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

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.   

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.

At our Oct 7 Mixer, BNL's newly minted interim director, John Hill will be present to give opening remarks and kick us off on a new year of impactful scientific AI collaborations.

Abstract: Weather extremes and strong seasonal-to- subseasonal variability pose growing challenges to urban populations, infrastructure, and energy systems. Yet, most cities remain data deserts: routine weather observations are sparse, with stations concentrated at airports rather than within the urban core. This lack of coverage limits our ability to monitor and predict fine-scale urban weather patterns precisely where they matter most. We present a new AI-driven framework for optimal sensor placement and urban weather monitoring. Unlike traditional approaches, our method leverages physics- based simulations together with Bayesian experimental design principles, but does so using a computationally efficient variational inference strategy that makes large-scale optimization tractable. This allows us to guide sensor networks in a way that minimizes information loss while capturing spatiotemporal variability at city scales. Applied to Phoenix, Arizona, our framework outperforms random sensor placement strategies, especially when only a limited number of sensors can be deployed. Importantly, the same AI models that guide sensor placement also function as a real-time nowcasting tool, providing urban weather information over the entire domain, beyond sensor locations. Together, these capabilities offer a scalable pathway to reduce urban data deserts, enhance monitoring of weather extremes, and improve resilience planning for energy, transportation, and public health systems.

Biography: Dr. Katia Lamer is an atmospheric scientist and the Director of the Center for Multiscale Applied Sensing at Brookhaven National Laboratory. Originally from Canada, she earned her B.S. and M.S. in Atmospheric and Oceanic Sciences from McGill University and a Ph.D. in Meteorology from Penn State University. Her research focuses on atmospheric boundary layer processes and remote sensing technologies, with a strong emphasis on data science. At Brookhaven, she is known for her work with the CMAS mobile observatories and its facility that connect fundamental atmospheric science to real-world applications, improving weather prediction, environmental monitoring, and urban climate resilience. Her work has been featured in public outlets such as New Scientist and Wired. Dr. Lamer also serves as an invited member of the World Meteorological Organization's Data Assimilation and Observing Systems Working Group, and the American Meteorological Society's Boundary Layer and Turbulence Committee. puting, communications and sensing, all enabled by AI.

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

Optimization and Machine Learning - presented by Yifan Sun

Abstract: Optimization is a growing topic of interest in the machine learning community. It starts out as an option to check in Tensorflow (SGD? Adam? Adagrad?), but as we get more into the how and why of these options, we uncover many fundamental principles relating to operations research, control theory, and dynamical systems, dating back as far as the Cold World era. 

In this talk I will give a broad overview of some of the important optimization themes in machine learning. I will try to give connections between tools we are used to seeing in popular packages 
and fundamental optimization concepts like duality, convexity, contractive operators, etc. While we cannot hope to completely cover this diverse research area, I hope to provide a glimpse of this exciting research area that is permeating more and more into the machine learning world. 

Bio: Yifan Sun received her PhD in Electrical Engineering from the University of California Los Angeles in 2015, with research focusing on convex optimization and semidefinite programming. She was then Technicolor Research and Innovation, focusing on machine learning and 
data science applications. More recently, she completed two postdocs focusing on optimization, at the University of British Columbia in Vancouver, Canada and INRIA, in Paris, France.

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.

The overall purpose of this seminar is to bring together people with interests in Computer Vision theory and techniques and to examine current research issues. This course will be appropriate for people who already took a Computer Vision graduate course or already had research experience in Computer Vision. To enroll in this course, you must either: (1) be in the PhD program or (2) receive permission from the instructors.

Each seminar will consist of multiple short talks (around 10 minutes) by multiple people. Students can register for 1 credit for CSE 656. Registered students must attend and present a minimum of 2 or 3 talks. Everyone else is welcome to attend. Fill in https://forms.gle/pCVXovgfMfQwGqG38 to subscribe to our mailing list for further announcement.