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
Face Editing with Machine Learning presented by Zhixin Shu

ABSTRACT: The face is the most informative feature of humans and has been a long-standing research topic in Computer Vision and Graphics. Images of faces are also ubiquitous in photography and social media, and people have devoted significant resources to capturing and editing face images. Face editing can be broadly viewed as the encoding, manipulation and the decoding of some representations for face images. The challenges are that we want to manipulate an image in a controllable way and generate results that are both desirable and as realistic as possible. This thesis explores different Machine Learning-based face-editing approaches. I discuss the role of machine learning for achieving desirable edits by learning both the physical aspects as well as the statistical manifold of human faces. In my work for eye-editing, I discuss the importance of understanding multiple physical elements of a face image, such as shape, illumination, pose, etc. In a deep-learning-based approach, I introduce image formation domain knowledge to the construction and training of a neural network. This network provides transparent access to the disentangled representations of the aforementioned physical properties. With this network, we can achieve various face editing tasks in forms of representation manipulation. After that, I introduce Deforming Autoencoders, a network that learns to disentangle shape and appearance in an unsupervised manner. This disentanglement is beneficial for the learning of some other factors of variations, such as illumination and facial expression. In an extension of Deforming Autoencoders, we incorporate non-rigid structure-from-motion to learn a 3D morphable model for faces that only requires an image set for training. At last, I describe an image-to-image network for 3D face reconstruction, which also utilizes structure-from-motion in deep learning. With real face images in training, this network not only reconstructs 3D faces more accurately than prior art but also has better generalization ability in real-life testing cases.

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

Jianda Chen, EBNN - Improving the stability and accuracy of PDE-ML hybrid AGCMs

Boyang Li, CDS - Accelerating Materials Discovery using Machine Learning

Jaehye on Do, NPP Isotopes - Using LLMs for Isotopes Research and Production

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

Meeting ID: 161 528 9117
Passcode: 991382

Join us to share your thoughts about teaching, learning, and AI!

The landscape of higher education is rapidly evolving with the integration of Artificial Intelligence (AI). Through the Institute on AI, Pedagogy, and the Curriculum with AAC&U, we are exploring ways that we can better address AI in teaching and learning. We want to hear your experiences, your concerns, and your ideas.

This is an open discussion for all faculty and staff to share their perspectives on the opportunities and challenges AI presents in our academic environment.

We'll be exploring critical questions like:

  • In the age of AI, what are the opportunities you see for enriching the classroom and curriculum? How can it enhance student learning or your professional practice?

  • What are the most significant challenges and concerns that AI raises for you regarding academics, student integrity, or your workload?

  • What resources (tools, training, technical support, policy guidance, etc.) do you need to feel confident and successful in the age of AI?

Dates/Times:

  • Tuesday, 2/3 at 2pm

  • Friday, 2/6 at 9:30am

Please register in advance for the Zoom link.

Can't Make It? Share Your Feedback!

We understand schedules are tight. If you cannot attend the live discussion, you can still share your thoughts! Join our AI Zoom Room to share your thoughts via video recording or email rose.tirotta-esposito@stonybrook.edu with your comments and ideas.

Videos will not be shared publicly and comments will only be shared in aggregate.

Your input is vital. From pedagogy to assessment, your insights will be critical. We look forward to a thoughtful and productive conversation!

  • Dr. Rose Tirotta-Esposito (Assistant Provost; Director of CELT)

  • Dr. Elizabeth Hewitt (Associate Professor in the Department of Technology and Society (DTS) in the College of Engineering and Applied Sciences)

  • Chris Kretz (Associate Librarian and Head of Academic Engagement at SBU Libraries)

  • Prof. Rajiv Lajmi (Assistant Professor in the School of Health Professions and Chair of Applied Health Informatics)

  • Dr. Matthew Salzano (Assistant Professor in the Department of Communication in the School of Communication and Journalism)

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 Ph.D. program or (2) receive permission from the instructors. Each seminar will consist of multiple short talks (around 15 minutes) by multiple students. Students can register for 1 credit for CSE656. Registered students must attend and present a minimum of 2 talks. Registered students must attend in person. Up to 3 absences will be excused. Everyone else is welcome to attend. The seminar will be taught by Prof. Chao Chen, chao.chen.1@stonybrook.edu.

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.

Abstract: Large Language Models (LLMs) have transitioned from standalone prediction interfaces into integrated systems that incorporate content protection, external knowledge retrieval, and multi-step reasoning. While these functional layers expand model capabilities, they also introduce complex, inter-component dependencies that create novel and systemic security risks. This research provides a systematic deconstruction of the structural vulnerabilities emerging across these functional layers.

In this proposal, we evaluate the security boundaries of LLM systems through three pivotal dimensions:
The Content Layer: We present Watermark under Fire, revealing the inherent fragility of content-based tracing mechanisms under adaptive perturbations and highlighting the limitations of surface-level safety measures.
The Retrieval Layer: We introduce GraphRAG under Fire to examine the security of topology-aware knowledge integration. We reveal how graph-based indexing can be exploited as a structural lever for high-success poisoning attacks.
The Reasoning Layer: We detail AutoRAN, the first framework demonstrating the hijacking of internal safety reasoning in Large Reasoning Models (LRMs). This work proves that the transparency of the reasoning process itself creates a critical and exploitable attack surface.

Collectively, these studies demonstrate a systemic failure of add-on safety mechanisms in securing the broader LLM ecosystem. By identifying recurring patterns of exploitation across different system layers, this research provides the necessary foundation for transitioning from reactive patching to a more unified and architecturally-grounded approach to AI trustworthiness.

Speaker: Jiacheng Liang

Zoom: https://stonybrook.zoom.us/j/6669990420?pwd=dkY0eEw5YXpPSWo3RUE4OE1oVW90UT09&omn=97367037382
Meeting ID: 666 999 0420
Passcode: 075299
AI + Music Seminar - The meeting will consist of introductions and organizational discussions, aimed at understanding participants' interests. We'll discuss what the seminars can focus on going forward.


Dates: 

Wednesday, March 3, 2021 - 6:00pm to 7:30pm

Location: 

Zoom - contact events@cs.stonybrook.edu for Zoom info.

Event Description: 

Women in Computer Science (WiCS), the Society of Women Engineers (SWE), and the Stony Brook Robotics Team (SBRT) are collaborating to host an event called Inspiring Women in STEM Academia: A Community Dialogue to address the lack of female representation in STEM academia. 
 

All are invited to attend so they may gain a better understanding of the challenges faced by their female colleagues and hear perspectives on how they can offer support in the workplace. Given the shockingly disproportionate number of female professionals in STEM academia, we feel that this event would be extremely beneficial for male faculty to listen to and amplify their voices.

It will begin with a discussion panel consisting of Stony Brook professors and faculty who will provide valuable insight into the issue. From there, we will split into smaller discussion groups where student and faculty attendees will be able to voice their opinions, hear about the thoughts/experiences of others, and participate in an engaging discussion with panelists.

The event will be held on March 3rd from 6:00 - 7:30 PM on Zoom.
 

The following Stony Brook faculty will be panelists:

Dr. Aruna Balasubramanian - Computer Science Professor, WiCS Advisor, WPhD Advisor

Dr. Xinwei Mao - Civil Engineering Assistant Professor

Urszula Zalewski - Director of Experiential Learning, Career Center Advisor (Healthcare)

Dr. Heather Lynch - Ecology and Evolution Professor, Lynch Lab for Quantitative Ecology

Karen Kernan - URECA Director, Simons Summer Research Program Director

Dr. Eszter Boros - Chemistry Assistant Professor, Boros Lab

Dr. Maria Nagan - Chemistry Lecturer, Nagan Research Lab