Abstract Driving intelligence test is critical to the development and deployment of autonomous vehicles. The prevailing approach tests autonomous vehicles in life- like simulations of the naturalistic driving environment. However, due to the high dimensionality of the environment and the rareness of safety-critical events, hundreds of millions of miles would be required to demonstrate the safety performance of autonomous vehicles, which is severely inefficient. We discover that sparse but adversarial adjustments to the naturalistic driving environment, resulting in the naturalistic and adversarial driving environment, can significantly reduce the required test miles without loss of evaluation unbiasedness. By training the background vehicles to learn when to execute what adversarial maneuver, the proposed environment becomes an intelligent environment for driving intelligence testing. We demonstrate the effectiveness of the proposed environment in a highway-driving simulation. Comparing with the naturalistic driving environment, the proposed environment can accelerate the evaluation process by multiple orders of magnitude. ZOOM LINK: Meeting ID: 950 6760 3617; Passcode: 426506 https://stonybrook.zoom.us/j/95067603617?pwd=dXQybEprSkNlTFY3WHlWYjViUG95UT09 Bio Professor Henry Liu is a professor in the Department of Civil and Environmental Engineering at the University of Michigan, Ann Arbor. He is also a Research Professor at the University of Michigan Transportation Research Institute and the Director for the Center for Connected and Automated Transportation (USDOT Region 5 University Transportation Center). Prof. Liu conducts interdisciplinary research at the interface between civil and mechanical engineering. Specifically, his scholarly interests concern traffic flow monitoring, modeling, and control, as well as testing and evaluation of connected and automated vehicles. He has published more than 100 refereed journal papers and is listed as one of the top 50 leading authors in the past 50 years (1969-2019) in the prestigious Transportation Research journal. Professor Liu and his work have been widely recognized in public media for promoting smart transportation innovations. He has appeared on media outlets including CNBC, Forbes, Technode, etc. In 2019, Professor Liu was invited to testify on national transportation research agenda in front of the US House Subcommittee on Research and Technology. Professor Liu has nurtured a new generation of scholars, and some of his PhD students and postdocs have joined first class universities such as Columbia University, Purdue University, RPI, etc. Prof. Liu is the managing editor of Journal of Intelligent Transportation Systems.

Abstract: My presentation will be focused on introducing the use of Screenomics, a passive sensing approach that directly collects time-intensive data from participants' smartphones, to observe and analyze adolescents' digital behaviors across multiple timescales. I will present our completed and ongoing efforts using Screenomics to (1) evaluate the biases of self-reports of screen time and app use, (2) describe how adolescents use their smartphones during school hours and overnight, (3) examine longitudinal associations between adolescents' social media use and mental health, and (4) capture adolescents' communication pattern with parents. I will also introduce the theoretical framework and study plan for a new NIH-funded project that aims to identify adolescents' social media management strategies (SMMS) and how SMMS are related to adolescents' actual social media use and mental health. I will conclude with a discussion of future directions for interventions to promote healthy digital practices among adolescents.

Bio: Xiaoran Sun, Ph.D., is an assistant professor in the Department of Family Social Science, College of Education and Human Development at University of Minnesota (UMN). She is the director of the UMN Technology, Teens, and Families Lab and a core faculty of the Learning Informatics Lab. She is also affiliated with the UMN Data Science Initiative and the Minnesota Population Center. Her research is mainly focused on using innovative approaches, such as passive sensing and machine learning, to examine children's and parents' use of technology and the implications for their wellbeing. Her work is being funded by the U.S. National Institute of Mental Health and the Spencer Foundation.

What AI tools are available to help with the scholarly research process? Are they helpful? What do they do and is it worth the time and energy to try them out? Join librarian Christine Fena to explore and compare established and emerging AI research tools such as Elicit, Scite, Consensus, and Undermind. The online workshop will provide a starting point to understanding what these tools are, the basics of how they work, and how AI research assistants might bring changes to your search process in the future. All are welcome!



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Zoom Like a Pro! Unlock Whiteboard, Polls, AI Companion, and more to supercharge student participation. This hands-on workshop explores innovative ways to use Zoom's built-in tools to enhance active learning activities in your classes. Learn how to utilize the Whiteboard feature to make collaborative work more engaging, use Polling and Quizzes for instant feedback, AI Companion for summary, and Breakout Sessions for group activities. Register here: https://stonybrook.zoom.us/meeting/register/tJckf--rpj4pGdRV0ItgTW8Lk7gn_RuykByO#/registration

Abstract: Computational pathology has revolutionized cancer diagnosis and research through the analysis of digitized whole slide images (WSIs). However, the giga-pixel size of these images presents profound technical challenges, creating two intertwined bottlenecks: computational inefficiency and label inefficiency. The immense data scale makes standard end-to-end (E2E) training of deep neural networks infeasible due to prohibitive GPU memory requirements, while the reliance on expert pathologists for annotations makes obtaining high-quality labeled data a tedious and expensive process. This proposal confronts these dual challenges by developing a series of novel model architectures, training paradigms, and self-supervised learning methods designed to create a more efficient and effective framework for WSI analysis.

To improve computational efficiency, this proposal first introduces a locally supervised learning paradigm that enables E2E training on entire WSIs by partitioning a network into gradient-isolated modules, circumventing the memory bottleneck of backpropagation. Second, it presents Prompt-MIL, a parameter-efficient fine-tuning framework that reduces the number of trainable parameters, memory consumption, and training time by fine-tuning only few prompts to guide large pre-trained models. Third, this work advances the efficient architecture on WSIs by developing novel State-Space Models (SSMs). It proposes 2DMamba, the first intrinsic Mamba architecture that preserves the crucial 2D spatial structure of images, overcoming the spatial discrepancy inherent in 1D models. Fourth, to address the inefficiency of multi-directional scans in Mamba models, including 2DMamba, it presents Locally Bi-directional Mamba (LBMamba), which introduces a novel, hardware-aware local backward scan that integrates bi-directional scan into a single forward pass, significantly improving throughput performance trade-off. Lastly, it proposes an extension to the LBMamba, warp-level Bi-directional Mamba (WLBMamba) that extends the thread-level bidirectional scan to warp-level bidirectional scan that further improves the throughput performance trade-off.

To improve label efficiency, this proposal proposes a Precise Location-based Matching strategy for self-supervised dense contrastive learning. By allowing a local patch in one augmented view to match multiple overlapping patches in another, creates a more accurate correspondence, leading to superior feature representations for dense prediction tasks like segmentation and detection.

In summary, this proposal presents a holistic investigation into the efficiency bottlenecks in computational pathology. Through these combined contributions in model architecture, training paradigms, and self-supervised learning, this work establishes a more scalable, efficient, and powerful computational framework for analyzing giga-pixel pathology images.

Speaker: Jingwei Zhang

Location: Old Computer Science Room 2114

Zoom: https://stonybrook.zoom.us/j/95187903649?pwd=tV0CNxLu1QKqw7hGmcE1h0rJ2C6n1b.1
Meeting ID: 951 8790 3649 | Passcode: 488916
Join CELT on Tuesday, March 31 for a focused, one-hour overview on how to redesign and future-proof assessments in the age of AI! This session will cover three key areas: leveraging AI as a co-pilot for developing effective exam questions, designing authentic assessments, and exploring how AI can strategically support active learning structures like Team-Based Learning (TBL), Project-Based Learning (PBL), and Scenario-Based Learning (SBL).

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Abstract : Humans reason about everyday situations by making commonsense-based inferences, derived both from explicitly stated information and implicit, unstated knowledge. In this thesis, I investigate whether NLP models have different aspects of causal knowledge about events and how to improve their understanding of narratives and plans.
Answering questions about why people perform actions in a narrative can test whether NLP systems contain and can effectively apply causal knowledge about events. I introduce TellMeWhy, a dataset concerning why characters in short narratives perform the actions described. An evaluation of then SOTA finetuned models show that they are far worse than humans. To improve models, it is important to understand what aspects of causal knowledge they need and how to best use external sources to inject this knowledge. In KnowWhy, I analyze different ways of injecting knowledge into models, which is difficult since we do not know apriori what type of knowledge will be needed to answer a question, hence requiring a ranking model to pick the most important inference. Results show that this retrieved knowledge helps models of all sizes, thereby improving their understanding of narratives.
Next, I study whether models can reason about causal aspects of plans. I focus on testing whether they understand the underlying causal dependencies reflected in the temporal order of a plan's steps. I introduce CAT-Bench, and find that SOTA models are underwhelming, and that model answers are not consistent across questions about the same step pairs. In their current state, these models cannot yet reliably be used for complex user-facing tasks. I then measure contemporary models' ability to perform user-facing and user-centric plan customization. I introduce the use of semi-symbolic edits in large language model (LLM) based agents and test several multi-LLM-agent architectures for plan customization. While LLMs still lack the ability to understand complex customization hints, my results suggest that LLM-based architectures may be worth exploring further for other customization applications. Finally, I distill complex reasoning capabilities into small language models (SLMs) using synthetic data that reflects a decomposition-then-editing process for plan customization. I demonstrate that explicitly teaching this latent causal reasoning significantly improves the quality of SLM-generated customizations. Overall, my work has improved how well NLP models understand complex reasoning associated with events in different contexts.

Speaker: Yash Kumar Lal

Location: NCS 220 or Zoom https://stonybrook.zoom.us/j/95849648243?pwd=dgPpZtDpgwQrK9z1SaPpNbBifaorzk.1

Abstract: Implicit functions have long been a fundamental representation for both 2D and 3D objects in computer graphics, playing a significant role in the field's early development. With the rise of 3D deep learning and the rapid advancement of neural rendering techniques, implicit representations of 3D shapes have regained significant attention in recent years. In this talk, I will present several recent research projects focusing on implicit function-based 3D reconstruction and neural rendering. Furthermore, I will discuss potential future developments in this dynamic and rapidly evolving field.

Biography: Ying He is an Associate Professor at the College of Computing and Data Science, Nanyang Technological University, where he also serves as the Director of the Centre for Augmented and Virtual Reality. His research interests lie in geometric computation and analysis, with applications spanning computer graphics, 3D vision, computer-aided design, multimedia, and wireless sensor networks. Dr. He is an active member of the technical program committees for major conferences on geometric modeling and has served on the editorial boards of IEEE Transactions on Visualization and Computer Graphics, Computer Graphics Forum, and Computational Visual Media. He has also taken on key leadership roles as General/Program Co-Chair for several conferences, including Shape Modeling International (SMI) 2022, Solid and Physical Modeling (SPM) 2022 & 2023, Geometric Modeling and Processing (GMP) 2014 & 2021, and Computational Visual Media (CVM) 2020. For more information, please visit https://personal.ntu.edu.sg/yhe/

Location: NCS 115