regularizer, enabling high-quality sparse-view reconstruction from as few as three input images. Next, we introduce an explicit 3D representation for 3D generation, built from multi-view depth and RGB images with 3D Gaussian features. This design allows the model to use 2D generative priors while enforcing multi-view consistency through epipolar attention. We then address the computational bottleneck in image and video synthesis with importance-based token merging. Our method uses importance signals available during generation to preserve critical information while merging redundant tokens. Finally, we enable efficient mixed-resolution diffusion transformers via phase-aligned attention. This approach stabilizes attention under mixed-resolution token grids and unlocks high-fidelity image and video generation at reduced cost. Taken together, these contributions reduce the data requirements, representational overhead, and computational demands, thereby providing a foundation for high-quality, scalable, and efficient visual generation.

Speaker: Haoyu Wu

Location: NCS 120

You are cordially invited to attend the biweekly Brookhaven AI Mixer (BAM). BAM includes three short talks on AI research happening at BNL, followed by an open mixer over coffee and snacks for everyone to network and discuss all things AI. The first half hour will consist of presentations that will be available via ZOOM, and the second half hour will be for in person only networking.

Join us every other Tuesday at noon in CDSD's Training Room (building 725, 2nd floor) to learn about interesting AI methods and applications, engage with potential collaborators, prepare for pending FASST funding calls, and build a community of AI for Science at BNL.

Speakers

Kriti Chopra, Computing & Data Sciences (CDS)
Thomas Flynn, Computing & Data Sciences (CDS)
Wenjie Liao, Chemistry Division

Tuesday, January 7, 2025, 12:00 pm -- CDS, Bldg. 725, Training Room

Join ZoomGov Meeting: https://bnl.zoomgov.com/j/1615289117?pwd=Hqkbj9itxWrFnkhZ8rQXHPInO2gxdF.1

Meeting ID: 161 528 9117
Passcode: 991382