Yihui Ren
AI Department, Brookhaven National Laboratory
Abstract: Artificial intelligence is rapidly changing how we approach scientific discovery and engineering design. However, realizing the potential of modern AI in scientific domains requires going beyond simply applying models developed for natural language: scientific data have distinct structures, and scientific workflows often require tight integration among learned representations, domain knowledge, simulation, and numerical optimization.
In this talk, I will present two recent efforts that explore complementary directions for building AI systems for science and engineering. First, FM4NPP investigates whether the foundation-model paradigm can scale to experimental nuclear and particle physics. Using more than 11 million particle-collision events, we develop a self-supervised learning framework for sparse detector data and demonstrate scaling up to 188 million parameters. The resulting representations generalize across diverse downstream tasks and enable data-efficient adaptation with lightweight task-specific components.
Second, I will present AutoSizer, an LLM-driven framework for automatic sizing of analog and mixed-signal circuits. Rather than asking an LLM to perform numerical optimization directly, AutoSizer uses the LLM as a reasoning and orchestration layer that interprets circuit behavior, analyzes simulation feedback, and adaptively refines the optimization process.
Together, these studies illustrate two complementary roles for modern AI in scientific workflows: learning reusable representations from complex scientific data and reasoning over computational tools to guide challenging design and optimization problems. I will discuss the lessons from these efforts and opportunities toward more general, scalable, and reusable AI systems for science and engineering.
Short bio: Dr. Yihui (Ray) Ren is a Senior Computational Scientist in AI/ML and the AI-Codesign Group Lead in the Artificial Intelligence Department within Brookhaven National Laboratory's Computing and Data Sciences Directorate. His research focuses on AI for Science, AI-hardware codesign, real-time AI systems, and the evaluation of emerging AI hardware architectures. He also develops advanced AI methodologies for nuclear safeguards applications. Dr. Ren's key contributions include unpaired domain mapping techniques to mitigate domain shift, generative surrogate modeling, object detection and video synthesis using event-based cameras, and the development of AI foundation models such as the Foundation Model for Nuclear and Particle Physics (FM4NPP). Prior to joining Brookhaven National Laboratory in 2018, he was a postdoctoral researcher at Virginia Tech. He received his Ph.D. in Physics from the University of Notre Dame in 2015.
Location: NCS 120
Keynote speakers: Christopher Nguyen (Aitomatic), Yann LeCun (AMI Labs), Akihisa Shiozaki (House of Representatives, Japan), Suresh Venkatarayalu (Honeywell), Jimmy Rhee (ASI Foundry), and Toshikazu Okuya (Commerce and Information Policy Bureau).
Register here to attend.
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.
Tuesday, January 7, 2025, 12:00 pm -- CDS, Bldg. 725, Training Room
Speakers
Maria Zawadowicz, EBNN--ML for Atmospheric Aerosol Research
Mohammad Atif, CDS--An Extensible Digital Twin Framework
Guang Zhao, CDS--Pareto Prompt Optimization
Join ZoomGov Meeting: https://bnl.zoomgov.com/j/1615289117?pwd=Hqkbj9itxWrFnkhZ8rQXHPInO2gxdF.1
Meeting ID: 161 528 9117
Passcode: 991382
Economy and Language Vitality
Abstract: In this paper I discuss some aspects of language vitality that have been overlooked in the dominant discourse on language endangerment and loss, especially the ecosystemic factors that have enabled the dominant language to prevail. These include the socioeconomic structures in which competing languages have coexisted. Globalization, national demographic size, and political power do not always explain why some languages have become extinct. One must explain the specific ecosystemic conditions in which a group has been (dis)advantaged politically and economically. Is it true that the language-shifters are ashamed of their cultural traditions and that in European colonies they have always done so to speak European languages because these are prestigious? What does the migration and contact history of mankind since the development of long-distance trade and the rise of empires tell us about language shifts and the vitality of cultures, including languages? Are Indigenous peoples the only ones who have lost their languages?
Speaker: Salikoko S. Mufwene is the Edward Carson Waller Distinguished Service Professor in the Dept. of Linguistics, the Dept. of Race, Diaspora, and Indigeneity, and the College at the University of Chicago. His research area is evolutionary linguistics, focused on the phylogenetic emergence of languages and language speciation, and on language vitality. He has authored and (co-)edited dozens of books and has published hundreds of articles, book chapters, and book reviews. He is a fellow of the Linguistic Society of America, of the American Philosophical Society, and of the American Academy of Arts and Sciences. He assumed the Chaire Mondes francophones at the Clollège de France for the 2023-24 academic year.
Faculty Response Panel:
- S. N. Sridhar (Panel Chair; SUNY Distinguished Service Professor, Asian and Asian American Studies
and Mattoo Center for India Studies) - Manisha Desai (Executive Director of Center for Changing Systems of Power; Empowerment Charitable Trust
Endowed Professor in Global Citizenship; Professor of Sociology and Women, Gender, and Sexuality Studies,
Senior Research Associate, United Nations Research Institute for Social Development) - Sara Hamideh (Associate Professor, Marine and Atmospheric Sciences)
- Janet Ward (Professor, Philosophy; Associate Provost for Arts, Humanities, and Social Sciences Initiatives)
Location: Humanities 1006
Find Shortcuts? (Naoya Inoue, http://naoya-i.github.io/)
ABSTRACT: Recent studies have suggested that natural language understanding (NLU) systems learn to exploit superficial, task-unrelated cues (a.k.a. annotation artifacts) in current datasets. This prevents the community from reliably measuring the progress of NLU systems. In this talk, I will discuss two latest studies from our research team: (i) analysis of annotation artifacts in commonsense causal reasoning and (ii) creation of benchmark for evaluating NLU systems' internal reasoning.
---------------------------------------------------------------------------------------------------------------------------------------------
---------------------------------------------------------------------------------------------------------------------------------------------
Learning graph-structured sparse models (Baojian Zhou, https://baojianzhou.github.io/)
ABSTRACT: Learning graph-structured sparse models has recently received significant attention thanks to their broad applicability to many important real-world problems. However, such models, of more effective and stronger interpretability compared with their counterparts, are difficult to learn due to optimization challenges. In this talk, we will discuss how to learn graph-structured sparse models under stochastic and online learning settings. Some interesting related problems will also be discussed.
Abstract: Yes, scalable quantum computing should actually work! Sooner than many expect, which will create a huge headache when it breaks the encryption currently used to protect the Internet. But no, we don't think quantum computing can do most of what the popular articles promise in AI and optimization and so forth. Come to this talk to learn about why!
Speaker: Scott Aaronson is Schlumberger Chair of Computer Science at the University of Texas at Austin, and founding director of its Quantum Information Center. He received his bachelor's from Cornell University and his PhD from UC Berkeley. Aaronson's research has focused mainly on the capabilities and limits of quantum computers. His first book, Quantum Computing Since Democritus, was published in 2013 by Cambridge University Press. He received the National Science Foundation's Alan T. Waterman Award, the United States PECASE Award, the Tomassoni-Chisesi Prize in Physics, and the ACM Prize in Computing, and is a Fellow of the ACM and the AAAS and a member of the National Academy of Sciences. He blogs at Shtetl-Optimized, https://www.scottaaronson.com/blog.
Location: Della Pietra Family Auditorium (SCGP 103)
Le Lu, Ph.D
Executive Director, PAII Inc
Johns Hopkins University
IEEE Fellow, MICCAI Board Member
Time: Wednesday, April 14, 2021 3:00 pm - 4:00 pm
Zoom Meeting
https://stonybrook.zoom.us/j/
Meeting ID: 956 1719 7636 Passcode: 924293
Title:
In Search of Effective and Reproducible Clinical Imaging Biomarkers for Population Health and Oncology Applications of Screening, Diagnosis and Prognosis
Bio:
Le Lu received a PhD in 2007 from Johns Hopkins University. During his first six years at Siemens, he made significant contributions to the company's CT colonography and Lung CAD product lines. From 2013 to 2017, Dr. Lu served as a staff scientist in the Radiology and Imaging Sciences department of the National Institutes of Health Clinical Center. He then went on to found Nvidia's medical image analysis group and he held the position of senior research manager until June 2018. Since then, he has been the Executive Director at PAII Inc., Bethesda Research lab, Maryland, USA which has become one of the leading industrial research labs in medical imaging. He was the main technical leader for two of the most-impactful public radiology image dataset releases (NIH ChestXray14, NIH DeepLesion 2018). He won NIH Clinical Center Director Award in 2017, NIH Mentor of the year award in 2015, and won numerous best paper awards in MICCAI and RSNA from 2016 to 2020 (over 10000 citations). In 2021, He was elected into IEEE Fellow class cited for his contribution to machine learning for cancer detection and diagnosis, and MICCAI society board member (MICCAI-Industry Workgroup Chair). He is currently an Associate Editor for IEEE Trans. Pattern Analysis and Machine Intelligence and IEEE Signal Processing Letters. He has served as an Area Chair for recent MICCAI, AAAI, CVPR, WACV, ICIP and ICHI conferences for 14 times.
Abstract:
This talk will first give an overall on the work of employing deep learning to permit novel clinical workflows in two population health tasks, namely using conventional ultrasound for liver steatosis screening and quantitative reporting; osteoporosis screening via conventional X-ray imaging and AI readers. These two tasks were generally considered as infeasible tasks for human readers, but as proved by our scientific and clinical studies and peer-reviewed publications, they are suitable for AI readers. AI can be a supplementary and useful tool to assist physicians for cheaper and more convenient/precision patient management. Next, the main part of this talk describes a roadmap on three key problems in pancreatic cancer imaging solution: early screening, precision differential diagnosis, and deep prognosis on patient survival prediction. (1) Based on a new self- learning framework, we train the pancreatic ductal adenocarcinoma (PDAC) segmentation model using a larger quantity of patients (≈1,000, four institutions), with a mix of annotated/unannotated venous or multi-phase CT images. Pseudo annotations are generated by combining two teacher models with different PDAC segmentation specialties on unannotated images, and can be further refined by a teaching assistant model that identifies associated vessels around the pancreas. Our approach makes it technically feasible for robust large-scale PDAC screening from multi-institutional multi-phase partially-annotated CT scans. (2) We propose a holistic segmentation-mesh classification network (SMCN) to provide patient-level diagnosis, by fully utilizing the geometry and location information. SMCN learns the pancreas and mass segmentation task and builds an anatomical correspondence-aware organ mesh model by progressively deforming a pancreas prototype on the raw segmentation mask. Our results are comparable to a multimodality clinical test that combines clinical, imaging, and molecular testing for clinical management of patients with cysts. (3) Accurate preoperative prognosis of resectable PDACs for personalized treatment is highly desired in clinical practice. We present a novel deep neural network for the survival prediction of resectable PDAC patients, 3D Contrast-Enhanced Convolutional Long Short-Term Memory network (CE- ConvLSTM), to derive the tumor attenuation signatures from CE-CT imaging studies. Our framework can significantly improve the prediction performances upon existing state-of-the-art survival analysis methods. This deep tumor signature has evidently added values (as a predictive biomarker) to be combined with the existing clinical staging system.
More information can be found at:
https://bmi.
This workshop focuses on how to use AI Deep Research for investigating a topic. Go from a basic search to utilize Gemini and Perplexity to find information. We will show you our steps to evaluate and gain deeper insights from the results.
Register here for the online session.