An interactive session to discover how to create ALT text tags from images and create high-impact visuals, from identification to communicating ideas with images.

Discover how to use AI to create ALT text from images as well as identify objects in your environment, and build relatable visuals for high-impact presentations. Images communicate ideas as a way to understand concepts. AI-generated images have helped allow anyone to create these.

In this session, you will

  1. Creating image ALT Tags
  2. Transform ideas into images that are visually appealing
  3. Identify objects from visuals

Register here.

Zoom Link: https://stonybrook.zoom.us/j/98533029054?pwd=5FXO6lWGTJssCADEYkYbA7sjaacPRX.1

Meeting ID: 985 3302 9054

Passcode: 436997

Abstract:

Semantic segmentation, the task of assigning a semantic label to each pixel in an image, is a fundamental problem in the field of Computer Vision. with crucial applications in domains like autonomous driving, drone imagery and medical image analysis. Despite advancements in deep learning architectures, state-of-the-art models still heavily depend on large-scale pixel-level annotations, which are costly and time-consuming to acquire. To address this issue, Semi-Supervised Segmentation (SSS) has emerged as a promising solution, leveraging a small set of labeled images alongside a larger corpus of unlabeled data to reduce the annotation burden. In this proposal, I aim to investigate the challenges of SSS and propose approaches to address them. Existing SSS methods rely on a teacher-student framework to generate pseudo-labels for unlabeled images, which are then used for model training. However, this approach presents two major challenges. Pixel-level consistency fails to effectively capture contextual information, and pseudo-labels are noisy, especially in the early stages of training. To address the challenge of noisy pseudo-labels, existing methods rely on confidence-based thresholding to identify reliable pseudo-labels. However, during early training phases, when the model is poorly calibrated, this approach can select high-confidence but noisy pseudo-labels. To address this, we propose a novel approach that reduces reliance on model confidence to select reliable pseudo-labels. Our method employs an ensemble of a segmentation model and an object detection model to select more reliable pseudo-labels, which are then used to weight pseudo-labels using rank statistics, reducing the influence of noisy labels in training. Next, to address both the challenge of capturing contextual information and noisy pseudo-labels I introduce a novel Multi-scale Patch-based Multi-label Classifier (MPMC), which incorporates patch-level contextual information and reduces the impact of noisy pixel pseudo-labels by using the predictions of the patch-level Multi-label classifier to detect noisy labels, enhancing overall segmentation performance. While my work so far has focused on effectively utilizing unlabeled data to improve segmentation performance, as part of our future work, I will explore the use of textual information, such as category descriptions, for segmentation tasks. In limited labeled data scenarios it is more challenging to align visual features with textual features from large language models (LLMs).

The event will take place on Zoom and will feature two distinguished guest speakers: SBU alumnus, Velchamy Sankarlingam, president of Product and Engineering at Zoom, and Simeon Ananou, vice president for Information Technology and CIO at Stony Brook University. The discussion will be moderated by Haresh Gurnani, dean of the College of Business at Stony Brook University.

Exploring AI's Impact on Communication and Connection

Artificial Intelligence (AI) has rapidly evolved, becoming an integral part of various industries, including education and business. This event aims to delve into how AI is reshaping the way we learn and work, particularly in enhancing communication and fostering human connections. Velchamy Sankarlingam, an SBU alumnus and a key figure at Zoom, will share his insights on how AI-driven tools are revolutionizing virtual communication platforms, making interactions more seamless and effective.

Simeon Ananou, with his extensive experience in information technology, will provide a perspective on how AI is being integrated into educational institutions to improve learning outcomes and administrative efficiency. His role at Stony Brook University places him at the forefront of implementing innovative technologies that benefit both students and staff.

A Conversation Led by Expertise

Dean Haresh Gurnani, known for his leadership and expertise in business education, will guide the conversation, ensuring that the discussion remains focused on the practical implications of AI. He will explore how AI is not only boosting productivity but also enriching overall experiences in the workplace and educational settings. The event will include an interactive Q&A session, allowing attendees to engage directly with the speakers and gain deeper insights into the topics discussed.

As AI continues to develop, events like this are crucial for understanding its impact and potential. Stony Brook University's College of Business is committed to providing platforms for such important discussions, fostering an environment where innovation and education intersect.

This event is open to all. Please visit https://www.givecampus.com/schools/StonyBrookUniversity/events/artificial-intelligence-reshaping-learning-and-work to register.

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.

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.

Abstract: This presentation will begin by outlining key challenges facing the modern power grid and summarizing our group's research efforts to address them. It will then discuss how AI and machine learning are reshaping the grid modernization. The major focus of the talk will highlight a range of AI/ML applications we have developed in recent years to enhance grid operation, planning, control, and security.

Biography: Meng Yue is currently leading the Grid Modernization and Security Group in the Interdisciplinary Science Department at Brookhaven National Laboratory (BNL). He received his Ph. D. from Michigan State University in electrical engineering. His major research interests include power system modeling, simulation, and control, and applications of AI/ML- and quantum machine learning and quantum computing in operation, planning, and security of the future grid.

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.

Location: CDS, Bldg. 725, Training Room

Join ZoomGov Meeting: https://bnl.zoomgov.com/j/1604383624?pwd=ffQ5cUPNxTI7nzClKQO6cnsNbhF9Vf.1

Meeting ID: 160 438 3624
Passcode: 558449

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 and Edge Processing Co-Design for Radiation Detectors

Abstract: Artificial Intelligence (AI) offers exciting new opportunities for enhancing the performance of radiation detectors, ultimately leading to improved physics outcomes. Furthermore, with the explosive growth in data rates being seen by next-generation radiation detectors, deployment of AI algorithms at the edge by embedding intelligence within or near the detector front-end can be transformative. Such integration enables real-time data filtering, noise suppression, feature extraction, and adaptive control, while reducing downstream bandwidth and power consumption. This talk will cover three efforts that bring AI to the forefront of detector technology. First, we demonstrate how AI-based algorithms can be used for position reconstruction in virtual Frisch-grid (VFG) detectors by compensating for charge transport distortions and detector non- uniformities, leading to significantly enhanced fidelity in imaging of gamma-ray interactions. Second, we present a smart readout application specific integrated circuit (ASIC) that combines digital signal processing with co-designed artificial neural networks to enable on-chip regression and classification of detector signals, while meeting stringent constraints on accuracy, speed, and area. Finally, we introduce our recent efforts related to the development of electro-photonic processing architectures that integrate CMOS electronics and silicon photonics for near-sensor AI acceleration. These architectures aim to leverage cross-disciplinary co-design from algorithms to hardware, to achieve low latency and energy-efficient processing of detector data.

Biography: Dr. Prashansa Mukim is an early-career researcher in the Instrumentation Department at BNL, where she works on the design of front-end electronics for extreme environments and the development of co-design methodologies for novel processing modalities and beyond-CMOS technologies. Prior to joining BNL, she was a post-doctoral researcher at the National Institute of Standards and Technology (NIST) in Maryland, where she focused on characterizing the properties of CMOS circuits at cryogenic temperatures and applications of spintronic devices for neuromorphic computing. She received her Ph.D. in Electrical and Computer Engineering from the University of California, Santa Barbara, in 2021.

Location: CDS, Bldg. 725, Training Room

Join ZoomGov Meeting: https://bnl.zoomgov.com/j/1608585935?pwd=UemgEkqijfNf3vIJIGuOa2MdjsunaT.1

Meeting ID: 160 858 5935
Passcode: 076033


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.
Virtual Talk: Contextual Modeling for Natural Language Understanding, Generation and Grounding by Rui Zhang

Zoom link to come.

Abstract: Natural language is a fundamental form of information and communication. In both human-human and human-computer communication, people reason about the context of text and world state to understand language and produce language response. In this talk, I present 
several deep-neural-network-based systems that first understand the meaning of language grounded in various contexts where the language is used, and then generate effective language responses in different forms for information access and human-computer communication. First, 
I will introduce Speaker Interaction RNNs for addressee and response selection in multi-party conversations based on explicit representations for different discourse participants. Then, I will 
present a text summarization approach for generating email subject lines by optimizing quality scores in a reinforcement learning framework. Finally, I will show an editing-based multi-turn SQL query generation system towards intelligent natural language interfaces to databases. 

Bio: Rui Zhang is a final-year PhD student at Yale University advised by Professor Dragomir Radev. His research interest lies in various natural language processing problems in understanding, generation, and grounding. He has been working on (1) End-to-End Neural Modeling for Entities, Sentences, Documents and Multi-party Multi-turn Dialogues, (2) Text Summarization for Emails, News and Scientific Articles, (3) Cross-lingual Information Retrieval for Low-Resource Languages, (4) Context-Dependent Text-to-SQL Semantic Parsing in Human-Computer Interaction. Rui Zhang has published papers and served as Program Committee members at top-tier NLP and AI conferences including ACL, NAACL, EMNLP, AAAI and CoNLL. During his PhD, he has done research internships at IBM Thomas J. Watson Research Center, Grammarly Research and Google AI. He was a graduate student at the University of Michigan and got his Bachelor's degrees at both the University of Michigan and Shanghai Jiao Tong University from the UM-SJTU Joint Institute.