Abstract:
Large language models (LLMs) have transformed the way humans write code, bringing unprecedented automation to software development. In this talk, I will first provide an overview of my research on enhancing LLMs' code intelligence, optimizing each step of the development pipeline towards more complex software engineering tasks. I will then delve into my key contributions, focusing on how to equip LLMs with a deeper, more comprehensive understanding of software programs. Finally, I will discuss the future of AI-driven software engineering, envisioning a new era of automation that is more reliable, intelligent, and cost-efficient.

Bio:
Yangruibo (Robin) Ding is a Ph.D. candidate in the Department of Computer Science at Columbia University. His research is at the intersection of Software Engineering and Machine Learning, focusing on developing large language models (LLMs) for code. He trains LLMs to generate, analyze, and refine software programs and constructs benchmarks to systematically evaluate LLMs in solving software engineering tasks. He also studies how to improve LLMs' reasoning capability to tackle complex programming tasks, such as debugging and patching. His interdisciplinary research has been published in top-tier conferences of software engineering, programming languages, natural language processing, and machine learning. He won an ACM SIGSOFT Distinguished Paper Award, an IEEE TSE Best Paper Runner-up, and received an IBM Ph.D. Fellowship.
Location:
NCS 120
CSE 600 Seminar Series | Fall 2025


Abstract: Virtual worlds are prevalent in applications ranging from entertainment, healthcare, retail, to workforce training. With the demand for virtual content growing exponentially, the market for such content is valued at over $200 Billion, which is accelerating the need for advanced computational solutions. In this talk, I will focus on a key challenge in virtual content creation: simulating autonomous agents.
I begin by overviewing this problem domain, through the lens of a physics-based dynamics simulation, which enables the simulation of thousands of agents at interactive rates with GPU programming, achieving a level of performance previously unattainable.
Next, I'll present our recent results in Deep Reinforcement Learning for multi-agent navigation, which enable refined, reward-based strategies to control agent movement. We demonstrate how these techniques can simulate realistic crowds, with broad applications in pedestrians, robots, and swarms. Lastly, I conclude my talk by discussing our lab's work-at-large and the wide range of research opportunities in this emerging area.

Speaker: Tomer Weiss is a professor with New Jersey Institute of Technology since 2020. He received the best student, presentation, and best paper awards in various ACM SIGGRAPH conferences for his work on simulating multi-agent crowds. He was also a finalist in both ACM SIGGRAPH Thesis Fast Forward, and the ACM SIGGRAPH Asia Doctoral Symposium in 2018. He received his PhD in computer science from UCLA in 2018. His research interests include multi-agent dynamics, scene understanding, and interactive visual computing.
Abstract: Jailbreak attacks circumvent LLMs' built-in safeguards by concealing harmful queries within adversarial prompts. While most existing defenses attempt to mitigate the effects of adversarial prompts, they often prove inadequate as adversarial prompts can take arbitrary, adaptive forms. This paper introduces RobustKV, a novel jailbreak defense that takes a fundamentally different approach by selectively removing critical tokens of harmful queries from key-value (KV) caches. Intuitively, for an adversarial prompt to be effective, its tokens must achieve sufficient `importance' (measured by attention scores), which consequently lowers the importance of tokens in the concealed harmful query. Therefore, by carefully evicting the KVs of low-ranked tokens, RobustKV minimizes the harmful query's presence in the KV cache, thus preventing the LLM from generating informative responses. Extensive evaluation using benchmark datasets and models demonstrates that RobustKV effectively counters state-of-the-art jailbreak attacks while maintaining the LLM's performance on benign queries. Notably, RobustKV creates an interesting effectiveness-evasiveness dilemma for the adversary, leading to its robustness against adaptive attacks.

Speaker: Tanqiu Jiang

Where: NCS 220 and Zoom (https://stonybrook.zoom.us/j/6406956411)
Abstract: Artificial Intelligence (AI) is no longer a futuristic concept -- it is here, but its development, benefits, and risks remain unevenly distributed across industries, nations, and social groups. In this talk, Jieshu presents her research on the societal dimensions of AI from two perspectives: the forces shaping AI's development (backward-looking) and its current and potential impact on society (forward-looking). She first examines disparities in AI, including women's underrepresentation in AI patents and the geographic concentration of AI innovation, highlighting inequalities in who creates AI and who benefits from it. She then explores AI's societal impact, focusing on workforce transformation and the need for GenAI literacy. She will also discuss AI patents, AI's role in climate change mitigation and adaptation, potential environmental biases in LLMs, and gender-specific patterns in AI portrayals in science fiction.

Bio: Jieshu Wang is a Postdoctoral Research Scholar at Arizona State University (ASU), focusing on the social dimensions of artificial intelligence (AI). With a background in engineering, economics, communication, and science and technology studies, she examines how AI both shapes and is shaped by broader societal forces. Her research employs interdisciplinary methods to explore the social, political, and economic factors influencing AI development, as well as its role in innovation, the economy, the future of work, climate change mitigation, and popular culture. Jieshu holds a Ph.D. in Human and Social Dimensions of Science and Technology from ASU. She is also a science book translator and has translated six books.

Location: Old Computer Science, room 1310
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.

Abstract: How do humans learn the sound patterns of their language? Despite a variety of methods and advances in phonotactic learning, there is still a paucity of computational research, methods and data for languages with tones. In this talk, I will explore this question specifically in light of tone languages, where pitch plays a crucial role in distinguishing words' meaning. I provide an implementation of the Bottom-Up Factor Inference Algorithm over Autosegmental Representations (BUFIA-AR), which learns the rules governing possible tone patterns. Using a dataset of Hausa, a West African tone language, the algorithm successfully identifies patterns that are not permitted in the language. These results (i) confirm long-standing linguistic generalizations, (ii) make more specific predictions about exceptional cases, and (iii) reveal previously unnoticed patterns. The results show how mathematical models of sound structure can be brought into dialogue with both linguistic theory and computational learning, highlighting the broader potential of formal approaches to capture human linguistic knowledge.

Bio: Han Li is a fifth-year Ph.D. student in Linguistics department, specializing in computational linguistics under the supervision of Professor Jeff Heinz. Her research focuses on how sound patterns in language can be formally represented and computationally learned, bridging theoretical linguistics and computer science.

Location: Institute for Advanced Computational Science, Seminar Room

Zoom Meeting: https://stonybrook.zoom.us/j/94043459206?pwd=3ra47h8HghOFRfobRBjZaDMyTwialr.1
Meeting ID: 940 4345 9206
Passcode: 332717

Abstract: Many foundation models for digital pathology have been released recently. Benchmarking available methods then becomes paramount to get a clearer view of the research landscape. For this reason, we introduce THUNDER, a tile-level benchmark for digital pathology foundation models, allowing for efficient comparison of many models on diverse datasets with a series of downstream tasks, studying their feature spaces and assessing the robustness and uncertainty of predictions informed by their embeddings. Such foundation models are often used as feature extractors and combined with Multiple Instance Learning (MIL) aggregators at downstream time. Such aggregation must be efficient and reliable. We will focus on two specific examples of this: (I) HistAug, a fast and efficient generative model for controllable augmentations in the latent space of foundation models to perform data augmentation for MIL, and (ii) CAR-MIL, a method based on counterfactual attention regularisation to improve the reliability of attention maps of MIL methods.

Short-bio: Pierre Marza is a Postdoctoral Researcher at CentraleSupelec in the Biomathematics team of the MICS lab, studying Computer Vision and Deep Learning for Medical Imaging, with a focus on Digital Pathology. Prior to this, he was a PhD student at INSA Lyon, in the LIRIS and CITI labs, advised by Christian Wolf, and co-advised by Laetita Matignon and Olivier Simonin. He studied Visual Navigation, Embodied AI, Spatial Reasoning, more specifically how to learn to represent 3D space, generalize to new environments and master diverse tasks from light supervision.

Location: NCS 220

Zoom: https://stonybrook.zoom.us/j/94798224254?pwd=CFraer25qnpORbJ14aAVHRwaSJOjJM.1
The coach who led Team USA to four Math Olympiad gold medals shares his blueprint for staying irreplaceable in an AI-driven world.

As artificial intelligence transforms our world, what skills will remain uniquely human? How can we prepare for careers in an automated future?

Join Carnegie Mellon mathematics professor Po-Shen Loh for insights on navigating the AI revolution by embracing our humanity.

Dr. Loh brings a distinctive perspective shaped by his dual expertise: serving as national coach of the USA Mathematical Olympiad team (which has won four gold medals under his leadership) and developing innovative solutions for real-world challenges from pandemic response to educational technology.

Through his nationwide speaking tour that reached 250 audiences across 100 cities, he has refined a practical framework for thriving alongside AI.

In this presentation, Dr. Loh will explore how creative problem-solving, judgment, and communication become more valuable as automation grows -- and how students and professionals can build those strengths now.

The session includes real-world examples, guidance for education and careers, and a Q&A.

Speaker: Po-Shen Loh is a social entrepreneur and inventor, working across the spectrum of mathematics, education, and healthcare.

A math professor at Carnegie Mellon University, he also served a decade-long term as the national coach of the USA International Mathematical Olympiad (IMO) team, taking the team to gold on numerous occasions.

He has pioneered numerous innovations and has been featured in or co-created YouTube videos with more than 25 million views.

Location: Wang Center Theater

The series is offered by Stony Brook University's Institute for Creative Problem Solving in collaboration with the National Museum of Mathematics (MoMath) and Brookhaven National Laboratory.

The event is free but space is limited. Please register to reserve your space.

Abstract: Recent work in NLP uses debates between multiple LLMs to arrive at a more accurate conclusion. Earlier chain-of-thought prompting also shows improvements in accuracy when the model is asked to provide step-by-step reasoning in its response. Many publications since have developed strategies to improve the reasoning of model output with the goal of generating a more accurate result. However, even when asked to provide problem solving steps, the content of the reasoning provided by models is not well studied for all tasks and sometimes contains errors or conflicting statements even when the final result is correct. In fact, when evaluated across reasoning tasks, evidence shows that LLMs are not learning how to reason but are instead mimicking relevant solutions from their training sets.
By studying and evaluating the argumentation that LLMs provide, we can determine factors that may benefit or hinder the model's ability to give a complete, cohesive, and thorough answer. While there are signs that LLMs pattern match, finding where, when, and why this fails is valuable, as there may be ways to help the model imitate solutions that are more relevant to the task it is attempting to solve. Determining when pattern matching is not enough could show an area of improvement for future generations of LLMs. This research may separately aid in work on human-(AI)agent and inter-agent interaction. Specifically, frameworks could be used to determine when and why other models or humans are convinced by LLM-generated responses and which argument methods cause other models to change their response. Our current research in systematic versus heuristic cues shows that large language models sometimes present systematic or heuristic reasoning patterns based on prompting. Future research aims to explore other methods of classifying argumentation.

Speaker: Kiera Gross

Joining link: https://meet.google.com/xae-ywpv-udo
Abstract:

What is the nature of linguistic knowledge, and how is it acquired from limited data? In recent years, the program of subregular linguistics has identified formal language classes expressive enough to account for most phenomena in natural language but also sufficiently limited to be efficiently learned from positive data. An advantage to these formal learning algorithms is that they come with mathematically proven guarantees about their performance, and it is easy to reason about how and why they behave the way they do.

In this talk, I discuss the Multi Tier-based 2-Strictly Local Inference Algorithm (MT2SLIA), which probably learns the syntactically relevant class of 2-Factor Muti Tier-based Strictly Local (2FMSTL) tree languages. This algorithm efficiently learns from a polynomially-sized sample of positive data by identifying missing substructures and generalizing these as constraints over tiers in a principled manner.

I will introduce a working prototype implementation of this algorithm and demonstrate its behavior on a curated sample of natural language data to show how it can learn relevant syntactic patterns.

Bio:

Logan Swanson is a third year PhD student in the Department of Linguistics at Stony Brook University. He is advised by Dr. Jefferey Heinz and Dr. Thomas Graf. His interests include learning theory, computational syntax, and language change. His current research focuses on understanding the learning-theoretic elements of natural language by designing, implementing, and testing learning algorithms for linguistically relevant formal language classes.

*Please note: this seminar will be held in person (IACS Seminar Room w/ food provided) and online.

Join Zoom Meeting
https://stonybrook.zoom.us/j/95707958315?pwd=6ITUJ0ffCXjRJb4wpt0KMDTApfSLZ0.1

Meeting ID: 957 0795 8315
Passcode: 920473