Description:

Curious about what AI image generation tools are out there and how they work? Come down to the library Galleria space (outside the Central Reading Room) to see some demonstrations and learn more about them.

Librarians Chris Kretz and Ahmad Pratama, along with David Ecker of DoIT, will be hosting Explore AI demos from Monday - Wednesday this week on different topics. Whether you're new to AI or an experienced user, stop by and take a look!

Location: Library Galleria

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. 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.

We meet every other Tuesday at noon in CDSD's Training Room (building 725, room 2-124) 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.

Abstract: Identifying model Hamiltonians is a vital step toward creating predictive models of materials. W​e combined Bayesian optimization with the EDRIXS numerical package to infer Hamiltonian parameters from resonant inelastic X-ray scattering (RIXS) spectra within the single atom approximation. To evaluate the efficacy of our method, we tested it on experimental RIXS spectra for several materials and demonstrated that it can reproduce results obtained from hand-fitted parameters to a precision similar to expert human analysis while providing a more systematic mapping of parameter space. Our work provides a key first step toward solving the inverse scattering problem to extract effective multi- orbital models from information-dense RIXS measurements, which can be applied to a host of quantum materials.

Biography: Marton Lajer is a postdoctoral researcher at the Condensed Matter Physics and Materials Science Department, Brookhaven National Laboratory. Marton​ obtained his PhD in theoretical physics at the Eotvos Lorand University, Hungary, in 2021. He was a​ junior research fellow at the Wigner Research Centre for Physics in Budapest before joining BNL in September 2022. His background spans various analytical and performance-critical numerical methods, mostly in the context of low- dimensional quantum field theories and quantum many-body systems. His research currently focuses on incorporating AI-enhanced methods to various problems in inelastic spectroscopy.

In addition to our speaker, we will have a number of CDS staff in attendance with expertise in AI methods and applications including image analysis, foundation models development, and inverse problem solving.

Location: CDS, Bldg. 725, Training Room

Join ZoomGov Meeting: https://bnl.zoomgov.com/j/1604383624?pwd=ffQ5cUPNxTI7nzClKQO6cnsNbhF9Vf.1

Meeting ID: 160 438 3624
Passcode: 558449

Please Note: Due to a funding shortfall, we are for the time being no longer able to provide pizza and sodas for these events. We will have coffee though, and all are of course welcome to bring their lunch.


https://stonybrook.zoom.us/j/94414957054?pwd=V1JMc2EwSnVGMFdaUlNobE9DSHU4dz09#success
ID: 94414957054
Password: 094758

Speaker: Heather J. Lynch


Bio:  Dr. Heather J. Lynch is an Associate Professor of Ecology & Evolution at Stony Brook University. Prior to Stony Brook, Dr. Lynch was an Adjunct Professor of Applied Math and Statistics at UC Santa Cruz and a Research Scientist in the Biology Department at the University Maryland. Dr. Lynch received her A.B. in Physics from Princeton University in 2000, an A.M. in Physics from Harvard University in 2004, and a Ph.D. in Organismal and Evolutionary Biology from Harvard University in 2006. Dr. Lynch's research is focused on spatial population dynamics of Antarctic penguins, with a particular focus on statistical and mathematical models to integrate patchy time series with remote sensing imagery. These data will allow Dr. Lynch and colleagues to develop mathematical models to explore how coloniality constrains the colonization and extinction of individual habitat patches and, ultimately, the metapopulation dynamics of colonial seabirds.   
West Campus - SAC- Student Activities Center - Ballrooms A & B 100 Nicolls Road Stony Brook NY 11794 Job Fair.jpg The Career Center invites Alumni Employers and Job Seekers to the IT/Computer Science Job and Internship Fair this spring. Job Seekers: A job fair is an opportunity for you to present yourself professionally in person to a potential employer, while showcasing your communication skills. Get more information Alumni Employers: Held in both the fall and spring semesters, this event is ideal for employers looking to fill internship, co-op, part-time and full-time opportunities in the field of information technology (i.e. Software Engineering, Network Administration, Web Development, etc.). Register here to recruit top SBU talent.
Abstract: DeepSeek-R1-Zero has shown that reinforcement learning (RL) at scale can directly enhance the reasoning capabilities of LLMs without supervised fine-tuning. In this work, we critically examine R1-Zero-like training by analyzing its two core components: base models and RL. We investigate a wide range of base models, including DeepSeek-V3-Base, to understand how pretraining characteristics influence RL performance. Our analysis reveals that DeepSeek-V3-Base already exhibit ''Aha moment'', while Qwen2.5 base models demonstrate strong reasoning capabilities even without prompt templates, suggesting potential pretraining biases. Additionally, we identify an optimization bias in Group Relative Policy Optimization (GRPO), which artificially increases response length (especially for incorrect outputs) during training. To address this, we introduce Dr. GRPO, an unbiased optimization method that improves token efficiency while maintaining reasoning performance. Leveraging these insights, we present a minimalist R1-Zero recipe that achieves 43.3% accuracy on AIME 2024 with a 7B base model, establishing a new state-of-the-art.

Speaker: Md. Saqib Hasan

Location: CS2311

Abstract: Millions of individuals living in disadvantaged communities are burdened by poverty, illegal drug activities, health concerns, and the lack of reliable and affordable access to facilities (e.g., schools, hospitals, and transit stations). To address these societal problems efficiently with broad support, initiatives have called to engage agents (e.g., residents, community leaders, or stakeholders) and consider their preferences on community improvement decisions to make collective community decisions. In this talk, we will focus on our ongoing AI-empowered collective decision-making approaches to improve the accessibility of individuals to facilities by (a) locating facilities to provide essential services and (b) strengthening existing infrastructures via structural modifications (e.g., constructing new roads, bridges, multi-use paths, or shuttle services) subject to individuals' preferences on the locations of the facilities and which communities to improve access, respectively. In particular, we will discuss our (theoretical and algorithmic) studies on modeling these approaches under several settings (e.g., accounting for fairness and agent preferences) and designing fair, transparent, strategy proof, and (approximately) optimal mechanisms to elicit (true) individual preferences and determine collective community decisions in order to improve facility accessibility. Finally, we will discuss other ongoing and future collective decision-making efforts in urban planning and public health (i.e., our recent studies on substance use research) to improve communities.

Bio: Hau Chan is an assistant professor in the School of Computing at the University of Nebraska-Lincoln. He received his Ph.D. in Computer Science from Stony Brook University in 2015 and completed three years of Postdoctoral Fellowships, including at the Laboratory for Innovation Science at Harvard University in 2018. His main research lies in multi-agent aspects of AI for Society and Social Good, focusing on developing modeling and algorithmic foundations for tackling societal problems involving agents and predicting agent behavior in societal contexts, leveraging AI, game theory, mechanism design, and machine learning to better inform policymaking and (collective) decision-making. His team has been addressing societal challenges and fairness issues in various domains, including security (e.g., reducing vulnerability), public health (e.g., reducing substance use and homelessness), and urban planning (e.g., improving accessibility to public facilities), collaborating with domain experts. His research has been supported by NSF, NIH, and USCYBERCOM. He has received several Best Paper Awards at SDM and AAMAS and distinguished/outstanding SPC/PC member recognitions at IJCAI and WSDM. He has given tutorials and talks on computational game theory and mechanism design at venues such as AAMAS and IJCAI, including an Early Career Spotlight at IJCAI 2022. He has served as co-chairs for the AI and Social Good Track, Demonstration Track, Student Activities, Doctoral Consortium, Job Fair, Scholarships, Finance, and Diversity & Inclusion Activities at AAAI, AAMAS, and IJCAI.

Location: Old Computer Science, room 1310

Join Zoom Meeting
https://stonybrook.zoom.us/j/98079526509?pwd=Wkt5eURhVDN5VE56TUloS2h5V1Jodz09

Meeting ID: 980 7952 6509
Passcode: 949941



Abstract Over the last decade, artificial neural networks have undergone a revolution, catalyzed by better
supervised learning algorithms. However, in stark contrast to young animals (including humans), training such
networks requires enormous numbers of labeled examples, leading to the belief that animals must rely instead
mainly on unsupervised learning. The reason is that most animal behavior is not the result of clever learning
algorithms--supervised or unsupervised-- but is encoded in the genome. Specifically, animals are born with
highly structured brain connectivity, which enables them to learn very rapidly. Because the wiring diagram is
far too complex to be specified explicitly in the genome, it must be compressed through a genomic
bottleneck. I will describe results showing how the genomic bottleneck algorithm can lead to dramatic
compression of networks and better generalization, particularly for rapid transfer in supervised and
reinforcement learning.

Anthony Zador is professor of neuroscience at CSHL.

Talk Title: Knowledge-enhanced LLMs and Human-AI Collaboration Frameworks for Creativity Support


Abstract:

Large language models (LLMs) constitute a paradigm shift in Natural Language Processing and Artificial Intelligence. To build AI systems that are human-centered, I propose we need knowledge-aware models and human-AI collaboration frameworks to help them solve tasks ultimately aligning these models better with human values. In this talk, I will discuss my research agenda for human-centered AI with a case study on creativity that focuses on how to augment LMs with external knowledge, build effective human-AI collaboration frameworks as well as theoretically grounded robust evaluation protocols for measuring capabilities of NLG systems. I will begin by describing knowledge-enhanced methods for creative text generation such as metaphors. Next, I will describe how content creators can collaborate and benefit from the creative capabilities of text-to-image-based AI models. Finally, I will focus on the design and development of theoretically grounded evaluation protocols to benchmark the creative capabilities of Large Language Models in both producing as well as assessing creative text. I will end this talk by highlighting the current limitations of existing models and future directions toward building better models that will enable efficient and trustworthy human-AI collaboration systems.


Bio:

Tuhin Chakrabarty is a final-year Ph.D. candidate in the Natural Language Processing group within the Computer Science department at Columbia University. His research is supported by the Columbia Center of Artificial Intelligence & Technology (CAIT) & an Amazon Science Ph.D. Fellowship. He was also a Computational Journalism fellow at NYTimes R&D and an intern at the Allen Institute of Artificial Intelligence, Salesforce Research, and Deepmind. His research interests are broadly in Natural Language Processing, Computer Vision, and Human-Computer Interaction with a special focus on Human-Centered Methods for Understanding, Generation, and Evaluation of Creativity. His work has been recognized at top natural language processing and human-computer interaction conferences and journals such as ACL, NAACL, EMNLP, TACL, and CHI. He has been involved in organizing several workshops and tutorials at NLP conferences such Figurative Language Processing workshop at EMNLP 2022, NAACL 2024, and the tutorial on Creative Text Generation at EMNLP 2023. His work on AI and creativity has been mentioned in mainstream news media such as The Hollywood Reporter and more recently The Washington Post.

Join Zoom Meeting https://stonybrook.zoom.us/j/97103601583?pwd=TnpGMXdpeEd1N0hZcXppS1BLNFJhZz09 (ID: 97103601583, passcode: 004031) Join by phone (US) +1 646-931-3860 (passcode: 004031) Joining instructions: https://www.google.com/url?q=https://applications.zoom.us/addon/invitation/detail?meetingUuid%3DILacj94mRvSXgTYt0Cqs1w%253D%253D%26signature%3D9f2f1e7e603bbcb9034724d084eea8846c19a38b7436180170dfc3f1d718b425%26v%3D1&sa=D&source=calendar&usg=AOvVaw3MsNgLSPMRl8L5i6BosYrB Meeting host: H.Andrew.Schwartz@stonybrook.edu

Join Zoom Meeting:
https://stonybrook.zoom.us/j/97103601583?pwd=TnpGMXdpeEd1N0hZcXppS1BLNFJhZz09