Talk by Zhenhua Liu to be followed by AI Institute updates


Abstract: Decision making with uncertainty has been studied in multiple communities extensively. Recently, online optimization has gained popularity partially because of its promising performance guarantees by incorporating predictions. In this talk, I will provide an overview of our work on algorithm designs for online optimization and its applications. Then, I will talk about our recent work in ACM Sigmetrics 2019 on choosing predictions and control algorithms simultaneously and dynamically. Finally, I will discuss some ongoing efforts and collaboration opportunities.

Bio: Zhenhua Liu is currently an assistant professor in the Department of Applied Mathematics and Statistics at Stony Brook University. He is also affiliated with the Department of Computer Science, the AI Institute and the Smart Energy Technology Cluster. He received his PhD degree in Computer Science from California Institute of Technology. His current research interests include cloud computing, online optimization and learning, smart grid, market design and distributed control. His research combines rigorous analysis and system design, and goes from theory, to prototype, and eventually to industry to make real impacts.

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.

Join Zoom Meeting https://stonybrook.zoom.us/j/97103601583?pwd=TnpGMXdpeEd1N0hZcXppS1BLNFJhZz09 (ID: 97103601583, passcode: 004031) Join by phone (US) +1 646-931-3860 (passcode: 004031) Joining instructions: https://www.google.com/url?q=https://applications.zoom.us/addon/invitation/detail?meetingUuid%3DILacj94mRvSXgTYt0Cqs1w%253D%253D%26signature%3D9f2f1e7e603bbcb9034724d084eea8846c19a38b7436180170dfc3f1d718b425%26v%3D1&sa=D&source=calendar&usg=AOvVaw3MsNgLSPMRl8L5i6BosYrB Meeting host: H.Andrew.Schwartz@stonybrook.edu

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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.

Come learn of the exciting research being done across so many fields using AI! The recipients of AI3's seed awards will present their work in our showcase on November 17, 2025 and we would love to see you there!

The schedule is listed below.

Location: New Computer Science Room 120

Session 1 - 10:30 AM to 11:45

Kevin Reed, PI, Introducing the AI Techniques in Assessing the Future Changes of Extreme Precipitation and Associated Flood Risks
Co-PIs: Tangnyu Song, Ishrat Dollan
Consultant: Jayesh Rathi

Ruwen Qin, PI, AI-Assisted Analysis of Materials in Recycling Streams
Consultant: Vismay Vora

Giuseppe Gazzola, PI, Using AI to Investigate National Literatures: Italy, France, Spain 1733- 1794
Consultant: Jayesh Rathi

Joseph Lemelin, PI, IAE2^3: AI Ecologies
Co-PIs: Katherine Johnston, Aruna Balasubramanian, Matthew Salzano

Niranjan Balasubramanian, Co-PI, Molecular Foundations for Sustainability: Data Analytics for Sustainable Cellulose Scaffolding Modifications to Remediate Diverse Water Contamination Challenges
PI: Benjamin Hsiao, Co-PI: I. V. Ramakrishnan

Owen Rambow, PI,Achieving Common Ground Through Language and Vision in Mixed-Initiative Human-Machine Communication Via zoom
Co-PI Susan Brennan

Session 2 - 12:30 PM to 1:45

Jack McSweeney, PI, Developing Machine Learning Approaches to Classify Internal Waves
Consultant: Vismay Vora

Eric Josephs, PI, Learning Design Rules to Personalize Precision CRISPR Gene Therapies with Interpretable AI
Consultant: Deboparna Banerjee

Shyam Sharma, PI, Fostering Writing-to-Learn Skills through Critical AI Literacy: A Faculty Development and Student Support Program
Co-PIs: Rose Tirotta-Esposito, Christine Fena

Ritwik Banerjee, PI, A Pragmatic Approach to AI for Digital Media Integrity: Combating Complex Misinformation Through Fallacies and Propaganda
Co-PI: Ruobing Li

Ziyu Shu, Co-PI, Novel Clinical Applications of Deep Image Prior-based CT Image Reconstruction
PI: Xin Qian, Co-PIs: Tiezhi Zhang, Zhaozheng Yin

Prateek Prasanna, Co-PI, An Artificial Intelligence-Driven Clinical Decision Support Tool for the Management of Abdominal Aortic Aneurysm
PI: Apostolos Tassiopoulos, Co-PI's: Mary Saltz, Janos Hajagos, Tahsin Kurc



Presenters will give a 5-minute talk with 2 minutes for Q & A.
The Collective Surgical Consciousness: Artificial Intelligence & the Future of Surgery Guest speaker Doctor Ozanan Meireles, the Director of the Surgical AI and Innovation Lab at Massachusetts General Hospital and a faculty member at Harvard Medical School, presents The Collective Surgical Consciousness: Artificial Intelligence & the Future of Surgery. Objectives: * Become familiar with the subfields of AI used in surgery * Understand the importance of a potential paradigm shift in surgical practice, training, and continue medical development * The importance of data acquisition, sharing and ownership, and development of machine learning algorithms


Abstract: In high-dimensional data spaces, vast empty regions often exist where no known data points are present. These empty spaces are not merely gaps but hold untapped potential for discovering novel configurations, optimizing parameters, and improving decision-making processes. However, traditional exploration techniques struggle to identify and leverage these regions due to the curse of dimensionality. To address this, we introduce the Empty Space Search Algorithm (ESA), a scalable, physics-inspired method that systematically identifies and explores these uncharted voids. ESA operates by modeling the data space as a dynamic system, using a repulsion-attraction mechanism to locate optimal empty space configurations (ESCs) without requiring exhaustive search. Building upon ESA, we present GapMiner, a visual analytics system that integrates human-in-the-loop AI to iteratively refine and validate ESCs. GapMiner combines parallel coordinate visualization, interactive optimization, and deep learning-based predictive modeling to enhance the efficiency of empty space exploration. This methodology has broad applications, including accelerating convergence in evolutionary algorithms through a more diverse initial population, optimizing adversarial learning strategies, and discovering novel parameter configurations in reinforcement learning. Our approach demonstrates that empty space is not just an absence of data but a frontier for new possibilities in high-dimensional problem-solving.
Bio: Xinyu Zhang received his B.E. in Computer Science from Shandong University, Taishan College, in 2019. He is currently a final-year Ph.D. candidate in the Department of Computer Science at Stony Brook University, advised by Prof. Klaus Mueller. His research focuses on multivariate data analysis, scientific visualization, and reinforcement learning. He has published multiple papers in top-tier journals and conferences, including IEEE TVCG and NeurIPS.
*this seminar will be held in person (food provided on a first come, first serve basis), and online (zoom link below)!
Topic: IACS Student Seminar Speaker: Xinyu Zhang
Time: Feb 26, 2025 12:00 PM Eastern Time (US and Canada)
Join Zoom Meeting
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Meeting ID: 918 4821 8975
Passcode: 027337
Abstract: The capacity to adapt machine learning models to various contexts, information, and objectives is particularly valuable. In this thesis, I focus on developing Class Conditional Guided Models. These are models that can be adaptively biased towards a class of interest via a conditional input. My primary focus lies in the efficiency of these models. They are constructed to require training only once, with the ability to quickly and conveniently adapt during testing time without necessitating fine-tuning or retraining.
Firstly, I propose RelationVAE, a novel generative model designed for few-shot scenarios, utilizing the prior knowledge of class similarity relationships. RelationVAE is designed to condition on the embeddings of the neighbor classes (i.e. classes with similarity relationships), to generate more reliable samples by making them more similar to the neighbor class. This enables adaptation of the generative model to the provided prior knowledge about class relationships.
As a second focus, I introduce scGAN, a shadow segmentation technique that enables adaptation to varying shadow distributions in different testing environments. scGAN is designed to condition on a sensitivity parameter, a scalar, to control the amount of the shadow detected. In the testing phase, the parameter is set to appropriate values, allowing the model to quickly adapt to specific test environments.
In my third contribution, I propose S-SEG, a methodology for fine-grained counting allowing adaptation to different granularities of fine-grained classes. In fine-grained problems, the distinction between classes is subtle and inconsistent across images, leading to variations in the granularity of the target class from one image to another. S-SEG is designed to be conditioned on an additional input, the sensitivity parameter, to control the granularities of the target class during inference.
My fourth contribution is a text-to-image synthesis method which allows controlling the number of the generated objects of a target class. I propose to generate an intermediate condition, the density map, which reflects the number of objects, together with their layout. This intermediate condition is used to effectively guide the generative model to generate objects with accurate counts.

Speaker: Vu Nguyen

Zoom: https://stonybrook.zoom.us/j/97114455337?pwd=Z4rB9dWcstlahUIs8PRrvQ9b2ZK2Df.1
Meeting ID: 971 1445 5337
Passcode: 272300

Join the Office of Educational Effectiveness' upcoming workshop on the transformative potential of AI tools to enhance program assessment. Learn how to leverage AI to create targeted learning objectives, detailed rubrics, and precise benchmarks that will elevate the quality and effectiveness of your program assessment process. Join in-person on Oct. 17 at 10:30 am or virtually on Oct. 21 at 12 pm.

Register in advance: https://calendar.stonybrook.edu/site/office-educational-effectiveness/event/leveraging-ai-in-assessment-zoom/

https://stonybrook.zoom.us/j/94414957054?pwd=V1JMc2EwSnVGMFdaUlNobE9DSHU4dz09#success
ID: 94414957054
Password: 094758

Speaker: Heather J. Lynch


Bio:  Dr. Heather J. Lynch is an Associate Professor of Ecology & Evolution at Stony Brook University. Prior to Stony Brook, Dr. Lynch was an Adjunct Professor of Applied Math and Statistics at UC Santa Cruz and a Research Scientist in the Biology Department at the University Maryland. Dr. Lynch received her A.B. in Physics from Princeton University in 2000, an A.M. in Physics from Harvard University in 2004, and a Ph.D. in Organismal and Evolutionary Biology from Harvard University in 2006. Dr. Lynch's research is focused on spatial population dynamics of Antarctic penguins, with a particular focus on statistical and mathematical models to integrate patchy time series with remote sensing imagery. These data will allow Dr. Lynch and colleagues to develop mathematical models to explore how coloniality constrains the colonization and extinction of individual habitat patches and, ultimately, the metapopulation dynamics of colonial seabirds.   

The International Conference on Machine Learning (ICML) is the premier gathering of professionals dedicated to the advancement of the branch of artificial intelligence known as machine learning.

ICML is globally renowned for presenting and publishing cutting-edge research on all aspects of machine learning used in closely related areas like artificial intelligence, statistics and data science, as well as important application areas such as machine vision, computational biology, speech recognition, and robotics.

ICML is one of the fastest growing artificial intelligence conferences in the world. Participants at ICML span a wide range of backgrounds, from academic and industrial researchers, to entrepreneurs and engineers, to graduate students and postdocs.


For more information and registration, visit the official website.