AI Innovation Institute

Google Docs has been updated to integration Gemini AI. Learn about the integrations that gives you more features within Google Docs. There are also AI Automations that will keep you updated when things happen with documents.

In this session, you will:

1. Learn about the AI features within Google Docs
2. Learn about workflow to be notified of changes to Docs
3. Understand the features that allow AI to work

Register here.

Title: Sustainable NLP

Time: Friday 4/29, 2:40 PM

Location: NCS 120

Abstract:


Natural language processing (NLP) technology has supercharged many real-world applications ranging from intelligent personal assistants (like Alexa, Siri, and Google Assistant) to commercial search engines such as Google and Bing. But current NLP applications use extremely large neural models, making them (i) expensive to deploy on servers, requiring large amounts of compute resources and power, and (ii) impossible to run on mobile devices, making on-device, privacy-preserving applications impractical.

In the first part of the talk, I will describe systems optimizations we have developed that significantly reduce the compute and memory requirement of NLP models. The optimizations we developed can be applied broadly and results in over 10x reduction in latency when deployed on mobile devices. In the second part of the talk, I will describe our recent work on predicting energy consumption of NLP models. Existing energy prediction approaches are not accurate, making it difficult for developers and practitioners to reason about their models in terms of power. We use a multi-level regression approach that produces highly accurate and interpretable energy predictions.



Bio:
Aruna Balasubramanian is an Associate Professor at Stony Brook University. She received her Ph.D from the University of Massachusetts Amherst, where her dissertation won the UMass outstanding dissertation award and was the SIGCOMM dissertation award runner up. She works in the area of networked systems. Her current work consists of two threads: (1) significantly improving Quality of Experience of Internet applications, and (2) improving the usability, accessibility, and privacy of mobile systems. She is the recipient of the SIGMobile Rockstar award, a Ubicomp best paper award, a Computing Innovation Fellowship, a VMWare Early Career award, several Google research awards, an

TITLE: Sampling Using Langevin Diffusions Beyond the Worst-Case by Andrej Risteski of CMU


ABSTRACT: Many tasks involving generative models involve being able to sample from distributions parametrized as p(x) = e^{-f(x)}/Z where Z is the normalizing constant, for some function f whose values and gradients we can query. This mode of access to f is natural -- for instance sampling from posteriors in latent-variable models. Classical results show that a natural random walk, Langevin diffusion, mixes rapidly when f is convex. Unfortunately, even in simple examples, the applications listed above will entail working with functions f that are nonconvex.

We exhibit instances where Langevin diffusion (combined with other tools) can provably be shown to mix rapidly in instances of relevance in practice: distributions p that are multimodal, as well as distributions p that have a natural manifold structure on their level sets. 

Presented by Stony Brook University Department of Biomedical Informatics and Long Island Network for Clinical and Translational Science (LINCATS).

The seminar aims to empower participants with the knowledge and skills necessary to harness AI effectively in clinical practice and research. It will equip attendees with practical insights, case studies, and interactive discussions led by experts in both AI and medicine, fostering a collaborative environment where attendee can explore how to overcome barriers and maximize the potential of AI in transforming modern healthcare delivery.

All Stony Brook Audiences Welcome.
Please note: This exciting event is open to all Stony Brook Faculty/Staff/Students. While the overarching theme for this event is the application of AI in medicine, the event is designed to bridge the professional practice gap that exists between cutting-edge AI research and its practical implementation in clinical settings, While AI holds immense promise for transforming healthcare delivery, many physicians and researchers lack the foundational knowledge and practical skills needed to effectively integrate AI into their daily practices.

THIS CONFERENCE IS FOR STONY BROOK UNIVERSITY & HOSPITAL FACULTY/STAFF & STUDENTS ONLY.

Registration link: https://cme.stonybrookmedicine.edu/continuing-medical-education/conferences/235/bench-to-bedside-understanding-the-practical-application-of-ai-in-medicine-2024/10/17/2024

FOR QUESTIONS
joseph.cesaria@stonybrookmedicine.edu
mary.saltz@stonybookmedicine.edu

Abstract: Robot control has evolved from optimization-based controllers---precise but task-specific---through deep reinforcement learning's learned policies, to Vision-Language-Action (VLA) models that leverage pretrained vision-language backbones for language-conditioned manipulation across diverse tasks.
Despite their promise, VLAs exhibit a critical limitation: they function primarily as trajectory learners rather than skill learners. Recent evaluations reveal that VLAs often fail when faced with even minor variations in object initialization or environmental conditions, suggesting they memorize specific trajectories rather than acquiring generalizable manipulation skills. Attempts to address this through 3D spatial representations have shown limited success, indicating that the missing component may be more fundamental than geometric understanding alone.
This work argues that World Models (WMs)---internal representations that predict future states given actions---constitute the missing piece for robust VLA systems. We present one completed contribution and two ongoing investigations.
We developed a dual-layer world model for human-robot interaction that anticipates both physical scene evolution and latent human preferences for assistive tasks. Building on these foundations, we present ongoing work probing VLA internal representations to verify implicit world model existence, and propose a WM-VLA integration approach operating in the native visual domain through embedding prediction and image decoding.
Together, these contributions and investigations establish a foundation for WM-VLA systems, pointing toward robust, generalizable robot policies.
Speaker: Jason Qin
Location: NCS 220

Abstract: Facial emotion understanding aims to recognize, represent, and interpret human affect from facial behavior, and it is important for affective computing, human-computer interaction, digital humans, and mental health assessment. Existing work has represented facial emotion through discrete emotion categories, continuous affective dimensions such as valence and arousal, facial landmarks or geometry, and more recently semantic or language-based emotion descriptors. With the development of deep learning, supervised facial expression recognition has achieved strong performance on benchmark datasets, while self-supervised learning, multimodal large language models, and controllable facial generation have introduced new ways to learn emotion-related facial representations from images, videos, text, audio, and speech. However, many current models still rely heavily on manually annotated emotion labels, third-party perception judgments, or multimodal contextual cues, making it difficult to determine how much emotional information is captured directly from facial behavior itself, especially in naturalistic and clinically meaningful settings. Because these limitations make it challenging to evaluate whether facial representations capture emotionally meaningful behavior in real-world interactions, we propose to study facial emotion understanding through the relationship between facial behavior and language-derived emotional expression in psychiatric interview videos. Using a large dataset of mental health interviews, we extract multiple types of facial representations, including Action Unit features from FaceReader and OpenFace, non-AU facial behavior features from OpenFace, and 3D facial representations from EMOCA and SMIRK. We train segment-aligned transformer regressors to predict language-derived emotional targets, including valence, arousal, and RoBERTa-derived semantic-affective features from transcript segments. The results show that all facial representations achieve meaningful predictive performance across MSE and Pearson correlation metrics in both within-participant and between-participant evaluation settings. This indicates that facial behavior encodes information related to linguistic emotion at multiple levels: moment-to-moment emotional variation within individuals and broader affective differences across individuals. These findings suggest that structured facial representations can support vision-based emotion understanding in naturalistic mental health interviews and motivate future work on self-supervised, personalized, and controllable facial emotion models.

Speaker: Shao-Yu Chang

Zoom: https://stonybrook.zoom.us/j/3679036240?omn=98419305450

Abstract: At XTX Markets, we view algorithmic trading as one of the most compelling real-world frontiers for deep learning and foundation models. Every day, our systems generate forecasts for tens of thousands of financial instruments and execute over $300B in global trading volume: fully automated, with no discretionary human intervention. This domain combines massive data scale with high noise, adversarial dynamics, and frequent regime shifts, making it both scientifically challenging and commercially impactful. For machine learning researchers, it serves as a rigorous proving ground where advances in time-series modeling, large-scale optimization, representation learning, and foundation models can translate directly into measurable real-world outcomes. This talk will provide a high-level overview of our research agenda, infrastructure, and key open challenges at the intersection of large-scale AI and quantitative finance.

Speaker: Dr. Zhangyang Atlas Wang is the Research Director at XTX Markets, one of the world's leading high-frequency trading firms. He founded and leads the firm's AI Lab in New York City, focused on developing large-scale foundation models for financial time series and market data, powered by XTX's proprietary AI infrastructure. He is currently on leave from his position as the Temple Foundation Endowed Associate Professor at The University of Texas at Austin. His academic research has received numerous awards, and he has mentored a broad network of Ph.D. students and postdoctoral researchers. Many of his alumni now hold tenure-track faculty positions (eight to date) or senior research roles in industry (nineteen and counting). For more information about his group and alumni, please visit: https://www.vita-group.space/team.

Location: NCS 120

Refreshments will be served after the seminar in the first-floor atrium.

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https://stonybrookuniversity.co1.qualtrics.com/jfe/form/SV_a9PVlBw0E1Bal1A?