Abstract: Many scientific and engineering challenges, such as the design of materials or molecules or the control of experimental systems, rely on the existence of fast predictive models that can evaluate potential designs or control policies. Traditionally this has been accomplished through numerical simulation; more recently data-driven machine learning methods have been applied. However, both approaches leave gaps: physical modeling can be accurate and extrapolates well to previously-unstudied conditions, but it is often computationally expensive and relies on physics approximations that may not be valid. Machine learning can generalize from massive amounts of real-world or simulation data, but suffers from physical grounding and extrapolation into new regimes, as well as in settings where large data sets do not exist.
In this talk I explore an intermediate regime, which is hybrid reduced order models: fast simplified physics approximations where some of the unknown or approximated equations are replaced with data-driven machine learning components. Examples include coarse-grained models where the full macroscopic equations cannot be derived from first-principles microscopic equations, multiscale models with unknown closure terms or sub-grid parameterization schemes, and low-order or latent dynamical systems that learn governing equations on a low-dimensional reduced state space. I discuss how such reduced systems can be identified from very limited data, much less than is often needed in traditional machine learning but at much lower time-to-solution than traditional numerical modeling. This facilitates not only system design and control but also uncertainty quantification approaches that search the space of possible equations for predictive models that can explain the data. I will focus on an example from materials science concerning the design of self-assembling block copolymer nanomaterials.

Speaker: Dr. Nathan Urban, Applied Mathematics Department, Brookhaven National Laboratory

Location: Laufer 101

Zoom: https://stonybrook.zoom.us/j/96090260834?pwd=mw8QTHbMOw9oeU9hazZeoq8bN4VIfH.1
Meeting ID: 960 9026 0834 Passcode: 374969

Learn how to use AI Studio to build mini-agents that could handle repetitive tasks, save your time, and keep yourself organized.

In this workshop, you will understand the basics of the platform and trigger real-time notifications for emails or calendar events. This will give you the basis for you to create your own.

Register to attend.

Time: May 5, 2022, Thursday, 02:00 PM Eastern Time (US and Canada)
Place: New Computer Science (NCS) Room 220, and Zoom

Zoom link: https://stonybrook.zoom.us/j/95948672934?pwd=d3ZDcUJkK3VweFBDVWhIVDhtaFU2Zz09
Meeting ID:  959 4867 2934
Passcode:  082036

Title:  Generative Adversarial Learning using Optimal Transport

Abstract: 

Generative Adversarial Learning (GAL) aims to learn a target distribution in an adversarial manner. A Generative Adversarial Network (GAN) is a concrete implementation of GAL using a discriminator and a generator that play a min-max game. GANs have been used in many machine learning and computer vision applications. However, GANs are known to be hard to train, mainly because a min-max saddle point optimization problem needs to be solved in GAL. In this thesis, I investigate several methods to improve generative adversarial learning using Optimal Transport (OT). 

Previous Wasserstein GANs (WGANs) do not compute the correct Wasserstein distance to train the discriminator. To address this problem, I propose WGAN-TS that uses the L1 transport cost and computes the correct Wasserstein distance to train the discriminator. To ensure the local convergence of WGANs, I propose WGAN-QC that adopts the quadratic transport cost. I prove that WGAN-QC not only computes the correct Wasserstein distance but also converges to a local equilibrium point. To compute the Wasserstein distance over the whole dataset, I propose to use Semi-Discrete Optimal Transport (SDOT) to match noise points and the real images during GAN training. To measure the quality of an SDOT map, I use the Maximum Relative Error (MRE) and the L_1 distance between the target distribution and the transported distribution obtained by an OT map. I propose statistical methods to estimate the MRE and the L_1 distance. I propose an efficient Epoch Gradient Descent algorithm for SDOT (SDOT-EGD). To deal with the 2D special case of GAL, I propose to use OT to learn 2D distributions. In particular, I adopt OT to match persistent diagrams in training a topology-aware GAN and learn density maps in the crowd counting task. Finally, I use OT and the topological maps of the crowd to improve the crowd counting performance and propose a topology-based metric to measure the quality of the crowd density maps.

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


Abstract:
Quantum Machine Learning (QML) holds significant promise for solving computational challenges across diverse domains. However, its practical deployment is constrained by the limitations of noisy intermediate-scale quantum (NISQ) devices, including noise, limited scalability, and trainability issues in variational quantum circuits (VQCs). We introduce the multi-chip ensemble VQC framework, which partitions high-dimensional computations across smaller quantum chips to enhance scalability, trainability, and noise resilience. We show that this approach mitigates barren plateaus, reduces quantum error bias and variance, and maintains robust generalization through controlled entanglement. Designed to align with current and emerging quantum hardware, the framework demonstrates strong potential for enabling scalable QML on near-term devices, as validated by experiments on standard benchmark datasets (MNIST, FashionMNIST, CIFAR-10).

IACS Student Seminar Speaker:
Junghoon Park, Seoul National University
BA in Economics, Seoul National University, Korea
PhD Candidate for Interdisciplinary Programme in Artificial Intelligence at Seoul National University
Visiting Researcher at Brookhaven National Laboratory


Current Research Interests
Quantum Machine Learning


Recent Papers
Park, J., Cha, J., Chen, S. Y.-C., Yoo, S., & Tseng, H.-H. (2025). Addressing the Current Challenges of Quantum Machine Learning through Multi-Chip Ensembles. In Review at ICML.
Park, J., Kim, K., & Cha, J. (2025). How to Assess AI Ethics: Suggestions for Ethical Rating Agencies. In Review at IJCAI.
Park, J., Cha, J., Chen, S. Y.-C., Yoo, S., & Tseng, H.-H. (2024, 15-20 Sept.). Over the Quantum Rainbow: Explaining Hybrid Quantum Reinforcement Learning. 2024 IEEE International Conference on Quantum Computing and Engineering (QCE).
Park, J., Lee, E., Cho, G., Hwang, H., Kim, B.-G., Kim, G., Joo, Y. Y., & Cha, J. (2024). Gene-Environment Pathways to Cognitive Intelligence and Psychotic-Like Experiences in Children. eLife, 12, RP88117. DOI:10.7554/eLife.88117

This seminar will be held in person (food provided!) in the IACS Seminar Room, and online (zoom link below!)
https://stonybrook.zoom.us/j/96548538719?pwd=jBmI43H68q2UkdcRRjVbTkgrC6F942.1
Meeting ID: 965 4853 8719
Passcode: 493290

Artificial Intelligence in Management 101 - Beginner Level is a practical, hands‑on learning experience designed to help supervisors and professionals work smarter--not harder--using AI responsibly and effectively. Participants explore how AI can increase productivity, improve communication, and support performance management through real workplace examples, live demonstrations, and interactive activities.

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Discover how Google Gemini can help you draft lesson plans, generate discussion questions, design activities, and create course materials in a fraction of the time.

You have a workshop to plan, need an engaging activity, and a stack of content to create, but not enough time? Google Gemini, available through your SBU Google account, can help you brainstorm, draft, and refine lesson plans, learning objectives, discussion prompts, rubrics, and more. This session is designed for anyone who is interested in using Gemini to help generate learning activities.

In this session, you will:

  1. Access Google Gemini using your SBU Google account
  2. Write effective prompts for lesson planning and content creation
  3. Generate and refine learning objectives, discussion questions, and activities
  4. Create or improve course materials such as rubrics, instructions, and assessment ideas
  5. Apply best practices for reviewing and adapting AI-generated content

Register here.
Abstract Driving intelligence test is critical to the development and deployment of autonomous vehicles. The prevailing approach tests autonomous vehicles in life- like simulations of the naturalistic driving environment. However, due to the high dimensionality of the environment and the rareness of safety-critical events, hundreds of millions of miles would be required to demonstrate the safety performance of autonomous vehicles, which is severely inefficient. We discover that sparse but adversarial adjustments to the naturalistic driving environment, resulting in the naturalistic and adversarial driving environment, can significantly reduce the required test miles without loss of evaluation unbiasedness. By training the background vehicles to learn when to execute what adversarial maneuver, the proposed environment becomes an intelligent environment for driving intelligence testing. We demonstrate the effectiveness of the proposed environment in a highway-driving simulation. Comparing with the naturalistic driving environment, the proposed environment can accelerate the evaluation process by multiple orders of magnitude. ZOOM LINK: Meeting ID: 950 6760 3617; Passcode: 426506 https://stonybrook.zoom.us/j/95067603617?pwd=dXQybEprSkNlTFY3WHlWYjViUG95UT09 Bio Professor Henry Liu is a professor in the Department of Civil and Environmental Engineering at the University of Michigan, Ann Arbor. He is also a Research Professor at the University of Michigan Transportation Research Institute and the Director for the Center for Connected and Automated Transportation (USDOT Region 5 University Transportation Center). Prof. Liu conducts interdisciplinary research at the interface between civil and mechanical engineering. Specifically, his scholarly interests concern traffic flow monitoring, modeling, and control, as well as testing and evaluation of connected and automated vehicles. He has published more than 100 refereed journal papers and is listed as one of the top 50 leading authors in the past 50 years (1969-2019) in the prestigious Transportation Research journal. Professor Liu and his work have been widely recognized in public media for promoting smart transportation innovations. He has appeared on media outlets including CNBC, Forbes, Technode, etc. In 2019, Professor Liu was invited to testify on national transportation research agenda in front of the US House Subcommittee on Research and Technology. Professor Liu has nurtured a new generation of scholars, and some of his PhD students and postdocs have joined first class universities such as Columbia University, Purdue University, RPI, etc. Prof. Liu is the managing editor of Journal of Intelligent Transportation Systems.
Abstract: To be helpful assistants, AI agents must be aware of their own capabilitiesand limitations. This includes knowing when to answer from parametricknowledge versus using tools, when to trust tool outputs, and when toabstain or hedge. Such capabilities are hard to teach through supervisedfine-tuning because they require constructing examples that reflect theagent's specific capabilities. We therefore propose a radically new approachto teaching agents what they know: collaborative self-play. We constructmulti-agent collaborations in which the group is rewarded for collectivelyarriving at correct answers. The desired meta-knowledge emerges fromthe incentives built into the structure of the interaction. We focus on smallsocieties of agents that have access to heterogeneous tools (corpus-specificretrieval), and therefore must collaborate to maximize their success withminimal effort. Experiments show that group-level rewards for multiagent communities can induce policies that transfer to improve tool use andselective prediction in single-agent scenarios. Speaker: Syed Mostofa Monsur Location: Old Computer Science Building - CS2311