Synthetic Dreams and Ghost Machines

Dr. Steven Skiena will join the Cinema for a presentation exploring the rapidly evolving worlds of artificial intelligence and robotics.

Dr. Skiena is Distinguished Teaching Professor of Computer Science and Associate Director of the AI Innovation Institute at Stony Brook University. A leading researcher in data science and algorithms, he is the author of several influential books on AI and computation, including The Algorithm Design Manual and The Data Science Design Manual.

The presentation will be followed by a screening of Ex Machina, Alex Garland's provocative science-fiction thriller exploring the seductive and unsettling boundaries between human consciousness and artificial intelligence.

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 10 minutes) by multiple people. Students can register for 1 credit for CSE 656. Registered students must attend and present a minimum of 2 or 3 talks. Everyone else is welcome to attend. Fill in https://forms.gle/pCVXovgfMfQwGqG38 to subscribe to our mailing list for further announcement.

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.

AI for Neutrino Oscillation Fits

Abstract: Neutrino oscillation experiments face the problem of performing likelihood fits in a very highdimensional space to extract the oscillation parameters from measured spectra. The current strategy for this is to fix all but a few parameters, reducing the dimensionality of the fit to a manageable number, but this risks missing correlations between the parameters, which can impact the systematics of the measurement. This is an area where artificial intelligence and machine learning could make great improvements. I will discuss the problem, explain how it is currently dealt with, and sketch one possible way of implementing AI to solve it, using a sampling method combining Smolyak's algorithm, for efficient sampling using sparse grids, with an adaptive grid refinement to increase sampling in regions that are more likely to contain the global minimum.

Speaker: Steven Linden is a physicist in the Instrumentation Department at BNL working on neutrino and dark matter experiments. He got his PhD from Yale in 2010 doing analysis on the MiniBooNE experiment and then worked on various dark matter detectors (MiniCLEAN, Pico, SENSEI) at SNOLAB in Canada for nearly ten years before moving to BNL.

Location: CDS, Bldg. 725, Training Room

Join ZoomGov Meeting: https://bnl.zoomgov.com/j/1614473319?pwd=e4QSSgFHqDzHx870ixJpwuG3yqBere.1

Meeting ID: 161 447 3319
Passcode: 733283

A talk by Jerome Zhengrong Liang entitled, Machine Learning from Original Images to Texture Patterns: A Paradigm Shift from Non-Medical Application to Medical Diagnosis. Abstract: Artificial intelligence (AI) research for medical diagnosis started soon after human began to use computer, initially called artificial neural network (ANN) and now convolutional neural network (CNN). ANN has been mainly explored to classify the experts' handcrafted features from the original (or raw) images, while CNN has been mainly explored directly on the raw images for both tasks of extracting abstract features and classifying the features. Experimental evidences have been shown that CNN can be trained by a large number of the raw images with experts' scores (or labels) to match or even surpass the experts' performance for both non-medical and medical diagnosis applications. However, the performances of the CNN models as well as the experts on medical diagnosis dropped dramatically when the labels of the raw images were replaced by the corresponding medical pathological reports. Accumulated medical knowledge reveals that the lesion heterogeneity is a footprint of lesion evolution and ecology, and the heterogeneity is an indicator of lesion progress and response to medical intervention. The heterogeneity can be reflected by the image contrast distribution (or texture patterns) across the lesion volume. Image textures have been shown as an effective descriptor of the lesion heterogeneity for computer-aided diagnosis. Can we map the raw images into texture patterns (or images) and train CNN to learn from the texture images? This question is the central theme of this presentation with application to CT Colonography or virtual colonoscopy, a game from AlphaGo to PolypGo. Bio: Jerome Zhengrong Liang, PhD, IEEE Fellow Imaging Research and Informatics Laboratory Department of Radiology, Stony Brook University
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.

Join the Department of Computer Science as we welcome Lyle Ungar, University of Pennsylvania, who will be delivering a lecture on 'Measuring Cultural Variation using Natural Language Processing.' When: 11/08/24 @ 2:30 PM Where: New Computer Science Building, Room 120. Reception to follow. Abstract: Cultures vary widely in how they view the world, for example being more individualist or collectivist. Such cultural differences are, of course, reflected in the words that people use. We first show a variety of ways in which multilingual language models are not multicultural; they speak Hindi or Mandarin, but still think like Americans. In contrast, we then present a scalable method that uses embedding-derived lexica to successfully measure regional variation in culture. Bio: Lyle Ungar is a Professor of Computer and Information Science at the University of Pennsylvania, where he also holds secondary appointments in Psychology, Bioengineering, Genomics and Computational Biology, and Operations, Information and Decisions. His group uses natural language processing and explainable AI for psychological research, including analyzing social media and cell phone sensor data to better understand the drivers of physical and mental well-being. They are currently building socio-emotionally sensitive GPT-based tutors and coaches.

Join us to share your thoughts about teaching, learning, and AI!

The landscape of higher education is rapidly evolving with the integration of Artificial Intelligence (AI). Through the Institute on AI, Pedagogy, and the Curriculum with AAC&U, we are exploring ways that we can better address AI in teaching and learning. We want to hear your experiences, your concerns, and your ideas.

This is an open discussion for all faculty and staff to share their perspectives on the opportunities and challenges AI presents in our academic environment.

We'll be exploring critical questions like:

  • In the age of AI, what are the opportunities you see for enriching the classroom and curriculum? How can it enhance student learning or your professional practice?

  • What are the most significant challenges and concerns that AI raises for you regarding academics, student integrity, or your workload?

  • What resources (tools, training, technical support, policy guidance, etc.) do you need to feel confident and successful in the age of AI?

Dates/Times:

  • Tuesday, 2/3 at 2pm

  • Friday, 2/6 at 9:30am

Please register in advance for the Zoom link.

Can't Make It? Share Your Feedback!

We understand schedules are tight. If you cannot attend the live discussion, you can still share your thoughts! Join our AI Zoom Room to share your thoughts via video recording or email rose.tirotta-esposito@stonybrook.edu with your comments and ideas.

Videos will not be shared publicly and comments will only be shared in aggregate.

Your input is vital. From pedagogy to assessment, your insights will be critical. We look forward to a thoughtful and productive conversation!

  • Dr. Rose Tirotta-Esposito (Assistant Provost; Director of CELT)

  • Dr. Elizabeth Hewitt (Associate Professor in the Department of Technology and Society (DTS) in the College of Engineering and Applied Sciences)

  • Chris Kretz (Associate Librarian and Head of Academic Engagement at SBU Libraries)

  • Prof. Rajiv Lajmi (Assistant Professor in the School of Health Professions and Chair of Applied Health Informatics)

  • Dr. Matthew Salzano (Assistant Professor in the Department of Communication in the School of Communication and Journalism)

Abstract: AI agents are increasingly deployed in complex, interactive environments, yet their runtime remains a major bottleneck for training, evaluation, and real-world use. Typical agent behavior unfolds sequentially, where each action requires an API call that can incur substantial latency. For example, a game of chess between two state-of-the-art agents can take hours. We introduce speculative actions, a lossless acceleration framework for general agentic systems. Inspired by speculative execution in microprocessors and speculative decoding in LLM inference, our method uses faster models to predict likely future actions and executes them in parallel, committing only when predictions match. We evaluate speculative actions across gaming, e-commerce, and web search environments, and additionally study a lossy extension in an operating systems setting. Across domains, we achieve up to 55% next-action prediction accuracy, translating into substantial latency reductions. Finally, we present a cost-latency analysis that formalizes the tradeoff between speculative breadth and time savings. This analysis enables principled tuning and selective branch launching, to ensure multi-branch speculation delivers practical speedups without prohibitive cost growth.

Speaker: Kehan Qi

Location: Old Computer Science Building - CS2311

Abstract: This talk is about the two ends of LLM training: pre-training and in-deployment learning. I will present an approach to disentangle knowledge from skill in model pre-training. This brings about a new class of LLMs that externalize knowledge, with dramatically different characteristics from common LLMs along dimensions of scale, factuality, and updateability. On the other end, I will discuss two in-deployment learning methods. I will describe how in-context learning abilities extend beyond supervised settings, showing that LLMs display in-context reinforcement learning from rewards. Finally, if time allows, I will describe continual learning from implicit interaction signals, demonstrating that LLMs can retrospectively decode latent interaction cues by observing how humans respond to their outputs.

Bio: Yoav Artzi is an Associate Professor in the Department of Computer Science and Cornell Tech at Cornell University, a visiting faculty researcher at Google DeepMind, and arXiv's associate faculty director. His research focuses on language modeling and learning in interactive and situated scenarios. His work was acknowledged by awards and honorable mentions at ACL, EMNLP, NAACL, and IROS, as well as a TACL test-of-time award. Yoav holds a B.Sc. from Tel Aviv University and a Ph.D. from the University of Washington.

Location: NCS 120