The Della Pietra Lecture Series is pleased to present this lecture by Scott Aaronson:

Abstract: I'll survey some areas where I think theoretical computer science, math, and statistics can potentially contribute to the urgent quest to align powerful AI with humane values. These areas include: the watermarking of AI outputs, mechanistic interpretability (including Paul Christiano's No-Coincidence Principle, and succinct digests of the training process to aid interpretability), and theoretical guarantees for out-of-distribution generalization.

Speaker: Scott Aaronson is Schlumberger Chair of Computer Science at the University of Texas at Austin, and founding director of its Quantum Information Center. He received his bachelor's from Cornell University and his PhD from UC Berkeley. Aaronson's research has focused mainly on the capabilities and limits of quantum computers. His first book, Quantum Computing Since Democritus, was published in 2013 by Cambridge University Press. He received the National Science Foundation's Alan T. Waterman Award, the United States PECASE Award, the Tomassoni-Chisesi Prize in Physics, and the ACM Prize in Computing, and is a Fellow of the ACM and the AAAS and a member of the National Academy of Sciences. He blogs at Shtetl-Optimized, https://www.scottaaronson.com/blog.

Location: Della Pietra Family Auditorium (SCGP 103)

Talk Title: Knowledge-enhanced LLMs and Human-AI Collaboration Frameworks for Creativity Support


Abstract:

Large language models (LLMs) constitute a paradigm shift in Natural Language Processing and Artificial Intelligence. To build AI systems that are human-centered, I propose we need knowledge-aware models and human-AI collaboration frameworks to help them solve tasks ultimately aligning these models better with human values. In this talk, I will discuss my research agenda for human-centered AI with a case study on creativity that focuses on how to augment LMs with external knowledge, build effective human-AI collaboration frameworks as well as theoretically grounded robust evaluation protocols for measuring capabilities of NLG systems. I will begin by describing knowledge-enhanced methods for creative text generation such as metaphors. Next, I will describe how content creators can collaborate and benefit from the creative capabilities of text-to-image-based AI models. Finally, I will focus on the design and development of theoretically grounded evaluation protocols to benchmark the creative capabilities of Large Language Models in both producing as well as assessing creative text. I will end this talk by highlighting the current limitations of existing models and future directions toward building better models that will enable efficient and trustworthy human-AI collaboration systems.


Bio:

Tuhin Chakrabarty is a final-year Ph.D. candidate in the Natural Language Processing group within the Computer Science department at Columbia University. His research is supported by the Columbia Center of Artificial Intelligence & Technology (CAIT) & an Amazon Science Ph.D. Fellowship. He was also a Computational Journalism fellow at NYTimes R&D and an intern at the Allen Institute of Artificial Intelligence, Salesforce Research, and Deepmind. His research interests are broadly in Natural Language Processing, Computer Vision, and Human-Computer Interaction with a special focus on Human-Centered Methods for Understanding, Generation, and Evaluation of Creativity. His work has been recognized at top natural language processing and human-computer interaction conferences and journals such as ACL, NAACL, EMNLP, TACL, and CHI. He has been involved in organizing several workshops and tutorials at NLP conferences such Figurative Language Processing workshop at EMNLP 2022, NAACL 2024, and the tutorial on Creative Text Generation at EMNLP 2023. His work on AI and creativity has been mentioned in mainstream news media such as The Hollywood Reporter and more recently The Washington Post.

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CSE 600 Seminar Series | Fall 2025



Abstract:

We often talk about AI as if it begins with a dataset and ends with an application. But behind every model lie two sets of actors who are rarely acknowledged in technical documentation: the workers who train AI systems and the researchers who try to make sense of them. This talk brings both groups into view.
Dr. Ben Zhang will offer an on-the-ground examination of the prevailing values and invisible labor that underpin commercial AI production and data production. Drawing on ethnographic research inside AI data annotation centers in China, he introduces the concept of precision labor to unpack the labor dimension of constructing, managing, and performing technical accuracy. This concept highlights the hidden and excessive labor required to reconcile the ambiguity and uncertainty involved in AI training. A precision labor lens challenges the legitimacy and sustainability of the relentless pursuit of technical accuracy, raising new questions about its consequences and implications.
On the other end of the pipeline, as LLMs become embedded in society, social scientists like Dr. Jieshu Wang is scrutinizing their potential biases while employing them as research tools. She will present her recent work auditing LLM responses across different contexts, revealing that LLMs exhibit varying levels of environmental awareness and disproportionately reward institutional prestige in peer-review simulations. She also demonstrates how LLMs can serve as useful tools in social-science pipelines, e.g., extracting location information, inferring demographics, parsing citations, mapping social networks, and analyzing occupational data.
By placing these two worlds side by side - the labor of training AI and the scholarly efforts to study it - we show why responsible AI should go beyond the deployment phase - emphasizing fairness audits, and model explainability. It requires reimaging the values, labor regimes, and social science practices that shape AI systems from annotation to analysis.


Bios:

Dr. Jieshu Wang is an interdisciplinary researcher studying the human and social dimensions of artificial intelligence (AI) and how people can thrive in an AI-integrated future. She combines computational methods with qualitative insights to trace technology trends and understand their broader societal impact. She earned her Ph.D. in Human and Social Dimensions of Science and Technology from Arizona State University, after earlier degrees in Civil Engineering, Economics, and Science and Technology Studies. She has also worked as a patent examiner, an editor at a popular science magazine, and co-founded Synced (机器之心), an AI-focused media company in China. Her research looks both backward and forward. Backward-looking, she examines how AI are created, who creates them, and who is missing from the process. Forward-looking, she studies how AI is transforming the way we live, connect, invent, work, and adapt, as well as how AI might help address challenges such as climate change and workforce transitions.
Dr. Ben Zhang is an Assistant Professor in the Department of Technology. His research explores the production and sociotechnical impacts of AI systems in critical areas such as work, health, and sustainability. Drawing from his background in Human-Computer Interaction (HCI), Human-Centered AI, and Science and Technology Studies (STS), he employs a life-cycle-centered approach to holistically examine the promises and harms of these systems and to inform the design of responsible AI infrastructures across their development, deployment, and governance. Ben received his Ph.D. in Information Science from the University of Michigan. Ben's work has been supported by competitive awards and fellowships, including the University of Michigan Rackham Predoctoral Fellowship and the Weizenbaum Fellowship. His research has appeared in premier computing venues, including ACM CHI, ACM CSCW, and AAAI ICWSM.

Location: NCS 120
Postmortem Program Analysis from a Conventional Program Analysis Method to an AI-assisted Approach

Abstract: Despite the best efforts of developers, software inevitably contains flaws that may be leveraged as security vulnerabilities. Modern operating systems integrate various security mechanisms to prevent software faults from being exploited. To bypass these defenses and hijack program execution, an attacker needs to constantly mutate an exploit and make many attempts. While in their attempts, the exploit triggers a security vulnerability and makes the running process abnormally terminate.

After a program has crashed and abnormally terminated, it typically leaves behind a snapshot of its crashing state in the form of a core dump. While a core dump carries a large amount of information, which has long been used for software debugging, it barely serves as informative debugging aids in locating software faults, particularly memory corruption vulnerabilities. As such, previous research mainly seeks fully reproducible execution tracing to identify software vulnerabilities in crashes. However, such techniques are usually impractical for complex programs. Even for simple programs, the overhead of fully reproducible tracing may only be acceptable at the time of in-house testing.

In this talk, I will discuss how we tackle this issue by bridging program analysis with artificial intelligence (AI). More specifically, I will first talk about the history of postmortem program analysis, characterizing and disclosing their limitations. Second, I will introduce how we design a new reverse-execution approach for postmortem program analysis. Third, I will discuss how we integrate AI into our reverse-execution method to escalate its analysis efficiency and accuracy. Last but not least, as part of this talk, I will demonstrate the effectiveness of this AI-assisted postmortem program analysis framework by using massive amounts of real-world programs.

Bio: Dr. Xinyu Xing is an Assistant Professor at Pennsylvania State University. His research interests include exploring, designing and developing new program analysis and AI techniques to automate vulnerability discovery, failure reproduction, vulnerability diagnosis (and triage), exploit and security patch generation. His past research has been featured by many mainstream media and received the best paper awards from ACM CCS and ACSAC. Going beyond academic research, he also actively participates and hosts many world-class cybersecurity competitions (such as HITB and XCTF). As the founder of JD-OMEGA, his team has been selected for DEFCON/GeekPwn AI challenge grand final at Las Vegas. Currently, his research is mainly supported by NSF, ONR, NSA and industry partners.
Abstract: This dissertation addresses the methodological disconnect between Natural Language Processing (NLP) and human-centric analysis by shifting the unit of analysis from document to human behavior in two broad respects: (i) time-ordering: modeling documents as sequential person-indexed behavioral observations, and (ii) person-level semantics: evaluation and explainability of models by their latent structure of psychological constructs rather than just its predictive accuracy against narrow proxy measures. First, we consider the most basic implication of language as a person's behaviors when measuring their psychological constructs: relationship between language sample size and model's predictive performance. We empirically show that the state-of-the-art transformers are often over-parameterized for typical NLP dataset sizes and can be reduced in dimensionality without performance loss. Establishing the author as the unit of analysis naturally allows us to treat their behavior as a time-ordered sequence. Second, we introduce a longitudinal evaluation framework that establishes ecologically valid evaluation settings, namely, cross-sectional and prospective generalization, and separates error measurement of the model into within-person dynamics and between-person differences. We demonstrate that traditional NLP evaluations based on random document splits can yield reversed conclusions under ecologically valid generalization settings. To address this, we develop models that capture the trajectory of mental states (e.g., mood shifts) rather than static traits. Third, moving into person-level semantics, we evaluate the latent structure of large language models using a novel machine behavior analytic framework. We find that while GPT-4 achieves high predictive correlation with self-reports, its latent symptoms structure diverges from clinical understanding. Finally, we propose a method for modeling multidimensional behaviors, embedding concurrent behavioral signals alongside language to predict future states. Taken together, this work suggests that operationalizing language as behavior advances NLP methods into a rigorous instrument for valid psychological inquiry.

Speaker: Adithya Ganesan

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Abstract: Drawing on group-theoretic and information-theoretic foundations, we propose information lattice learning (ILL) as a general framework to learn rules of a signal (e.g., an image or a probability distribution). In our definition, a rule is a coarsened signal used to help us gain one interpretable insight about the original signal. To make full sense of what might govern the signal's intrinsic structure, we seek multiple disentangled rules arranged in a hierarchy, called a lattice. Compared to representation/rule-learning models optimized for a specific task (e.g., classification), ILL focuses on explainability: it is designed to mimic human experiential learning and discover rules akin to those humans can distill and comprehend. We will detail the mathematical foundations and algorithms of ILL, and illustrate how it addresses the fundamental question what makes X an X by creating rule-based explanations designed to help humans understand. Our focus is on explaining X rather than (re)generating it. We show ILL's efficacy and interpretability on benchmarks and assessments, as well as a demonstration of ILL-enhanced classifiers achieving human-level digit recognition using only one or a few MNIST training examples (1-10 per class). We present applications in knowledge discovery, using ILL to distill music theory from scores and chemical laws from molecules and further revealing connections between them. We close with some early work on understanding the principles that govern scattering amplitudes in Super Yang-Mills theory, rather than just predicting them.

Biography: Lav R. Varshney is the Della Pietra Infinity Professor and inaugural director of the AI Innovation Institute at Stony Brook University. He is co-founder and CEO of Kocree, Inc., a startup company building novel human-controllable AI for discovery and creativity, and chief scientist of Ensaras, Inc., a startup company focused on AI and wastewater treatment. He holds appointments at RAND Corporation and at Brookhaven National Laboratory. He was previously on the faculty of the University of Illinois Urbana-Champaign, a visiting scholar at Northwestern's Kellogg School of Management, a principal research scientist at Salesforce Research AI, and a research staff member at IBM Research. He is a former White House staffer, having served on the National Security Council staff as a White House Fellow, where he contributed to national AI and wireless communications policy. His research interests include information theory and artificial intelligence. He received his B.S. degree from Cornell University and his S.M. and Ph.D. degrees from the Massachusetts Institute of Technology.

Location: Room 102
Abstract: Molecular learning has become an emerging field of AI, driving breakthroughs in drug discovery, protein design, and materials design. For high-stakes scientific tasks, however, predictive accuracy alone is not sufficient: models must also be interpretable and trustworthy. Our work aims to study molecular learning under a unified explainability perspective across two major model families: Graph Neural Networks (GNNs) and Large Language Models (LLMs).

GNNs are natural choices for molecular graphs and achieve strong performance on many molecular tasks. To enhance explainability, many GNN explanation methods have been proposed and work well for 2D GNNs. However, 3D GNNs introduce two key challenges: producing chemically meaningful substructures and reducing fidelity loss caused by dense geometric graphs. To address these challenges, I present two methods. 3DGraphX decomposes dense 3D graphs into chemically meaningful 3D motifs, enabling compact explanations that align with chemical intuition. EDMA introduces an energy-based discrete mask approximation approach to reduce the discrepancy between the soft mask optimized during training and the hard mask used for explanation, improving explanation fidelity.

LLMs present different characteristics and challenges compared with GNNs. LLMs can provide a certain level of explanation through step-by-step reasoning, and their natural-language outputs are easy for humans to understand and interpret. However, because LLMs are trained for general-purpose tasks, their performance on scientific tasks often lags behind specialized GNNs. To improve performance, existing methods guide LLMs by providing suggestions through brief feedback, retrieval-augmented generation (RAG), or planner agents. However, these approaches face several limitations, such as vague guidance, introduced bias problems, and high computational cost. To fill the gap, I propose RL-Guider, a lightweight reinforcement-learning agent that converts evaluation feedback into input-specific guidance for molecular optimization. RL-Guider improves over time by accumulating historical experience and transfers efficiently across different LLMs while preserving interpretability.

Together, these efforts aim to provide explanations that are scientifically meaningful and faithful, while also preserving or improving performance on molecular tasks to better meet real scientific needs.

Speaker: Xufeng Liu

Location: New Computer Science-1-Room 115

Academic Integrity and CELT invite faculty to an open discussion on managing the challenges of classroom technology and AI, handling misconduct suspicions and reports, and accessing resources like syllabus templates and recommended policy language. Our goal is to promote transparency about policies and procedures and empower faculty with easily accessible resources and strategies.

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This virtual presentation series is designed to inform the Stony Brook University research community about the Research Funding Landscape of key topic areas. Our Strategic Research Initiatives team will provide insight into the rapidly shifting funding environment using policy briefs, budgetary priorities, and relevant legislation. We will highlight federal and state priorities in the current and upcoming years to help Stony Brook researchers develop strategies for pursuing funding in a rapidly shifting environment. This series is moderated by Mónica Bugallo, Interim Vice President for Research & Innovation.

Join us for the third in the series, focused on the artificial intelligence landscape:


Translating the Funding Landscape for Stony Brook Researchers: Artificial Intelligence
Presented by Catherine Chen, Ph.D., Research Development Associate
Faculty Respondent: Assistant Professor Nav Nidhi Rajput, Department of Materials Science and Chemical Engineering
Wednesday, April 22, 2026 at 2 pm to 3 pm

Registration is Required