Defending Software Systems from Cyber Attack Campaigns Presented by R. Sekar The DNC hack of 2016, the Equifax breach of 2017, and the spate of ransomware campaigns in 2019 demonstrate the formidable challenges we face in securing our network and software systems against highly stealthy and sophisticated adversaries. In this talk, I will describe two avenues of research we have been pursuing to help tilt the table against such powerful adversaries. The first is software hardening techniques that make software vulnerabilities harder to exploit. To maximize their applicability and ease of use, our techniques are implemented into compilers, or they directly transform binary code. I will outline some of the exciting new developments we have had in this area over the years, including randomization, memory safety, information-flow tracking, control-flow integrity, and code-pointer integrity. We complement this first line of defense with techniques for analyzing and understanding attack campaigns that manage to slip past all deployed defenses. Our techniques can sift through logs consisting of hundreds of millions of events to zoom in on attack activity that may span just a few hundred events. I will describe our experience in mapping out several DARPA-sponsored red team attack campaigns.

Abstract: The advent of ChatGPT has redrawn the boundary of pedagogical discourse, where the dyadic configuration of teacher-student has, for many, become triadic -- one that includes AI as an relevant third party, not to be missed or dismissed. Within applied linguistics, AI-focused research has predominantly targeted the teaching and learning of writing (Fang & Han, 2025). The work on AI and speaking, on the other hand, has largely involved perception studies documenting its positive impact on learners' willingness to communicate (Goh & Aryadoust, 2025). In this talk, I explore the role of AI in the teaching and learning of speaking, and in particular, the development of interactional competence. Based on a corpus of learner-AI interactions, I demonstrate the ways in which ChatGPT excels and fails at acting as a useful conversation partner, with a view towards furthering our ongoing deliberation on the affordances and constraints of AI in language education.

Speaker: Hansun Zhang Waring (Teachers College, Columbia University)

Hansun Zhang Waring is Professor of Linguistics and Education at Columbia University and founder The Language and Social Interaction Working Group (LANSI). As an applied linguist and a conversation analyst, Hansun is interested in all things interaction -- (second language) pedagogical interaction, communication with the public, parent-child interaction, and human-AI interaction (HAI). Her work has appeared in leading journals in applied linguistics and discourse analysis as well as numerous book volumes, some of which she (co-)authored or co-edited. She is on the editorial boards of Chinese Language and Discourse (CLD), Classroom Discourse (CD), and International Review of Applied Linguistics (IRAL).

Location: Wang Center, Lecture Hall #1

If you need special accommodation, please contact chikako.nakamura@stonybrook.edu.

Abstract: Machine learning (ML) systems fueled by neural networks have entered our daily lives and led to scientific breakthroughs, but many open questions remain. After a nod toward the question of rigor with ML and recent progress, I'll turn to the theory of neural networks. I will argue that understanding neural networks inevitably leads to ideas from field theory (FT), which was already realized in the simplest case in the 1990s, and I will review some essential FT-for-NN results. I will then propose that the connection might be more general, an NN-FT correspondence of sorts, with neural networks providing a way to define a field theory. I'll end with comments on known results including the origin of interactions and various symmetries, but I will also list some open questions. The apparent non-sequitur in the title will be used as a rhetorical device to explore where we are and where we'd like to go.

https://scgp.stonybrook.edu/calendar/full-calendar
Join CELT on Tuesday, March 31 for a focused, one-hour overview on how to redesign and future-proof assessments in the age of AI! This session will cover three key areas: leveraging AI as a co-pilot for developing effective exam questions, designing authentic assessments, and exploring how AI can strategically support active learning structures like Team-Based Learning (TBL), Project-Based Learning (PBL), and Scenario-Based Learning (SBL).

Register here.
The Hudson River Estuary (HRE) and New York Bight (NYB) are closely connected, with HRE acting as crucial areas where many NYB marine species spawn and grow. Understanding how these biotic and abiotic environments interact, especially with rapid climate change, is key to better managing fisheries and conserving ecosystems. To better understand the HRE-NYB ecosystem, we develop a comprehensive ecosystem model that links physical and biological processes. Using data from long-term monitoring programs, we analyze ecological patterns and identify key factors regulating the ecosystem. We use this information to develop a model that mimics the food web from tiny plankton to large predators in the ecosystem. This model can help us better understand how changes in the environment, like rising temperatures, and human activities such as fishing affect marine lives and ecosystem over time. The insights from this model can support smarter fisheries management and efforts to conserve marine ecosystems in the HRE-NYB region.

IACS Student Seminar Speaker: Xiangyan Yang, Dept. of Applied Math & Statistics

Location: IACS Seminar Room or Zoom

Join Zoom Meeting: https://stonybrook.zoom.us/j/91650247483?pwd=fvAGEwadplJh7jFC5RWcdvZ5NWPJth.1
Meeting ID: 916 5024 7483
Passcode: 631055

Topic: AI Seminar: Stanley Bak
Time: Monday Nov 1, 2021 12:00 PM Eastern Time (US and Canada)

Join Zoom Meeting
https://stonybrook.zoom.us/j/91227496273?pwd=M3EyUDlzK3Vzd2pDOGpDU1ZjN0k1UT09

Abstract: The field of formal verification has traditionally looked at proving properties about finite state machines or software programs. The surge in deep learning has been accompanied by a surge of progress in trying to apply mathematical and algorithmic techniques to prove things about the function being computed by a neural network.

This talk formalizes the neural network verification problem and describes technical methods for neural network verification based on reachability analysis. Improvements to analysis efficiency will be given, as well as research directions for further exploration. We also include an objective comparison performed this last summer trying to evaluate the best existing verification methods in terms of speed and network size. The competition was performed on common hardware and involved the participation of twelve international teams (the tool authors) on a common set of benchmarks. 

Biography: 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 strives to develop practical formal methods that are both scalable and useful, which demands developing new theory, programming efficient tools and building experimental systems.
Stanley Bak received a Bachelor's degree in Computer Science from Rensselaer Polytechnic Institute (RPI) in 2007 (summa cum laude), and a Master's degree in Computer Science from the University of Illinois at Urbana-Champaign (UIUC) in 2009. He completed his PhD from the Department of Computer Science at UIUC in 2013. He received the Founders Award of Excellence for his undergraduate research at RPI in 2004, the Debra and Ira Cohen Graduate Fellowship from UIUC twice, in 2008 and 2009, and was awarded the Science, Mathematics and Research for Transformation (SMART) Scholarship from 2009 to 2013. From 2013 to 2018, Stanley was a Research Computer Scientist at the US Air Force Research Lab (AFRL), both in the Information Directorate in Rome, NY, and in the Aerospace Systems Directorate in Dayton, OH. He helped run Safe Sky Analytics, a research consulting company investigating verification and autonomous systems, and performed teaching at Georgetown University before joining Stony Brook University as an assistant professor in Fall 2020.

The AI Community at Stony Brook University is proud to announce Datathon 2026.

Dive into data analysis and AI/ML, and get ready to build something big. In this year's underwater-themed event, enjoy a weekend of data analysis, hacking, networking, fun activities, and minigames.

Whether you're a seasoned developer, data scientist, designer, or completely new to hacking, this event is your chance to collaborate, learn data science, and create something impactful with data and AI/ML.

What is Datathon?

AI Community's Datathon is the premier data science competition at Stony Brook University, bringing together students of all skill levels for a weekend of data exploration, analysis, and innovation. Just like a typical hackathon, you will be using your skills to build your dream project.

Unlike a regular hackathon, Datathon is focused on data science. You will be given a set of data to work with, analyze, and apply to your project. You can also find your own data to use. Your project will be presented to a panel of judges consisting of professors and industry professionals!

Who Can Participate

  • Students of all skill levels and majors are welcome.
  • Come with a team or find one at the event or on Discord.
  • This event is open to SBU and non-SBU students.

* Non-SBU Undergraduate Students are ineligible to receive prizes
* You must be 18+ or older (Excludes minors who are active SBU students)

Location: SAC Ballroom B

Register here.

Stony Brook University Libraries invites students, faculty, & staff to join a conversation about how AI is transforming the private sector workforce. As AI tools move from experimentation to everyday business use, companies are rethinking roles, skill sets, leadership, and long-term strategy. This discussion-based event will focus on the fast-paced changes and directions at tech companies and their possible impact. This event will be particularly relevant for students preparing for an AI influenced job market and how to position themselves for opportunities in a rapidly evolving professional landscape.

The discussion will be led by Tariq Khan, Senior Director of Private Cloud Solutions at Hewlett Packard Enterprise. Tariq is a technology leader and architect with experience across private cloud, hybrid cloud, and data center platforms. He is responsible for shaping the technology architecture and strategic direction of HPE's Private Cloud offerings across on premises and cloud integrated environments.

Light refreshments will be served.


Location: Melville Library, NRR, Learning Lab
Abstract: Large language models are prone to memorizing some of their training data. Memorized (and possibly sensitive) samples can then be extracted at generation time by adversarial or benign users. There is hope that model alignment---a standard training process that tunes a model to harmlessly follow user instructions---would mitigate the risk of extraction. However, we develop two novel attacks that undo a language model's alignment and recover thousands of training examples from popular proprietary aligned models such as OpenAI's ChatGPT. Our work highlights the limitations of existing safeguards to prevent training data leakage in production language models.

Speaker: Pegah Alipoormolabashi

Location: CS2311

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.

Learning Generalizable Program and Architecture Representations for Performance Modeling

Abstract: Performance modeling is an essential tool in many areas of computer science and engineering. However, existing performance modeling approaches have limitations, such as high computational cost, narrow flexibility, or restricted accuracy/generality. To address these limitations, this talk introduces PerfVec, a novel deep learning-based performance modeling framework that learns high-dimensional and independent/orthogonal program and microarchitecture representations. Once learned, a program representation can be used to predict its performance on any microarchitecture, and likewise, a microarchitecture representation can be applied in the performance prediction of any program. Additionally, PerfVec yields a foundation model that captures the performance essence of instructions, which can be directly used by developers in numerous performance modeling-related tasks without incurring its training cost. The evaluation demonstrates that PerfVec is more general and efficient than previous approaches. This talk will also introduce how PerfVec's design principles can benefit broader research areas.

Biography: Lingda Li is a computer scientist at Brookhaven National Laboratory. He is generally interested in computer architecture and programming model research, with focus on simulation/modeling, memory systems, and machine learning. Before joining BNL, he worked at the Department of Computer Science of Rutgers University as a postdoc to carry out GPGPU research. He obtained a PhD in computer architecture from the Microprocessor Research and Development Center at Peking University.

Location: CDS, Bldg. 725, Training Room

Join ZoomGov Meeting: https://bnl.zoomgov.com/j/1605837856?pwd=kYqJs4bVBt4E0cMCWR6GXH3wxzOoiw.1

Meeting ID: 160 583 7856
Passcode: 161580