AI Innovation Institute

Educational objectives:

1. Describe how bias enters AI systems across the development pipeline, using aesthetic facialevaluation as a case study, and identify mitigation strategies applicable at each stage.
2. Apply the PROBAST+AI risk-of-bias framework to critically appraise the methodologicalquality of a published prediction model using regression or machine learning methods.
3. Compare the discriminative performance of multiple modeling architectures against anexisting clinical risk calculator on a large benchmarking dataset.
4. Recognize the complementary roles of clinical expertise and informatics methodology indefining clinically meaningful AI project objectives, predictors, and evaluation criteria.

Speaker: Dr. Anisha R. Kumar, MD Clinical Assistant Professor, Surgery and Biomedical Informatics, Stony Brook University School of Medicine


Location: MART Building, Room 7M-0602 (7th Floor)

Remote Access: https://stonybrook.zoom.us/j/95617197636?pwd=KytzZ2pVRG9SZGpKZUtpNXJISjNjZz09Meeting ID: 95617197636 | Passcode: 924293

Qualitative data can be challenging to analyze and interpret effectively. In this workshop, SBU Libraries' Data Literacies Lead, Ahmad Pratama will show you how to extract meaningful insights from textual data, including understanding sentiment trends. Learn to explore qualitative data with Python using word clouds, basic natural language processing (NLP) techniques, and lexicon-based sentiment analysis with VADER.
RSVP via link: https://t.e2ma.net/click/t70ivh/5wwlu4oe/hy5q96

Abstract:
Artificial intelligence (AI)-based methods and computational materials science continue to make inroads into accelerated materials design and development. I will review Al-enabled advances made in the subfield of polymer informatics, with a particular focus on the design of application-specific practical polymeric materials. I will describe exemplar design attempts within a few critical and emerging application spaces, including materials designs for storing, producing, and conserving energy, and those that can prepare us for a sustainable economy powered by recyclable and/or biodegradable polymers. Al- powered workflows help efficiently search the staggeringly large chemical and configurational space of materials, using modern machine-learning (ML) algorithms to solve forward and inverse materials design problems. A practical informatics-based design protocol involves creating a set of application-specific target property criteria, building ML model predictors for those relevant target properties, enumerating or generating a tangible population of viable polymers, and selecting candidates that meet design recommendations. The protocol will be demonstrated for several energy and sustainability-related applications. Finally, I will offer an outlook on the lingering obstacles that must be overcome to achieve widespread adoption of informatics-driven protocols in industrial-scale materials development.

Speaker Bio:
Prof. Ramprasad is the Regents' Entrepreneur, Michael E. Tennenbaum Family Chair and Georgia Research Alliance Eminent Scholar in the School of Materials Science & Engineering at the Georgia Institute of Technology. He is also the CEO and co-founder of Matmerize, Inc. His area of expertise is the development and application of computational and machine learning tools to accelerate sustainable materials development aimed at energy production, storage and utilization. Prof. Ramprasad received his B. Tech. in Metallurgical Engineering at the Indian Institute of Technology, Madras, India, an M.S. degree in Materials Science & Engineering at the Washington State University, and a Ph.D. degree also in Materials Science & Engineering at the University of Illinois, Urbana-Champaign.
Prof. Ramprasad is a Fellow of the Materials Research Society, a Fellow of the American Physical Society, an elected member of the Connecticut Academy of Science and Engineering, and the recipient of the Alexander von Humboldt Fellowship and the Max Planck Society Fellowship for Distinguished Scientists. He has authored or co-authored over 300 peer-reviewed journal articles, 8 book chapters and 8 patents, and has delivered over 300 invited talks at Universities and Conferences worldwide. He is a member of the Editorial Advisory Boards of npj Computational Materials, ACS Materials Letters and Journal of Physical Chemistry A/B/C. He created and chaired the inaugural 2022 Gordon Research Conference on Computational Materials Science and Engineering.

Location: Room 301, Engineering Building

The biennial conference takes place Sun. Oct 19th through Thu the 23rd, 2025 at the Honolulu Convention Center.
October 19 - 20: Workshops
October 21 - 23: Main Conference
More information can be found here.

To truly understand human language, we must look at words in the context of the human generating the language. Factors such as demographics, personality, modes of communication, and emotional states have shown to play a crucial role in NLP models pre-LLMs era. Steps of mathematically defining the inclusion of human context in language modeling and more will be discussed with Nikita Soni, a PhD student at Stony Brook University co-advised by H. Andrew Schwartz and Niranjan Balasubramanian. She is the lead organizer of the workshop on human-centered large language modeling.

Please register for the STEM Speaker Series Zoom event here

Please RSVP for the STEM Speaker Series in-person event here

Join a collaborative workshop series designed to help you redesign writing-intensive assignments in the age of AI. The program includes four self-paced modules, weekly interactive workshops (September 18, 25, and October 2, 9), student resources, and a SUNY-wide virtual symposium. Applications are accepted through August 31.

https://you.stonybrook.edu/campusannouncements/2026/06/22/june-22-2026/

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 15 minutes) by multiple students. Students can register for 1 credit for CSE656. Registered students must attend and present a minimum of 2 talks. Everyone else is welcome to attend. Fill in https://forms.gle/q6UG9ygauLp2a8Po8 to subscribe to our mailing list for further announcement.

Abstract: Recent studies have highlighted the vulnerability of Natural Language Processing (NLP) and Vision-Language Models (VLMs) to backdoor attacks, posing significant security risks. Understanding these attack strategies is crucial for assessing model robustness and developing effective defenses. This thesis proposal aims to investigate the vulnerability of language and vision-language models, analyze abnormal behaviors in backdoor-attacked models, and develop defense methods to enhance safety of modern machine learning models at deployment.


We investigate the internal mechanisms of backdoored NLP models, identifying a distinct attention focus drifting phenomenon, where trigger tokens hijack attention regardless of the input context. Through comprehensive qualitative and quantitative analysis, we provide insights into the underlying mechanisms that enable backdoor attacks. Building on these insights, we propose detection methods to differentiate backdoored models from clean ones, through inspecting both the attention distribution and the model predictions. To better understand the vulnerability, we develop advanced backdoor attack strategies targeting language models in classification tasks. For BERT variants, we introduce Trojan Attention Loss (TAL), a novel method that directly manipulates attention patterns to enhance backdoor effectiveness, ensuring stealth and robustness. Vision-Language Models have demonstrated strong performance in recent years. Yet their vulnerability is largely underexplored. We investigate advanced backdoor attack strategies on Vision-Language Models, focusing on image-to-text generation tasks. We demonstrate how backdoors can be embedded in complex multimodal tasks while maintaining semantic integrity under poisoned inputs. Additionally, we propose innovative techniques for injecting backdoors without requiring access to the original training data, expanding the feasibility of real-world attacks.

This proposal provides novel insights into the internal mechanisms of backdoored models, propose effective detection strategies, and develop advanced attack techniques that expose critical vulnerabilities. These findings underscore the urgent need for robust security measures to defend against emerging backdoor threats in deep learning models. The results have been published in top venues including ICLR, ECCV, NAACL, EMNLP, etc.

Speaker: Weimin Lyu


Zoom link: https://stonybrook.zoom.us/j/99880605139?pwd=cfWbRG6n9v3GXEa7OqvXa5cOp5eLBv.1
Meeting ID: 998 8060 5139
Passcode: 843302

Please join us on Friday for a CSE 600 talk by CS Faculty, Stanley Bak. During this semester, please periodically check the CSE 600 schedule for the latest talk updates.

Title:  Formal Verification Methods for Cyber-Physical Systems and Neural Networks

Time: Friday 4/1, 2:40 PM

Location:  NCS 120

Abstract: Formal verification methods in Computer Science strive to prove properties about all possible executions of a system, and are an alternative development approach to testing when correctness is paramount. Traditionally these have been applied to hardware circuits, state-machine protocols, or software source code. Prof. Stanley Bak will discuss his research on extending formal verification approaches to more complex areas including cyber-physical systems and neural networks.

Speaker Bio: Stanley Bak is an assistant professor in the Department of Computer Science at Stony Brook University investigating the verification of autonomy, cyber-physical systems, and neural networks. He received a PhD from the University of Illinois at Urbana-Champaign (UIUC) in 2013, and worked for four years in the Verification and Validation (V&V) group in the Aerospace Systems Directorate at the Air Force Research Laboratory (AFRL). He received the AFOSR Young Investigator Research Program (YIP) award in 2020.