The Future Histories Studio will host Young Maeng, an artist and professor at California State University, Fresno, for a talk exploring the intersection of artificial intelligence (AI) and traditional painting, examining how two seemingly disparate fields can converge to create new artistic expressions.

The lecture is part of the Future History Studio series at Stony Brook University, a platform dedicated to examining the evolving relationship between technology, art, and society.

Young will discuss her innovative approach to expanded painting, an integration of AI-generated images and traditional techniques such as Korean ink and acrylic painting. Through this fusion, she visualizes complex philosophical and ethical questions about the coexistence of humans, nature, and AI companion robots. The lecture will highlight the broader implications of AI in the art world, touching on how AI technologies challenge conventional notions of creativity and human-centric perspectives in art.

Speaker Bio:

Young Maeng is an artist and professor at California State University, Fresno, whose work explores the intersection of artificial intelligence (AI) and traditional painting techniques such as Korean ink and acrylic.

Maeng's innovative approach to expanded painting blends AI technology with traditional methods to visualize complex philosophical and ethical questions surrounding the coexistence of humans, nature, and AI companion robots.

Location: Future Histories Studio
Register here: https://www.eventbrite.ca/e/ai-and-painting-tickets-1021050809457?aff=oddtdtcreator

Abstract:

It is known that models like large language models (LLMs) can often suggest colloquial plans given verbal descriptions of tasks, yet they are unable to reliably provide executable and verifiable plans given formally specified environments. In this talk, I will discuss a strand of efforts to have LLMs generate accurate and explainable plans in textual simulations. Instead of directly generating the plan or actions, LLMs are prompted to generate Planning Domain Definition Language (PDDL) that specifies the environment (domain file) and the task (problem file), which can then be deterministically solved with an off-the-shelf planner. In a 3-phase study, my collaborators and I first observed that it is possible but very challenging for LLMs to generate long-form code such as PDDL domain and problem files given textual specifications. Next, we devise methodologies for LLMs to iteratively generate and refine problem files while exploring a partially-observed, simulated, textual environment. Finally, we show that domain files are even more difficult to generate correctly, even on well-established planning tasks such as BlocksWorld. Finally, I will discuss ongoing efforts to improve said ability of structured generation and promising frontiers to explore.

Bio:
Li Harry Zhang is an assistant professor at Drexel University, focusing on Natural Language Processing (NLP) and artificial intelligence (AI). He obtained his PhD degree from the University of Pennsylvania advised by Prof. Chris Callison-Burch. Prior, he obtained his Bachelor's degree at the University of Michigan mentored by Prof. Rada Mihalcea and Prof. Dragomir Radev. His current research uses large language models (LLMs) to reason and plan via symbolic and structured representations. He has published more than 20 peer-reviewed papers in NLP and AI conferences, such as ACL, EMNLP, and AACL, that have been cited more than 1,000 times. He also consistently serves as Area Chair, Session Chair, and reviewer in those venues. Being a musician, producer, and content creator having over 50,000 subscribers, he is also passionate in the research of AI music and creativity.

How to Succeed in Language Design Without Really Trying presented by Professor Brian Kernighan

ABSTRACT: Why do some languages succeed while others fall by the wayside? I've helped create nearly a dozen languages (mostly small) over the years; a handful are still in widespread use, while others have languished or simply disappeared. I've also been present at the creation of several other languages, including some really major ones. In this talk I'll give my humble, but correct, opinion on factors that affect success and failure, and try to offer some insight into what to do if you're trying to design a new language yourself, and why that might be a good thing.

BIO: Brian Kernighan received a PhD in electrical engineering from Princeton in 1969. He joined the Computer Science department at Princeton in 2000, after many years at Bell Labs. He is a co-creator of several programming languages, including AWK and AMPL, and of a number of tools for document preparation. He is the co-author of a dozen books and some technical papers, and holds 5 patents.
He is a member of the National Academy of Engineering and of the American Academy of Arts and Sciences. His research areas include programming languages, tools and interfaces that make computers easier to use, often for non-specialist users. He has also written two books on technology for
non-technical audiences: Understanding the Digital World in 2017 and Millions, Billions, Zillions: Defending Yourself in a World of Too Many Numbers, published in 2018. His most recent book, Unix: A History and a Memoir, was published in October 2019.
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
Please join us for the next CSE 600 Seminar this Friday, October 11th, at 2:30pm in New Computer Science 120 given by Assistant Professor Mohammad Javad Amiri. Abstract: Today's distributed transaction processing systems must deal with untrustworthy environments where multiple mutually distrustful entities communicate with each other, and maintain data on untrusted infrastructure. Byzantine Fault-Tolerant (BFT) protocols have recently been extensively used by distributed transaction processing systems to establish consensus on the order of transactions. However, the proliferation of different BFT protocols has made it difficult to navigate the BFT landscape, let alone determine the protocol that best meets application needs. Moreover, as novel smart contracts, modern hardware, and new cloud platforms arise, future-proof distributed transaction processing systems need to be designed with full-stack adaptivity in mind. This talk presents our vision for a reinforcement learning (RL)-based distributed transaction processing system that adjusts effectively in real-time to changing fault scenarios and workloads.










Abstract:
Quantifying similarity is a central notion in science and data analysis, pervading everything from phylogenetic trees to the foundation of clustering. Unfortunately, despite being examined and applied for decades, traditional similarity and distance metrics have fundamental drawbacks. The key problem is that all of them are only defined over pairs of objects, so they scale quadratically when one tries to compare N objects. The present explosion in the amount of data available to us requires new ways to process information, and while some current algorithms can handle millions of points, we need alternatives applicable to billions. This is what motivated us to develop a new framework that can compare any number of objects at the same time. With this, we achieve an unprecedented linear scaling when comparing multiple objects. Here we will discuss the main properties of this formalism, along with its applications in drug design and to the analysis of Molecular Dynamics (MD) simulations. Our indices have proven to be incredibly versatile when applied to chemical space exploration and visualization, allowing us to rigorously quantify the chemical diversity of very large molecular libraries. This has led to the creation of several algorithms to sample important regions in chemical space, including a more efficient way of identifying the prevalence of activity cliffs. Additionally, our indices provide a convenient route to sample complex MD trajectories, allowing to identify representative structures very efficiently. Moreover, we can also cluster biological ensembles in a more robust way than with standard algorithms, which has led to our group's work on MDANCE, a very flexible and efficient open-source clustering module. Drop by if you want to know how we clustered one billion molecules!


Speaker:
Assistant Professor, Department of Chemistry and Quantum Theory Project
University of Florida, Gainesville
Website: https://quintana.chem.ufl.edu/

Location:
Laufer Center Lecture Hall 101

Imagine machines that can see beyond human limitations--drones locating hidden survivors, cameras predicting structural failures, or medical devices detecting tumors beneath the skin. Traditional vision systems are constrained by the boundaries of human perception, missing vast information present in light interactions. This talk explores the development of advanced vision systems that capture underutilized dimensions of light, model intricate light-scene interactions, and extract hidden 3D information--around corners, beneath surfaces, and at high speeds. By jointly developing novel imaging hardware, efficient rendering models, and physics-based learning algorithms, we aim to transcend conventional vision capabilities--unlocking critical applications in autonomous navigation, structural monitoring, and non-invasive medical imaging.

Speaker Bio:


Akshat Dave is a Postdoctoral Associate at MIT Media Lab in the Camera Culture group working with Prof. Ramesh Raskar. He received his Ph.D. from Rice University ECE Department in 2023 where he was advised by Prof. Ashok Veeraraghavan. His research lies at the intersection of applied optics, computer graphics, and computer vision. His research focuses on developing vision systems that go beyond human perception. His work has been recognized by Rice University's Best Thesis Award, OSA Best Paper Prize, and fellowships by Texas Instruments and Qualcomm.

Abstract:

Conventional approaches to scientific discovery often prioritize building larger sensors, gathering more data, and scaling up computational power. In this talk, I will present a complementary perspective: extracting insights hidden in the data we already have. The key lies in using AI not as a black-box predictor, but as a tool for interpreting data through its underlying physical process.

I will demonstrate how AI, when integrated with the physics of light propagation, can serve as a computational lens to overcome fundamental limitations in fields ranging from biomedicine to astrophysics. Specifically, I will showcase two compelling applications: non-invasive imaging through scattering biological tissues, and detecting faint exoplanets against the overwhelming brightness of their host stars.

These methods represent a departure from traditional learning-based approaches that rely on fitting models to training labels and hoping for generalization. Instead, with physics-informed strategies that decode how light propagates, we can transform raw measurements into scientifically meaningful insights--without requiring costly hardware upgrades or human-annotated datasets. Finally, I will outline future directions for combining AI with physical principles, enabling us to unlock more phenomena once considered hidden and accelerating discoveries in healthcare, astronomy, and beyond.

Short Bio:

Brandon Y. Feng is a Postdoctoral Associate at MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) and a Visiting Scientist at the Harvard-Smithsonian Center for Astrophysics. His research bridges artificial intelligence and physics to expand the limits of human and machine vision. He develops AI-driven methods that reveal hidden patterns in complex visual data, driving breakthroughs in areas such as exoplanet detection and imaging through scattering tissues. His work has been published in top venues, including Science Advances, CVPR, ICCV, ECCV, and NeurIPS, and has been featured in Science.org, New Scientist, and Phys.org. He holds a Ph.D. in Computer Science from the University of Maryland, along with a B.A. in Computer Science and Statistics and an M.S. in Statistics from the University of Virginia.

Location: NCS 220