Abstract:
Quantifying similarity is a central notion in science and data analysis, pervading everything from phylogenetic trees to the foundation of clustering. Unfortunately, despite being examined and applied for decades, traditional similarity and distance metrics have fundamental drawbacks. The key problem is that all of them are only defined over pairs of objects, so they scale quadratically when one tries to compare N objects. The present explosion in the amount of data available to us requires new ways to process information, and while some current algorithms can handle millions of points, we need alternatives applicable to billions. This is what motivated us to develop a new framework that can compare any number of objects at the same time. With this, we achieve an unprecedented linear scaling when comparing multiple objects. Here we will discuss the main properties of this formalism, along with its applications in drug design and to the analysis of Molecular Dynamics (MD) simulations. Our indices have proven to be incredibly versatile when applied to chemical space exploration and visualization, allowing us to rigorously quantify the chemical diversity of very large molecular libraries. This has led to the creation of several algorithms to sample important regions in chemical space, including a more efficient way of identifying the prevalence of activity cliffs. Additionally, our indices provide a convenient route to sample complex MD trajectories, allowing to identify representative structures very efficiently. Moreover, we can also cluster biological ensembles in a more robust way than with standard algorithms, which has led to our group's work on MDANCE, a very flexible and efficient open-source clustering module. Drop by if you want to know how we clustered one billion molecules!
Speaker:
Assistant Professor, Department of Chemistry and Quantum Theory Project
University of Florida, Gainesville
Website: https://quintana.chem.ufl.edu/
Location:
Laufer Center Lecture Hall 101
Abstract:
Artificial intelligence (AI)-based methods and computational materials science continue to make inroads into accelerated materials design and development. I will review Al-enabled advances made in the subfield of polymer informatics, with a particular focus on the design of application-specific practical polymeric materials. I will describe exemplar design attempts within a few critical and emerging application spaces, including materials designs for storing, producing, and conserving energy, and those that can prepare us for a sustainable economy powered by recyclable and/or biodegradable polymers. Al- powered workflows help efficiently search the staggeringly large chemical and configurational space of materials, using modern machine-learning (ML) algorithms to solve forward and inverse materials design problems. A practical informatics-based design protocol involves creating a set of application-specific target property criteria, building ML model predictors for those relevant target properties, enumerating or generating a tangible population of viable polymers, and selecting candidates that meet design recommendations. The protocol will be demonstrated for several energy and sustainability-related applications. Finally, I will offer an outlook on the lingering obstacles that must be overcome to achieve widespread adoption of informatics-driven protocols in industrial-scale materials development.
Speaker Bio:
Prof. Ramprasad is the Regents' Entrepreneur, Michael E. Tennenbaum Family Chair and Georgia Research Alliance Eminent Scholar in the School of Materials Science & Engineering at the Georgia Institute of Technology. He is also the CEO and co-founder of Matmerize, Inc. His area of expertise is the development and application of computational and machine learning tools to accelerate sustainable materials development aimed at energy production, storage and utilization. Prof. Ramprasad received his B. Tech. in Metallurgical Engineering at the Indian Institute of Technology, Madras, India, an M.S. degree in Materials Science & Engineering at the Washington State University, and a Ph.D. degree also in Materials Science & Engineering at the University of Illinois, Urbana-Champaign.
Prof. Ramprasad is a Fellow of the Materials Research Society, a Fellow of the American Physical Society, an elected member of the Connecticut Academy of Science and Engineering, and the recipient of the Alexander von Humboldt Fellowship and the Max Planck Society Fellowship for Distinguished Scientists. He has authored or co-authored over 300 peer-reviewed journal articles, 8 book chapters and 8 patents, and has delivered over 300 invited talks at Universities and Conferences worldwide. He is a member of the Editorial Advisory Boards of npj Computational Materials, ACS Materials Letters and Journal of Physical Chemistry A/B/C. He created and chaired the inaugural 2022 Gordon Research Conference on Computational Materials Science and Engineering.
Location: Room 301, Engineering Building
Artificial intelligence (AI)-based methods and computational materials science continue to make inroads into accelerated materials design and development. I will review Al-enabled advances made in the subfield of polymer informatics, with a particular focus on the design of application-specific practical polymeric materials. I will describe exemplar design attempts within a few critical and emerging application spaces, including materials designs for storing, producing, and conserving energy, and those that can prepare us for a sustainable economy powered by recyclable and/or biodegradable polymers. Al- powered workflows help efficiently search the staggeringly large chemical and configurational space of materials, using modern machine-learning (ML) algorithms to solve forward and inverse materials design problems. A practical informatics-based design protocol involves creating a set of application-specific target property criteria, building ML model predictors for those relevant target properties, enumerating or generating a tangible population of viable polymers, and selecting candidates that meet design recommendations. The protocol will be demonstrated for several energy and sustainability-related applications. Finally, I will offer an outlook on the lingering obstacles that must be overcome to achieve widespread adoption of informatics-driven protocols in industrial-scale materials development.
Speaker Bio:
Prof. Ramprasad is the Regents' Entrepreneur, Michael E. Tennenbaum Family Chair and Georgia Research Alliance Eminent Scholar in the School of Materials Science & Engineering at the Georgia Institute of Technology. He is also the CEO and co-founder of Matmerize, Inc. His area of expertise is the development and application of computational and machine learning tools to accelerate sustainable materials development aimed at energy production, storage and utilization. Prof. Ramprasad received his B. Tech. in Metallurgical Engineering at the Indian Institute of Technology, Madras, India, an M.S. degree in Materials Science & Engineering at the Washington State University, and a Ph.D. degree also in Materials Science & Engineering at the University of Illinois, Urbana-Champaign.
Prof. Ramprasad is a Fellow of the Materials Research Society, a Fellow of the American Physical Society, an elected member of the Connecticut Academy of Science and Engineering, and the recipient of the Alexander von Humboldt Fellowship and the Max Planck Society Fellowship for Distinguished Scientists. He has authored or co-authored over 300 peer-reviewed journal articles, 8 book chapters and 8 patents, and has delivered over 300 invited talks at Universities and Conferences worldwide. He is a member of the Editorial Advisory Boards of npj Computational Materials, ACS Materials Letters and Journal of Physical Chemistry A/B/C. He created and chaired the inaugural 2022 Gordon Research Conference on Computational Materials Science and Engineering.
Location: Room 301, Engineering Building
Prepare your Business for the AI-driven future with DocItUSA's Document Management Solutions.
In today's digital world, businesses need to leverage the benefits of the Digital Cloud Age by streamlining Document Organization, Storage, and Accessibility.
Michael Feingold, of Digital Onesource Consulting Solutions/DOCS Consulting, Inc. will show you how DocItUSA equips your company with the tools to efficiently capture, classify, and retrieve documents and enable seamless AI integration.
https://nysbdc.ecenterdirect.com/events/1019400
In today's digital world, businesses need to leverage the benefits of the Digital Cloud Age by streamlining Document Organization, Storage, and Accessibility.
Michael Feingold, of Digital Onesource Consulting Solutions/DOCS Consulting, Inc. will show you how DocItUSA equips your company with the tools to efficiently capture, classify, and retrieve documents and enable seamless AI integration.
https://nysbdc.ecenterdirect.com/events/1019400
Communication-Efficient Heterogeneity-Aware Machine Learning System and Architecture by Xuehai Qian
ABSTRACT: The key success of deep learning is the increasing size of models that can achieve high accuracy. At the same time, it is difficult to train the complex models with large data sets. Therefore, it is crucial to accelerate training with distributed systems and architectures, where communication and heterogeneity are two key challenges. In this talk, I will present two heterogeneity-aware decentralized training protocols without communication bottleneck. Specifically, Hop supports arbitrary iteration gap between workers by novel queue-based synchronization which can tolerate heterogeneity with system techniques. Prague uses randomized communication to tolerate heterogeneity with a new training algorithm based on partial reduce -- an efficient communication primitive. If time permits, I will present the systematic tensor partitioning for training on heterogeneous accelerator arrays (e.g., GPU/TPU). We believe that our principled approaches are crucial for achieving high-performance and efficient distributed training.
BIO: Xuehai Qian is an assistant professor at University of Southern California. His research interests include domain-specific systems and architectures, performance tuning and resource management of cloud systems and parallel computer architectures. He received his PhD from the University of Illinois Urbana Champaign and was a postdoc at UC Berkeley. He is the recipient of W.J Poppelbaum Memorial Award at UIUC, NSF CRII and CAREER Award, and the inaugural ACSIC (American Chinese Scholar In Computing) Rising Star Award.
ABSTRACT: The key success of deep learning is the increasing size of models that can achieve high accuracy. At the same time, it is difficult to train the complex models with large data sets. Therefore, it is crucial to accelerate training with distributed systems and architectures, where communication and heterogeneity are two key challenges. In this talk, I will present two heterogeneity-aware decentralized training protocols without communication bottleneck. Specifically, Hop supports arbitrary iteration gap between workers by novel queue-based synchronization which can tolerate heterogeneity with system techniques. Prague uses randomized communication to tolerate heterogeneity with a new training algorithm based on partial reduce -- an efficient communication primitive. If time permits, I will present the systematic tensor partitioning for training on heterogeneous accelerator arrays (e.g., GPU/TPU). We believe that our principled approaches are crucial for achieving high-performance and efficient distributed training.
BIO: Xuehai Qian is an assistant professor at University of Southern California. His research interests include domain-specific systems and architectures, performance tuning and resource management of cloud systems and parallel computer architectures. He received his PhD from the University of Illinois Urbana Champaign and was a postdoc at UC Berkeley. He is the recipient of W.J Poppelbaum Memorial Award at UIUC, NSF CRII and CAREER Award, and the inaugural ACSIC (American Chinese Scholar In Computing) Rising Star Award.
The Collective Surgical Consciousness: Artificial Intelligence & the Future of Surgery
Guest speaker Doctor Ozanan Meireles, the Director of the Surgical AI and Innovation Lab at Massachusetts General Hospital and a faculty member at Harvard Medical School, presents The Collective Surgical Consciousness: Artificial Intelligence & the Future of Surgery.
Objectives:
* Become familiar with the subfields of AI used in surgery
* Understand the importance of a potential paradigm shift in surgical practice, training, and continue medical development
* The importance of data acquisition, sharing and ownership, and development of machine learning algorithms
AI3, SBU Libraries and IACS present
at International Love Data Week
sponsored by The Office of the Provost and
Educational and Institutional Effectiveness (EIE)
Special Talk and Panel Discussion
How I Learned to Stop Worrying and Love AI (For Now)
with Paul Fain from The Job and Work Shift
A reporter's take on what we know--and what we don't know--about AI's emerging impacts on the labor market. The discussion will include the latest research from economists and the AI labs themselves about how workers are using AI, and current thinking among experts on how the tech's rapid deployment will play out across job roles, industries, and regions.
Limited Seats!
Registration is required.
at International Love Data Week
sponsored by The Office of the Provost and
Educational and Institutional Effectiveness (EIE)
Special Talk and Panel Discussion
How I Learned to Stop Worrying and Love AI (For Now)
with Paul Fain from The Job and Work Shift
A reporter's take on what we know--and what we don't know--about AI's emerging impacts on the labor market. The discussion will include the latest research from economists and the AI labs themselves about how workers are using AI, and current thinking among experts on how the tech's rapid deployment will play out across job roles, industries, and regions.
Panel discussion to follow with:
- Lav Varshney, Della Pietra Infinity Professor and inaugural director of the AI Innovation Institute
- Nicholas Johnson, Director of AI, SBU Libraries
- Marianna Savoca, Associate Vice President for Career Readiness and Experiential Education
Limited Seats!
Registration is required.
The Department of AI and Society (AIS) at the University at Buffalo is hosting a two-day AI and Society Workshop focused on building AI systems by society, for society. This workshop brings together researchers and community organizers to explore how AI systems can be developed through meaningful collaboration across disciplines.
Topics include:
- Labor and AI
- Public services and AI
- Community-centered AI systems
- Intersections of humanities, social sciences, arts, and computing
The vision of UB's Department of AI and Society is to create a future where AI systems are built by society, for society. AIS centers community engagement at every stage of AI development through collaboration across disciplines and sectors. AIS was established with a $5 million grant from SUNY, and this workshop is made possible through that support.
Who Should Attend?
- Researchers
- Students
- Community organizers
- Practitioners interested in AI's societal impact
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.
Embodied Intelligence at Scientific User Facilities
Abstract: This presentation explores the active work integrating artificial intelligence and robotics at the National Synchrotron Light Source II, and a perspective for the future. Through various case studies, we highlight the optimization of operations, improved experimental outcomes, and the orchestration of distributed multimodal experiments. This ongoing development includes collaborators from across the light and neutron sources in the DOE complex. We will elaborate on the open-source Bluesky project, and its capabilities to support adaptive and autonomous experiments. Additionally, we will discuss how Bluesky can be integrated with open-source robotic control software to unlock new flexible automation for autonomous scientific research, which scales to new experiments and continues to leverage human ingenuity.
Biography: Dr. Phillip M. Maffettone is an Associate Computational Scientist in the Data Science and Systems Integration Division at NSLS-II. His research focuses on accelerating scientific discovery at user facilities through the integration of robotics, artificial intelligence (AI), and advanced experiment orchestration systems. He leads the N3XTware project, constructing the software architecture for the next 12 beamlines to be built at NSLS-II. Prior to this he built the brain on the world's first mobile robotic scientist at the University of Liverpool, and later spearheaded the machine learning platform for a biotechnology start-up, BigHat Biosciences. He holds a DPhil in Inorganic Chemistry from the University of Oxford and a B.S. in Chemical Engineering from the University at Buffalo.
Location: CDS, Bldg. 725, Training Room
Link: https://bnl.zoomgov.com/j/16049713 31?pwd=nc5CV3cOFrdYxordFieP W07tIDmwYb.1
Meeting ID: 160 497 1331
Passcode: 289875
Stony Brook Radiology Department Invites you to the AI Research Seminar
Abstract: This talk traces the evolution of AI in neuroradiology through three connected stages: Precision Care, General Medical Intelligence, and Discovery. It moves from multimodal deep learning for stroke outcome prediction, to large-scale foundation models and agent-based medical AI, to imaging biomarkers such as brain aging derived from routine scans. It closes with early-stage work on Galen, a framework for reproducible, scalable, and transparent biomedical imaging AI development and analysis.
Speaker: Yongkai Liu, PhD, Instructor, Department of Radiology (Neuroimaging & Neurointervention), Stanford University
Join Microsoft Teams: https://teams.microsoft.com/meet/221225483255320?p=Anban9lwgOgAq3jaHj
Meeting ID: 221 225 483 255 320
Passcode: kJ7fF3ou
Abstract: This talk traces the evolution of AI in neuroradiology through three connected stages: Precision Care, General Medical Intelligence, and Discovery. It moves from multimodal deep learning for stroke outcome prediction, to large-scale foundation models and agent-based medical AI, to imaging biomarkers such as brain aging derived from routine scans. It closes with early-stage work on Galen, a framework for reproducible, scalable, and transparent biomedical imaging AI development and analysis.
Speaker: Yongkai Liu, PhD, Instructor, Department of Radiology (Neuroimaging & Neurointervention), Stanford University
Join Microsoft Teams: https://teams.microsoft.com/meet/221225483255320?p=Anban9lwgOgAq3jaHj
Meeting ID: 221 225 483 255 320
Passcode: kJ7fF3ou
As part of a grant project funded by the AI3 Institute, a group of instructors participated in a faculty development program, Fostering Writing-to-Learn Skills with Critical AI Literacy: A Faculty Development and Student Support Program. This program was developed to support instructors across campus with navigating/integrating AI in their courses specifically around writing intensive/involved assignments. We would like to invite anyone interested to the culmination of this program, a mini-symposium, where the participants will share practical changes they made or are making around writing intensive/involved assignments and AI.
Location: Wang 201
A light lunch will be served. Please register by Friday, November 7th.