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

Chuntian Cao, CDS AID - Neural Network Potential (NNP) for Battery Electrolytes

Yeonju Go, NPP Physics - Generative AI for High-Energy Nuclear Physics

Gilchan Park, CDS AID - Graph RAG: Indexing, Retrieval and Generation

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

Meeting ID: 161 528 9117
Passcode: 991382

The Institute for AI-Driven Discovery and Innovation hosts Dr. Mary
Simoni for a talk on her music and its intersection with AI, as part
of the Music and AI Seminars series.

The event will be held on Thursday, December 10, 2020, at 3:00 PM.

Abstract: Mary Simoni, Dean of Humanities, Arts & Social Sciences at
Rensselaer Polytechnic Institute will discuss her research in the use
of computer algorithms and technology in the composition and
performance of music. The talk will feature compositions inspired by
Augmented Transition Networks (ATNs), employ motion tracking to
control synthesis parameters, and a work in progress that employs
machine learning using training data that juxtaposes classical music
with COVID-19. During this talk, participants will be introduced to
several technologies that support music information retrieval, machine
learning, and algorithmic composition such as jSymbolic, Weka, and
Common Music.

Zoom details below:
https://stonybrook.zoom.us/j/98236706900?pwd=bDFEZFZtaHBWU0cyL0wxK3UrdUpIdz09
Meeting ID: 982 3670 6900
Passcode: 133945  

Please join us this Friday, February 13th for the CSE 600 seminar given by Associate Professor Debswapna Bhattacharya, from the Department of Computer Science at Virginia Tech.

Abstract: Building a model of a biological system that can provide actionable hypotheses to form a solid foundation for experimental and theoretical analyses is one of the key challenges in biology and medicine. In this talk, I will present my group's ongoing work in developing, evaluating, and disseminating a new generation of computational methods for biomolecular modeling powered by artificial intelligence (AI) and machine learning (ML). First, I will introduce a new generation of AI/ML methods for improved modeling and characterization of protein-nucleic acid assemblies by deep graph learning using embeddings from biological large language models (LLMs) as well as geometric attention-enabled pairing of heterogeneous biological LLMs, a previously unexplored avenue. Then, I will present a novel generative deep learning model based on equivariant flow matching for end-to-end generation of all-atom RNA 3D structural ensemble. Finally, I will outline my future research directions on attaining atomic-level accuracy in computational modeling of biomolecules and their assemblies at scale.

Speaker: Debswapna Bhattacharya is an Associate Professor in the Department of Computer Science at Virginia Tech. He received his Ph.D. in Computer Science from the University of Missouri-Columbia in 2016. Before joining Virginia Tech in 2022, he was an Assistant Professor at Auburn University from 2017 to 2021. His research interests lie at the intersection of computational biology and machine learning, with a particular focus on artificial intelligence for computational structural biology, specifically in modeling and characterization of biomolecular structures and interactions. His research group has been developing novel computational and data-driven methods, software, and information systems for diverse biomolecular modeling problems, ranking among the best methods in community-wide blind assessments and serving the worldwide community of biomedical users. He received various research awards (NSF CAREER Award, NIH Maximizing Investigators' Research Award, NSF National AI Research Resource Award) and numerous institutional honors (National Distinction and Outstanding Contributor at Virginia Tech, Ginn Faculty Fellowship at Auburn University, Outstanding Engineering Faculty Award at Auburn University).
Location: NCS 120
What comes after today's large language models and deep neural networks? Join the Computing Community Consortium (CCC) for a virtual 30-min community chat led by David Jensen, CCC Council Member and lead author of the new CCC whitepaper, Envisioning Possible Futures for AI Research. Jensen will explore paradigm-shifting AI Research Futures like Neuro-Symbolic, Embodied, Multi-Agent, and Quantum AI, and then open the floor to the audience for an engaging Q&A discussion.

Register here.
Abstract: Large language models are prone to memorizing some of their training data. Memorized (and possibly sensitive) samples can then be extracted at generation time by adversarial or benign users. There is hope that model alignment---a standard training process that tunes a model to harmlessly follow user instructions---would mitigate the risk of extraction. However, we develop two novel attacks that undo a language model's alignment and recover thousands of training examples from popular proprietary aligned models such as OpenAI's ChatGPT. Our work highlights the limitations of existing safeguards to prevent training data leakage in production language models.

Speaker: Pegah Alipoormolabashi

Location: CS2311
https://meetings.cshl.edu/meetings.aspx?meet=naisys&year=20  


November 9 - 12, 2020 Virtual
Abstract Deadline: August 14, 2020


Organizers:

Raia Hadsell, DeepMind, United Kingdom
Blake Richards, Mila, Québec AI Institute, Canada
Anthony Zador, Cold Spring Harbor Laboratory

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The current COVID-19 situation is challenging and difficult for all of us - we hope this virtual conference will allow colleagues to share and discuss their latest research, while under travel and stay-at-home restrictions.

Because of the ongoing COVID-19/SARS-CoV-2 outbreak, CSHL and the organizers have now reached the difficult decision to restructure the meeting on From Neuroscience to Artificially Intelligent Systems into a virtual meeting scheduled November 9-12, 2020.  NAISys will begin at 10 am (EDT)  on Monday, November 9 and ending with an afternoon session on Thursday, November 12, 2020. Oral sessions will be confined to later morning and afternoon sessions EST to maximize access by participants from around the world. 

*********

Artificial intelligence (AI) and neural networks have long drawn on neuroscience for inspiration. However, in spite of tremendous recent advances in AI, natural intelligence is still far more adept at interacting with the real world in real-time, adapting to changes, and doing so under significant physical and energetic constraints. The goal of this meeting is to bring together researchers at the intersection of AI and neuroscience, and to identify insights from neuroscience that can help catalyze the development of next-generation artificial systems.

Abstracts are welcomed on all scientific topics related to how principles and insights from neuroscience can lead to better artificial intelligence. Topics of interest include but are not limited to network architectures, computing with spiking networks, learning algorithms, active perception, inductive bias, meta-learning, and online learning. Please note that abstracts should be ONE page (~2900 characters).   




Keynote speakers (pending reconfirmation):Yoshua Bengio, Université de Montréal
Yann Lecun, NYU/Facebook


Invited Speakers (pending reconfirmation):Kwabena Boahen, Stanford University
Dmitri Chklovskii, Simons Foundation
Anne Churchland, Cold Spring Harbor Laboratory
Claudia Clopath, Imperial College London, UK
Jim DiCarlo, MIT
Chelsea Finn, Stanford University
Surya Ganguli, Stanford University
Jeff Hawkins, Numenta
Konrad Kording, University of Pennsylvania
Timothy Lillicrap, DeepMind
Yael Niv, Princeton University
Bruno Olshausen, UC Berkeley
Cristina Savin, New York University
Terry Sejnowski, Salk Institute for Biological Studies
Sebastian Seung, Princeton University
Eero Simoncelli, New York University
Sara A. Solla, Northwestern University
David Sussillo, Google Brain
Andreas Tolias, Baylor College of Medicine


New and revised abstracts should be submitted by the resubmission deadline, Friday, August 14. Individuals originally selected for talks should assume they will still be speaking, but we may select additional talks based on the number of invited and selected speakers who cannot reconfirm.

Abstracts should contain only new and unpublished material and must be submitted electronically by the abstract deadline. Selection of material for oral and poster presentation will be made by the organizers and individual session chairs. Status (talk/poster) of abstracts will be posted on our web site as soon as decisions have been made by the organizers.

We are eager to have as many students and postdocs as possible to attend since they are likely to benefit most from this meeting. We have applied for funds from industry and foundations to partially support graduate students and postdocs. Apply in writing stating need for financial support to Catie Carr at carr@cshl.edu. Preference is given to those submitting abstracts. 

All questions pertaining to registration, fees, abstract submission or any other matters may be directed to Catie Carr at carr@cshl.edu.

We anticipate the following support :

National Science Foundation

Social Media:

The designated hashtag for this meeting is #cshlNeuroAI. Note that you must obtain permission from an individual presenter before live-tweeting or discussing his/her talk, poster, or research results on social media. Click the Policies tab above to see our full Confidentiality & Reporting Policy.


Pricing:

Virtual Academic Package: $285
Virtual Graduate Student Package: $175
Virtual Corporate Package: $425

Lab Group Discounts (not departmental or institutional discounts):

Labs registering 4 people: 20% discount off applicable fees
Labs registering 5 people: 25% discount off applicable fees
Labs registering 6 people: 30% discount off applicable fees

To be eligible for lab group discounts, please submit a list of lab members planning to attend in advance of registration to Catie Carr  to establish appropriate discounted fees. Please include a link to your lab web page for verification purposes. Prior payments will be included in the group discount calculation.

IBRO/International Brain Research Organization are generously supporting the participation of a limited number of individuals from US/Canadian Minority Serving Institutions (check eligibility): $25
Limit: 65 attendees / limit per institution: 5 (contact Catie Carr  to confirm eligibility/availability prior to registering) 

Reduced Pricing for Individuals from US/Canadian Minority Serving Institutions (check eligibility): $50

The Renaissance School Of Medicine Department of Scientific Affairs and its Single Cell Genomics facility are excited to host a special seminar and discussion on AI and single cell genomics analysis:

With the decreasing cost of sequencing, many biobanks and large research cohorts have moved to whole genome sequencing (WGS) and single-cell RNA-seq. However, making use of this deluge of data remains a challenge. I will discuss statistical and deep learning approaches that we are exploring to address the challenge of noncoding variant interpretation, including our work as part of the Alzheimer's disease sequencing project.

Speaker: David A. Knowles, PhD. Asst. Professor of Computer Science, Interdisciplinary Appointee in Systems Biology, Columbia University Core Faculty Member, New York Genome Center

Join us in person: Health Science Tower Level 3, Lecture Hall 5
I will be holding an informal 2-week short optimization course, to try
to cover a few important proofs in the field. The goal will be depth
over breadth, with focus on:

 - convergence proofs for gradient descent and stochastic gradient descent
 - energy functions and continuous time optimization
 - estimate sequences and Nesterov acceleration

and, time permitting, additional topics like variance reduction,
quasi-Newton methods, and Frank-Wolfe methods. If we go super fast, we
can spend a few days at the end brainstorming interesting research
project ideas.

Details: NCS 220 6:15pm-7:45pm, Monday-Friday, Feb 7-Feb 18.

In person only, since I plan to use the whiteboard (but may be recorded)

More details will be uploaded here (notes, specific schedule):
https://sites.google.com/view/optimization-short-course/home